diff --git a/.dockerignore b/.dockerignore index 6738c2b3e5ce2de8ba1ea170c480fc6aeb53894e..e447e440408e5d00fa8985e8ef75bad10b7a532a 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,20 +1,16 @@ -.git -.github -.venv -.remember -.letta -.claude -__pycache__ -*.pyc -*.pyo -*.pyd -*.log -run_*.log -run*.log -*.txt -WORKER_COMPLETE -autoresearch_loop.log -overlay/data/ -overlay/state_store/ -overlay/htm_rust/target/ -overlay/hydra-core/target/ +# Keep HF runtime image context deterministic and small. +**/__pycache__/ +**/*.py[cod] +**/.pytest_cache/ +**/.mypy_cache/ +**/.ruff_cache/ +**/.venv/ +**/target/ +**/logs/ +**/*.log +**/*.out +**/*.pt +**/*.safetensors +**/*.parquet +**/*.npz +**/.git/ diff --git a/Dockerfile b/Dockerfile index d9034e496577002f91afa8d8c3f6316819799803..b21d52887880c913a86c65cf5cb3e3339ec0e161 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,128 +1,124 @@ -FROM pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel +FROM pytorch/pytorch:2.5.1-cuda12.1-cudnn9-devel -ARG HTM_CUDA_ARCH=sm_86 - -ENV DEBIAN_FRONTEND=noninteractive \ - PIP_NO_CACHE_DIR=1 \ - PYTHONUNBUFFERED=1 \ - CARGO_HOME=/root/.cargo \ - RUSTUP_HOME=/root/.rustup \ - PATH=/root/.cargo/bin:${PATH} - -RUN apt-get update && apt-get install -y --no-install-recommends \ - git curl ca-certificates build-essential pkg-config libssl-dev && \ - rm -rf /var/lib/apt/lists/* - -RUN curl https://sh.rustup.rs -sSf | bash -s -- -y --profile minimal --default-toolchain stable - -RUN pip install --upgrade pip setuptools wheel && \ - pip install \ - maturin \ - huggingface_hub \ - datasets \ - requests \ - pyarrow \ - rustbpe \ - pandas \ - tiktoken \ - pydantic \ - ninja \ - packaging \ - einops - -# Mamba-3 fused CUDA kernel stack (mandatory — NO fallback allowed). -# -# We install PRE-BUILT manylinux wheels from the official state-spaces/mamba -# and Dao-AILab/causal-conv1d GitHub releases. Compiling mamba_ssm from source -# on HF Spaces' cpu-basic builder (~16GB RAM) OOMKills even with MAX_JOBS=1 — -# nvcc on the templated selective-scan/chunk-scan kernels needs 8–12GB per TU. -# -# Wheel selection for base image pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel: -# - Python 3.11 (cp311) — matches PyTorch 2.6.0 image -# - CUDA 12.x wheels (cu12) — matches host CUDA 12.4 -# - PyTorch 2.6 ABI (torch2.6) — exact torch match -# - cxx11abiFALSE — standard PyTorch pip build -# -# Versions: mamba_ssm 2.3.1 (first stable with Mamba3 class) + causal_conv1d -# 1.6.1.post4 (matching ABI). Both are CUDA-compiled, no build toolchain needed -# on the Space builder. -# -# Step A: install the published v2.3.1 prebuilt wheel (compiled CUDA ops -# for selective_scan, layernorm_gated, ssd_*, causal_conv1d, etc). -RUN pip install \ - 'https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.6.1.post4/causal_conv1d-1.6.1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl' \ - 'https://github.com/state-spaces/mamba/releases/download/v2.3.1/mamba_ssm-2.3.1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl' && \ - python -c "import importlib.metadata as m; print('installed mamba_ssm=' + m.version('mamba_ssm') + ' causal_conv1d=' + m.version('causal_conv1d'))" - -# -# Step B: graft the Mamba3 class + its pure-Triton ops subtree from mamba-ssm -# main. v2.3.1 is the latest release but Mamba3 landed post-release; the new -# files under ops/triton/mamba3/ are ALL pure Python @triton.jit kernels with -# zero compiled-CUDA dependencies (verified: every import in that subtree is -# triton/torch/python — no .so files, no nvcc). So we install the v2.3.1 wheel -# (for its compiled ops) and overlay the main-branch Mamba3 sources on top. -# -# This avoids the source-build OOM on the cpu-basic HF Space builder and the -# missing-file error the smoke hit on the last attempt. -# Download grafted mamba3 module + triton ops subtree -RUN SITE=/opt/conda/lib/python3.11/site-packages/mamba_ssm && \ - BASE=https://raw.githubusercontent.com/state-spaces/mamba/main && \ - curl -fsSL "$BASE/mamba_ssm/modules/mamba3.py" -o "$SITE/modules/mamba3.py" && \ - mkdir -p "$SITE/ops/triton/mamba3" && \ - for f in __init__.py angle_dt.py mamba3_mimo_rotary_step.py mamba3_mimo_utils.py mamba3_siso_bwd.py mamba3_siso_combined.py mamba3_siso_fwd.py mamba3_siso_step.py utils.py; do \ - curl -fsSL "$BASE/mamba_ssm/ops/triton/mamba3/$f" -o "$SITE/ops/triton/mamba3/$f"; \ - done - -# Replace mamba_ssm/__init__.py with a minimal one that only imports Mamba3 -# (pure-Triton, works). The shipped __init__.py eagerly imports -# selective_scan_cuda.so which has a libtorch C++ ABI mismatch on this base -# image ("undefined symbol: _ZN3c107WarningC1E..."). Since training only needs -# Mamba3 (grafted from main), we skip all compiled-CUDA imports. -COPY mamba_ssm_init.py /opt/conda/lib/python3.11/site-packages/mamba_ssm/__init__.py - -# Structural check (no triton init — triton has no GPU on the builder) -RUN SITE=/opt/conda/lib/python3.11/site-packages/mamba_ssm && \ - test -f "$SITE/modules/mamba3.py" && \ - test -f "$SITE/ops/triton/mamba3/mamba3_siso_combined.py" && \ - test -s "$SITE/__init__.py" && \ - echo "mamba3 graft + __init__ override verified" - -# Optional tilelang for MIMO path — pure-python, cheap; SISO Mamba3 works without. -RUN pip install tilelang || echo "[dockerfile] tilelang optional install failed — continuing" - -# Triton version decision: FORCE 3.5.1 — the only version with both mamba3 -# APIs (set_allocator + tl.make_tensor_descriptor). torch 2.6's _inductor -# imports AttrsDescriptor from triton.compiler.compiler which was removed in -# triton 3.4+, but mamba_ssm/__init__.py shims AttrsDescriptor as a stub -# before any torch._inductor import path runs, so the incompatibility is -# neutralized. Build-time assert verifies mamba3's two required APIs. -RUN pip install --force-reinstall --no-deps 'triton==3.5.1' && \ - python -c "import triton; from triton import language as tl; \ - assert hasattr(triton, 'set_allocator'), 'missing triton.set_allocator'; \ - assert hasattr(tl, 'make_tensor_descriptor'), 'missing tl.make_tensor_descriptor'; \ - print(f'triton={triton.__version__} set_allocator+make_tensor_descriptor OK, AttrsDescriptor shimmed in mamba_ssm/__init__.py')" - -WORKDIR /workspace -COPY overlay /workspace/feather -COPY entrypoint.py /app/entrypoint.py -WORKDIR /workspace/feather - -RUN python - <<'PY' -from pathlib import Path -for sh in Path('/workspace/feather/scripts').glob('*.sh'): - raw = sh.read_bytes() - norm = raw.replace(b'\r\n', b'\n') - if norm != raw: - sh.write_bytes(norm) -PY +# Default target is HF Jobs a10g-large (NVIDIA A10G, Ampere GA102, sm_86). +# Override at build time for other cards, e.g. --build-arg FEATHER_GPU_ARCH=sm_90a. +ARG FEATHER_GPU_ARCH=sm_86 +ARG FEATHER_TORCH_CUDA_ARCH_LIST=8.6 + +ENV DEBIAN_FRONTEND=noninteractive \ + PIP_NO_CACHE_DIR=1 \ + PYTHONUNBUFFERED=1 \ + CARGO_HOME=/root/.cargo \ + RUSTUP_HOME=/root/.rustup \ + HTM_CUDA_ARCH=${FEATHER_GPU_ARCH} \ + TORCH_CUDA_ARCH_LIST=${FEATHER_TORCH_CUDA_ARCH_LIST} \ + PATH=/root/.cargo/bin:${PATH} + +RUN apt-get update && apt-get install -y --no-install-recommends \ + git curl ca-certificates build-essential pkg-config libssl-dev && \ + rm -rf /var/lib/apt/lists/* + +RUN curl https://sh.rustup.rs -sSf | bash -s -- -y --profile minimal --default-toolchain stable + +RUN pip install --upgrade pip setuptools wheel && \ + pip install \ + maturin \ + huggingface_hub \ + datasets \ + requests \ + pyarrow \ + rustbpe \ + pandas \ + tiktoken \ + pydantic \ + ninja \ + packaging \ + einops + +# Mamba-3 fused CUDA kernel stack (mandatory — NO fallback allowed). +# +# We install PRE-BUILT manylinux wheels from the official state-spaces/mamba +# and Dao-AILab/causal-conv1d GitHub releases. Compiling mamba_ssm from source +# on HF Spaces' cpu-basic builder (~16GB RAM) OOMKills even with MAX_JOBS=1 — +# nvcc on the templated selective-scan/chunk-scan kernels needs 8–12GB per TU. +# +# Wheel selection for base image pytorch/pytorch:2.5.1-cuda12.1-cudnn9-devel: +# - Python 3.11 (cp311) — matches PyTorch 2.5.1 image +# - CUDA 12.x wheels (cu12) — compatible with CUDA 12.1 base +# - PyTorch 2.5 ABI (torch2.5) — exact torch match +# - cxx11abiFALSE — standard PyTorch pip build +# +# Versions: mamba_ssm 2.3.0 + causal_conv1d 1.6.0 (matching torch2.5 ABI). +# Both are CUDA-compiled, no build toolchain needed +# on the Space builder. +# +# Step A: install the published v2.3.0 prebuilt wheel (compiled CUDA ops +# for selective_scan, layernorm_gated, ssd_*, causal_conv1d, etc). +RUN pip install \ + 'https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.6.0/causal_conv1d-1.6.0+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl' \ + 'https://github.com/state-spaces/mamba/releases/download/v2.3.0/mamba_ssm-2.3.0+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl' && \ + python -c "import importlib.metadata as m; print('installed mamba_ssm=' + m.version('mamba_ssm') + ' causal_conv1d=' + m.version('causal_conv1d'))" + +# +# Step B: graft the Mamba3 class + its pure-Triton ops subtree from mamba-ssm +# main. v2.3.1 is the latest release but Mamba3 landed post-release; the new +# files under ops/triton/mamba3/ are ALL pure Python @triton.jit kernels with +# zero compiled-CUDA dependencies (verified: every import in that subtree is +# triton/torch/python — no .so files, no nvcc). So we install the v2.3.1 wheel +# (for its compiled ops) and overlay the main-branch Mamba3 sources on top. +# +# This avoids the source-build OOM on the cpu-basic HF Space builder and the +# missing-file error the smoke hit on the last attempt. +# Download grafted mamba3 module + triton ops subtree +RUN SITE=/opt/conda/lib/python3.11/site-packages/mamba_ssm && \ + BASE=https://raw.githubusercontent.com/state-spaces/mamba/main && \ + curl -fsSL "$BASE/mamba_ssm/modules/mamba3.py" -o "$SITE/modules/mamba3.py" && \ + mkdir -p "$SITE/ops/triton/mamba3" && \ + for f in __init__.py angle_dt.py mamba3_mimo_rotary_step.py mamba3_mimo_utils.py mamba3_siso_bwd.py mamba3_siso_combined.py mamba3_siso_fwd.py mamba3_siso_step.py utils.py; do \ + curl -fsSL "$BASE/mamba_ssm/ops/triton/mamba3/$f" -o "$SITE/ops/triton/mamba3/$f"; \ + done + +# Replace mamba_ssm/__init__.py with a minimal one that only imports Mamba3 +# (pure-Triton, works). The shipped __init__.py eagerly imports +# selective_scan_cuda.so which has a libtorch C++ ABI mismatch on this base +# image ("undefined symbol: _ZN3c107WarningC1E..."). Since training only needs +# Mamba3 (grafted from main), we skip all compiled-CUDA imports. +COPY mamba_ssm_init.py /opt/conda/lib/python3.11/site-packages/mamba_ssm/__init__.py + +# Structural check (no triton init — triton has no GPU on the builder) +RUN SITE=/opt/conda/lib/python3.11/site-packages/mamba_ssm && \ + test -f "$SITE/modules/mamba3.py" && \ + test -f "$SITE/ops/triton/mamba3/mamba3_siso_combined.py" && \ + test -s "$SITE/__init__.py" && \ + echo "mamba3 graft + __init__ override verified" + +# Optional tilelang for MIMO path — pure-python, cheap; SISO Mamba3 works without. +RUN pip install tilelang || echo "[dockerfile] tilelang optional install failed — continuing" + +# Triton version decision: FORCE 3.4.0 — first line with both mamba3 +# APIs (set_allocator + tl.make_tensor_descriptor) while avoiding the 3.5.x +# driver-discovery regression seen on HF A10G (`0 active drivers` despite +# torch.cuda being available). torch 2.5's _inductor expects older Triton +# internals, but mamba_ssm/__init__.py shims AttrsDescriptor as a stub +# before any torch._inductor import path runs, so the incompatibility is +# neutralized. Build-time assert verifies mamba3's two required APIs. +RUN pip install --force-reinstall --no-deps 'triton==3.4.0' && \ + python -c "import triton; from triton import language as tl; \ + assert hasattr(triton, 'set_allocator'), 'missing triton.set_allocator'; \ + assert hasattr(tl, 'make_tensor_descriptor'), 'missing tl.make_tensor_descriptor'; \ + print(f'triton={triton.__version__} set_allocator+make_tensor_descriptor OK, AttrsDescriptor shimmed in mamba_ssm/__init__.py')" + +WORKDIR /workspace +COPY overlay /workspace/feather +COPY entrypoint.py /app/entrypoint.py +WORKDIR /workspace/feather RUN python -m py_compile hydra/training.py prepare.py train.py && \ bash -n scripts/run_domain_expanded_pretrain.sh - + RUN export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} && \ - export HTM_CUDA_ARCH=${HTM_CUDA_ARCH} && \ - export CARGO_BUILD_JOBS=1 && \ - maturin build --release -j 1 --features gpu --manifest-path htm_rust/Cargo.toml && \ + echo "building htm_rust GPU kernels for HTM_CUDA_ARCH=${HTM_CUDA_ARCH} TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}" && \ + maturin build --release --features gpu --manifest-path htm_rust/Cargo.toml && \ pip install htm_rust/target/wheels/htm_rust-*.whl - -CMD ["python", "/app/entrypoint.py"] + +CMD ["python", "/app/entrypoint.py"] diff --git a/entrypoint.py b/entrypoint.py index 2377445bbfd6d7a548a8ff8da0a49a84f09b987a..15b0380a5c6afc4bd3033f85a8203ae4d0b1f556 100644 --- a/entrypoint.py +++ b/entrypoint.py @@ -1,227 +1,267 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import json -import os -import subprocess -import sys -import time -from http.server import BaseHTTPRequestHandler, HTTPServer -from pathlib import Path -from threading import Thread - - -# ============================================================================= -# EARLY CUDA FABRIC MANAGER KICK (before ANY CUDA-touching imports) -# ============================================================================= -# On H200 hosts, cudaGetDeviceCount can return Error 802 "system not yet -# initialized" on first use, because nvidia-fabricmanager on the host -# synchronizes with the container's first driver call. Once any NVML/CUDA -# call succeeds once (even just nvidia-smi), the fabric is up for the rest -# of the container lifetime. -# -# Our previous approach (wait in a subprocess before training) didn't work -# because the "initialization failed" state persisted across calls in the -# same container. The real fix: kick the driver exactly once with -# nvidia-smi, which is what successfully-working baseline containers do -# implicitly via their first torch.cuda call. -# -# Must happen BEFORE `import torch` (because any import that eagerly calls -# cudaGetDeviceCount will cache the Error 802 state). -def _early_cuda_kick() -> None: - deadline = time.time() + 120.0 - attempt = 0 - while time.time() < deadline: - attempt += 1 - r = subprocess.run(['nvidia-smi'], capture_output=True, text=True, timeout=30) - if r.returncode == 0 and 'H200' in (r.stdout or '') or 'H100' in (r.stdout or '') \ - or 'A100' in (r.stdout or '') or r.returncode == 0: - print(f'[boot] nvidia-smi OK on attempt {attempt}', flush=True) - break - print(f'[boot] nvidia-smi attempt {attempt} rc={r.returncode} stderr={(r.stderr or "")[:120]}', - flush=True) - time.sleep(2) - # After nvidia-smi, probe torch in a subprocess so any latent error state - # doesn't leak into the main process's CUDA context. - probe = 'import torch; import sys; sys.exit(0 if torch.cuda.is_available() else 1)' - torch_deadline = time.time() + 120.0 - t_attempt = 0 - while time.time() < torch_deadline: - t_attempt += 1 - r = subprocess.run([sys.executable, '-c', probe], capture_output=True, text=True, timeout=60) - if r.returncode == 0: - print(f'[boot] torch.cuda.is_available() = True after {t_attempt} probe(s)', flush=True) - return - if t_attempt == 1: - print(f'[boot] torch cuda probe {t_attempt}: {(r.stderr or "")[:200]}', flush=True) - time.sleep(2) - print('[boot] WARNING: torch.cuda never became ready — training will likely fail', flush=True) - - -_early_cuda_kick() - -# Hydrate triton compilation cache from HF Hub before any triton/mamba_ssm import. -# triton_cache_setup.py is copied next to this file by the job bash command. -try: - import triton_cache_setup as _tcs - _tcs.setup() -except ImportError: - print('[boot] triton_cache_setup not found; skipping cache hydrate', flush=True) - -from huggingface_hub import HfApi # noqa: E402 (import after cuda kick) - -REPO_ROOT = Path('/workspace/feather') -CACHE_ROOT = Path.home() / '.cache' / 'autoresearch' -LOG_FILE = REPO_ROOT / 'run_domain_expanded.log' -JOB_ID = os.environ.get('JOB_ID', 'local-job') -OUTPUT_REPO = os.environ.get('HF_REPO_ID', 'icarus112/feather-pretrain-checkpoints') -TOKEN = os.environ.get('HF_TOKEN') -RUNTIME_MODE = os.environ.get('FEATHER_RUNTIME_MODE', 'space') -APP_PORT = int(os.environ.get('PORT', '7860')) - - -class _HealthHandler(BaseHTTPRequestHandler): - def do_GET(self): - if self.path in ('/', '/health', '/healthz', '/ready'): - payload = { - 'status': 'ok', - 'mode': RUNTIME_MODE, - 'job_id': JOB_ID, - } - body = json.dumps(payload).encode('utf-8') - self.send_response(200) - self.send_header('Content-Type', 'application/json') - self.send_header('Content-Length', str(len(body))) - self.end_headers() - self.wfile.write(body) - return - self.send_response(404) - self.end_headers() - - def log_message(self, format, *args): - return - - -def _start_health_server() -> HTTPServer: - server = HTTPServer(('0.0.0.0', APP_PORT), _HealthHandler) - thread = Thread(target=server.serve_forever, daemon=True) - thread.start() - print(f'[space] health server listening on 0.0.0.0:{APP_PORT}', flush=True) - return server - - -def upload_artifact(api: HfApi, path: Path, dest: str) -> None: - if not path.exists(): - print(f'[upload] skip missing {path}', flush=True) - return - api.upload_file( - path_or_fileobj=str(path), - path_in_repo=dest, - repo_id=OUTPUT_REPO, - repo_type='model', - ) - print(f'[upload] uploaded {path} -> {OUTPUT_REPO}/{dest}', flush=True) - - -def _wait_for_cuda_ready(timeout_s: int = 120) -> None: - """Block until CUDA is fully initialized or timeout. - - On H200 hosts with NVSwitch/fabric manager, nvidia driver setup can race - with container start. cudaGetDeviceCount can return CUDA_ERROR_SYSTEM_NOT_READY - (error 802) for the first few seconds, and any import that triggers - @triton.autotune (e.g. mamba_ssm, torch amp utilities) blows up with - "0 active drivers" if it happens during that window. - - We pre-init CUDA in a throwaway Python subprocess (so any error state does - not leak into the main training process) and retry until torch.cuda - reports ready. - """ - import time as _t - probe = ( - "import torch; " - "import sys; " - "avail = torch.cuda.is_available(); " - "count = torch.cuda.device_count() if avail else 0; " - "sys.exit(0 if (avail and count > 0) else 1)" - ) - deadline = _t.time() + timeout_s - attempt = 0 - while _t.time() < deadline: - attempt += 1 - r = subprocess.run(['python', '-c', probe], capture_output=True, text=True) - if r.returncode == 0: - print(f'[job] CUDA ready after {attempt} probe(s)', flush=True) - return - if attempt == 1: - print(f'[job] CUDA not ready yet (will retry up to {timeout_s}s): {r.stderr.strip()[:200]}', flush=True) - _t.sleep(2) - print(f'[job] CUDA still not ready after {timeout_s}s — continuing anyway (training will likely fail)', flush=True) - - -def run_job_mode() -> int: - os.chdir(REPO_ROOT) - os.environ.setdefault('HYDRA_TIME_BUDGET', '43200') - os.environ.setdefault('HYDRA_TARGET_SHARDS', '2048') - os.environ.setdefault('HYDRA_DOWNLOAD_WORKERS', '16') - os.environ.setdefault('HYDRA_CKPT_INTERVAL', '1000') - os.environ.setdefault('HYDRA_RESUME_CKPT', str(CACHE_ROOT / 'latest.pt')) - - # CUDA readiness was kicked at module import via _early_cuda_kick. Keep - # the wait as a second safety net — no-op if CUDA already ready. - _wait_for_cuda_ready() - - cmd = [ - 'bash', - './scripts/run_domain_expanded_pretrain.sh', - '--target-shards', os.environ['HYDRA_TARGET_SHARDS'], - '--download-workers', os.environ['HYDRA_DOWNLOAD_WORKERS'], - ] - print('[job] starting Feather domain-expanded pretrain', flush=True) - print(f'[job] command={cmd}', flush=True) - proc = subprocess.run(cmd, check=False) - - # Push triton compilation cache back to HF Hub for next run. - try: - import triton_cache_setup as _tcs - _tcs.teardown() - except Exception as _tcs_err: - print(f'[triton_cache] teardown error (non-fatal): {_tcs_err}', flush=True) - - if TOKEN: - api = HfApi(token=TOKEN) - try: - api.create_repo(repo_id=OUTPUT_REPO, repo_type='model', private=True, exist_ok=True) - except Exception as e: - print(f'[upload] create_repo warning: {type(e).__name__}: {e}', flush=True) - prefix = f'jobs/{JOB_ID}' - try: - upload_artifact(api, LOG_FILE, f'{prefix}/run_domain_expanded.log') - upload_artifact(api, CACHE_ROOT / 'latest.pt', f'{prefix}/latest.pt') - upload_artifact(api, CACHE_ROOT / 'pretrain_final.pt', f'{prefix}/pretrain_final.pt') - except Exception as e: - print(f'[upload] upload warning: {type(e).__name__}: {e}', flush=True) - else: - print('[upload] HF_TOKEN not set; skipping artifact upload', flush=True) - - return proc.returncode - - -def run_space_mode() -> int: - server = _start_health_server() - print('[space] Feather runtime image ready', flush=True) - try: - while True: - time.sleep(3600) - finally: - server.shutdown() - server.server_close() - - -def main() -> int: - if RUNTIME_MODE == 'job': - return run_job_mode() - return run_space_mode() - - -if __name__ == '__main__': - raise SystemExit(main()) +#!/usr/bin/env python3 +from __future__ import annotations + +import json +import os +import subprocess +import sys +import time +from http.server import BaseHTTPRequestHandler, HTTPServer +from pathlib import Path +from threading import Thread + + +def _prepend_library_path(*paths: str) -> None: + """Expose injected NVIDIA driver libraries before torch/triton imports.""" + existing = [p for p in os.environ.get('LD_LIBRARY_PATH', '').split(':') if p] + merged = [] + for p in paths: + if p and p not in merged: + merged.append(p) + for p in existing: + if p not in merged: + merged.append(p) + os.environ['LD_LIBRARY_PATH'] = ':'.join(merged) + + +_prepend_library_path( + # HF Jobs injects the host driver under /usr/local/nvidia. Prefer that + # over CUDA toolkit/compat libcuda stubs; using /usr/local/cuda/compat here + # made A10G PyTorch report Error 803 despite nvidia-smi working. + '/usr/local/nvidia/lib64', + '/usr/local/nvidia/lib', + '/usr/lib/x86_64-linux-gnu', +) + + +# ============================================================================= +# EARLY CUDA FABRIC MANAGER KICK (before ANY CUDA-touching imports) +# ============================================================================= +# On HF GPU hosts, cudaGetDeviceCount can transiently return not-ready errors +# on first use. H200 fabric-manager is the worst case; A10G is usually ready +# immediately, but the same early kick keeps the runtime deterministic. +# synchronizes with the container's first driver call. Once any NVML/CUDA +# call succeeds once (even just nvidia-smi), the fabric is up for the rest +# of the container lifetime. +# +# Our previous approach (wait in a subprocess before training) didn't work +# because the "initialization failed" state persisted across calls in the +# same container. The real fix: kick the driver exactly once with +# nvidia-smi, which is what successfully-working baseline containers do +# implicitly via their first torch.cuda call. +# +# Must happen BEFORE `import torch` (because any import that eagerly calls +# cudaGetDeviceCount will cache the Error 802 state). +def _early_cuda_kick() -> None: + deadline = time.time() + 120.0 + attempt = 0 + while time.time() < deadline: + attempt += 1 + r = subprocess.run(['nvidia-smi'], capture_output=True, text=True, timeout=30) + if r.returncode == 0: + gpu_line = next((ln.strip() for ln in (r.stdout or '').splitlines() if any(g in ln for g in ('A10', 'A100', 'H100', 'H200', 'RTX'))), 'gpu=unknown') + print(f'[boot] nvidia-smi OK on attempt {attempt}: {gpu_line}', flush=True) + break + print(f'[boot] nvidia-smi attempt {attempt} rc={r.returncode} stderr={(r.stderr or "")[:120]}', + flush=True) + time.sleep(2) + # After nvidia-smi, probe torch in a subprocess so any latent error state + # doesn't leak into the main process's CUDA context. + probe = 'import torch; import sys; sys.exit(0 if torch.cuda.is_available() else 1)' + torch_deadline = time.time() + 120.0 + t_attempt = 0 + while time.time() < torch_deadline: + t_attempt += 1 + r = subprocess.run([sys.executable, '-c', probe], capture_output=True, text=True, timeout=60) + if r.returncode == 0: + print(f'[boot] torch.cuda.is_available() = True after {t_attempt} probe(s)', flush=True) + return + if t_attempt == 1: + print(f'[boot] torch cuda probe {t_attempt}: {(r.stderr or "")[:200]}', flush=True) + time.sleep(2) + print('[boot] WARNING: torch.cuda never became ready — training will likely fail', flush=True) + + +_early_cuda_kick() + +# Hydrate triton compilation cache from HF Hub before any triton/mamba_ssm import. +# triton_cache_setup.py is copied next to this file by the job bash command. +try: + import triton_cache_setup as _tcs + _tcs.setup() +except ImportError: + print('[boot] triton_cache_setup not found; skipping cache hydrate', flush=True) + +from huggingface_hub import HfApi # noqa: E402 (import after cuda kick) + +REPO_ROOT = Path('/workspace/feather') +CACHE_ROOT = Path.home() / '.cache' / 'autoresearch' +LOG_FILE = REPO_ROOT / 'run_domain_expanded.log' +JOB_ID = os.environ.get('JOB_ID', 'local-job') +OUTPUT_REPO = os.environ.get('HF_REPO_ID', 'icarus112/feather-pretrain-checkpoints') +TOKEN = os.environ.get('HF_TOKEN') +RUNTIME_MODE = os.environ.get('FEATHER_RUNTIME_MODE', 'space') +APP_PORT = int(os.environ.get('PORT', '7860')) + + +class _HealthHandler(BaseHTTPRequestHandler): + def do_GET(self): + if self.path in ('/', '/health', '/healthz', '/ready'): + payload = { + 'status': 'ok', + 'mode': RUNTIME_MODE, + 'job_id': JOB_ID, + } + body = json.dumps(payload).encode('utf-8') + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.send_header('Content-Length', str(len(body))) + self.end_headers() + self.wfile.write(body) + return + self.send_response(404) + self.end_headers() + + def log_message(self, format, *args): + return + + +def _start_health_server() -> HTTPServer: + server = HTTPServer(('0.0.0.0', APP_PORT), _HealthHandler) + thread = Thread(target=server.serve_forever, daemon=True) + thread.start() + print(f'[space] health server listening on 0.0.0.0:{APP_PORT}', flush=True) + return server + + +def upload_artifact(api: HfApi, path: Path, dest: str) -> None: + if not path.exists(): + print(f'[upload] skip missing {path}', flush=True) + return + api.upload_file( + path_or_fileobj=str(path), + path_in_repo=dest, + repo_id=OUTPUT_REPO, + repo_type='model', + ) + print(f'[upload] uploaded {path} -> {OUTPUT_REPO}/{dest}', flush=True) + + +def _wait_for_cuda_ready(timeout_s: int = 120) -> None: + """Block until CUDA is fully initialized or timeout. + + On H200 hosts with NVSwitch/fabric manager, nvidia driver setup can race + with container start. cudaGetDeviceCount can return CUDA_ERROR_SYSTEM_NOT_READY + (error 802) for the first few seconds, and any import that triggers + @triton.autotune (e.g. mamba_ssm, torch amp utilities) blows up with + "0 active drivers" if it happens during that window. + + We pre-init CUDA in a throwaway Python subprocess (so any error state does + not leak into the main training process) and retry until torch.cuda + reports ready. + """ + import time as _t + probe = ( + "import torch; " + "import sys; " + "avail = torch.cuda.is_available(); " + "count = torch.cuda.device_count() if avail else 0; " + "torch.empty(1, device='cuda') if (avail and count > 0) else None; " + "from triton.runtime import driver; " + "driver.active.get_current_device(); " + "sys.exit(0 if (avail and count > 0) else 1)" + ) + deadline = _t.time() + timeout_s + attempt = 0 + while _t.time() < deadline: + attempt += 1 + r = subprocess.run(['python', '-c', probe], capture_output=True, text=True) + if r.returncode == 0: + print(f'[job] CUDA/Triton ready after {attempt} probe(s)', flush=True) + return + if attempt == 1: + print(f'[job] CUDA not ready yet (will retry up to {timeout_s}s): {r.stderr.strip()[:200]}', flush=True) + _t.sleep(2) + print(f'[job] CUDA still not ready after {timeout_s}s — continuing anyway (training will likely fail)', flush=True) + + +def run_job_mode() -> int: + os.chdir(REPO_ROOT) + os.environ.setdefault('HYDRA_TIME_BUDGET', '43200') + os.environ.setdefault('HYDRA_TARGET_SHARDS', '2048') + os.environ.setdefault('HYDRA_DOWNLOAD_WORKERS', '16') + os.environ.setdefault('HYDRA_CKPT_INTERVAL', '1000') + os.environ.setdefault('HYDRA_RESUME_CKPT', str(CACHE_ROOT / 'latest.pt')) + os.environ.setdefault('FEATHER_GPU_PROFILE', 'a10g-large') + os.environ.setdefault('HTM_CUDA_ARCH', 'sm_86') + os.environ.setdefault('TORCH_CUDA_ARCH_LIST', '8.6') + os.environ.setdefault('TRITON_CACHE_DIR', f"/workspace/triton_cache/{os.environ['FEATHER_GPU_PROFILE']}") + os.environ.setdefault('TRITON_CACHE_REPO', f"icarus112/feather-triton-cache-{os.environ['FEATHER_GPU_PROFILE']}") + print(f"[job] gpu_profile={os.environ['FEATHER_GPU_PROFILE']} htm_cuda_arch={os.environ['HTM_CUDA_ARCH']} torch_cuda_arch={os.environ['TORCH_CUDA_ARCH_LIST']}", flush=True) + + # CUDA readiness was kicked at module import via _early_cuda_kick. Keep + # the wait as a second safety net — no-op if CUDA already ready. + _wait_for_cuda_ready() + + cmd = [ + 'bash', + './scripts/run_domain_expanded_pretrain.sh', + '--target-shards', os.environ['HYDRA_TARGET_SHARDS'], + '--download-workers', os.environ['HYDRA_DOWNLOAD_WORKERS'], + ] + print('[job] ensuring retina.npz before training...', flush=True) + try: + sys.path.insert(0, str(REPO_ROOT)) + from subsystems.sdr_retina import build_retina + build_retina() + except Exception as _retina_err: + print(f'[job] retina bootstrap warning (train.py may still build it): {_retina_err}', flush=True) + print('[job] starting Feather domain-expanded pretrain', flush=True) + print(f'[job] command={cmd}', flush=True) + proc = subprocess.run(cmd, check=False) + + # Push triton compilation cache back to HF Hub for next run. + try: + import triton_cache_setup as _tcs + _tcs.teardown() + except Exception as _tcs_err: + print(f'[triton_cache] teardown error (non-fatal): {_tcs_err}', flush=True) + + if TOKEN: + api = HfApi(token=TOKEN) + try: + api.create_repo(repo_id=OUTPUT_REPO, repo_type='model', private=True, exist_ok=True) + except Exception as e: + print(f'[upload] create_repo warning: {type(e).__name__}: {e}', flush=True) + prefix = f'jobs/{JOB_ID}' + try: + upload_artifact(api, LOG_FILE, f'{prefix}/run_domain_expanded.log') + upload_artifact(api, CACHE_ROOT / 'latest.pt', f'{prefix}/latest.pt') + upload_artifact(api, CACHE_ROOT / 'pretrain_final.pt', f'{prefix}/pretrain_final.pt') + except Exception as e: + print(f'[upload] upload warning: {type(e).__name__}: {e}', flush=True) + else: + print('[upload] HF_TOKEN not set; skipping artifact upload', flush=True) + + return proc.returncode + + +def run_space_mode() -> int: + server = _start_health_server() + print('[space] Feather runtime image ready', flush=True) + try: + while True: + time.sleep(3600) + finally: + server.shutdown() + server.server_close() + + +def main() -> int: + if RUNTIME_MODE == 'job': + return run_job_mode() + return run_space_mode() + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/mamba_ssm_init.py b/mamba_ssm_init.py index 5304a70909d1243c62e19ed7c2533f15297172d0..69e5e1aa942519d7434247442cdc64354fe22786 100644 --- a/mamba_ssm_init.py +++ b/mamba_ssm_init.py @@ -1,101 +1,69 @@ -# mamba_ssm package init — minimal override to avoid broken selective_scan_cuda.so -# ABI mismatch with the base image's libtorch. -# -# The upstream __init__.py eagerly imports selective_scan_cuda which fails on -# pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel (undefined c10::Warning ctor -# symbol). We only need Mamba3 (grafted from main, pure-Triton), so we skip -# all compiled-CUDA imports here and let Mamba3 load directly. - -__version__ = "2.3.1+feather-graft" - -# selective_scan_fn / mamba_inner_fn are shimmed to None — they are NOT used -# by the Feather training path (which is Mamba3-only). If any import path -# hits this, it will get a clear AttributeError instead of an obscure ImportError. -selective_scan_fn = None -mamba_inner_fn = None - -# --- triton API compatibility shims ----------------------------------------- -# Version matrix is hostile: torch 2.6 pins triton==3.2.0 because torch._inductor -# imports AttrsDescriptor from triton.compiler.compiler — removed in triton 3.4+. -# Grafted Mamba3 (from mamba-ssm main) needs triton.set_allocator and -# tl.make_tensor_descriptor, both added in triton 3.3+. No single triton version -# satisfies both simultaneously. We run on triton 3.5.1 (latest, has both mamba3 -# APIs) and shim AttrsDescriptor as a stub dataclass for torch._inductor. The -# stub is never actually invoked at runtime because the codebase does not use -# torch.compile — but importing torch._inductor.* still requires the symbol to -# exist at module load time. +# mamba_ssm package init — minimal override to avoid broken selective_scan_cuda.so +# ABI mismatch with the base image's libtorch. +# +# The upstream __init__.py eagerly imports selective_scan_cuda which fails on +# pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel (undefined c10::Warning ctor +# symbol). We only need Mamba3 (grafted from main, pure-Triton), so we skip +# all compiled-CUDA imports here and let Mamba3 load directly. + +__version__ = "2.3.1+feather-graft" + +# selective_scan_fn / mamba_inner_fn are shimmed to None — they are NOT used +# by the Feather training path (which is Mamba3-only). If any import path +# hits this, it will get a clear AttributeError instead of an obscure ImportError. +selective_scan_fn = None +mamba_inner_fn = None + +# --- triton API compatibility shims ----------------------------------------- +# Version matrix is hostile: torch 2.6 pins triton==3.2.0 because torch._inductor +# imports AttrsDescriptor from triton.compiler.compiler — removed in triton 3.4+. +# Grafted Mamba3 (from mamba-ssm main) needs triton.set_allocator and +# tl.make_tensor_descriptor, both added in triton 3.3+. No single triton version +# satisfies both simultaneously. We run on triton 3.5.1 (latest, has both mamba3 +# APIs) and shim AttrsDescriptor as a stub dataclass for torch._inductor. The +# stub is never actually invoked at runtime because the codebase does not use +# torch.compile — but importing torch._inductor.* still requires the symbol to +# exist at module load time. import triton as _triton # noqa: E402 if not hasattr(_triton, "set_allocator"): - def _noop_set_allocator(_fn): # pragma: no cover - return None - _triton.set_allocator = _noop_set_allocator - -import triton.compiler.compiler as _tcc # noqa: E402 -if not hasattr(_tcc, "AttrsDescriptor"): - class _AttrsDescriptorShim: - """Stub for torch._inductor compatibility on triton >= 3.4. - torch._inductor.runtime.hints imports this at module load but the - constructor is only called inside torch.compile paths. Accept any - args/kwargs so the import itself succeeds.""" - def __init__(self, *args, **kwargs): - self.args = args - self.kwargs = kwargs - - @classmethod - def from_hints(cls, *args, **kwargs): - return cls(*args, **kwargs) - - _tcc.AttrsDescriptor = _AttrsDescriptorShim - -# triton_key: removed in triton 3.5, used by torch._inductor.codecache for -# FxGraphCache key derivation. Return a stable string so caching still works. -if not hasattr(_tcc, "triton_key"): - def _triton_key_shim(): - import triton as _t - return f"triton-{_t.__version__}-shim" - _tcc.triton_key = _triton_key_shim + def _noop_set_allocator(_fn): # pragma: no cover + return None + _triton.set_allocator = _noop_set_allocator + +import triton.compiler.compiler as _tcc # noqa: E402 +if not hasattr(_tcc, "AttrsDescriptor"): + class _AttrsDescriptorShim: + """Stub for torch._inductor compatibility on triton >= 3.4. + torch._inductor.runtime.hints imports this at module load but the + constructor is only called inside torch.compile paths. Accept any + args/kwargs so the import itself succeeds.""" + def __init__(self, *args, **kwargs): + self.args = args + self.kwargs = kwargs -# Triton 3.5 wheels can occasionally load with an empty backend registry in -# HF Jobs environments (driver.active -> "0 active drivers"), even though the -# NVIDIA backend module is present and CudaDriver.is_active() is True. -# Patch _create_driver to directly select CudaDriver when registry discovery -# returns empty. -import importlib as _importlib # noqa: E402 -_triton_driver_mod = _importlib.import_module("triton.runtime.driver") -if getattr(_triton_driver_mod, "backends", None) == {}: - from triton.backends.nvidia import driver as _nvidia_driver # noqa: E402 + @classmethod + def from_hints(cls, *args, **kwargs): + return cls(*args, **kwargs) - def _create_driver_shim(): - if hasattr(_nvidia_driver, "CudaDriver") and _nvidia_driver.CudaDriver.is_active(): - return _nvidia_driver.CudaDriver() - raise RuntimeError( - "Triton backend registry is empty and NVIDIA CudaDriver is not active" - ) + _tcc.AttrsDescriptor = _AttrsDescriptorShim - _triton_driver_mod._create_driver = _create_driver_shim - if hasattr(_triton_driver_mod, "driver") and hasattr(_triton_driver_mod.driver, "reset_active"): - _triton_driver_mod.driver.reset_active() +# triton_key: removed in triton 3.5, used by torch._inductor.codecache for +# FxGraphCache key derivation. Return a stable string so caching still works. +if not hasattr(_tcc, "triton_key"): + def _triton_key_shim(): + import triton as _t + return f"triton-{_t.__version__}-shim" + _tcc.triton_key = _triton_key_shim -_triton_compiler_mod = _importlib.import_module("triton.compiler.compiler") -if getattr(_triton_compiler_mod, "backends", None) == {}: - from triton.backends import Backend as _Backend # noqa: E402 - from triton.backends.nvidia.compiler import CUDABackend as _CUDABackend # noqa: E402 - from triton.backends.nvidia.driver import CudaDriver as _CudaDriver # noqa: E402 +# Suppress torch.compile/_dynamo errors globally — we don't rely on torch.compile +# for performance in this codebase (Muon + mamba3 CUDA kernels already fused), +# so fall back to eager on any dynamo failure rather than crashing. This is +# defense-in-depth against further triton API drift. +try: + import torch._dynamo # noqa: F401 — triggers dynamo module init + torch._dynamo.config.suppress_errors = True +except Exception: # pragma: no cover + pass - _triton_compiler_mod.backends["nvidia"] = _Backend( - compiler=_CUDABackend, - driver=_CudaDriver, - ) - -# Suppress torch.compile/_dynamo errors globally — we don't rely on torch.compile -# for performance in this codebase (Muon + mamba3 CUDA kernels already fused), -# so fall back to eager on any dynamo failure rather than crashing. This is -# defense-in-depth against further triton API drift. -try: - import torch._dynamo # noqa: F401 — triggers dynamo module init - torch._dynamo.config.suppress_errors = True -except Exception: # pragma: no cover - pass - -# Expose Mamba3 at top level to match `from mamba_ssm import Mamba3`. -from mamba_ssm.modules.mamba3 import Mamba3 # noqa: E402 +# Expose Mamba3 at top level to match `from mamba_ssm import Mamba3`. +from mamba_ssm.modules.mamba3 import Mamba3 # noqa: E402 diff --git a/overlay/.dockerignore b/overlay/.dockerignore index 6aa36bbea6fdefa1cf487e66c87b91ba535c02d4..675f920af42988ad539e5f24a11233c714b50fb6 100644 --- a/overlay/.dockerignore +++ b/overlay/.dockerignore @@ -1,20 +1,20 @@ -.git -.github -.venv -.remember -.letta -.claude -__pycache__ -*.pyc -*.pyo -*.pyd -*.log -run_*.log -run*.log -*.txt -WORKER_COMPLETE -autoresearch_loop.log -data/ -state_store/ -htm_rust/target/ -hydra-core/target/ +.git +.github +.venv +.remember +.letta +.claude +__pycache__ +*.pyc +*.pyo +*.pyd +*.log +run_*.log +run*.log +*.txt +WORKER_COMPLETE +autoresearch_loop.log +data/ +state_store/ +htm_rust/target/ +hydra-core/target/ diff --git a/overlay/configs/__init__.py b/overlay/configs/__init__.py index 43ff2ca1946e212dfaa187501e2cd2ed9053caa4..14154e8e4eb30cb9f9779ab0f41f4d2df9d78161 100644 --- a/overlay/configs/__init__.py +++ b/overlay/configs/__init__.py @@ -1,5 +1,5 @@ -from configs.hardware_config import HardwareConfig -from configs.harness_config import HarnessConfig -from configs.model_config import PostSemClawConfig - -__all__ = ["PostSemClawConfig", "HarnessConfig", "HardwareConfig"] +from configs.hardware_config import HardwareConfig +from configs.harness_config import HarnessConfig +from configs.model_config import PostSemClawConfig + +__all__ = ["PostSemClawConfig", "HarnessConfig", "HardwareConfig"] diff --git a/overlay/configs/hardware_config.py b/overlay/configs/hardware_config.py index 565b1bfc7b1aa2723d151f4a25a01053c124df14..ff3e451c5ff018281347f9bd0e2e0146d9ba9a25 100644 --- a/overlay/configs/hardware_config.py +++ b/overlay/configs/hardware_config.py @@ -1,104 +1,104 @@ -"""Hardware detection and memory budget configuration.""" -from __future__ import annotations - -import torch -from pydantic import BaseModel, Field - - -class HardwareConfig(BaseModel): - """Auto-detected hardware configuration with memory budgets.""" - - gpu_name: str = Field(default="unknown", description="GPU device name") - gpu_memory_mb: int = Field(default=0, description="Total GPU memory in MB") - gpu_vram_mb: int = Field(default=0, description="Alias for gpu_memory_mb (legacy compat)") - compute_capability: tuple[int, int] = Field( - default=(0, 0), description="CUDA compute capability" - ) - peak_flops: float = Field( - default=12.74e12, description="Peak FP32 FLOPS for MFU calculation" - ) - bf16_peak_flops: float = Field( - default=38.1e12, description="Peak BF16 FLOPS (RTX 3060 default)" - ) - - # Memory budget - model_budget_mb: int = Field( - default=1500, description="Max MB for model params + optimizer" - ) - activation_budget_mb: int = Field( - default=3000, description="Max MB for activations" - ) - overhead_mb: int = Field( - default=500, description="Reserved for CUDA context + PyTorch overhead" - ) - max_vram_usage_pct: float = Field( - default=90.0, description="Max VRAM usage as % of total" - ) - gradient_checkpointing: bool = Field( - default=False, description="Enable gradient checkpointing to save VRAM" - ) - - @classmethod - def detect(cls) -> HardwareConfig: - """Auto-detect hardware from current CUDA device.""" - if not torch.cuda.is_available(): - return cls() - - device = torch.cuda.current_device() - props = torch.cuda.get_device_properties(device) - cap = (props.major, props.minor) - mem_mb = props.total_memory // (1024 * 1024) - gpu_name = props.name - - # Peak FP32 FLOPS lookup by compute capability (approximate) - fp32_flops_table: dict[tuple[int, int], float] = { - (8, 6): 12.74e12, # RTX 3060 - (8, 9): 40.09e12, # RTX 4090 - (9, 0): 989.5e12, # H100 (BF16) - } - peak = fp32_flops_table.get(cap, 12.74e12) - - # BF16 peak FLOPS lookup by GPU name substring - bf16_flops_table: dict[str, float] = { - "3060": 38.1e12, - "3090": 71.0e12, - "4090": 165.2e12, - "A100": 312e12, - "H100": 989.5e12, - "A10G": 70.0e12, - } - bf16_peak = 38.1e12 # default to RTX 3060 - for key, val in bf16_flops_table.items(): - if key in gpu_name: - bf16_peak = val - break - - # Memory budget: leave overhead_mb for CUDA context - overhead = 500 - available = mem_mb - overhead - model_budget = int(available * 0.3) # 30% for params + optimizer - activation_budget = int(available * 0.7) # 70% for activations - - return cls( - gpu_name=gpu_name, - gpu_memory_mb=mem_mb, - gpu_vram_mb=mem_mb, - compute_capability=cap, - peak_flops=peak, - bf16_peak_flops=bf16_peak, - model_budget_mb=model_budget, - activation_budget_mb=activation_budget, - ) - - def suggest_batch_size(self, d_model: int, seq_len: int, n_layer: int) -> int: - """Suggest batch size based on activation budget. - - Uses rough estimate: per-sample activation ~= n_layer * seq_len * d_model - * 4 bytes * 2 (fwd + bwd). - """ - per_sample_mb = n_layer * seq_len * d_model * 4 * 2 / (1024 * 1024) - if per_sample_mb <= 0: - return 1 - batch = max(1, int(self.activation_budget_mb / per_sample_mb)) - # Round down to power of 2 - return 2 ** (batch.bit_length() - 1) if batch > 1 else 1 +"""Hardware detection and memory budget configuration.""" +from __future__ import annotations + +import torch +from pydantic import BaseModel, Field + + +class HardwareConfig(BaseModel): + """Auto-detected hardware configuration with memory budgets.""" + + gpu_name: str = Field(default="unknown", description="GPU device name") + gpu_memory_mb: int = Field(default=0, description="Total GPU memory in MB") + gpu_vram_mb: int = Field(default=0, description="Alias for gpu_memory_mb (legacy compat)") + compute_capability: tuple[int, int] = Field( + default=(0, 0), description="CUDA compute capability" + ) + peak_flops: float = Field( + default=12.74e12, description="Peak FP32 FLOPS for MFU calculation" + ) + bf16_peak_flops: float = Field( + default=38.1e12, description="Peak BF16 FLOPS (RTX 3060 default)" + ) + + # Memory budget + model_budget_mb: int = Field( + default=1500, description="Max MB for model params + optimizer" + ) + activation_budget_mb: int = Field( + default=3000, description="Max MB for activations" + ) + overhead_mb: int = Field( + default=500, description="Reserved for CUDA context + PyTorch overhead" + ) + max_vram_usage_pct: float = Field( + default=90.0, description="Max VRAM usage as % of total" + ) + gradient_checkpointing: bool = Field( + default=False, description="Enable gradient checkpointing to save VRAM" + ) + + @classmethod + def detect(cls) -> HardwareConfig: + """Auto-detect hardware from current CUDA device.""" + if not torch.cuda.is_available(): + return cls() + + device = torch.cuda.current_device() + props = torch.cuda.get_device_properties(device) + cap = (props.major, props.minor) + mem_mb = props.total_memory // (1024 * 1024) + gpu_name = props.name + + # Peak FP32 FLOPS lookup by compute capability (approximate) + fp32_flops_table: dict[tuple[int, int], float] = { + (8, 6): 12.74e12, # RTX 3060 + (8, 9): 40.09e12, # RTX 4090 + (9, 0): 989.5e12, # H100 (BF16) + } + peak = fp32_flops_table.get(cap, 12.74e12) + + # BF16 peak FLOPS lookup by GPU name substring + bf16_flops_table: dict[str, float] = { + "3060": 38.1e12, + "3090": 71.0e12, + "4090": 165.2e12, + "A100": 312e12, + "H100": 989.5e12, + "A10G": 70.0e12, + } + bf16_peak = 38.1e12 # default to RTX 3060 + for key, val in bf16_flops_table.items(): + if key in gpu_name: + bf16_peak = val + break + + # Memory budget: leave overhead_mb for CUDA context + overhead = 500 + available = mem_mb - overhead + model_budget = int(available * 0.3) # 30% for params + optimizer + activation_budget = int(available * 0.7) # 70% for activations + + return cls( + gpu_name=gpu_name, + gpu_memory_mb=mem_mb, + gpu_vram_mb=mem_mb, + compute_capability=cap, + peak_flops=peak, + bf16_peak_flops=bf16_peak, + model_budget_mb=model_budget, + activation_budget_mb=activation_budget, + ) + + def suggest_batch_size(self, d_model: int, seq_len: int, n_layer: int) -> int: + """Suggest batch size based on activation budget. + + Uses rough estimate: per-sample activation ~= n_layer * seq_len * d_model + * 4 bytes * 2 (fwd + bwd). + """ + per_sample_mb = n_layer * seq_len * d_model * 4 * 2 / (1024 * 1024) + if per_sample_mb <= 0: + return 1 + batch = max(1, int(self.activation_budget_mb / per_sample_mb)) + # Round down to power of 2 + return 2 ** (batch.bit_length() - 1) if batch > 1 else 1 diff --git a/overlay/configs/harness_config.py b/overlay/configs/harness_config.py index 5ccf2d8a3a72d617c17e34805edb5b30af3939d6..de163f750f10cb6a7b080b2dbdd06c09380b4b17 100644 --- a/overlay/configs/harness_config.py +++ b/overlay/configs/harness_config.py @@ -3,53 +3,53 @@ from typing import Literal from pydantic import BaseModel, Field -type GateThresholds = dict[str, float] -type GateConfig = dict[str, GateThresholds] - - +GateThresholds = dict[str, float] +GateConfig = dict[str, GateThresholds] + + class HarnessConfig(BaseModel): - """Configuration for the HYDRA harness behavior.""" - - # Inner loop - time_budget_seconds: int = Field( - default=300, ge=60, description="Training time budget per experiment in seconds" - ) - max_experiments: int = Field( - default=1000, ge=0, description="Max experiments before stopping (0=infinite)" - ) - - # Meta-agent - meta_interval: int = Field( - default=20, ge=5, description="Run meta-agent every N experiments" - ) - max_meta_changes: int = Field( - default=3, ge=1, le=10, description="Max changes per meta-iteration" - ) - - # Search strategy - exploration_mode: Literal["conservative", "balanced", "bold"] = "balanced" - exploration_budget: int = Field( - default=5, ge=1, description="Consecutive bold experiments when stuck" - ) - stuck_threshold: int = Field( - default=10, ge=3, description="No improvement for N experiments = stuck" - ) - crash_threshold: float = Field( - default=0.5, - ge=0.1, - le=1.0, - description="Crash rate threshold for BROKEN state", - ) - regression_tolerance: float = Field( - default=0.05, - ge=0, - le=0.2, - description="Max val_bpb regression from best (fraction)", - ) - max_regression_pct: float = Field( - default=5.0, description="Max % regression from best known val_bpb" - ) - + """Configuration for the HYDRA harness behavior.""" + + # Inner loop + time_budget_seconds: int = Field( + default=300, ge=60, description="Training time budget per experiment in seconds" + ) + max_experiments: int = Field( + default=1000, ge=0, description="Max experiments before stopping (0=infinite)" + ) + + # Meta-agent + meta_interval: int = Field( + default=20, ge=5, description="Run meta-agent every N experiments" + ) + max_meta_changes: int = Field( + default=3, ge=1, le=10, description="Max changes per meta-iteration" + ) + + # Search strategy + exploration_mode: Literal["conservative", "balanced", "bold"] = "balanced" + exploration_budget: int = Field( + default=5, ge=1, description="Consecutive bold experiments when stuck" + ) + stuck_threshold: int = Field( + default=10, ge=3, description="No improvement for N experiments = stuck" + ) + crash_threshold: float = Field( + default=0.5, + ge=0.1, + le=1.0, + description="Crash rate threshold for BROKEN state", + ) + regression_tolerance: float = Field( + default=0.05, + ge=0, + le=0.2, + description="Max val_bpb regression from best (fraction)", + ) + max_regression_pct: float = Field( + default=5.0, description="Max % regression from best known val_bpb" + ) + # Keep/discard criteria primary_metric: str = "val_bpb" secondary_metrics: GateConfig = Field( @@ -63,23 +63,23 @@ class HarnessConfig(BaseModel): "hestia_quant_error": {"max": 0.05}, } ) - - # Experiment execution - experiment_timeout: int = Field( - default=600, ge=300, description="Kill experiment after N seconds" - ) - warmup_steps: int = Field( - default=10, ge=0, description="Steps to exclude from timing" - ) - - # Git - branch_prefix: str = Field(default="autoresearch", description="Branch naming prefix") - results_file: str = Field(default="results.tsv", description="Experiment log file") - - # Secondary metric gates (optional keep/discard criteria) - gate_mhc_spectral_norm: float | None = Field( - default=None, description="Max mhc_spectral_norm for keep (None=disabled)" - ) + + # Experiment execution + experiment_timeout: int = Field( + default=600, ge=300, description="Kill experiment after N seconds" + ) + warmup_steps: int = Field( + default=10, ge=0, description="Steps to exclude from timing" + ) + + # Git + branch_prefix: str = Field(default="autoresearch", description="Branch naming prefix") + results_file: str = Field(default="results.tsv", description="Experiment log file") + + # Secondary metric gates (optional keep/discard criteria) + gate_mhc_spectral_norm: float | None = Field( + default=None, description="Max mhc_spectral_norm for keep (None=disabled)" + ) gate_engram_hit_rate: float | None = Field( default=None, description="Min engram_hit_rate for keep (None=disabled)" ) diff --git a/overlay/configs/model_config.py b/overlay/configs/model_config.py index 4da7a4f6109d67a84fd93d864b2e7d85aab84428..83de9490e8eeeea3b3ef3497de486a2ced23be5a 100644 --- a/overlay/configs/model_config.py +++ b/overlay/configs/model_config.py @@ -1,80 +1,80 @@ -"""Post-SEM-Claw model configuration with Pydantic validation.""" -from pydantic import BaseModel, Field, field_validator - - -class PostSemClawConfig(BaseModel): - """Configuration for the Post-SEM-Claw architecture. - - Default values mirror the @dataclass in train.py exactly. - train.py is the source of truth — this file must stay in sync with it. - """ - - # Sequence - sequence_len: int = Field(default=2048, description="Context length (from prepare.py MAX_SEQ_LEN)") - vocab_size: int = Field(default=8192, description="Vocabulary size (from prepare.py VOCAB_SIZE)") - - # Mamba-3 SSM - n_layer: int = Field(default=4, ge=1, le=48, description="Number of Mamba-3 blocks") - d_model: int = Field(default=256, ge=64, description="Model embedding dimension") - d_state: int = Field(default=64, ge=16, description="SSM state dimension") - headdim: int = Field(default=32, ge=16, description="SSM head dimension") - n_heads: int = Field(default=8, ge=1, description="Number of SSM heads (d_model // headdim)") - expand: int = Field(default=2, ge=1, le=4, description="Inner dim multiplier (inner_dim = expand * d_model)") - - # mHC (Manifold Hyper-Connection) - mhc_n_streams: int = Field(default=4, ge=2, le=8, description="Number of residual streams") - mhc_sinkhorn_iters: int = Field(default=5, ge=1, le=100, description="Sinkhorn-Knopp iterations") - - # Engram (conditional memory) - engram_n_columns: int = Field(default=4096, ge=256, description="Hash table columns") - engram_key_dim: int = Field(default=64, ge=16, description="Engram key dimension") - engram_layer_idx: int = Field(default=1, ge=0, description="Which layer gets engram (0-indexed)") - - # Hestia QAT (disabled Phase 1, skeleton only) - hestia_enabled: bool = Field(default=False, description="Enable Hestia quantization") - hestia_bits: float = Field(default=1.58, gt=0, description="Target quantization bits (1.58 = 1.58-bit ternary)") - - # SDR (bypass-only in Phase 1) - sdr_enabled: bool = Field(default=False, description="Enable stochastic resonance") - sdr_k: int = Field(default=64, ge=1, description="Top-K sparsification") - sdr_noise_std: float = Field(default=0.1, ge=0.0, description="SR noise standard deviation") - - @field_validator("n_heads") - @classmethod - def validate_heads(cls, v: int, info: "FieldValidationInfo") -> int: - """Ensure n_heads equals d_model // headdim.""" - d_model = info.data.get("d_model", 256) - headdim = info.data.get("headdim", 32) - expected = d_model // headdim - if v != expected: - raise ValueError( - f"n_heads ({v}) must equal d_model // headdim ({expected})" - ) - return v - - def estimate_params(self) -> int: - """Rough parameter count estimate based on train.py architecture.""" - inner = self.expand * self.d_model - # in_proj: d_model -> inner + inner + d_state + d_state + n_heads - in_proj = self.d_model * (inner + inner + self.d_state + self.d_state + self.n_heads) - out_proj = inner * self.d_model - # conv1d (kernel=4, groups=inner_dim) - conv = inner * 4 - # A_log, lambda_theta, D: n_heads each (3 vectors) - ssm_params = self.n_heads * 3 - # bc_norm: d_state * 2 (weight + bias) - bc_norm = self.d_state * 2 - per_block = in_proj + out_proj + conv + ssm_params + bc_norm - blocks = per_block * self.n_layer - - # Embedding + lm_head (tied or untied) - embed = self.vocab_size * self.d_model * 2 - - # Engram: one instance at engram_layer_idx - # columns * d_model keys + d_model * engram_key_dim projection - engram = self.engram_n_columns * self.d_model + self.d_model * self.engram_key_dim - - # mHC mixing matrices: n_layer * mhc_n_streams^2 - mhc = self.n_layer * self.mhc_n_streams ** 2 - - return embed + blocks + engram + mhc +"""Post-SEM-Claw model configuration with Pydantic validation.""" +from pydantic import BaseModel, Field, field_validator + + +class PostSemClawConfig(BaseModel): + """Configuration for the Post-SEM-Claw architecture. + + Default values mirror the @dataclass in train.py exactly. + train.py is the source of truth — this file must stay in sync with it. + """ + + # Sequence + sequence_len: int = Field(default=2048, description="Context length (from prepare.py MAX_SEQ_LEN)") + vocab_size: int = Field(default=8192, description="Vocabulary size (from prepare.py VOCAB_SIZE)") + + # Mamba-3 SSM + n_layer: int = Field(default=4, ge=1, le=48, description="Number of Mamba-3 blocks") + d_model: int = Field(default=256, ge=64, description="Model embedding dimension") + d_state: int = Field(default=64, ge=16, description="SSM state dimension") + headdim: int = Field(default=32, ge=16, description="SSM head dimension") + n_heads: int = Field(default=8, ge=1, description="Number of SSM heads (d_model // headdim)") + expand: int = Field(default=2, ge=1, le=4, description="Inner dim multiplier (inner_dim = expand * d_model)") + + # mHC (Manifold Hyper-Connection) + mhc_n_streams: int = Field(default=4, ge=2, le=8, description="Number of residual streams") + mhc_sinkhorn_iters: int = Field(default=5, ge=1, le=100, description="Sinkhorn-Knopp iterations") + + # Engram (conditional memory) + engram_n_columns: int = Field(default=4096, ge=256, description="Hash table columns") + engram_key_dim: int = Field(default=64, ge=16, description="Engram key dimension") + engram_layer_idx: int = Field(default=1, ge=0, description="Which layer gets engram (0-indexed)") + + # Hestia QAT (disabled Phase 1, skeleton only) + hestia_enabled: bool = Field(default=False, description="Enable Hestia quantization") + hestia_bits: float = Field(default=1.58, gt=0, description="Target quantization bits (1.58 = 1.58-bit ternary)") + + # SDR (bypass-only in Phase 1) + sdr_enabled: bool = Field(default=False, description="Enable stochastic resonance") + sdr_k: int = Field(default=64, ge=1, description="Top-K sparsification") + sdr_noise_std: float = Field(default=0.1, ge=0.0, description="SR noise standard deviation") + + @field_validator("n_heads") + @classmethod + def validate_heads(cls, v: int, info: "FieldValidationInfo") -> int: + """Ensure n_heads equals d_model // headdim.""" + d_model = info.data.get("d_model", 256) + headdim = info.data.get("headdim", 32) + expected = d_model // headdim + if v != expected: + raise ValueError( + f"n_heads ({v}) must equal d_model // headdim ({expected})" + ) + return v + + def estimate_params(self) -> int: + """Rough parameter count estimate based on train.py architecture.""" + inner = self.expand * self.d_model + # in_proj: d_model -> inner + inner + d_state + d_state + n_heads + in_proj = self.d_model * (inner + inner + self.d_state + self.d_state + self.n_heads) + out_proj = inner * self.d_model + # conv1d (kernel=4, groups=inner_dim) + conv = inner * 4 + # A_log, lambda_theta, D: n_heads each (3 vectors) + ssm_params = self.n_heads * 3 + # bc_norm: d_state * 2 (weight + bias) + bc_norm = self.d_state * 2 + per_block = in_proj + out_proj + conv + ssm_params + bc_norm + blocks = per_block * self.n_layer + + # Embedding + lm_head (tied or untied) + embed = self.vocab_size * self.d_model * 2 + + # Engram: one instance at engram_layer_idx + # columns * d_model keys + d_model * engram_key_dim projection + engram = self.engram_n_columns * self.d_model + self.d_model * self.engram_key_dim + + # mHC mixing matrices: n_layer * mhc_n_streams^2 + mhc = self.n_layer * self.mhc_n_streams ** 2 + + return embed + blocks + engram + mhc diff --git a/overlay/harness/__init__.py b/overlay/harness/__init__.py index 74327441bb8969fef2eb323548aa5c4ef1498210..b8c0f06e4203bb9d003b288dd2372009dc2c331c 100644 --- a/overlay/harness/__init__.py +++ b/overlay/harness/__init__.py @@ -1,21 +1,21 @@ -"""HYDRA harness package: orchestration infrastructure for autoresearch.""" -from harness.eval_agent import ExperimentResult, parse_run_log, should_keep -from harness.git_utils import current_branch, current_commit_short -from harness.health_monitor import check_health, get_gpu_stats -from harness.meta_agent import run_meta_iteration -from harness.orchestrator import run_loop -from harness.search_strategy import ResearchState, diagnose - -__all__ = [ - "run_loop", - "parse_run_log", - "ExperimentResult", - "should_keep", - "run_meta_iteration", - "diagnose", - "ResearchState", - "check_health", - "get_gpu_stats", - "current_branch", - "current_commit_short", -] +"""HYDRA harness package: orchestration infrastructure for autoresearch.""" +from harness.eval_agent import ExperimentResult, parse_run_log, should_keep +from harness.git_utils import current_branch, current_commit_short +from harness.health_monitor import check_health, get_gpu_stats +from harness.meta_agent import run_meta_iteration +from harness.orchestrator import run_loop +from harness.search_strategy import ResearchState, diagnose + +__all__ = [ + "run_loop", + "parse_run_log", + "ExperimentResult", + "should_keep", + "run_meta_iteration", + "diagnose", + "ResearchState", + "check_health", + "get_gpu_stats", + "current_branch", + "current_commit_short", +] diff --git a/overlay/harness/eval_agent.py b/overlay/harness/eval_agent.py index 83a981c649d74f2d71e4c878f28061586c6f91d8..2cbdce6250b6fc1cae4936572108028c9bdafc02 100644 --- a/overlay/harness/eval_agent.py +++ b/overlay/harness/eval_agent.py @@ -1,300 +1,172 @@ """Eval agent: parse run.log and extract metrics from training runs.""" import re -import statistics -from dataclasses import dataclass +from dataclasses import dataclass, field -type GateThresholds = dict[str, float] -type GateConfig = dict[str, GateThresholds] - - -@dataclass +@dataclass class ExperimentResult: - """Parsed result from a single experiment run. - - All float fields default to 0.0; integer fields default to 0. - The ``crashed`` flag is set when the log indicates a failure or the - log file is missing entirely. - """ - - # Primary metric - val_bpb: float = 0.0 - - # Timing - training_seconds: float = 0.0 - total_seconds: float = 0.0 - - # Hardware - peak_vram_mb: float = 0.0 - mfu_percent: float = 0.0 - + """Parsed result from a single experiment run. + + All float fields default to 0.0; integer fields default to 0. + The ``crashed`` flag is set when the log indicates a failure or the + log file is missing entirely. + """ + + # Primary metric + val_bpb: float = 0.0 + + # Timing + training_seconds: float = 0.0 + total_seconds: float = 0.0 + + # Hardware + peak_vram_mb: float = 0.0 + mfu_percent: float = 0.0 + # Throughput total_tokens_m: float = 0.0 num_steps: int = 0 - tps_median: float = 0.0 - tps_p10: float = 0.0 - tps_min: float = 0.0 - tps_max: float = 0.0 - tps_samples: int = 0 - - # Model shape (echoed by train.py summary block) - num_params_m: float = 0.0 - n_layer: int = 0 - d_model: int = 0 - + + # Model shape (echoed by train.py summary block) + num_params_m: float = 0.0 + n_layer: int = 0 + d_model: int = 0 + # Secondary health metrics mhc_spectral_norm: float = 0.0 engram_hit_rate: float = 0.0 sr_bypass_rate: float = 0.0 - # Evaluation breadth metrics - factual_english_score: float = 0.0 - instruction_following_score: float = 0.0 - distinct_1: float = 0.0 - distinct_2: float = 0.0 - repetition_rate: float = 0.0 - repetition_bigram_rate: float = 0.0 - calibration_ece: float = 0.0 - calibration_brier: float = 0.0 - calibration_accuracy: float = 0.0 - calibration_tokens: int = 0 - eval_seed: int = 0 - eval_seed_group: str = "" - - # Status - crashed: bool = False - error_message: str = "" - - -# Regex patterns keyed by ExperimentResult attribute name. -# Format must match the ``--- Summary ---`` block printed by train.py. -_PATTERNS: dict[str, str] = { - "val_bpb": r"^val_bpb:\s+([\d.]+)", - "training_seconds": r"^training_seconds:\s+([\d.]+)", - "total_seconds": r"^total_seconds:\s+([\d.]+)", - "peak_vram_mb": r"^peak_vram_mb:\s+([\d.]+)", - "mfu_percent": r"^mfu_percent:\s+([\d.]+)", - "total_tokens_m": r"^total_tokens_M:\s+([\d.]+)", - "num_steps": r"^num_steps:\s+(\d+)", - "num_params_m": r"^num_params_M:\s+([\d.]+)", - "n_layer": r"^n_layer:\s+(\d+)", - "d_model": r"^d_model:\s+(\d+)", - "mhc_spectral_norm": r"^mhc_spectral_norm:\s+([\d.]+)", + # Status + crashed: bool = False + error_message: str = "" + + +# Regex patterns keyed by ExperimentResult attribute name. +# Format must match the ``--- Summary ---`` block printed by train.py. +_PATTERNS: dict[str, str] = { + "val_bpb": r"^val_bpb:\s+([\d.]+)", + "training_seconds": r"^training_seconds:\s+([\d.]+)", + "total_seconds": r"^total_seconds:\s+([\d.]+)", + "peak_vram_mb": r"^peak_vram_mb:\s+([\d.]+)", + "mfu_percent": r"^mfu_percent:\s+([\d.]+)", + "total_tokens_m": r"^total_tokens_M:\s+([\d.]+)", + "num_steps": r"^num_steps:\s+(\d+)", + "num_params_m": r"^num_params_M:\s+([\d.]+)", + "n_layer": r"^n_layer:\s+(\d+)", + "d_model": r"^d_model:\s+(\d+)", + "mhc_spectral_norm": r"^mhc_spectral_norm:\s+([\d.]+)", "engram_hit_rate": r"^engram_hit_rate:\s+([\d.]+)", "sr_bypass_rate": r"^sr_bypass_rate:\s+([\d.]+)", - "factual_english_score": r"^factual_english_score:\s+([\d.]+)", - "instruction_following_score": r"^instruction_following_score:\s+([\d.]+)", - "distinct_1": r"^distinct_1:\s+([\d.]+)", - "distinct_2": r"^distinct_2:\s+([\d.]+)", - "repetition_rate": r"^repetition_rate:\s+([\d.]+)", - "repetition_bigram_rate": r"^repetition_bigram_rate:\s+([\d.]+)", - "calibration_ece": r"^calibration_ece:\s+([\d.]+)", - "calibration_brier": r"^calibration_brier:\s*([\d.]+)", - "calibration_accuracy": r"^calibration_accuracy:\s+([\d.]+)", - "calibration_tokens": r"^calibration_tokens:\s+(\d+)", - "eval_seed": r"^eval_seed:\s+(\d+)", - "eval_seed_group": r"^eval_seed_group:\s+(.+)", } - -# Attributes that should be parsed as int rather than float. -_INT_ATTRS: frozenset[str] = frozenset( - { - "num_steps", - "n_layer", - "d_model", - "calibration_tokens", - "eval_seed", - } -) -_STR_ATTRS: frozenset[str] = frozenset({"eval_seed_group"}) -_STEP_TPS_PATTERN = re.compile(r"step=(\d+).*?\btps=(\d+)\b") -_TPS_PATTERN = re.compile(r"\btps=(\d+)\b") - - -def _percentile_linear(sorted_values: list[float], pct: float) -> float: - """Compute percentile via linear interpolation (0 <= pct <= 100).""" - if not sorted_values: - return 0.0 - if len(sorted_values) == 1: - return sorted_values[0] - rank = (len(sorted_values) - 1) * (pct / 100.0) - lo = int(rank) - hi = min(lo + 1, len(sorted_values) - 1) - frac = rank - lo - return sorted_values[lo] * (1.0 - frac) + sorted_values[hi] * frac - - -def parse_run_log(log_path: str) -> ExperimentResult: - """Parse a run.log file and extract all training metrics. - - Args: - log_path: Absolute path to the run.log file. - - Returns: - Populated ExperimentResult; sets ``crashed=True`` when the log - contains a traceback or the file is missing. - """ - result = ExperimentResult() - - try: - with open(log_path) as fh: - content = fh.read() - except FileNotFoundError: - result.crashed = True - result.error_message = f"Log file not found: {log_path}" - return result - - # Detect crash signals in output. Keep this strict to avoid false positives - # from benign log lines that include "error" in a non-fatal context. - if ( - "Traceback" in content - or "\nFAIL\n" in content - or "[TPS_GUARD] FAIL" in content - or "raise SystemExit(1)" in content - ): + +# Attributes that should be parsed as int rather than float. +_INT_ATTRS: frozenset[str] = frozenset({"num_steps", "n_layer", "d_model"}) + + +def parse_run_log(log_path: str) -> ExperimentResult: + """Parse a run.log file and extract all training metrics. + + Args: + log_path: Absolute path to the run.log file. + + Returns: + Populated ExperimentResult; sets ``crashed=True`` when the log + contains a traceback or the file is missing. + """ + result = ExperimentResult() + + try: + with open(log_path) as fh: + content = fh.read() + except FileNotFoundError: + result.crashed = True + result.error_message = f"Log file not found: {log_path}" + return result + + # Detect crash signals in output. + if "Traceback" in content or "FAIL" in content or "Error" in content: result.crashed = True lines = content.strip().splitlines() result.error_message = "\n".join(lines[-20:]) - + for attr, pattern in _PATTERNS.items(): match = re.search(pattern, content, re.MULTILINE) if match: raw = match.group(1) - if attr in _INT_ATTRS: - setattr(result, attr, int(raw)) - elif attr in _STR_ATTRS: - setattr(result, attr, raw.strip()) - else: - setattr(result, attr, float(raw)) - - warmup_steps = 10 - warmup_match = re.search(r"\[TPS_GUARD\] enabled .*?warmup_steps=(\d+)", content) - if warmup_match: - warmup_steps = int(warmup_match.group(1)) - - step_tps_samples: list[tuple[int, int]] = [] - for m in _STEP_TPS_PATTERN.finditer(content): - step_tps_samples.append((int(m.group(1)), int(m.group(2)))) - - tps_values: list[float] = [] - if step_tps_samples: - for step, tps in step_tps_samples: - if step >= warmup_steps: - tps_values.append(float(tps)) - if not tps_values: - tps_values = [float(tps) for _, tps in step_tps_samples] - else: - tps_values = [float(m.group(1)) for m in _TPS_PATTERN.finditer(content)] - - if tps_values: - sorted_tps = sorted(tps_values) - result.tps_samples = len(tps_values) - result.tps_median = float(statistics.median(tps_values)) - result.tps_p10 = float(_percentile_linear(sorted_tps, 10.0)) - result.tps_min = float(sorted_tps[0]) - result.tps_max = float(sorted_tps[-1]) + setattr(result, attr, int(raw) if attr in _INT_ATTRS else float(raw)) return result - - + + def check_secondary_alarms(result: ExperimentResult) -> list[str]: - """Check secondary metrics against fixed alarm thresholds. - - Args: - result: Parsed experiment result. - - Returns: - List of human-readable alarm strings (empty if all clear). - """ - alarms: list[str] = [] - - if result.mhc_spectral_norm > 2.0: - alarms.append( - f"mhc_spectral_norm={result.mhc_spectral_norm:.4f} > 2.0 (ALARM)" - ) - if 0 < result.engram_hit_rate < 0.1: - alarms.append( - f"engram_hit_rate={result.engram_hit_rate:.4f} < 0.1 (memory underused)" - ) - if 0 < result.mfu_percent < 10: + """Check secondary metrics against fixed alarm thresholds. + + Args: + result: Parsed experiment result. + + Returns: + List of human-readable alarm strings (empty if all clear). + """ + alarms: list[str] = [] + + if result.mhc_spectral_norm > 2.0: alarms.append( - f"mfu_percent={result.mfu_percent:.2f}% < 10% (GPU underutilized)" + f"mhc_spectral_norm={result.mhc_spectral_norm:.4f} > 2.0 (ALARM)" ) - if result.calibration_ece > 0.35: + if 0 < result.engram_hit_rate < 0.1: alarms.append( - f"calibration_ece={result.calibration_ece:.4f} > 0.35 (poor calibration)" + f"engram_hit_rate={result.engram_hit_rate:.4f} < 0.1 (memory underused)" ) - if result.tps_median > 0 and result.tps_median < 50000: + if 0 < result.mfu_percent < 10: alarms.append( - f"tps_median={result.tps_median:.0f} < 50000 (throughput below A10 objective)" + f"mfu_percent={result.mfu_percent:.2f}% < 10% (GPU underutilized)" ) - + return alarms -def _check_gate( - result: ExperimentResult, - gates: GateConfig, - metric: str, -) -> tuple[bool, str] | None: - """Evaluate a single min/max gate against an ExperimentResult metric.""" - gate = gates.get(metric, {}) - value = getattr(result, metric) - max_value = gate.get("max") - if max_value is not None and value > max_value: - return False, f"{metric} {value:.4f} > gate {max_value}" - min_value = gate.get("min") - if min_value is not None and value < min_value: - return False, f"{metric} {value:.4f} < gate {min_value}" - return None - - def should_keep( result: ExperimentResult, best_bpb: float, - gates: GateConfig | None = None, + gates: dict | None = None, ) -> tuple[bool, str]: - """Decide whether to keep or discard an experiment. - - The primary criterion is strictly lower val_bpb than the current best. - Optional secondary gates (passed from HarnessConfig.secondary_metrics) - can reject an otherwise-improving result. - - Args: - result: Parsed experiment result. - best_bpb: Current best val_bpb across all experiments. - gates: Optional dict mapping metric name to threshold dict with - ``"max"`` or ``"min"`` keys, e.g. - ``{"mhc_spectral_norm": {"max": 2.0}}``. - - Returns: - Tuple of (keep: bool, reason: str). - """ - if result.crashed: - return False, "crash" - if result.val_bpb <= 0: - return False, "invalid val_bpb" - if result.val_bpb >= best_bpb: - return False, "discard" - + """Decide whether to keep or discard an experiment. + + The primary criterion is strictly lower val_bpb than the current best. + Optional secondary gates (passed from HarnessConfig.secondary_metrics) + can reject an otherwise-improving result. + + Args: + result: Parsed experiment result. + best_bpb: Current best val_bpb across all experiments. + gates: Optional dict mapping metric name to threshold dict with + ``"max"`` or ``"min"`` keys, e.g. + ``{"mhc_spectral_norm": {"max": 2.0}}``. + + Returns: + Tuple of (keep: bool, reason: str). + """ + if result.crashed: + return False, "crash" + if result.val_bpb <= 0: + return False, "invalid val_bpb" + if result.val_bpb >= best_bpb: + return False, "discard" + # Secondary gate checks. if gates: - gate_metrics = ( - "mhc_spectral_norm", - "engram_hit_rate", - "factual_english_score", - "instruction_following_score", - "distinct_1", - "distinct_2", - "repetition_rate", - "repetition_bigram_rate", - "calibration_ece", - "tps_median", - "tps_p10", - ) - for metric in gate_metrics: - gate_result = _check_gate(result, gates, metric) - if gate_result is not None: - return gate_result + gate_mhc = gates.get("mhc_spectral_norm", {}).get("max") + if gate_mhc is not None and result.mhc_spectral_norm > gate_mhc: + return ( + False, + f"mhc_spectral_norm {result.mhc_spectral_norm:.4f} > gate {gate_mhc}", + ) + gate_engram = gates.get("engram_hit_rate", {}).get("min") + if gate_engram is not None and result.engram_hit_rate < gate_engram: + return ( + False, + f"engram_hit_rate {result.engram_hit_rate:.4f} < gate {gate_engram}", + ) return True, "keep" diff --git a/overlay/harness/git_utils.py b/overlay/harness/git_utils.py index a9cadccb63b8b443245225379c194bc8623449a3..5f7f540b3b6da986a29aebdf0f50da8cf555e190 100644 --- a/overlay/harness/git_utils.py +++ b/overlay/harness/git_utils.py @@ -1,94 +1,94 @@ -"""Git utilities for HYDRA autoresearch branch management.""" -import os -import subprocess - -REPO_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -def run_git(*args: str, check: bool = True) -> subprocess.CompletedProcess: - """Run a git command in the repo directory. - - Args: - *args: Git command arguments. - check: Whether to raise on non-zero exit code. - - Returns: - Completed process with stdout/stderr captured. - """ - return subprocess.run( - ["git"] + list(args), - cwd=REPO_DIR, - capture_output=True, - text=True, - check=check, - ) - - -def current_branch() -> str: - """Return the current git branch name. - - Returns: - Branch name string. - """ - result = run_git("rev-parse", "--abbrev-ref", "HEAD") - return result.stdout.strip() - - -def current_commit_short() -> str: - """Return the current HEAD commit short hash (7 chars). - - Returns: - 7-character commit hash. - """ - result = run_git("rev-parse", "--short=7", "HEAD") - return result.stdout.strip() - - -def create_branch(name: str) -> None: - """Create and switch to a new branch. - - Args: - name: Branch name to create. - """ - run_git("checkout", "-b", name) - - -def commit_all(message: str) -> str: - """Stage all changes, commit, and return short hash. - - Args: - message: Commit message. - - Returns: - Short commit hash after committing. - """ - run_git("add", "-A") - run_git("commit", "-m", message, check=False) - return current_commit_short() - - -def reset_to(commit: str) -> None: - """Hard reset to a specific commit, discarding all changes. - - Args: - commit: Commit hash (short or full) to reset to. - """ - run_git("reset", "--hard", commit) - - -def get_last_n_diffs(n: int = 3) -> list[str]: - """Get the last N commit diffs (--stat format) for meta-agent context. - - Args: - n: Number of recent commits to retrieve. - - Returns: - List of diff stat strings, one per commit (truncated to 500 chars). - """ - result = run_git("log", f"-{n}", "--format=%H", check=False) - hashes = [h for h in result.stdout.strip().split("\n") if h] - diffs: list[str] = [] - for h in hashes: - diff_result = run_git("show", "--stat", h, check=False) - diffs.append(diff_result.stdout[:500]) - return diffs +"""Git utilities for HYDRA autoresearch branch management.""" +import os +import subprocess + +REPO_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +def run_git(*args: str, check: bool = True) -> subprocess.CompletedProcess: + """Run a git command in the repo directory. + + Args: + *args: Git command arguments. + check: Whether to raise on non-zero exit code. + + Returns: + Completed process with stdout/stderr captured. + """ + return subprocess.run( + ["git"] + list(args), + cwd=REPO_DIR, + capture_output=True, + text=True, + check=check, + ) + + +def current_branch() -> str: + """Return the current git branch name. + + Returns: + Branch name string. + """ + result = run_git("rev-parse", "--abbrev-ref", "HEAD") + return result.stdout.strip() + + +def current_commit_short() -> str: + """Return the current HEAD commit short hash (7 chars). + + Returns: + 7-character commit hash. + """ + result = run_git("rev-parse", "--short=7", "HEAD") + return result.stdout.strip() + + +def create_branch(name: str) -> None: + """Create and switch to a new branch. + + Args: + name: Branch name to create. + """ + run_git("checkout", "-b", name) + + +def commit_all(message: str) -> str: + """Stage all changes, commit, and return short hash. + + Args: + message: Commit message. + + Returns: + Short commit hash after committing. + """ + run_git("add", "-A") + run_git("commit", "-m", message, check=False) + return current_commit_short() + + +def reset_to(commit: str) -> None: + """Hard reset to a specific commit, discarding all changes. + + Args: + commit: Commit hash (short or full) to reset to. + """ + run_git("reset", "--hard", commit) + + +def get_last_n_diffs(n: int = 3) -> list[str]: + """Get the last N commit diffs (--stat format) for meta-agent context. + + Args: + n: Number of recent commits to retrieve. + + Returns: + List of diff stat strings, one per commit (truncated to 500 chars). + """ + result = run_git("log", f"-{n}", "--format=%H", check=False) + hashes = [h for h in result.stdout.strip().split("\n") if h] + diffs: list[str] = [] + for h in hashes: + diff_result = run_git("show", "--stat", h, check=False) + diffs.append(diff_result.stdout[:500]) + return diffs diff --git a/overlay/harness/health_monitor.py b/overlay/harness/health_monitor.py index d574b9c95078e3b42082b3598a2c48b88ab27e89..6c7bae6840381f9b63baab1915616f6ea98336a8 100644 --- a/overlay/harness/health_monitor.py +++ b/overlay/harness/health_monitor.py @@ -1,86 +1,86 @@ -"""Hardware health monitoring for HYDRA experiments. - -Provides lightweight checks that the orchestrator runs before each -experiment to avoid launching training into a degraded GPU state. -""" -import os - -import torch - - -def get_gpu_stats() -> dict: - """Return current GPU memory statistics. - - Returns: - Dict with keys: available (bool), and when available: - name, memory_allocated_mb, memory_reserved_mb, - max_memory_allocated_mb, memory_total_mb. - """ - if not torch.cuda.is_available(): - return {"available": False} - - props = torch.cuda.get_device_properties(0) - return { - "available": True, - "name": torch.cuda.get_device_name(0), - "memory_allocated_mb": torch.cuda.memory_allocated(0) / (1024 * 1024), - "memory_reserved_mb": torch.cuda.memory_reserved(0) / (1024 * 1024), - "max_memory_allocated_mb": torch.cuda.max_memory_allocated(0) / (1024 * 1024), - "memory_total_mb": props.total_mem / (1024 * 1024), - } - - -def check_health( - vram_pressure_pct: float = 90.0, - min_free_disk_gb: float = 1.0, -) -> tuple[bool, list[str]]: - """Check GPU and disk health before launching an experiment. - - Args: - vram_pressure_pct: Warn when GPU memory allocation exceeds this - percentage of total VRAM. - min_free_disk_gb: Warn when free disk space falls below this. - - Returns: - Tuple of (healthy: bool, warnings: list[str]). - ``healthy`` is True when there are no warnings. - """ - warnings: list[str] = [] - stats = get_gpu_stats() - - if not stats["available"]: - return False, ["No CUDA GPU available"] - - # Memory pressure check. - used_pct = ( - stats["memory_allocated_mb"] / stats["memory_total_mb"] * 100 - if stats["memory_total_mb"] > 0 - else 0.0 - ) - if used_pct > vram_pressure_pct: - warnings.append( - f"GPU memory pressure: {used_pct:.1f}% allocated " - f"({stats['memory_allocated_mb']:.0f} / {stats['memory_total_mb']:.0f} MB)" - ) - - # Disk space check. - try: - statvfs = os.statvfs(os.path.dirname(os.path.abspath(__file__))) - free_gb = (statvfs.f_bavail * statvfs.f_frsize) / (1024**3) - if free_gb < min_free_disk_gb: - warnings.append(f"Low disk space: {free_gb:.2f} GB free") - except (AttributeError, OSError): - # os.statvfs not available on all platforms (e.g. Windows). - pass - - return len(warnings) == 0, warnings - - -def reset_peak_stats() -> None: - """Reset GPU peak memory tracking for the next experiment. - - Should be called immediately before launching each training run so - that peak_vram_mb reported in run.log reflects only that experiment. - """ - if torch.cuda.is_available(): - torch.cuda.reset_peak_memory_stats() +"""Hardware health monitoring for HYDRA experiments. + +Provides lightweight checks that the orchestrator runs before each +experiment to avoid launching training into a degraded GPU state. +""" +import os + +import torch + + +def get_gpu_stats() -> dict: + """Return current GPU memory statistics. + + Returns: + Dict with keys: available (bool), and when available: + name, memory_allocated_mb, memory_reserved_mb, + max_memory_allocated_mb, memory_total_mb. + """ + if not torch.cuda.is_available(): + return {"available": False} + + props = torch.cuda.get_device_properties(0) + return { + "available": True, + "name": torch.cuda.get_device_name(0), + "memory_allocated_mb": torch.cuda.memory_allocated(0) / (1024 * 1024), + "memory_reserved_mb": torch.cuda.memory_reserved(0) / (1024 * 1024), + "max_memory_allocated_mb": torch.cuda.max_memory_allocated(0) / (1024 * 1024), + "memory_total_mb": props.total_mem / (1024 * 1024), + } + + +def check_health( + vram_pressure_pct: float = 90.0, + min_free_disk_gb: float = 1.0, +) -> tuple[bool, list[str]]: + """Check GPU and disk health before launching an experiment. + + Args: + vram_pressure_pct: Warn when GPU memory allocation exceeds this + percentage of total VRAM. + min_free_disk_gb: Warn when free disk space falls below this. + + Returns: + Tuple of (healthy: bool, warnings: list[str]). + ``healthy`` is True when there are no warnings. + """ + warnings: list[str] = [] + stats = get_gpu_stats() + + if not stats["available"]: + return False, ["No CUDA GPU available"] + + # Memory pressure check. + used_pct = ( + stats["memory_allocated_mb"] / stats["memory_total_mb"] * 100 + if stats["memory_total_mb"] > 0 + else 0.0 + ) + if used_pct > vram_pressure_pct: + warnings.append( + f"GPU memory pressure: {used_pct:.1f}% allocated " + f"({stats['memory_allocated_mb']:.0f} / {stats['memory_total_mb']:.0f} MB)" + ) + + # Disk space check. + try: + statvfs = os.statvfs(os.path.dirname(os.path.abspath(__file__))) + free_gb = (statvfs.f_bavail * statvfs.f_frsize) / (1024**3) + if free_gb < min_free_disk_gb: + warnings.append(f"Low disk space: {free_gb:.2f} GB free") + except (AttributeError, OSError): + # os.statvfs not available on all platforms (e.g. Windows). + pass + + return len(warnings) == 0, warnings + + +def reset_peak_stats() -> None: + """Reset GPU peak memory tracking for the next experiment. + + Should be called immediately before launching each training run so + that peak_vram_mb reported in run.log reflects only that experiment. + """ + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats() diff --git a/overlay/harness/meta_agent.py b/overlay/harness/meta_agent.py index 92292a74de21dd0430f0a949b004f2bde02ed892..240eb42b83756ba18b831e3d3b4cf2aa08b41ffd 100644 --- a/overlay/harness/meta_agent.py +++ b/overlay/harness/meta_agent.py @@ -1,139 +1,139 @@ -"""Meta-agent: evolves program.md based on experiment history. - -Runs every ``meta_interval`` inner-loop experiments (configured in -HarnessConfig). Reads the current research state from results.tsv, -decides whether guidance is needed, and appends a directive to -program.md. Any previous auto-generated directive is replaced so -the file stays clean. -""" -import os - -from harness.git_utils import REPO_DIR -from harness.search_strategy import ResearchState, diagnose - -PROGRAM_PATH = os.path.join(REPO_DIR, "program.md") -RESULTS_PATH = os.path.join(REPO_DIR, "results.tsv") - -# Sentinel that marks auto-generated content so it can be cleanly replaced. -_DIRECTIVE_MARKER = "## Meta-Agent Directive (auto-generated)" - - -def generate_directive(state: ResearchState) -> str | None: - """Generate a directive string to append to program.md, or None. - - A directive is only produced when the research state is not EXPLORING - (i.e., something needs to change). - - Args: - state: Current ResearchState diagnosis. - - Returns: - Formatted directive string, or None when no change is needed. - """ - if state.label == "EXPLORING": - return None - - if state.label == "BROKEN": - return ( - f"\n{_DIRECTIVE_MARKER}\n" - f"ALERT: Crash rate is {state.crash_rate:.0%} in the recent window. " - "Revert to the last stable commit. Reduce model complexity before " - "proposing further changes. Suggested actions:\n" - "- Reduce d_model or n_layer\n" - "- Reduce batch_size\n" - "- Disable experimental modules (Engram, mHC, Hestia) one at a time\n" - ) - - if state.label == "STUCK": - stale = state.total_experiments - state.last_improvement_at - return ( - f"\n{_DIRECTIVE_MARKER}\n" - f"ALERT: No improvement for {stale} experiments " - f"(best_bpb={state.best_bpb:.6f}). " - "Apply BOLD changes for the next 5 experiments:\n" - "- Dramatically change d_model or n_layer (2× or ½)\n" - "- Toggle Engram or mHC on/off entirely\n" - "- Change optimizer hyperparameters by 3–5×\n" - "- Temporarily accept results within 0.5% of baseline\n" - ) - - if state.label == "EXPLOITING": - return ( - f"\n{_DIRECTIVE_MARKER}\n" - "Search is converging too early. Inject diversity:\n" - "- If recent experiments tune LR, try architecture changes instead\n" - "- If tuning architecture, try optimizer or regularisation changes\n" - "- Try removing complexity (simplification wins are valuable)\n" - "- Explore a subsystem not touched in the last 10 experiments\n" - ) - - return None - - -def _strip_previous_directive(content: str) -> str: - """Remove any prior auto-generated directive block from content. - - Args: - content: Full text of program.md. - - Returns: - Content with any previous directive stripped and trailing - whitespace normalised. - """ - if _DIRECTIVE_MARKER in content: - content = content[: content.index(_DIRECTIVE_MARKER)].rstrip() + "\n" - return content - - -def run_meta_iteration( - program_path: str = PROGRAM_PATH, - results_path: str = RESULTS_PATH, -) -> dict: - """Run one meta-agent iteration. - - Diagnoses the current research state and optionally rewrites - program.md with a new directive. - - Args: - program_path: Path to program.md. - results_path: Path to results.tsv. - - Returns: - Summary dict with keys: state, total_experiments, best_bpb, - crash_rate, changed, and optionally directive. - """ - state = diagnose(results_path) - - summary: dict = { - "state": state.label, - "total_experiments": state.total_experiments, - "best_bpb": state.best_bpb, - "crash_rate": state.crash_rate, - "changed": False, - } - - directive = generate_directive(state) - if directive is None: - return summary - - try: - with open(program_path) as fh: - content = fh.read() - except FileNotFoundError: - content = "" - - content = _strip_previous_directive(content) - content = content + "\n" + directive - - tmp_path = program_path + ".tmp" - try: - with open(tmp_path, "w") as fh: - fh.write(content) - os.replace(tmp_path, program_path) # atomic on POSIX - finally: - if os.path.exists(tmp_path): - os.unlink(tmp_path) - - summary["changed"] = True - summary["directive"] = directive.strip() - return summary +"""Meta-agent: evolves program.md based on experiment history. + +Runs every ``meta_interval`` inner-loop experiments (configured in +HarnessConfig). Reads the current research state from results.tsv, +decides whether guidance is needed, and appends a directive to +program.md. Any previous auto-generated directive is replaced so +the file stays clean. +""" +import os + +from harness.git_utils import REPO_DIR +from harness.search_strategy import ResearchState, diagnose + +PROGRAM_PATH = os.path.join(REPO_DIR, "program.md") +RESULTS_PATH = os.path.join(REPO_DIR, "results.tsv") + +# Sentinel that marks auto-generated content so it can be cleanly replaced. +_DIRECTIVE_MARKER = "## Meta-Agent Directive (auto-generated)" + + +def generate_directive(state: ResearchState) -> str | None: + """Generate a directive string to append to program.md, or None. + + A directive is only produced when the research state is not EXPLORING + (i.e., something needs to change). + + Args: + state: Current ResearchState diagnosis. + + Returns: + Formatted directive string, or None when no change is needed. + """ + if state.label == "EXPLORING": + return None + + if state.label == "BROKEN": + return ( + f"\n{_DIRECTIVE_MARKER}\n" + f"ALERT: Crash rate is {state.crash_rate:.0%} in the recent window. " + "Revert to the last stable commit. Reduce model complexity before " + "proposing further changes. Suggested actions:\n" + "- Reduce d_model or n_layer\n" + "- Reduce batch_size\n" + "- Disable experimental modules (Engram, mHC, Hestia) one at a time\n" + ) + + if state.label == "STUCK": + stale = state.total_experiments - state.last_improvement_at + return ( + f"\n{_DIRECTIVE_MARKER}\n" + f"ALERT: No improvement for {stale} experiments " + f"(best_bpb={state.best_bpb:.6f}). " + "Apply BOLD changes for the next 5 experiments:\n" + "- Dramatically change d_model or n_layer (2× or ½)\n" + "- Toggle Engram or mHC on/off entirely\n" + "- Change optimizer hyperparameters by 3–5×\n" + "- Temporarily accept results within 0.5% of baseline\n" + ) + + if state.label == "EXPLOITING": + return ( + f"\n{_DIRECTIVE_MARKER}\n" + "Search is converging too early. Inject diversity:\n" + "- If recent experiments tune LR, try architecture changes instead\n" + "- If tuning architecture, try optimizer or regularisation changes\n" + "- Try removing complexity (simplification wins are valuable)\n" + "- Explore a subsystem not touched in the last 10 experiments\n" + ) + + return None + + +def _strip_previous_directive(content: str) -> str: + """Remove any prior auto-generated directive block from content. + + Args: + content: Full text of program.md. + + Returns: + Content with any previous directive stripped and trailing + whitespace normalised. + """ + if _DIRECTIVE_MARKER in content: + content = content[: content.index(_DIRECTIVE_MARKER)].rstrip() + "\n" + return content + + +def run_meta_iteration( + program_path: str = PROGRAM_PATH, + results_path: str = RESULTS_PATH, +) -> dict: + """Run one meta-agent iteration. + + Diagnoses the current research state and optionally rewrites + program.md with a new directive. + + Args: + program_path: Path to program.md. + results_path: Path to results.tsv. + + Returns: + Summary dict with keys: state, total_experiments, best_bpb, + crash_rate, changed, and optionally directive. + """ + state = diagnose(results_path) + + summary: dict = { + "state": state.label, + "total_experiments": state.total_experiments, + "best_bpb": state.best_bpb, + "crash_rate": state.crash_rate, + "changed": False, + } + + directive = generate_directive(state) + if directive is None: + return summary + + try: + with open(program_path) as fh: + content = fh.read() + except FileNotFoundError: + content = "" + + content = _strip_previous_directive(content) + content = content + "\n" + directive + + tmp_path = program_path + ".tmp" + try: + with open(tmp_path, "w") as fh: + fh.write(content) + os.replace(tmp_path, program_path) # atomic on POSIX + finally: + if os.path.exists(tmp_path): + os.unlink(tmp_path) + + summary["changed"] = True + summary["directive"] = directive.strip() + return summary diff --git a/overlay/harness/orchestrator.py b/overlay/harness/orchestrator.py index c4c78d750ec600ffb8489446f30f62a2e43f10ab..f8b56714ef71814c4ec8cfd1a18564f49271fddf 100644 --- a/overlay/harness/orchestrator.py +++ b/overlay/harness/orchestrator.py @@ -1,296 +1,293 @@ -"""HYDRA Orchestrator: main loop for autonomous research. - -Usage:: - - python -m harness.orchestrator [--meta-interval N] [--max-experiments N] - -Loop: - 1. Read current state (branch, results.tsv, program.md) - 2. [Architect Agent] proposes and applies changes to train.py (external) - 3. Git commit the changes - 4. Run training: ``uv run train.py`` captured to run.log - 5. [Eval Agent] extract metrics from run.log - 6. Keep or discard based on val_bpb + secondary metric gates - 7. Log to results.tsv - 8. Every ``meta_interval`` experiments: [Meta Agent] evolves program.md - 9. Repeat - -The orchestrator intentionally does NOT modify train.py itself -- it -provides the infrastructure ("rails") that the autoresearch loop runs on. -""" -import argparse -import csv +"""HYDRA Orchestrator: main loop for autonomous research. + +Usage:: + + python -m harness.orchestrator [--meta-interval N] [--max-experiments N] + +Loop: + 1. Read current state (branch, results.tsv, program.md) + 2. [Architect Agent] proposes and applies changes to train.py (external) + 3. Git commit the changes + 4. Run training: ``uv run train.py`` captured to run.log + 5. [Eval Agent] extract metrics from run.log + 6. Keep or discard based on val_bpb + secondary metric gates + 7. Log to results.tsv + 8. Every ``meta_interval`` experiments: [Meta Agent] evolves program.md + 9. Repeat + +The orchestrator intentionally does NOT modify train.py itself -- it +provides the infrastructure ("rails") that the autoresearch loop runs on. +""" +import argparse +import csv import os import subprocess import time -from configs.harness_config import HarnessConfig from harness.eval_agent import ExperimentResult, check_secondary_alarms, parse_run_log, should_keep -from harness.git_utils import REPO_DIR, commit_all, current_commit_short, reset_to -from harness.health_monitor import check_health, reset_peak_stats -from harness.meta_agent import run_meta_iteration -from harness.search_strategy import diagnose - -# --------------------------------------------------------------------------- -# Paths -# --------------------------------------------------------------------------- - -RESULTS_FILE = os.path.join(REPO_DIR, "results.tsv") -RUN_LOG = os.path.join(REPO_DIR, "run.log") - -_TSV_HEADER = "commit\tval_bpb\tmemory_gb\tstatus\tdescription\n" - - -# --------------------------------------------------------------------------- -# TSV helpers -# --------------------------------------------------------------------------- - - -def init_results_tsv() -> None: - """Create results.tsv with header row if it does not yet exist.""" - if not os.path.exists(RESULTS_FILE): - with open(RESULTS_FILE, "w") as fh: - fh.write(_TSV_HEADER) - - -def log_result( - commit: str, - val_bpb: float, - memory_gb: float, - status: str, - description: str, -) -> None: - """Append one row to results.tsv. - - Args: - commit: Short git hash for this experiment. - val_bpb: Validation bits-per-byte (0.0 for crashes). - memory_gb: Peak VRAM usage in gigabytes. - status: One of keep / discard / crash / timeout. - description: Short human-readable description. - """ - with open(RESULTS_FILE, "a") as fh: - fh.write( - f"{commit}\t{val_bpb:.6f}\t{memory_gb:.2f}\t{status}\t{description}\n" - ) - - -def count_experiments() -> int: - """Count the number of experiment rows in results.tsv. - - Returns: - Row count excluding the header line (0 when file does not exist). - """ - if not os.path.exists(RESULTS_FILE): - return 0 - with open(RESULTS_FILE) as fh: - return max(0, sum(1 for _ in fh) - 1) - - -def _load_best_bpb() -> float: - """Scan results.tsv for the best (lowest positive) val_bpb seen so far. - - Returns: - Best val_bpb, or ``float("inf")`` when no valid result exists. - """ - if not os.path.exists(RESULTS_FILE): - return float("inf") - best = float("inf") - with open(RESULTS_FILE) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - try: - bpb = float(row.get("val_bpb", "0") or "0") - except ValueError: - continue - if 0 < bpb < best: - best = bpb - return best - - -# --------------------------------------------------------------------------- -# Experiment execution -# --------------------------------------------------------------------------- - - -def run_experiment(timeout: int = 600) -> str: - """Launch ``uv run train.py`` and capture all output to run.log. - - Args: - timeout: Kill the process after this many seconds. - - Returns: - One of ``"ok"``, ``"timeout"``, or ``"error"``. - """ - try: - with open(RUN_LOG, "w") as log_file: - proc = subprocess.run( - ["uv", "run", "train.py"], - cwd=REPO_DIR, - stdout=log_file, - stderr=subprocess.STDOUT, - timeout=timeout, - ) - return "ok" if proc.returncode == 0 else "error" - except subprocess.TimeoutExpired: - return "timeout" - except Exception as exc: # noqa: BLE001 - with open(RUN_LOG, "a") as log_file: - log_file.write(f"\nOrchestrator error: {exc}\n") - return "error" - - -# --------------------------------------------------------------------------- -# Main loop -# --------------------------------------------------------------------------- - - +from harness.git_utils import REPO_DIR, commit_all, current_commit_short, reset_to +from harness.health_monitor import check_health, reset_peak_stats +from harness.meta_agent import run_meta_iteration +from harness.search_strategy import diagnose + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- + +RESULTS_FILE = os.path.join(REPO_DIR, "results.tsv") +RUN_LOG = os.path.join(REPO_DIR, "run.log") + +_TSV_HEADER = "commit\tval_bpb\tmemory_gb\tstatus\tdescription\n" + + +# --------------------------------------------------------------------------- +# TSV helpers +# --------------------------------------------------------------------------- + + +def init_results_tsv() -> None: + """Create results.tsv with header row if it does not yet exist.""" + if not os.path.exists(RESULTS_FILE): + with open(RESULTS_FILE, "w") as fh: + fh.write(_TSV_HEADER) + + +def log_result( + commit: str, + val_bpb: float, + memory_gb: float, + status: str, + description: str, +) -> None: + """Append one row to results.tsv. + + Args: + commit: Short git hash for this experiment. + val_bpb: Validation bits-per-byte (0.0 for crashes). + memory_gb: Peak VRAM usage in gigabytes. + status: One of keep / discard / crash / timeout. + description: Short human-readable description. + """ + with open(RESULTS_FILE, "a") as fh: + fh.write( + f"{commit}\t{val_bpb:.6f}\t{memory_gb:.2f}\t{status}\t{description}\n" + ) + + +def count_experiments() -> int: + """Count the number of experiment rows in results.tsv. + + Returns: + Row count excluding the header line (0 when file does not exist). + """ + if not os.path.exists(RESULTS_FILE): + return 0 + with open(RESULTS_FILE) as fh: + return max(0, sum(1 for _ in fh) - 1) + + +def _load_best_bpb() -> float: + """Scan results.tsv for the best (lowest positive) val_bpb seen so far. + + Returns: + Best val_bpb, or ``float("inf")`` when no valid result exists. + """ + if not os.path.exists(RESULTS_FILE): + return float("inf") + best = float("inf") + with open(RESULTS_FILE) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + try: + bpb = float(row.get("val_bpb", "0") or "0") + except ValueError: + continue + if 0 < bpb < best: + best = bpb + return best + + +# --------------------------------------------------------------------------- +# Experiment execution +# --------------------------------------------------------------------------- + + +def run_experiment(timeout: int = 600) -> str: + """Launch ``uv run train.py`` and capture all output to run.log. + + Args: + timeout: Kill the process after this many seconds. + + Returns: + One of ``"ok"``, ``"timeout"``, or ``"error"``. + """ + try: + with open(RUN_LOG, "w") as log_file: + proc = subprocess.run( + ["uv", "run", "train.py"], + cwd=REPO_DIR, + stdout=log_file, + stderr=subprocess.STDOUT, + timeout=timeout, + ) + return "ok" if proc.returncode == 0 else "error" + except subprocess.TimeoutExpired: + return "timeout" + except Exception as exc: # noqa: BLE001 + with open(RUN_LOG, "a") as log_file: + log_file.write(f"\nOrchestrator error: {exc}\n") + return "error" + + +# --------------------------------------------------------------------------- +# Main loop +# --------------------------------------------------------------------------- + + def run_loop( meta_interval: int = 20, max_experiments: int | None = None, experiment_timeout: int = 600, - secondary_gates: dict[str, dict[str, float]] | None = None, + secondary_gates: dict | None = None, ) -> None: - """Run the HYDRA autoresearch loop. - - This function runs indefinitely (or until ``max_experiments`` is reached - or the user interrupts with Ctrl-C). - - Args: - meta_interval: Run the meta-agent every N experiments. - max_experiments: Hard stop after this many experiments (None = infinite). - experiment_timeout: Seconds before a training run is killed. - secondary_gates: Optional gate thresholds forwarded to - :func:`~harness.eval_agent.should_keep`. - """ + """Run the HYDRA autoresearch loop. + + This function runs indefinitely (or until ``max_experiments`` is reached + or the user interrupts with Ctrl-C). + + Args: + meta_interval: Run the meta-agent every N experiments. + max_experiments: Hard stop after this many experiments (None = infinite). + experiment_timeout: Seconds before a training run is killed. + secondary_gates: Optional gate thresholds forwarded to + :func:`~harness.eval_agent.should_keep`. + """ init_results_tsv() - if secondary_gates is None: - secondary_gates = HarnessConfig().to_secondary_gates() best_bpb = _load_best_bpb() - experiment_num = count_experiments() - - print( - f"HYDRA Orchestrator starting. " - f"Experiments so far: {experiment_num}, Best BPB: {best_bpb:.6f}" - ) - - while max_experiments is None or experiment_num < max_experiments: - experiment_num += 1 - - # ------------------------------------------------------------------ - # Pre-flight health check - # ------------------------------------------------------------------ - healthy, hw_warnings = check_health() - if hw_warnings: - print(f" [health] {hw_warnings}") - - # ------------------------------------------------------------------ - # Periodic meta-agent update - # ------------------------------------------------------------------ - if experiment_num > 1 and experiment_num % meta_interval == 0: - print(f"\n=== Meta-agent iteration at experiment {experiment_num} ===") - meta_result = run_meta_iteration() - print( - f" state={meta_result['state']} " - f"best_bpb={meta_result['best_bpb']:.6f} " - f"changed={meta_result['changed']}" - ) - if meta_result.get("directive"): - print(f" directive: {meta_result['directive'][:120]}") - - # ------------------------------------------------------------------ - # Record baseline commit so we can reset on failure / discard - # ------------------------------------------------------------------ - pre_commit = current_commit_short() - - # ------------------------------------------------------------------ - # Run experiment - # ------------------------------------------------------------------ - print(f"\n--- Experiment {experiment_num} ---") - reset_peak_stats() - t0 = time.time() - run_status = run_experiment(timeout=experiment_timeout) - elapsed = time.time() - t0 - print(f" run_status={run_status} elapsed={elapsed:.1f}s") - - # ------------------------------------------------------------------ - # Parse results - # ------------------------------------------------------------------ - result: ExperimentResult = parse_run_log(RUN_LOG) - - if result.crashed or run_status != "ok": - commit = current_commit_short() - err_short = ( - "timeout" - if run_status == "timeout" - else result.error_message[:80].replace("\n", " ") - ) - log_result(commit, 0.0, 0.0, "crash", err_short) - print(f" CRASH: {err_short}") - reset_to(pre_commit) - continue - - # ------------------------------------------------------------------ - # Secondary alarms (non-blocking -- logged but do not abort) - # ------------------------------------------------------------------ - alarms = check_secondary_alarms(result) - if alarms: - for alarm in alarms: - print(f" [alarm] {alarm}") - - # ------------------------------------------------------------------ - # Keep / discard - # ------------------------------------------------------------------ - keep, reason = should_keep(result, best_bpb, gates=secondary_gates) - commit = current_commit_short() - memory_gb = result.peak_vram_mb / 1024.0 - - if keep: - best_bpb = result.val_bpb - description = f"val_bpb improved to {result.val_bpb:.6f}" - log_result(commit, result.val_bpb, memory_gb, "keep", description) - print(f" KEEP: val_bpb={result.val_bpb:.6f} (new best)") - else: - description = f"{reason} val_bpb={result.val_bpb:.6f}" - log_result(commit, result.val_bpb, memory_gb, "discard", description) - print(f" DISCARD: val_bpb={result.val_bpb:.6f} ({reason})") - reset_to(pre_commit) - - print(f"\nHYDRA finished after {experiment_num} experiments. Best BPB: {best_bpb:.6f}") - - -# --------------------------------------------------------------------------- -# CLI entry point -# --------------------------------------------------------------------------- - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="HYDRA Autoresearch Orchestrator") - parser.add_argument( - "--meta-interval", - type=int, - default=20, - help="Run meta-agent every N experiments (default: 20)", - ) - parser.add_argument( - "--max-experiments", - type=int, - default=None, - help="Stop after N experiments; omit for infinite (default: infinite)", - ) - parser.add_argument( - "--experiment-timeout", - type=int, - default=600, - help="Kill training run after N seconds (default: 600)", - ) - args = parser.parse_args() - - try: - run_loop( - meta_interval=args.meta_interval, - max_experiments=args.max_experiments, - experiment_timeout=args.experiment_timeout, - ) - except KeyboardInterrupt: - print("\nOrchestrator stopped by user.") + experiment_num = count_experiments() + + print( + f"HYDRA Orchestrator starting. " + f"Experiments so far: {experiment_num}, Best BPB: {best_bpb:.6f}" + ) + + while max_experiments is None or experiment_num < max_experiments: + experiment_num += 1 + + # ------------------------------------------------------------------ + # Pre-flight health check + # ------------------------------------------------------------------ + healthy, hw_warnings = check_health() + if hw_warnings: + print(f" [health] {hw_warnings}") + + # ------------------------------------------------------------------ + # Periodic meta-agent update + # ------------------------------------------------------------------ + if experiment_num > 1 and experiment_num % meta_interval == 0: + print(f"\n=== Meta-agent iteration at experiment {experiment_num} ===") + meta_result = run_meta_iteration() + print( + f" state={meta_result['state']} " + f"best_bpb={meta_result['best_bpb']:.6f} " + f"changed={meta_result['changed']}" + ) + if meta_result.get("directive"): + print(f" directive: {meta_result['directive'][:120]}") + + # ------------------------------------------------------------------ + # Record baseline commit so we can reset on failure / discard + # ------------------------------------------------------------------ + pre_commit = current_commit_short() + + # ------------------------------------------------------------------ + # Run experiment + # ------------------------------------------------------------------ + print(f"\n--- Experiment {experiment_num} ---") + reset_peak_stats() + t0 = time.time() + run_status = run_experiment(timeout=experiment_timeout) + elapsed = time.time() - t0 + print(f" run_status={run_status} elapsed={elapsed:.1f}s") + + # ------------------------------------------------------------------ + # Parse results + # ------------------------------------------------------------------ + result: ExperimentResult = parse_run_log(RUN_LOG) + + if result.crashed or run_status != "ok": + commit = current_commit_short() + err_short = ( + "timeout" + if run_status == "timeout" + else result.error_message[:80].replace("\n", " ") + ) + log_result(commit, 0.0, 0.0, "crash", err_short) + print(f" CRASH: {err_short}") + reset_to(pre_commit) + continue + + # ------------------------------------------------------------------ + # Secondary alarms (non-blocking -- logged but do not abort) + # ------------------------------------------------------------------ + alarms = check_secondary_alarms(result) + if alarms: + for alarm in alarms: + print(f" [alarm] {alarm}") + + # ------------------------------------------------------------------ + # Keep / discard + # ------------------------------------------------------------------ + keep, reason = should_keep(result, best_bpb, gates=secondary_gates) + commit = current_commit_short() + memory_gb = result.peak_vram_mb / 1024.0 + + if keep: + best_bpb = result.val_bpb + description = f"val_bpb improved to {result.val_bpb:.6f}" + log_result(commit, result.val_bpb, memory_gb, "keep", description) + print(f" KEEP: val_bpb={result.val_bpb:.6f} (new best)") + else: + description = f"{reason} val_bpb={result.val_bpb:.6f}" + log_result(commit, result.val_bpb, memory_gb, "discard", description) + print(f" DISCARD: val_bpb={result.val_bpb:.6f} ({reason})") + reset_to(pre_commit) + + print(f"\nHYDRA finished after {experiment_num} experiments. Best BPB: {best_bpb:.6f}") + + +# --------------------------------------------------------------------------- +# CLI entry point +# --------------------------------------------------------------------------- + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="HYDRA Autoresearch Orchestrator") + parser.add_argument( + "--meta-interval", + type=int, + default=20, + help="Run meta-agent every N experiments (default: 20)", + ) + parser.add_argument( + "--max-experiments", + type=int, + default=None, + help="Stop after N experiments; omit for infinite (default: infinite)", + ) + parser.add_argument( + "--experiment-timeout", + type=int, + default=600, + help="Kill training run after N seconds (default: 600)", + ) + args = parser.parse_args() + + try: + run_loop( + meta_interval=args.meta_interval, + max_experiments=args.max_experiments, + experiment_timeout=args.experiment_timeout, + ) + except KeyboardInterrupt: + print("\nOrchestrator stopped by user.") diff --git a/overlay/harness/search_strategy.py b/overlay/harness/search_strategy.py index 87aa234df60feca62b0f8a5da69eb96b14e2c4c6..3f005dff4ad910afc308f5579a258b1dbbb9bfc2 100644 --- a/overlay/harness/search_strategy.py +++ b/overlay/harness/search_strategy.py @@ -1,153 +1,153 @@ -"""Search strategy for HYDRA's meta-evolution loop. - -Reads results.tsv and diagnoses the current research state as one of: - EXPLORING -- active improvement trend with diverse experiments - EXPLOITING -- narrowing in on a local optimum (low diversity) - STUCK -- no improvement for >= stuck_threshold experiments - BROKEN -- crash rate exceeds crash_threshold -""" -import csv -import os -from dataclasses import dataclass - - -@dataclass -class ResearchState: - """Diagnosis of the current research trajectory. - - Attributes: - label: One of EXPLORING, EXPLOITING, STUCK, BROKEN. - trend_improving: True when the second half of the recent window is - better (lower BPB) than the first half. - experiment_diversity: Rough 0–1 score based on unique description - prefixes in the recent window. - crash_rate: Fraction of recent experiments that crashed. - best_bpb: Lowest val_bpb seen across all experiments. - last_improvement_at: Ordinal of the experiment that set best_bpb. - total_experiments: Total rows in results.tsv (excluding header). - """ - - label: str - trend_improving: bool - experiment_diversity: float - crash_rate: float - best_bpb: float - last_improvement_at: int - total_experiments: int - - -def diagnose( - results_path: str, - window: int = 20, - stuck_threshold: int = 10, - crash_threshold: float = 0.5, -) -> ResearchState: - """Diagnose current research state from results.tsv. - - Args: - results_path: Path to the tab-separated results file. - window: Number of recent experiments to consider for trend/diversity. - stuck_threshold: Experiments without improvement before labelling STUCK. - crash_threshold: Crash fraction above which state becomes BROKEN. - - Returns: - ResearchState with diagnosis label and supporting statistics. - """ - if not os.path.exists(results_path): - return ResearchState( - label="EXPLORING", - trend_improving=False, - experiment_diversity=0.0, - crash_rate=0.0, - best_bpb=float("inf"), - last_improvement_at=0, - total_experiments=0, - ) - - rows: list[dict] = [] - with open(results_path) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - rows.append(row) - - if not rows: - return ResearchState( - label="EXPLORING", - trend_improving=False, - experiment_diversity=0.0, - crash_rate=0.0, - best_bpb=float("inf"), - last_improvement_at=0, - total_experiments=0, - ) - - total = len(rows) - recent = rows[-window:] - - # Crash rate in the recent window. - crashes = sum(1 for r in recent if r.get("status") == "crash") - crash_rate = crashes / len(recent) if recent else 0.0 - - # Best BPB overall and which experiment achieved it. - best_bpb = float("inf") - last_improvement_at = 0 - for i, row in enumerate(rows): - try: - bpb = float(row.get("val_bpb", "0") or "0") - except ValueError: - continue - if bpb > 0 and bpb < best_bpb: - best_bpb = bpb - last_improvement_at = i + 1 - - # Trend: is the second half of the recent window better than the first? - valid_bpbs = [ - float(r.get("val_bpb", "0") or "0") - for r in recent - if float(r.get("val_bpb", "0") or "0") > 0 - ] - trend_improving = False - if len(valid_bpbs) >= 4: - mid = len(valid_bpbs) // 2 - first_half_mean = sum(valid_bpbs[:mid]) / mid - second_half_mean = sum(valid_bpbs[mid:]) / (len(valid_bpbs) - mid) - trend_improving = second_half_mean < first_half_mean - - # Diversity: fraction of unique description prefixes (first 20 chars). - descriptions = {r.get("description", "")[:20] for r in recent} - diversity = min(1.0, len(descriptions) / max(1, len(recent))) - - # Classify state. - stale = total - last_improvement_at - if crash_rate > crash_threshold: - label = "BROKEN" - elif stale >= stuck_threshold: - label = "STUCK" - elif trend_improving and diversity > 0.3: - label = "EXPLORING" - else: - label = "EXPLOITING" - - return ResearchState( - label=label, - trend_improving=trend_improving, - experiment_diversity=diversity, - crash_rate=crash_rate, - best_bpb=best_bpb, - last_improvement_at=last_improvement_at, - total_experiments=total, - ) - - -def should_explore(results_path: str, n: int = 10) -> bool: - """Return True when no improvement has been seen in the last N experiments. - - Args: - results_path: Path to results.tsv. - n: Look-back window for improvement check. - - Returns: - True if the research loop should try bolder mutations. - """ - state = diagnose(results_path, window=n, stuck_threshold=n) - return state.label in ("STUCK", "BROKEN") +"""Search strategy for HYDRA's meta-evolution loop. + +Reads results.tsv and diagnoses the current research state as one of: + EXPLORING -- active improvement trend with diverse experiments + EXPLOITING -- narrowing in on a local optimum (low diversity) + STUCK -- no improvement for >= stuck_threshold experiments + BROKEN -- crash rate exceeds crash_threshold +""" +import csv +import os +from dataclasses import dataclass + + +@dataclass +class ResearchState: + """Diagnosis of the current research trajectory. + + Attributes: + label: One of EXPLORING, EXPLOITING, STUCK, BROKEN. + trend_improving: True when the second half of the recent window is + better (lower BPB) than the first half. + experiment_diversity: Rough 0–1 score based on unique description + prefixes in the recent window. + crash_rate: Fraction of recent experiments that crashed. + best_bpb: Lowest val_bpb seen across all experiments. + last_improvement_at: Ordinal of the experiment that set best_bpb. + total_experiments: Total rows in results.tsv (excluding header). + """ + + label: str + trend_improving: bool + experiment_diversity: float + crash_rate: float + best_bpb: float + last_improvement_at: int + total_experiments: int + + +def diagnose( + results_path: str, + window: int = 20, + stuck_threshold: int = 10, + crash_threshold: float = 0.5, +) -> ResearchState: + """Diagnose current research state from results.tsv. + + Args: + results_path: Path to the tab-separated results file. + window: Number of recent experiments to consider for trend/diversity. + stuck_threshold: Experiments without improvement before labelling STUCK. + crash_threshold: Crash fraction above which state becomes BROKEN. + + Returns: + ResearchState with diagnosis label and supporting statistics. + """ + if not os.path.exists(results_path): + return ResearchState( + label="EXPLORING", + trend_improving=False, + experiment_diversity=0.0, + crash_rate=0.0, + best_bpb=float("inf"), + last_improvement_at=0, + total_experiments=0, + ) + + rows: list[dict] = [] + with open(results_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + rows.append(row) + + if not rows: + return ResearchState( + label="EXPLORING", + trend_improving=False, + experiment_diversity=0.0, + crash_rate=0.0, + best_bpb=float("inf"), + last_improvement_at=0, + total_experiments=0, + ) + + total = len(rows) + recent = rows[-window:] + + # Crash rate in the recent window. + crashes = sum(1 for r in recent if r.get("status") == "crash") + crash_rate = crashes / len(recent) if recent else 0.0 + + # Best BPB overall and which experiment achieved it. + best_bpb = float("inf") + last_improvement_at = 0 + for i, row in enumerate(rows): + try: + bpb = float(row.get("val_bpb", "0") or "0") + except ValueError: + continue + if bpb > 0 and bpb < best_bpb: + best_bpb = bpb + last_improvement_at = i + 1 + + # Trend: is the second half of the recent window better than the first? + valid_bpbs = [ + float(r.get("val_bpb", "0") or "0") + for r in recent + if float(r.get("val_bpb", "0") or "0") > 0 + ] + trend_improving = False + if len(valid_bpbs) >= 4: + mid = len(valid_bpbs) // 2 + first_half_mean = sum(valid_bpbs[:mid]) / mid + second_half_mean = sum(valid_bpbs[mid:]) / (len(valid_bpbs) - mid) + trend_improving = second_half_mean < first_half_mean + + # Diversity: fraction of unique description prefixes (first 20 chars). + descriptions = {r.get("description", "")[:20] for r in recent} + diversity = min(1.0, len(descriptions) / max(1, len(recent))) + + # Classify state. + stale = total - last_improvement_at + if crash_rate > crash_threshold: + label = "BROKEN" + elif stale >= stuck_threshold: + label = "STUCK" + elif trend_improving and diversity > 0.3: + label = "EXPLORING" + else: + label = "EXPLOITING" + + return ResearchState( + label=label, + trend_improving=trend_improving, + experiment_diversity=diversity, + crash_rate=crash_rate, + best_bpb=best_bpb, + last_improvement_at=last_improvement_at, + total_experiments=total, + ) + + +def should_explore(results_path: str, n: int = 10) -> bool: + """Return True when no improvement has been seen in the last N experiments. + + Args: + results_path: Path to results.tsv. + n: Look-back window for improvement check. + + Returns: + True if the research loop should try bolder mutations. + """ + state = diagnose(results_path, window=n, stuck_threshold=n) + return state.label in ("STUCK", "BROKEN") diff --git a/overlay/htm_rust/Cargo.lock b/overlay/htm_rust/Cargo.lock index 96901ada01c92337a96240028daac65718c82c81..630f4625354d674ec495a4dab8e7348c3c331556 100644 --- a/overlay/htm_rust/Cargo.lock +++ b/overlay/htm_rust/Cargo.lock @@ -1,383 +1,383 @@ -# This file is automatically @generated by Cargo. -# It is not intended for manual editing. -version = 4 - -[[package]] -name = "autocfg" -version = "1.5.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" - -[[package]] -name = "cfg-if" -version = "1.0.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" - -[[package]] -name = "cudarc" -version = "0.12.1" -source = 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63ca071e23ab43cd1ae0d077b6fd33a5885f52f5..9f5e7fac3f8916f9007c03022c030471ca3efc91 100644 --- a/overlay/htm_rust/Cargo.toml +++ b/overlay/htm_rust/Cargo.toml @@ -1,37 +1,37 @@ -[package] -name = "htm_rust" -version = "0.1.0" -edition = "2021" -authors = ["Feather/HYDRA"] -description = "Numenta BAMI-spec Hierarchical Temporal Memory (Spatial Pooler + Temporal Memory) with pyo3 bindings" -license = "MIT" - -[lib] -name = "htm_rust" -crate-type = ["cdylib", "rlib"] - -[dependencies] -pyo3 = { version = "0.22", features = ["extension-module"] } -numpy = "0.22" -ndarray = "0.16" -rand = "0.8" -rand_xoshiro = "0.6" -# cudarc: CUDA Rust bindings with dynamic-loading (no link-time dep on libcuda). -# Kernels are embedded as PTX and JIT-compiled at runtime. -cudarc = { version = "0.12", default-features = false, features = ["dynamic-linking", "driver", "cuda-12010"], optional = true } - -[build-dependencies] -# Only required when building with --features gpu. We shell to nvcc directly -# so we don't need cc's cuda support (which drags in extra deps). - -[features] -default = [] -# `gpu` adds the HTMRegionGPU class, compiles .cu kernels to PTX at build time, -# and links cudarc. Without this feature the crate is pure-CPU and has no -# CUDA dependency at build or run time. -gpu = ["cudarc"] - -[profile.release] -opt-level = 3 -lto = "thin" -codegen-units = 1 +[package] +name = "htm_rust" +version = "0.1.0" +edition = "2021" +authors = ["Feather/HYDRA"] +description = "Numenta BAMI-spec Hierarchical Temporal Memory (Spatial Pooler + Temporal Memory) with pyo3 bindings" +license = "MIT" + +[lib] +name = "htm_rust" +crate-type = ["cdylib", "rlib"] + +[dependencies] +pyo3 = { version = "0.22", features = ["extension-module"] } +numpy = "0.22" +ndarray = "0.16" +rand = "0.8" +rand_xoshiro = "0.6" +# cudarc: CUDA Rust bindings with dynamic-loading (no link-time dep on libcuda). +# Kernels are embedded as PTX and JIT-compiled at runtime. +cudarc = { version = "0.12", default-features = false, features = ["dynamic-linking", "driver", "cuda-12010"], optional = true } + +[build-dependencies] +# Only required when building with --features gpu. We shell to nvcc directly +# so we don't need cc's cuda support (which drags in extra deps). + +[features] +default = [] +# `gpu` adds the HTMRegionGPU class, compiles .cu kernels to PTX at build time, +# and links cudarc. Without this feature the crate is pure-CPU and has no +# CUDA dependency at build or run time. +gpu = ["cudarc"] + +[profile.release] +opt-level = 3 +lto = "thin" +codegen-units = 1 diff --git a/overlay/htm_rust/build.rs b/overlay/htm_rust/build.rs index ee6d6bcc0411d58e3b446c453edd8e6d2808c061..7755b21a8179baf2f7456688a6ca1a3e2519b929 100644 --- a/overlay/htm_rust/build.rs +++ b/overlay/htm_rust/build.rs @@ -1,160 +1,168 @@ -//! Build script: compiles `.cu` kernel files to PTX when the `gpu` feature -//! is enabled. PTX files are embedded into the final Rust binary via -//! `include_str!` / `OUT_DIR` constants and JIT-loaded at runtime by cudarc. -//! -//! No-op when `gpu` feature is off — CPU-only builds have zero CUDA -//! toolchain dependency. -//! -//! nvcc lookup order: -//! 1. $NVCC env var -//! 2. `nvcc` on PATH -//! 3. `/usr/local/cuda-12.1/bin/nvcc` -//! 4. `/usr/local/cuda/bin/nvcc` -//! -//! Target: sm_90a (Hopper, H200 — enables cluster::sync, TMA, wgmma). Override with $HTM_CUDA_ARCH. - -use std::env; -use std::path::PathBuf; -use std::process::Command; - -fn main() { - // Re-run whenever we edit the build script or any kernel source. - println!("cargo:rerun-if-changed=build.rs"); - - let gpu = env::var_os("CARGO_FEATURE_GPU").is_some(); - if !gpu { - return; - } - - let out_dir = PathBuf::from(env::var("OUT_DIR").expect("OUT_DIR")); - let arch = env::var("HTM_CUDA_ARCH").unwrap_or_else(|_| "sm_90a".into()); - - // Base kernels — compile for any sm_80+ GPU. Each .cu file → one .ptx file. - let base_kernels: &[&str] = &[ - "sp_overlap", - "sp_topk", - "sp_learn", - "sp_duty", - "sp_boost_fused", - "tm_predict", - "tm_activate", - "tm_learn", - "tm_punish", - "tm_grow", - "tm_anomaly", - "tm_reset", - ]; - - // htm_fused_step now compiles for ALL architectures (sm_80+). - // On Hopper (sm_90+): uses cluster-distributed shared memory for hot state. - // On Ampere (sm_86) and other pre-Hopper: uses global memory reads/writes - // with grid.sync() for cross-block synchronization (cooperative launch). - let kernels: Vec<&str> = base_kernels.iter().chain(["htm_fused_step"].iter()).copied().collect(); - - let kernels_dir = PathBuf::from("src/gpu/kernels"); - for k in &kernels { - let src = kernels_dir.join(format!("{k}.cu")); - println!("cargo:rerun-if-changed={}", src.display()); - } - - - let nvcc = find_nvcc(); - println!("cargo:warning=htm_rust: nvcc = {nvcc}"); - println!("cargo:warning=htm_rust: target arch = {arch}"); - - // Prefer gcc-12 if present (CUDA 12.1 doesn't support gcc-13+ headers). - let host_compiler = env::var("HTM_CUDA_CCBIN") - .ok() - .or_else(|| { - for cand in ["/usr/bin/gcc-12", "/usr/bin/gcc-11"] { - if std::path::Path::new(cand).exists() { - return Some(cand.to_string()); - } - } - None - }); - - // Optionally patch the emitted PTX `.version` header down to match an - // older driver. Useful when the system driver (e.g. on WSL2) is older - // than the nvcc toolchain. Set HTM_PTX_VERSION to e.g. "7.8" or "8.0". - let ptx_version_override = env::var("HTM_PTX_VERSION").ok(); - - for k in kernels { - let src = kernels_dir.join(format!("{k}.cu")); - let ptx = out_dir.join(format!("{k}.ptx")); - if !src.exists() { - panic!("missing kernel source: {}", src.display()); - } - let mut cmd = Command::new(&nvcc); - // Note: `--use_fast_math` breaks bit-parity with host `expf`, which - // in turn flips boost tie-breaks in SP learning. We accept the tiny - // perf loss for correctness; the hot overlap kernel has no transcendentals. - cmd.args([ - "--ptx", - "-O3", - "-rdc=true", - "-arch", - &arch, - ]); - if let Some(cc) = &host_compiler { - cmd.args(["-ccbin", cc]); - } - cmd.arg("-o").arg(&ptx).arg(&src); - let status = cmd - .status() - .unwrap_or_else(|e| panic!("failed to spawn nvcc: {e}")); - if !status.success() { - panic!("nvcc failed for {}", src.display()); - } - - if let Some(ver) = &ptx_version_override { - // Read, patch, write. - let text = std::fs::read_to_string(&ptx) - .unwrap_or_else(|e| panic!("read {} failed: {e}", ptx.display())); - // Match `.version X.Y` where X and Y are digits. Replace whole line. - let patched: String = text - .lines() - .map(|line| { - let t = line.trim_start(); - if t.starts_with(".version ") { - format!(".version {ver}") - } else { - line.to_string() - } - }) - .collect::>() - .join("\n"); - std::fs::write(&ptx, patched) - .unwrap_or_else(|e| panic!("write {} failed: {e}", ptx.display())); - } - } - - // Export OUT_DIR for include_str! in Rust. - println!( - "cargo:rustc-env=HTM_GPU_PTX_DIR={}", - out_dir.display() - ); -} - -fn find_nvcc() -> String { - if let Ok(n) = env::var("NVCC") { - return n; - } - // Try PATH. - if Command::new("nvcc").arg("--version").output().is_ok() { - return "nvcc".into(); - } - for cand in [ - "/usr/local/cuda-12.1/bin/nvcc", - "/usr/local/cuda/bin/nvcc", - "/usr/local/cuda-12/bin/nvcc", - ] { - if std::path::Path::new(cand).exists() { - return cand.into(); - } - } - panic!( - "nvcc not found. Set $NVCC or install CUDA toolkit. \ - Tried PATH, /usr/local/cuda-12.1, /usr/local/cuda." - ); -} +//! Build script: compiles `.cu` kernel files to PTX when the `gpu` feature +//! is enabled. PTX files are embedded into the final Rust binary via +//! `include_str!` / `OUT_DIR` constants and JIT-loaded at runtime by cudarc. +//! +//! No-op when `gpu` feature is off — CPU-only builds have zero CUDA +//! toolchain dependency. +//! +//! nvcc lookup order: +//! 1. $NVCC env var +//! 2. `nvcc` on PATH +//! 3. `/usr/local/cuda-12.1/bin/nvcc` +//! 4. `/usr/local/cuda/bin/nvcc` +//! +//! Default target: sm_86 (Ampere A10G / RTX 30xx). Override with $HTM_CUDA_ARCH (e.g. sm_90a for H200). + +use std::env; +use std::path::PathBuf; +use std::process::Command; + +fn main() { + // Re-run whenever we edit the build script or any kernel source. + println!("cargo:rerun-if-changed=build.rs"); + + let gpu = env::var_os("CARGO_FEATURE_GPU").is_some(); + if !gpu { + return; + } + + let out_dir = PathBuf::from(env::var("OUT_DIR").expect("OUT_DIR")); + let arch = env::var("HTM_CUDA_ARCH").unwrap_or_else(|_| "sm_86".into()); + + // Base kernels — compile for any sm_80+ GPU. Each .cu file → one .ptx file. + let base_kernels: &[&str] = &[ + "sp_overlap", + "sp_topk", + "sp_learn", + "sp_duty", + "sp_boost_fused", + "tm_predict", + "tm_activate", + "tm_learn", + "tm_punish", + "tm_grow", + "tm_anomaly", + "tm_reset", + ]; + + // htm_fused_step now compiles for ALL architectures (sm_80+). + // On Hopper (sm_90+): uses cluster-distributed shared memory for hot state. + // On Ampere (sm_86) and other pre-Hopper: uses global memory reads/writes + // with grid.sync() for cross-block synchronization (cooperative launch). + let kernels: Vec<&str> = base_kernels.iter().chain(["htm_fused_step"].iter()).copied().collect(); + + let kernels_dir = PathBuf::from("src/gpu/kernels"); + for k in &kernels { + let src = kernels_dir.join(format!("{k}.cu")); + println!("cargo:rerun-if-changed={}", src.display()); + } + + + let nvcc = find_nvcc(); + println!("cargo:warning=htm_rust: nvcc = {nvcc}"); + println!("cargo:warning=htm_rust: target arch = {arch}"); + + // Prefer gcc-12 if present (CUDA 12.1 doesn't support gcc-13+ headers). + let host_compiler = env::var("HTM_CUDA_CCBIN") + .ok() + .or_else(|| { + for cand in ["/usr/bin/gcc-12", "/usr/bin/gcc-11"] { + if std::path::Path::new(cand).exists() { + return Some(cand.to_string()); + } + } + None + }); + + // Optionally patch the emitted PTX `.version` header down to match an + // older driver. Useful when the system driver (e.g. on WSL2) is older + // than the nvcc toolchain. Set HTM_PTX_VERSION to e.g. "7.8" or "8.0". + let ptx_version_override = env::var("HTM_PTX_VERSION").ok(); + + for k in kernels { + let src = kernels_dir.join(format!("{k}.cu")); + let ptx = out_dir.join(format!("{k}.ptx")); + if !src.exists() { + panic!("missing kernel source: {}", src.display()); + } + let mut cmd = Command::new(&nvcc); + // Note: `--use_fast_math` breaks bit-parity with host `expf`, which + // in turn flips boost tie-breaks in SP learning. We accept the tiny + // perf loss for correctness; the hot overlap kernel has no transcendentals. + cmd.args([ + "--ptx", + "-O3", + "-rdc=true", + "-arch", + &arch, + ]); + // `cooperative_groups::this_cluster()` is not declared for Ampere + // device compiles in CUDA 12.x, even if guarded by __CUDA_ARCH__ in + // some nvcc front-end phases. Define an explicit build-time kill + // switch for all non-Hopper targets so sm_86/A10G only sees the + // cooperative-grid path. + if !arch.starts_with("sm_90") { + cmd.arg("-DHTM_DISABLE_CLUSTER=1"); + } + if let Some(cc) = &host_compiler { + cmd.args(["-ccbin", cc]); + } + cmd.arg("-o").arg(&ptx).arg(&src); + let status = cmd + .status() + .unwrap_or_else(|e| panic!("failed to spawn nvcc: {e}")); + if !status.success() { + panic!("nvcc failed for {}", src.display()); + } + + if let Some(ver) = &ptx_version_override { + // Read, patch, write. + let text = std::fs::read_to_string(&ptx) + .unwrap_or_else(|e| panic!("read {} failed: {e}", ptx.display())); + // Match `.version X.Y` where X and Y are digits. Replace whole line. + let patched: String = text + .lines() + .map(|line| { + let t = line.trim_start(); + if t.starts_with(".version ") { + format!(".version {ver}") + } else { + line.to_string() + } + }) + .collect::>() + .join("\n"); + std::fs::write(&ptx, patched) + .unwrap_or_else(|e| panic!("write {} failed: {e}", ptx.display())); + } + } + + // Export OUT_DIR for include_str! in Rust. + println!( + "cargo:rustc-env=HTM_GPU_PTX_DIR={}", + out_dir.display() + ); +} + +fn find_nvcc() -> String { + if let Ok(n) = env::var("NVCC") { + return n; + } + // Try PATH. + if Command::new("nvcc").arg("--version").output().is_ok() { + return "nvcc".into(); + } + for cand in [ + "/usr/local/cuda-12.1/bin/nvcc", + "/usr/local/cuda/bin/nvcc", + "/usr/local/cuda-12/bin/nvcc", + ] { + if std::path::Path::new(cand).exists() { + return cand.into(); + } + } + panic!( + "nvcc not found. Set $NVCC or install CUDA toolkit. \ + Tried PATH, /usr/local/cuda-12.1, /usr/local/cuda." + ); +} diff --git a/overlay/htm_rust/pyproject.toml b/overlay/htm_rust/pyproject.toml index 847ed70cb2df00a6665ca9ac4ceda6e33548314e..8f244c679aafa0b497b23f3c70af673fbcf629fc 100644 --- a/overlay/htm_rust/pyproject.toml +++ b/overlay/htm_rust/pyproject.toml @@ -1,17 +1,17 @@ -[build-system] -requires = ["maturin>=1.4,<2.0"] -build-backend = "maturin" - -[project] -name = "htm_rust" -version = "0.1.0" -description = "Numenta BAMI-spec HTM (Spatial Pooler + Temporal Memory) in Rust with pyo3 bindings" -requires-python = ">=3.11" -classifiers = [ - "Programming Language :: Rust", - "Programming Language :: Python :: Implementation :: CPython", -] - -[tool.maturin] -features = ["pyo3/extension-module"] -module-name = "htm_rust" +[build-system] +requires = ["maturin>=1.4,<2.0"] +build-backend = "maturin" + +[project] +name = "htm_rust" +version = "0.1.0" +description = "Numenta BAMI-spec HTM (Spatial Pooler + Temporal Memory) in Rust with pyo3 bindings" +requires-python = ">=3.11" +classifiers = [ + "Programming Language :: Rust", + "Programming Language :: Python :: Implementation :: CPython", +] + +[tool.maturin] +features = ["pyo3/extension-module"] +module-name = "htm_rust" diff --git a/overlay/htm_rust/src/gpu/fused.rs b/overlay/htm_rust/src/gpu/fused.rs index eb197b4bda3c3a3b2b3cc55d200074dbde886596..1c1df1d8f1e8358e480557c99a185608cfcf1068 100644 --- a/overlay/htm_rust/src/gpu/fused.rs +++ b/overlay/htm_rust/src/gpu/fused.rs @@ -1,663 +1,702 @@ -//! Fused HTM megakernel launcher. -//! -//! Collapses the 12-kernel per-timestep pipeline (and the outer T-loop) into -//! a single kernel launch per forward. See `kernels/htm_fused_step.cu` for -//! the kernel design and the cross-block coherence strategy (grid barrier -//! via device counter with all blocks concurrently resident). -//! -//! Launch invariant: `grid_dim.x <= concurrent-block capacity`. Host code -//! probes the device SM count at construction and caps grid_dim.x -//! accordingly — otherwise the grid barrier deadlocks. -//! -//! Semantic change from the top-K pipeline: activation is per-column -//! threshold-based (local lateral inhibition) instead of global top-K. -//! A per-column `inhibition_threshold` is tracked and EMA-steered to hit -//! the sparsity target. This is a real architectural change and is -//! documented in `docs/GPU_HTM.md`. - -#![cfg(feature = "gpu")] - -use std::ffi::CString; -use std::sync::Arc; - -use cudarc::driver::{result, sys, CudaDevice, CudaSlice, DeviceRepr, DevicePtr, DriverError, - LaunchConfig}; -use cudarc::nvrtc::Ptx; - -use super::sp_gpu::SpatialPoolerGpu; -use super::tm_gpu::{TemporalMemoryGpu, MAX_SEGMENTS_PER_CELL, MAX_SYN_PER_SEGMENT}; - -const PTX_HTM_FUSED: &str = - include_str!(concat!(env!("HTM_GPU_PTX_DIR"), "/htm_fused_step.ptx")); - -/// Struct-by-value pointer pack — matches C-side `FusedPtrs`. -/// -/// NOTE: `barrier_counters` is kept as an ABI-compat dummy (always 0). The -/// C-side `FusedPtrs` still has the field at the same byte offset; removing -/// it here would shift all subsequent fields and break the layout. Worker A -/// will eventually delete the field from both sides once the kernel is -/// updated; until then we zero it. -#[repr(C)] -#[derive(Clone, Copy)] -pub struct FusedPtrs { - pub syn_bit: u64, - pub syn_perm: u64, - pub boost: u64, - pub active_duty: u64, - pub inhibition_threshold: u64, - pub seg_cell_id: u64, - pub seg_syn_count: u64, - pub syn_presyn: u64, - pub tm_syn_perm: u64, - pub cell_seg_count: u64, - pub cell_active_a: u64, - pub cell_active_b: u64, - pub cell_winner_a: u64, - pub cell_winner_b: u64, - pub inputs: u64, - pub cols_out: u64, - pub anom_out: u64, - /// ABI-compat dummy — always 0. No device memory is allocated for this - /// field; the cluster barrier replaces the old software DLB barrier. - pub barrier_counters: u64, - pub step_scratch: u64, -} - -unsafe impl DeviceRepr for FusedPtrs {} - -/// Launch-time config — matches C-side `FusedConfig` 1:1. -#[repr(C)] -#[derive(Clone, Copy)] -pub struct FusedConfig { - pub input_bits: u32, - pub n_columns: u32, - pub synapses_per_col: u32, - pub conn_thr: f32, - pub sp_inc: f32, - pub sp_dec: f32, - pub sparsity_target: f32, - pub duty_alpha: f32, - pub thr_adapt_rate: f32, - pub cells_per_column: u32, - pub n_cells: u32, - pub bits_words: u32, - pub max_segments_per_cell: u32, - pub synapses_per_segment: u32, - pub activation_threshold: u32, - pub learning_threshold: u32, - pub max_new_synapses: u32, - pub conn_thr_i16: i32, - pub perm_inc_i16: i32, - pub perm_dec_i16: i32, - pub predicted_seg_dec_i16: i32, - pub initial_perm_i16: i32, - pub t: u32, - pub learn: u32, - pub iter_seed: u32, - pub cooperative_grid_sync: u32, -} - -unsafe impl DeviceRepr for FusedConfig {} - -/// Cluster launch parameters probed at construction time. -#[derive(Clone, Copy, Debug, PartialEq, Eq)] -pub(crate) struct ClusterInfo { - /// Maximum cluster size supported by this device (0 = cluster unsupported). - pub max_cluster_size: u32, -} - -// There is only ONE launch mode: non-cooperative launch with Hopper Thread -// Block Cluster attribute (`CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION`). The old -// software DLB barrier and the cooperative-launch path are both removed. -// Cluster barriers replace both. -#[derive(Clone, Copy, Debug, PartialEq, Eq)] -pub(crate) struct FusedLaunchPlan { - pub grid_dim_x: u32, - pub block_dim_x: u32, - pub cooperative_grid_limit: u32, - pub sm_count: u32, -} - -fn fused_grid_cap_override() -> Option { - std::env::var("HTM_FUSED_GRID_CAP") - .ok() - .and_then(|s| s.parse::().ok()) - .map(|v| v.max(1)) -} - -pub(crate) fn plan_fused_launch( - sm_count: u32, - cooperative_supported: bool, - cooperative_grid_limit: u32, - grid_cap_override: Option, -) -> Result { - let sm_count = sm_count.max(1); - // 1024 threads/block exceeds the register file on Ampere (sm_86: 65536 - // regs/SM ÷ 1024 = 64 regs/thread; fused kernel needs ~80+). 256 gives - // 256 regs/thread which is ample. Compensate with more blocks via - // cooperative launch. On Hopper (228 KB smem, 255 regs/thread baseline), - // 1024 works fine, but 256 is safe everywhere. - let block_dim_x = 256u32; - - // Cluster launch path: cooperative launch is not required. Keep the probe - // result for residency estimation only. - if !cooperative_supported { - eprintln!("[htm_rust] INFO: cooperative launch unsupported; cluster path only."); - } - - // Tested grid_cap: 4 blocks = 30ms (too serial), 16 blocks = 10.8ms (parallel wins). - // Parallelism in SP overlap + TM predict stages outweighs grid.sync() cost. - let default_grid_cap = 16u32; - let grid_cap = grid_cap_override.unwrap_or(default_grid_cap); - let resident_bound = if cooperative_grid_limit > 0 { - cooperative_grid_limit.max(sm_count * 2) - } else { - sm_count * 2 - }; - Ok(FusedLaunchPlan { - grid_dim_x: resident_bound.min(grid_cap).max(1), - block_dim_x, - cooperative_grid_limit: resident_bound, - sm_count, - }) -} - -pub(super) struct RawFusedKernel { - module: sys::CUmodule, - pub(super) function: sys::CUfunction, - pub(super) function_batched: sys::CUfunction, -} - -unsafe impl Send for RawFusedKernel {} -unsafe impl Sync for RawFusedKernel {} - -impl Drop for RawFusedKernel { - fn drop(&mut self) { - unsafe { - let _ = result::module::unload(self.module); - } - } -} - -/// Owns fused-path-only device state: -/// - per-column inhibition threshold (replaces global top-K) -/// - ping-pong cell_active/cell_winner bitsets -/// - step_scratch (n_active, n_unpred per timestep) -/// - cluster launch capability info -pub struct FusedState { - dev: Arc, - pub(super) raw_kernel: RawFusedKernel, - - pub inhibition_threshold: CudaSlice, - pub cell_active_bits_a: CudaSlice, - pub cell_active_bits_b: CudaSlice, - pub cell_winner_bits_a: CudaSlice, - pub cell_winner_bits_b: CudaSlice, - pub step_scratch: CudaSlice, // length 6 - - pub grid_dim_x: u32, - pub block_dim_x: u32, - pub cooperative_grid_limit: u32, - pub iter_counter: u32, - - /// Hopper cluster launch capability (0 = unsupported). - pub cluster_info: ClusterInfo, - - // Config mirror (read-only after init). - #[allow(dead_code)] - pub initial_threshold: f32, -} - -impl FusedState { - pub fn new( - dev: Arc, - n_columns: usize, - cells_per_column: usize, - initial_threshold: f32, - ) -> Result { - let n_cells = n_columns * cells_per_column; - assert!(n_cells % 32 == 0, "n_cells must be divisible by 32 for bitsets"); - let bits_words = n_cells / 32; - - let mut inhibition_threshold = dev.alloc_zeros::(n_columns)?; - let init_vec = vec![initial_threshold; n_columns]; - dev.htod_sync_copy_into(&init_vec, &mut inhibition_threshold)?; - - let cell_active_bits_a = dev.alloc_zeros::(bits_words)?; - let cell_active_bits_b = dev.alloc_zeros::(bits_words)?; - let cell_winner_bits_a = dev.alloc_zeros::(bits_words)?; - let cell_winner_bits_b = dev.alloc_zeros::(bits_words)?; - let step_scratch = dev.alloc_zeros::(6)?; - - unsafe { - result::ctx::set_current(*dev.cu_primary_ctx())?; - } - if dev.get_func("htm_fused", "htm_fused_step").is_none() { - dev.load_ptx( - Ptx::from_src(PTX_HTM_FUSED), - "htm_fused", - &["htm_fused_step", "htm_fused_step_batched"], - )?; - } - let ptx = CString::new(PTX_HTM_FUSED).expect("PTX contains no interior nul bytes"); - let module = unsafe { result::module::load_data(ptx.as_ptr().cast()) }?; - let function = unsafe { - result::module::get_function(module, CString::new("htm_fused_step").unwrap()) - }?; - let function_batched = unsafe { - result::module::get_function(module, CString::new("htm_fused_step_batched").unwrap()) - }?; - - // Cluster size 16 on Hopper is "non-portable" (> 8 requires opt-in). - // Must set CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED=1 on - // every launched kernel function, otherwise cuLaunchKernelEx rejects - // the cluster dim with CUDA_ERROR_INVALID_CLUSTER_SIZE. - unsafe { - let attr = sys::CUfunction_attribute::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED; - // Ignore errors: older CUDA may lack the attribute, in which case - // only portable sizes (<= 8) work — plan_fused_launch caps at 8. - let _ = sys::lib().cuFuncSetAttribute(function, attr, 1); - let _ = sys::lib().cuFuncSetAttribute(function_batched, attr, 1); - } - - // Probe SM count. - let sm_count = match dev.attribute( - cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, - ) { - Ok(v) => v as u32, - Err(_) => 16u32, - }; - - // T1: Probe Hopper cluster launch capability. - let max_cluster_size = match dev.attribute( - cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH, - ) { - Ok(v) if v > 0 => { - // H200/sm_90a supports up to 16 blocks per cluster. - // There is no MAX_CLUSTER_SIZE attribute in CUDA 12.4; hard-code the - // Hopper maximum which is 16 (8 SMs × 2 blocks/SM = 16 blocks/cluster). - 16u32 - } - _ => 0u32, - }; - eprintln!("[htm_rust] cluster: max_cluster_size={}", max_cluster_size); - let cluster_info = ClusterInfo { max_cluster_size }; - - let cooperative_supported = matches!( - dev.attribute(sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH), - Ok(v) if v > 0 - ); - let cooperative_grid_limit = if cooperative_supported { - let blocks_per_sm = unsafe { - result::occupancy::max_active_block_per_multiprocessor(function, 1024, 0) - } - .ok() - .map(|v| v.max(0) as u32) - .unwrap_or(0); - sm_count.saturating_mul(blocks_per_sm) - } else { - 0 - }; - let launch_plan = plan_fused_launch( - sm_count, - cooperative_supported, - cooperative_grid_limit, - fused_grid_cap_override(), - ) - .map_err(|msg| { - // Surface as a CUDA-ish error so callers can propagate. - eprintln!("[htm_rust] FATAL: {msg}"); - DriverError(cudarc::driver::sys::CUresult::CUDA_ERROR_NOT_SUPPORTED) - })?; - - eprintln!( - "[htm_rust] fused kernel: sm_count={} grid_dim_x={} cooperative_grid_limit={} cluster_max={}", - launch_plan.sm_count, launch_plan.grid_dim_x, launch_plan.cooperative_grid_limit, - cluster_info.max_cluster_size, - ); - - Ok(Self { - dev, - raw_kernel: RawFusedKernel { module, function, function_batched }, - inhibition_threshold, - cell_active_bits_a, - cell_active_bits_b, - cell_winner_bits_a, - cell_winner_bits_b, - step_scratch, - grid_dim_x: launch_plan.grid_dim_x, - block_dim_x: launch_plan.block_dim_x, - cooperative_grid_limit: launch_plan.cooperative_grid_limit, - iter_counter: 0, - cluster_info, - initial_threshold, - }) - } - - /// Reset fused state. Called at region.reset(). - pub fn reset(&mut self) -> Result<(), DriverError> { - self.dev.memset_zeros(&mut self.cell_active_bits_a)?; - self.dev.memset_zeros(&mut self.cell_active_bits_b)?; - self.dev.memset_zeros(&mut self.cell_winner_bits_a)?; - self.dev.memset_zeros(&mut self.cell_winner_bits_b)?; - self.dev.memset_zeros(&mut self.step_scratch)?; - // Do NOT reset inhibition_threshold — it's learned state. A hard - // reset of TM state should NOT forget the sparsity calibration. - Ok(()) - } -} - -/// Launch the fused megakernel. Processes all T timesteps in one kernel. -/// -/// Uses `cuLaunchKernelEx` with `CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION=(16,1,1)` -/// when the device supports cluster launch, otherwise falls back to a plain -/// `launch_kernel`. For single-region launches, grid_dim_x <= 16 ensures the -/// entire grid fits in one cluster. -#[allow(clippy::too_many_arguments)] -pub fn launch_fused( - sp: &mut SpatialPoolerGpu, - tm: &mut TemporalMemoryGpu, - fused: &mut FusedState, - inputs_flat: &CudaSlice, - cols_out: &mut CudaSlice, - anom_out: &mut CudaSlice, - t: usize, - input_bits: usize, - learn: bool, -) -> Result<(), DriverError> { - // Reset step_scratch before each launch (safe re-entry). - sp.dev_ref().memset_zeros(&mut fused.step_scratch)?; - - fused.iter_counter = fused.iter_counter.wrapping_add(1); - - let cfg = FusedConfig { - input_bits: input_bits as u32, - n_columns: sp.n_columns_accessor() as u32, - synapses_per_col: sp.synapses_per_col_accessor() as u32, - conn_thr: sp.conn_thr_accessor(), - sp_inc: sp.inc_accessor(), - sp_dec: sp.dec_accessor(), - sparsity_target: sp.sparsity_accessor(), - duty_alpha: 1.0f32 / sp.duty_period_accessor().max(1.0), - thr_adapt_rate: 0.001f32, - cells_per_column: tm.cells_per_column as u32, - n_cells: tm.n_cells as u32, - bits_words: tm.bits_words as u32, - max_segments_per_cell: MAX_SEGMENTS_PER_CELL as u32, - synapses_per_segment: MAX_SYN_PER_SEGMENT as u32, - activation_threshold: tm.activation_threshold, - learning_threshold: tm.learning_threshold, - max_new_synapses: tm.max_new_synapse_count, - conn_thr_i16: tm.conn_thr_i16 as i32, - perm_inc_i16: tm.perm_inc_i16 as i32, - perm_dec_i16: tm.perm_dec_i16 as i32, - predicted_seg_dec_i16: tm.predicted_seg_dec_i16 as i32, - initial_perm_i16: tm.initial_perm_i16 as i32, - t: t as u32, - learn: if learn { 1 } else { 0 }, - iter_seed: fused.iter_counter, - cooperative_grid_sync: 1, - }; - - let ptrs = FusedPtrs { - syn_bit: *sp.syn_bit_accessor().device_ptr(), - syn_perm: *sp.syn_perm_accessor().device_ptr(), - boost: *sp.boost_accessor().device_ptr(), - active_duty: *sp.active_duty_accessor().device_ptr(), - inhibition_threshold: *fused.inhibition_threshold.device_ptr(), - seg_cell_id: *tm.seg_cell_id_accessor().device_ptr(), - seg_syn_count: *tm.seg_syn_count_accessor().device_ptr(), - syn_presyn: *tm.syn_presyn_accessor().device_ptr(), - tm_syn_perm: *tm.syn_perm_accessor().device_ptr(), - cell_seg_count: *tm.cell_seg_count_accessor().device_ptr(), - cell_active_a: *fused.cell_active_bits_a.device_ptr(), - cell_active_b: *fused.cell_active_bits_b.device_ptr(), - cell_winner_a: *fused.cell_winner_bits_a.device_ptr(), - cell_winner_b: *fused.cell_winner_bits_b.device_ptr(), - inputs: *inputs_flat.device_ptr(), - cols_out: *cols_out.device_ptr(), - anom_out: *anom_out.device_ptr(), - barrier_counters: 0u64, // ABI-compat dummy; cluster barrier replaces DLB. - step_scratch: *fused.step_scratch.device_ptr(), - }; - - let grid_x = fused.grid_dim_x; - let block_x = fused.block_dim_x; - let cu_stream = *sp.dev_ref().cu_stream(); - let use_cluster = fused.cluster_info.max_cluster_size > 0; - - unsafe { - result::ctx::set_current(*sp.dev_ref().cu_primary_ctx())?; - let mut kernel_params: [*mut std::ffi::c_void; 2] = [ - (&ptrs as *const FusedPtrs).cast_mut().cast(), - (&cfg as *const FusedConfig).cast_mut().cast(), - ]; - - if use_cluster { - // T10: Hopper cluster launch with CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION. - // cluster_dim=(16,1,1) maps the entire single-region grid into one cluster. - let mut attr: sys::CUlaunchAttribute = std::mem::zeroed(); - attr.id = sys::CUlaunchAttributeID::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; - attr.value.clusterDim.x = 16; - attr.value.clusterDim.y = 1; - attr.value.clusterDim.z = 1; - - let mut launch_cfg: sys::CUlaunchConfig = std::mem::zeroed(); - launch_cfg.gridDimX = grid_x; - launch_cfg.gridDimY = 1; - launch_cfg.gridDimZ = 1; - launch_cfg.blockDimX = block_x; - launch_cfg.blockDimY = 1; - launch_cfg.blockDimZ = 1; - launch_cfg.sharedMemBytes = 0; - launch_cfg.hStream = cu_stream; - launch_cfg.numAttrs = 1; - launch_cfg.attrs = &mut attr as *mut sys::CUlaunchAttribute; - - let ret = sys::lib().cuLaunchKernelEx( - &launch_cfg as *const sys::CUlaunchConfig, - fused.raw_kernel.function, - kernel_params.as_mut_ptr(), - std::ptr::null_mut(), - ); - if ret != sys::CUresult::CUDA_SUCCESS { - return Err(DriverError(ret)); - } - } else { - // Pre-Hopper: cooperative kernel launch. The fused kernel uses - // grid.sync() for cross-block synchronization which REQUIRES - // cuLaunchCooperativeKernel (normal launch silently crashes on - // the first grid.sync() call). - let ret = sys::lib().cuLaunchCooperativeKernel( - fused.raw_kernel.function, - grid_x, 1, 1, - block_x, 1, 1, - 0, // sharedMemBytes - cu_stream, - kernel_params.as_mut_ptr(), - ); - if ret != sys::CUresult::CUDA_SUCCESS { - return Err(DriverError(ret)); - } - } - } - - Ok(()) -} - -/// Single batched non-cooperative launch for B regions with DLB sync. Uses the same kernel -/// body; each block reads its region's FusedPtrs from a device-side array -/// indexed by blockIdx.y. All regions share the same config (same -/// input_bits/n_columns/etc.) so we pass one FusedConfig. -/// -/// This breaks through the CUDA cooperative-kernel device-level -/// serialization: multiple cooperative launches are serialized regardless -/// of stream, but one cooperative launch with grid.y=B processes all -/// regions in a single invocation — ~B× speedup vs B sequential launches. -#[allow(clippy::too_many_arguments)] -/// Low-level raw-pointer entry, called by PyO3 binding which holds the -/// mutable borrows. Safety: each `*mut HTMRegionGpu` must point to a live, -/// uniquely-borrowed region. All regions must be distinct. -pub(super) fn launch_fused_batched_raw( - region_ptrs: &[*mut super::HTMRegionGpu], - inputs_per_region: &[u64], - cols_per_region: &[u64], - anom_per_region: &[u64], - t: usize, - input_bits: usize, - learn: bool, -) -> Result<(), DriverError> { - let b = region_ptrs.len(); - assert_eq!(inputs_per_region.len(), b); - assert_eq!(cols_per_region.len(), b); - assert_eq!(anom_per_region.len(), b); - assert!(b >= 1, "need at least one region"); - - // Reset per-region step_scratch before each launch. - for &rp in region_ptrs.iter() { - let r = unsafe { &mut *rp }; - let dev = r.sp_gpu.dev_ref().clone(); - dev.memset_zeros(&mut r.fused_state.step_scratch)?; - r.fused_state.iter_counter = r.fused_state.iter_counter.wrapping_add(1); - } - - // Shared config — all regions use identical sp/tm parameters. - let (grid_x, block_x, function_batched, cu_stream, cu_ctx) = { - let r0 = unsafe { &*region_ptrs[0] }; - ( - r0.fused_state.grid_dim_x, - r0.fused_state.block_dim_x, - r0.fused_state.raw_kernel.function_batched, - *r0.sp_gpu.dev_ref().cu_stream(), - *r0.sp_gpu.dev_ref().cu_primary_ctx(), - ) - }; - - let cfg = { - let r = unsafe { &*region_ptrs[0] }; - FusedConfig { - input_bits: input_bits as u32, - n_columns: r.sp_gpu.n_columns_accessor() as u32, - synapses_per_col: r.sp_gpu.synapses_per_col_accessor() as u32, - conn_thr: r.sp_gpu.conn_thr_accessor(), - sp_inc: r.sp_gpu.inc_accessor(), - sp_dec: r.sp_gpu.dec_accessor(), - sparsity_target: r.sp_gpu.sparsity_accessor(), - duty_alpha: 1.0f32 / r.sp_gpu.duty_period_accessor().max(1.0), - thr_adapt_rate: 0.001f32, - cells_per_column: r.tm_gpu.cells_per_column as u32, - n_cells: r.tm_gpu.n_cells as u32, - bits_words: r.tm_gpu.bits_words as u32, - max_segments_per_cell: MAX_SEGMENTS_PER_CELL as u32, - synapses_per_segment: MAX_SYN_PER_SEGMENT as u32, - activation_threshold: r.tm_gpu.activation_threshold, - learning_threshold: r.tm_gpu.learning_threshold, - max_new_synapses: r.tm_gpu.max_new_synapse_count, - conn_thr_i16: r.tm_gpu.conn_thr_i16 as i32, - perm_inc_i16: r.tm_gpu.perm_inc_i16 as i32, - perm_dec_i16: r.tm_gpu.perm_dec_i16 as i32, - predicted_seg_dec_i16: r.tm_gpu.predicted_seg_dec_i16 as i32, - initial_perm_i16: r.tm_gpu.initial_perm_i16 as i32, - t: t as u32, - learn: if learn { 1 } else { 0 }, - iter_seed: r.fused_state.iter_counter, - cooperative_grid_sync: 1, - } - }; - - // Build B FusedPtrs per-region. - let ptrs_vec: Vec = (0..b) - .map(|i| { - let r = unsafe { &*region_ptrs[i] }; - FusedPtrs { - syn_bit: *r.sp_gpu.syn_bit_accessor().device_ptr(), - syn_perm: *r.sp_gpu.syn_perm_accessor().device_ptr(), - boost: *r.sp_gpu.boost_accessor().device_ptr(), - active_duty: *r.sp_gpu.active_duty_accessor().device_ptr(), - inhibition_threshold: *r.fused_state.inhibition_threshold.device_ptr(), - seg_cell_id: *r.tm_gpu.seg_cell_id_accessor().device_ptr(), - seg_syn_count: *r.tm_gpu.seg_syn_count_accessor().device_ptr(), - syn_presyn: *r.tm_gpu.syn_presyn_accessor().device_ptr(), - tm_syn_perm: *r.tm_gpu.syn_perm_accessor().device_ptr(), - cell_seg_count: *r.tm_gpu.cell_seg_count_accessor().device_ptr(), - cell_active_a: *r.fused_state.cell_active_bits_a.device_ptr(), - cell_active_b: *r.fused_state.cell_active_bits_b.device_ptr(), - cell_winner_a: *r.fused_state.cell_winner_bits_a.device_ptr(), - cell_winner_b: *r.fused_state.cell_winner_bits_b.device_ptr(), - inputs: inputs_per_region[i], - cols_out: cols_per_region[i], - anom_out: anom_per_region[i], - barrier_counters: 0u64, // ABI-compat dummy; cluster barrier replaces DLB. - step_scratch: *r.fused_state.step_scratch.device_ptr(), - } - }) - .collect(); - - // Upload FusedPtrs array to device (B * sizeof(FusedPtrs) bytes). - // FusedPtrs is repr(C) + DeviceRepr so htod_sync_copy handles it. - let dev = unsafe { &*region_ptrs[0] }.sp_gpu.dev_ref().clone(); - let ptrs_dev: CudaSlice = dev.htod_sync_copy(&ptrs_vec)?; - let ptrs_dev_ptr: u64 = *ptrs_dev.device_ptr(); - - // T10: Cluster launch for batched regions. - // Grid = (grid_x, B, 1) with cluster_dim=(16,1,1): each region (Y slice) - // occupies exactly one cluster of 16 blocks. All 8 clusters run concurrently - // on the H200's 132 SMs (8 × 16 = 128 blocks ≤ 132 SMs). - let use_cluster = { - let r0 = unsafe { &*region_ptrs[0] }; - r0.fused_state.cluster_info.max_cluster_size > 0 - }; - - unsafe { - result::ctx::set_current(cu_ctx)?; - let mut kernel_params: [*mut std::ffi::c_void; 2] = [ - (&ptrs_dev_ptr as *const u64).cast_mut().cast(), - (&cfg as *const FusedConfig).cast_mut().cast(), - ]; - - if use_cluster { - let mut attr: sys::CUlaunchAttribute = std::mem::zeroed(); - attr.id = sys::CUlaunchAttributeID::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; - attr.value.clusterDim.x = 16; - attr.value.clusterDim.y = 1; - attr.value.clusterDim.z = 1; - - let mut launch_cfg: sys::CUlaunchConfig = std::mem::zeroed(); - launch_cfg.gridDimX = grid_x; - launch_cfg.gridDimY = b as u32; - launch_cfg.gridDimZ = 1; - launch_cfg.blockDimX = block_x; - launch_cfg.blockDimY = 1; - launch_cfg.blockDimZ = 1; - launch_cfg.sharedMemBytes = 0; - launch_cfg.hStream = cu_stream; - launch_cfg.numAttrs = 1; - launch_cfg.attrs = &mut attr as *mut sys::CUlaunchAttribute; - - let ret = sys::lib().cuLaunchKernelEx( - &launch_cfg as *const sys::CUlaunchConfig, - function_batched, - kernel_params.as_mut_ptr(), - std::ptr::null_mut(), - ); - if ret != sys::CUresult::CUDA_SUCCESS { - return Err(DriverError(ret)); - } - } else { - // Pre-Hopper: cooperative kernel launch (grid.sync() requires it). - let ret = sys::lib().cuLaunchCooperativeKernel( - function_batched, - grid_x, b as u32, 1, - block_x, 1, 1, - 0, // sharedMemBytes - cu_stream, - kernel_params.as_mut_ptr(), - ); - if ret != sys::CUresult::CUDA_SUCCESS { - return Err(DriverError(ret)); - } - } - } - - Ok(()) -} +//! Fused HTM megakernel launcher. +//! +//! Collapses the 12-kernel per-timestep pipeline (and the outer T-loop) into +//! a single kernel launch per forward. See `kernels/htm_fused_step.cu` for +//! the kernel design and the cross-block coherence strategy (grid barrier +//! via device counter with all blocks concurrently resident). +//! +//! Launch invariant: `grid_dim.x <= concurrent-block capacity`. Host code +//! probes the device SM count at construction and caps grid_dim.x +//! accordingly — otherwise the grid barrier deadlocks. +//! +//! Semantic change from the top-K pipeline: activation is per-column +//! threshold-based (local lateral inhibition) instead of global top-K. +//! A per-column `inhibition_threshold` is tracked and EMA-steered to hit +//! the sparsity target. This is a real architectural change and is +//! documented in `docs/GPU_HTM.md`. + +#![cfg(feature = "gpu")] + +use std::ffi::CString; +use std::sync::Arc; + +use cudarc::driver::{result, sys, CudaDevice, CudaSlice, DeviceRepr, DevicePtr, DriverError, + LaunchConfig}; +use cudarc::nvrtc::Ptx; + +use super::sp_gpu::SpatialPoolerGpu; +use super::tm_gpu::{TemporalMemoryGpu, MAX_SEGMENTS_PER_CELL, MAX_SYN_PER_SEGMENT}; + +const PTX_HTM_FUSED: &str = + include_str!(concat!(env!("HTM_GPU_PTX_DIR"), "/htm_fused_step.ptx")); + +/// Struct-by-value pointer pack — matches C-side `FusedPtrs`. +/// +/// NOTE: `barrier_counters` is kept as an ABI-compat dummy (always 0). The +/// C-side `FusedPtrs` still has the field at the same byte offset; removing +/// it here would shift all subsequent fields and break the layout. Worker A +/// will eventually delete the field from both sides once the kernel is +/// updated; until then we zero it. +#[repr(C)] +#[derive(Clone, Copy)] +pub struct FusedPtrs { + pub syn_bit: u64, + pub syn_perm: u64, + pub boost: u64, + pub active_duty: u64, + pub inhibition_threshold: u64, + pub seg_cell_id: u64, + pub seg_syn_count: u64, + pub syn_presyn: u64, + pub tm_syn_perm: u64, + pub cell_seg_count: u64, + pub cell_active_a: u64, + pub cell_active_b: u64, + pub cell_winner_a: u64, + pub cell_winner_b: u64, + pub inputs: u64, + pub cols_out: u64, + pub anom_out: u64, + /// ABI-compat dummy — always 0. No device memory is allocated for this + /// field; the cluster barrier replaces the old software DLB barrier. + pub barrier_counters: u64, + pub step_scratch: u64, +} + +unsafe impl DeviceRepr for FusedPtrs {} + +/// Launch-time config — matches C-side `FusedConfig` 1:1. +#[repr(C)] +#[derive(Clone, Copy)] +pub struct FusedConfig { + pub input_bits: u32, + pub n_columns: u32, + pub synapses_per_col: u32, + pub conn_thr: f32, + pub sp_inc: f32, + pub sp_dec: f32, + pub sparsity_target: f32, + pub duty_alpha: f32, + pub thr_adapt_rate: f32, + pub cells_per_column: u32, + pub n_cells: u32, + pub bits_words: u32, + pub max_segments_per_cell: u32, + pub synapses_per_segment: u32, + pub activation_threshold: u32, + pub learning_threshold: u32, + pub max_new_synapses: u32, + pub conn_thr_i16: i32, + pub perm_inc_i16: i32, + pub perm_dec_i16: i32, + pub predicted_seg_dec_i16: i32, + pub initial_perm_i16: i32, + pub t: u32, + pub learn: u32, + pub iter_seed: u32, + pub cooperative_grid_sync: u32, +} + +unsafe impl DeviceRepr for FusedConfig {} + +/// Cluster launch parameters probed at construction time. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub(crate) struct ClusterInfo { + /// Maximum cluster size supported by this device (0 = cluster unsupported). + pub max_cluster_size: u32, +} + +// There is only ONE launch mode: non-cooperative launch with Hopper Thread +// Block Cluster attribute (`CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION`). The old +// software DLB barrier and the cooperative-launch path are both removed. +// Cluster barriers replace both. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub(crate) struct FusedLaunchPlan { + pub grid_dim_x: u32, + pub block_dim_x: u32, + pub cooperative_grid_limit: u32, + pub sm_count: u32, +} + +fn fused_grid_cap_override() -> Option { + std::env::var("HTM_FUSED_GRID_CAP") + .ok() + .and_then(|s| s.parse::().ok()) + .map(|v| v.max(1)) +} + +pub(crate) fn plan_fused_launch( + sm_count: u32, + cooperative_supported: bool, + cooperative_grid_limit: u32, + grid_cap_override: Option, +) -> Result { + let sm_count = sm_count.max(1); + // 1024 threads/block exceeds the register file on Ampere (sm_86: 65536 + // regs/SM ÷ 1024 = 64 regs/thread; fused kernel needs ~80+). 256 gives + // 256 regs/thread which is ample. Compensate with more blocks via + // cooperative launch. On Hopper (228 KB smem, 255 regs/thread baseline), + // 1024 works fine, but 256 is safe everywhere. + let block_dim_x = 256u32; + + // Cluster launch path: cooperative launch is not required. Keep the probe + // result for residency estimation only. + if !cooperative_supported { + eprintln!("[htm_rust] INFO: cooperative launch unsupported; cluster path only."); + } + + // Tested grid_cap: 4 blocks = 30ms (too serial), 16 blocks = 10.8ms (parallel wins). + // Parallelism in SP overlap + TM predict stages outweighs grid.sync() cost. + let default_grid_cap = 16u32; + let grid_cap = grid_cap_override.unwrap_or(default_grid_cap); + let resident_bound = if cooperative_grid_limit > 0 { + cooperative_grid_limit.max(sm_count * 2) + } else { + sm_count * 2 + }; + Ok(FusedLaunchPlan { + grid_dim_x: resident_bound.min(grid_cap).max(1), + block_dim_x, + cooperative_grid_limit: resident_bound, + sm_count, + }) +} + +pub(crate) fn plan_batched_grid_dim( + grid_dim_x: u32, + cooperative_grid_limit: u32, + batch_regions: usize, + use_cluster: bool, +) -> Result { + if use_cluster { + return Ok(grid_dim_x.max(1)); + } + + let batch_regions = batch_regions.max(1) as u32; + if cooperative_grid_limit == 0 { + return Err("COOPERATIVE_LAUNCH_TOO_LARGE: cooperative launch limit unavailable".into()); + } + + let max_grid_x = cooperative_grid_limit / batch_regions; + if max_grid_x == 0 { + return Err(format!( + "COOPERATIVE_LAUNCH_TOO_LARGE: batch_regions={batch_regions} exceeds cooperative_grid_limit={cooperative_grid_limit}" + )); + } + + Ok(grid_dim_x.min(max_grid_x).max(1)) +} + +pub(super) struct RawFusedKernel { + module: sys::CUmodule, + pub(super) function: sys::CUfunction, + pub(super) function_batched: sys::CUfunction, +} + +unsafe impl Send for RawFusedKernel {} +unsafe impl Sync for RawFusedKernel {} + +impl Drop for RawFusedKernel { + fn drop(&mut self) { + unsafe { + let _ = result::module::unload(self.module); + } + } +} + +/// Owns fused-path-only device state: +/// - per-column inhibition threshold (replaces global top-K) +/// - ping-pong cell_active/cell_winner bitsets +/// - step_scratch (n_active, n_unpred per timestep) +/// - cluster launch capability info +pub struct FusedState { + dev: Arc, + pub(super) raw_kernel: RawFusedKernel, + + pub inhibition_threshold: CudaSlice, + pub cell_active_bits_a: CudaSlice, + pub cell_active_bits_b: CudaSlice, + pub cell_winner_bits_a: CudaSlice, + pub cell_winner_bits_b: CudaSlice, + pub step_scratch: CudaSlice, // length 6 + + pub grid_dim_x: u32, + pub block_dim_x: u32, + pub cooperative_grid_limit: u32, + pub iter_counter: u32, + + /// Hopper cluster launch capability (0 = unsupported). + pub cluster_info: ClusterInfo, + + // Config mirror (read-only after init). + #[allow(dead_code)] + pub initial_threshold: f32, +} + +impl FusedState { + pub fn new( + dev: Arc, + n_columns: usize, + cells_per_column: usize, + initial_threshold: f32, + ) -> Result { + let n_cells = n_columns * cells_per_column; + assert!(n_cells % 32 == 0, "n_cells must be divisible by 32 for bitsets"); + let bits_words = n_cells / 32; + + let mut inhibition_threshold = dev.alloc_zeros::(n_columns)?; + let init_vec = vec![initial_threshold; n_columns]; + dev.htod_sync_copy_into(&init_vec, &mut inhibition_threshold)?; + + let cell_active_bits_a = dev.alloc_zeros::(bits_words)?; + let cell_active_bits_b = dev.alloc_zeros::(bits_words)?; + let cell_winner_bits_a = dev.alloc_zeros::(bits_words)?; + let cell_winner_bits_b = dev.alloc_zeros::(bits_words)?; + let step_scratch = dev.alloc_zeros::(6)?; + + unsafe { + result::ctx::set_current(*dev.cu_primary_ctx())?; + } + if dev.get_func("htm_fused", "htm_fused_step").is_none() { + dev.load_ptx( + Ptx::from_src(PTX_HTM_FUSED), + "htm_fused", + &["htm_fused_step", "htm_fused_step_batched"], + )?; + } + let ptx = CString::new(PTX_HTM_FUSED).expect("PTX contains no interior nul bytes"); + let module = unsafe { result::module::load_data(ptx.as_ptr().cast()) }?; + let function = unsafe { + result::module::get_function(module, CString::new("htm_fused_step").unwrap()) + }?; + let function_batched = unsafe { + result::module::get_function(module, CString::new("htm_fused_step_batched").unwrap()) + }?; + + // Cluster size 16 on Hopper is "non-portable" (> 8 requires opt-in). + // Must set CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED=1 on + // every launched kernel function, otherwise cuLaunchKernelEx rejects + // the cluster dim with CUDA_ERROR_INVALID_CLUSTER_SIZE. + unsafe { + let attr = sys::CUfunction_attribute::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED; + // Ignore errors: older CUDA may lack the attribute, in which case + // only portable sizes (<= 8) work — plan_fused_launch caps at 8. + let _ = sys::lib().cuFuncSetAttribute(function, attr, 1); + let _ = sys::lib().cuFuncSetAttribute(function_batched, attr, 1); + } + + // Probe SM count. + let sm_count = match dev.attribute( + cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, + ) { + Ok(v) => v as u32, + Err(_) => 16u32, + }; + + // T1: Probe Hopper cluster launch capability. + let max_cluster_size = match dev.attribute( + cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH, + ) { + Ok(v) if v > 0 => { + // H200/sm_90a supports up to 16 blocks per cluster. + // There is no MAX_CLUSTER_SIZE attribute in CUDA 12.4; hard-code the + // Hopper maximum which is 16 (8 SMs × 2 blocks/SM = 16 blocks/cluster). + 16u32 + } + _ => 0u32, + }; + eprintln!("[htm_rust] cluster: max_cluster_size={}", max_cluster_size); + let cluster_info = ClusterInfo { max_cluster_size }; + + let cooperative_supported = matches!( + dev.attribute(sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH), + Ok(v) if v > 0 + ); + let cooperative_grid_limit = if cooperative_supported { + let blocks_per_sm = unsafe { + // Must match plan_fused_launch(): the A10G/Ampere-safe fused + // kernel launch uses 256 threads/block, not the historical + // 1024-thread Hopper occupancy probe. + result::occupancy::max_active_block_per_multiprocessor(function, 256, 0) + } + .ok() + .map(|v| v.max(0) as u32) + .unwrap_or(0); + sm_count.saturating_mul(blocks_per_sm) + } else { + 0 + }; + let launch_plan = plan_fused_launch( + sm_count, + cooperative_supported, + cooperative_grid_limit, + fused_grid_cap_override(), + ) + .map_err(|msg| { + // Surface as a CUDA-ish error so callers can propagate. + eprintln!("[htm_rust] FATAL: {msg}"); + DriverError(cudarc::driver::sys::CUresult::CUDA_ERROR_NOT_SUPPORTED) + })?; + + eprintln!( + "[htm_rust] fused kernel: sm_count={} grid_dim_x={} cooperative_grid_limit={} cluster_max={}", + launch_plan.sm_count, launch_plan.grid_dim_x, launch_plan.cooperative_grid_limit, + cluster_info.max_cluster_size, + ); + + Ok(Self { + dev, + raw_kernel: RawFusedKernel { module, function, function_batched }, + inhibition_threshold, + cell_active_bits_a, + cell_active_bits_b, + cell_winner_bits_a, + cell_winner_bits_b, + step_scratch, + grid_dim_x: launch_plan.grid_dim_x, + block_dim_x: launch_plan.block_dim_x, + cooperative_grid_limit: launch_plan.cooperative_grid_limit, + iter_counter: 0, + cluster_info, + initial_threshold, + }) + } + + /// Reset fused state. Called at region.reset(). + pub fn reset(&mut self) -> Result<(), DriverError> { + self.dev.memset_zeros(&mut self.cell_active_bits_a)?; + self.dev.memset_zeros(&mut self.cell_active_bits_b)?; + self.dev.memset_zeros(&mut self.cell_winner_bits_a)?; + self.dev.memset_zeros(&mut self.cell_winner_bits_b)?; + self.dev.memset_zeros(&mut self.step_scratch)?; + // Do NOT reset inhibition_threshold — it's learned state. A hard + // reset of TM state should NOT forget the sparsity calibration. + Ok(()) + } +} + +/// Launch the fused megakernel. Processes all T timesteps in one kernel. +/// +/// Uses `cuLaunchKernelEx` with `CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION=(16,1,1)` +/// when the device supports cluster launch, otherwise falls back to a plain +/// `launch_kernel`. For single-region launches, grid_dim_x <= 16 ensures the +/// entire grid fits in one cluster. +#[allow(clippy::too_many_arguments)] +pub fn launch_fused( + sp: &mut SpatialPoolerGpu, + tm: &mut TemporalMemoryGpu, + fused: &mut FusedState, + inputs_flat: &CudaSlice, + cols_out: &mut CudaSlice, + anom_out: &mut CudaSlice, + t: usize, + input_bits: usize, + learn: bool, +) -> Result<(), DriverError> { + // Reset step_scratch before each launch (safe re-entry). + sp.dev_ref().memset_zeros(&mut fused.step_scratch)?; + + fused.iter_counter = fused.iter_counter.wrapping_add(1); + + let cfg = FusedConfig { + input_bits: input_bits as u32, + n_columns: sp.n_columns_accessor() as u32, + synapses_per_col: sp.synapses_per_col_accessor() as u32, + conn_thr: sp.conn_thr_accessor(), + sp_inc: sp.inc_accessor(), + sp_dec: sp.dec_accessor(), + sparsity_target: sp.sparsity_accessor(), + duty_alpha: 1.0f32 / sp.duty_period_accessor().max(1.0), + thr_adapt_rate: 0.001f32, + cells_per_column: tm.cells_per_column as u32, + n_cells: tm.n_cells as u32, + bits_words: tm.bits_words as u32, + max_segments_per_cell: MAX_SEGMENTS_PER_CELL as u32, + synapses_per_segment: MAX_SYN_PER_SEGMENT as u32, + activation_threshold: tm.activation_threshold, + learning_threshold: tm.learning_threshold, + max_new_synapses: tm.max_new_synapse_count, + conn_thr_i16: tm.conn_thr_i16 as i32, + perm_inc_i16: tm.perm_inc_i16 as i32, + perm_dec_i16: tm.perm_dec_i16 as i32, + predicted_seg_dec_i16: tm.predicted_seg_dec_i16 as i32, + initial_perm_i16: tm.initial_perm_i16 as i32, + t: t as u32, + learn: if learn { 1 } else { 0 }, + iter_seed: fused.iter_counter, + cooperative_grid_sync: 1, + }; + + let ptrs = FusedPtrs { + syn_bit: *sp.syn_bit_accessor().device_ptr(), + syn_perm: *sp.syn_perm_accessor().device_ptr(), + boost: *sp.boost_accessor().device_ptr(), + active_duty: *sp.active_duty_accessor().device_ptr(), + inhibition_threshold: *fused.inhibition_threshold.device_ptr(), + seg_cell_id: *tm.seg_cell_id_accessor().device_ptr(), + seg_syn_count: *tm.seg_syn_count_accessor().device_ptr(), + syn_presyn: *tm.syn_presyn_accessor().device_ptr(), + tm_syn_perm: *tm.syn_perm_accessor().device_ptr(), + cell_seg_count: *tm.cell_seg_count_accessor().device_ptr(), + cell_active_a: *fused.cell_active_bits_a.device_ptr(), + cell_active_b: *fused.cell_active_bits_b.device_ptr(), + cell_winner_a: *fused.cell_winner_bits_a.device_ptr(), + cell_winner_b: *fused.cell_winner_bits_b.device_ptr(), + inputs: *inputs_flat.device_ptr(), + cols_out: *cols_out.device_ptr(), + anom_out: *anom_out.device_ptr(), + barrier_counters: 0u64, // ABI-compat dummy; cluster barrier replaces DLB. + step_scratch: *fused.step_scratch.device_ptr(), + }; + + let grid_x = fused.grid_dim_x; + let block_x = fused.block_dim_x; + let cu_stream = *sp.dev_ref().cu_stream(); + let use_cluster = fused.cluster_info.max_cluster_size > 0; + + unsafe { + result::ctx::set_current(*sp.dev_ref().cu_primary_ctx())?; + let mut kernel_params: [*mut std::ffi::c_void; 2] = [ + (&ptrs as *const FusedPtrs).cast_mut().cast(), + (&cfg as *const FusedConfig).cast_mut().cast(), + ]; + + if use_cluster { + // T10: Hopper cluster launch with CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION. + // cluster_dim=(16,1,1) maps the entire single-region grid into one cluster. + let mut attr: sys::CUlaunchAttribute = std::mem::zeroed(); + attr.id = sys::CUlaunchAttributeID::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; + attr.value.clusterDim.x = 16; + attr.value.clusterDim.y = 1; + attr.value.clusterDim.z = 1; + + let mut launch_cfg: sys::CUlaunchConfig = std::mem::zeroed(); + launch_cfg.gridDimX = grid_x; + launch_cfg.gridDimY = 1; + launch_cfg.gridDimZ = 1; + launch_cfg.blockDimX = block_x; + launch_cfg.blockDimY = 1; + launch_cfg.blockDimZ = 1; + launch_cfg.sharedMemBytes = 0; + launch_cfg.hStream = cu_stream; + launch_cfg.numAttrs = 1; + launch_cfg.attrs = &mut attr as *mut sys::CUlaunchAttribute; + + let ret = sys::lib().cuLaunchKernelEx( + &launch_cfg as *const sys::CUlaunchConfig, + fused.raw_kernel.function, + kernel_params.as_mut_ptr(), + std::ptr::null_mut(), + ); + if ret != sys::CUresult::CUDA_SUCCESS { + return Err(DriverError(ret)); + } + } else { + // Pre-Hopper: cooperative kernel launch. The fused kernel uses + // grid.sync() for cross-block synchronization which REQUIRES + // cuLaunchCooperativeKernel (normal launch silently crashes on + // the first grid.sync() call). + let ret = sys::lib().cuLaunchCooperativeKernel( + fused.raw_kernel.function, + grid_x, 1, 1, + block_x, 1, 1, + 0, // sharedMemBytes + cu_stream, + kernel_params.as_mut_ptr(), + ); + if ret != sys::CUresult::CUDA_SUCCESS { + return Err(DriverError(ret)); + } + } + } + + Ok(()) +} + +/// Single batched non-cooperative launch for B regions with DLB sync. Uses the same kernel +/// body; each block reads its region's FusedPtrs from a device-side array +/// indexed by blockIdx.y. All regions share the same config (same +/// input_bits/n_columns/etc.) so we pass one FusedConfig. +/// +/// This breaks through the CUDA cooperative-kernel device-level +/// serialization: multiple cooperative launches are serialized regardless +/// of stream, but one cooperative launch with grid.y=B processes all +/// regions in a single invocation — ~B× speedup vs B sequential launches. +#[allow(clippy::too_many_arguments)] +/// Low-level raw-pointer entry, called by PyO3 binding which holds the +/// mutable borrows. Safety: each `*mut HTMRegionGpu` must point to a live, +/// uniquely-borrowed region. All regions must be distinct. +pub(super) fn launch_fused_batched_raw( + region_ptrs: &[*mut super::HTMRegionGpu], + inputs_per_region: &[u64], + cols_per_region: &[u64], + anom_per_region: &[u64], + t: usize, + input_bits: usize, + learn: bool, +) -> Result<(), DriverError> { + let b = region_ptrs.len(); + assert_eq!(inputs_per_region.len(), b); + assert_eq!(cols_per_region.len(), b); + assert_eq!(anom_per_region.len(), b); + assert!(b >= 1, "need at least one region"); + + // Reset per-region step_scratch before each launch. + for &rp in region_ptrs.iter() { + let r = unsafe { &mut *rp }; + let dev = r.sp_gpu.dev_ref().clone(); + dev.memset_zeros(&mut r.fused_state.step_scratch)?; + r.fused_state.iter_counter = r.fused_state.iter_counter.wrapping_add(1); + } + + // Shared config — all regions use identical sp/tm parameters. + let (grid_x, block_x, cooperative_grid_limit, function_batched, cu_stream, cu_ctx) = { + let r0 = unsafe { &*region_ptrs[0] }; + ( + r0.fused_state.grid_dim_x, + r0.fused_state.block_dim_x, + r0.fused_state.cooperative_grid_limit, + r0.fused_state.raw_kernel.function_batched, + *r0.sp_gpu.dev_ref().cu_stream(), + *r0.sp_gpu.dev_ref().cu_primary_ctx(), + ) + }; + + let cfg = { + let r = unsafe { &*region_ptrs[0] }; + FusedConfig { + input_bits: input_bits as u32, + n_columns: r.sp_gpu.n_columns_accessor() as u32, + synapses_per_col: r.sp_gpu.synapses_per_col_accessor() as u32, + conn_thr: r.sp_gpu.conn_thr_accessor(), + sp_inc: r.sp_gpu.inc_accessor(), + sp_dec: r.sp_gpu.dec_accessor(), + sparsity_target: r.sp_gpu.sparsity_accessor(), + duty_alpha: 1.0f32 / r.sp_gpu.duty_period_accessor().max(1.0), + thr_adapt_rate: 0.001f32, + cells_per_column: r.tm_gpu.cells_per_column as u32, + n_cells: r.tm_gpu.n_cells as u32, + bits_words: r.tm_gpu.bits_words as u32, + max_segments_per_cell: MAX_SEGMENTS_PER_CELL as u32, + synapses_per_segment: MAX_SYN_PER_SEGMENT as u32, + activation_threshold: r.tm_gpu.activation_threshold, + learning_threshold: r.tm_gpu.learning_threshold, + max_new_synapses: r.tm_gpu.max_new_synapse_count, + conn_thr_i16: r.tm_gpu.conn_thr_i16 as i32, + perm_inc_i16: r.tm_gpu.perm_inc_i16 as i32, + perm_dec_i16: r.tm_gpu.perm_dec_i16 as i32, + predicted_seg_dec_i16: r.tm_gpu.predicted_seg_dec_i16 as i32, + initial_perm_i16: r.tm_gpu.initial_perm_i16 as i32, + t: t as u32, + learn: if learn { 1 } else { 0 }, + iter_seed: r.fused_state.iter_counter, + cooperative_grid_sync: 1, + } + }; + + // Build B FusedPtrs per-region. + let ptrs_vec: Vec = (0..b) + .map(|i| { + let r = unsafe { &*region_ptrs[i] }; + FusedPtrs { + syn_bit: *r.sp_gpu.syn_bit_accessor().device_ptr(), + syn_perm: *r.sp_gpu.syn_perm_accessor().device_ptr(), + boost: *r.sp_gpu.boost_accessor().device_ptr(), + active_duty: *r.sp_gpu.active_duty_accessor().device_ptr(), + inhibition_threshold: *r.fused_state.inhibition_threshold.device_ptr(), + seg_cell_id: *r.tm_gpu.seg_cell_id_accessor().device_ptr(), + seg_syn_count: *r.tm_gpu.seg_syn_count_accessor().device_ptr(), + syn_presyn: *r.tm_gpu.syn_presyn_accessor().device_ptr(), + tm_syn_perm: *r.tm_gpu.syn_perm_accessor().device_ptr(), + cell_seg_count: *r.tm_gpu.cell_seg_count_accessor().device_ptr(), + cell_active_a: *r.fused_state.cell_active_bits_a.device_ptr(), + cell_active_b: *r.fused_state.cell_active_bits_b.device_ptr(), + cell_winner_a: *r.fused_state.cell_winner_bits_a.device_ptr(), + cell_winner_b: *r.fused_state.cell_winner_bits_b.device_ptr(), + inputs: inputs_per_region[i], + cols_out: cols_per_region[i], + anom_out: anom_per_region[i], + barrier_counters: 0u64, // ABI-compat dummy; cluster barrier replaces DLB. + step_scratch: *r.fused_state.step_scratch.device_ptr(), + } + }) + .collect(); + + // Upload FusedPtrs array to device (B * sizeof(FusedPtrs) bytes). + // FusedPtrs is repr(C) + DeviceRepr so htod_sync_copy handles it. + let dev = unsafe { &*region_ptrs[0] }.sp_gpu.dev_ref().clone(); + let ptrs_dev: CudaSlice = dev.htod_sync_copy(&ptrs_vec)?; + let ptrs_dev_ptr: u64 = *ptrs_dev.device_ptr(); + + // T10: Cluster launch for batched regions. + // Grid = (grid_x, B, 1) with cluster_dim=(16,1,1): each region (Y slice) + // occupies exactly one cluster of 16 blocks. All 8 clusters run concurrently + // on the H200's 132 SMs (8 × 16 = 128 blocks ≤ 132 SMs). + let use_cluster = { + let r0 = unsafe { &*region_ptrs[0] }; + r0.fused_state.cluster_info.max_cluster_size > 0 + }; + let grid_x = plan_batched_grid_dim(grid_x, cooperative_grid_limit, b, use_cluster) + .map_err(|msg| { + eprintln!("[htm_rust] FATAL: {msg}"); + DriverError(cudarc::driver::sys::CUresult::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE) + })?; + + unsafe { + result::ctx::set_current(cu_ctx)?; + let mut kernel_params: [*mut std::ffi::c_void; 2] = [ + (&ptrs_dev_ptr as *const u64).cast_mut().cast(), + (&cfg as *const FusedConfig).cast_mut().cast(), + ]; + + if use_cluster { + let mut attr: sys::CUlaunchAttribute = std::mem::zeroed(); + attr.id = sys::CUlaunchAttributeID::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; + attr.value.clusterDim.x = 16; + attr.value.clusterDim.y = 1; + attr.value.clusterDim.z = 1; + + let mut launch_cfg: sys::CUlaunchConfig = std::mem::zeroed(); + launch_cfg.gridDimX = grid_x; + launch_cfg.gridDimY = b as u32; + launch_cfg.gridDimZ = 1; + launch_cfg.blockDimX = block_x; + launch_cfg.blockDimY = 1; + launch_cfg.blockDimZ = 1; + launch_cfg.sharedMemBytes = 0; + launch_cfg.hStream = cu_stream; + launch_cfg.numAttrs = 1; + launch_cfg.attrs = &mut attr as *mut sys::CUlaunchAttribute; + + let ret = sys::lib().cuLaunchKernelEx( + &launch_cfg as *const sys::CUlaunchConfig, + function_batched, + kernel_params.as_mut_ptr(), + std::ptr::null_mut(), + ); + if ret != sys::CUresult::CUDA_SUCCESS { + return Err(DriverError(ret)); + } + } else { + // Pre-Hopper: cooperative kernel launch (grid.sync() requires it). + let ret = sys::lib().cuLaunchCooperativeKernel( + function_batched, + grid_x, b as u32, 1, + block_x, 1, 1, + 0, // sharedMemBytes + cu_stream, + kernel_params.as_mut_ptr(), + ); + if ret != sys::CUresult::CUDA_SUCCESS { + return Err(DriverError(ret)); + } + } + } + + // `ptrs_dev` is a per-call device array consumed by the async kernel. + // Keep it alive until the kernel has read it; otherwise dropping/freeing + // it immediately after launch can surface as a later unrelated CUDA error. + dev.synchronize()?; + + Ok(()) +} diff --git a/overlay/htm_rust/src/gpu/kernels/htm_fused_step.cu b/overlay/htm_rust/src/gpu/kernels/htm_fused_step.cu index 33db54273333e901357469876a2271b198ad879f..ec5ccdc21c2db12f6aef77418a26031cd2e8d6fe 100644 --- a/overlay/htm_rust/src/gpu/kernels/htm_fused_step.cu +++ b/overlay/htm_rust/src/gpu/kernels/htm_fused_step.cu @@ -1,677 +1,677 @@ -// Fused HTM megakernel — SP + TM, all T timesteps in a single launch. -// -// Design rationale: -// - Global top-K column selection requires cross-block synchronization at -// every timestep (grid.sync is unreliable on WSL2/sm_86 without rdc=true). -// - Replace with per-column threshold activation using local lateral -// inhibition: column c activates if overlap[c]*boost[c] > threshold[c]. -// Threshold is a per-column running-EMA learned scalar that steers the -// column's long-run activation rate toward the global sparsity target. -// - This is biologically grounded (GABAergic local inhibition) and supported -// by HTM theory (duty-cycle boost already drives this loop; we just -// change which lever the EMA pulls). -// -// Launch shape: -// grid = min(device SM count, 16) // hard cap — see below -// block = 1024 threads = 32 warps -// Each warp of 32 owns a contiguous column slice (n_columns / total_warps). -// -// Cross-block coherence: -// - Ping-pong buffers for cell_active/cell_winner: write _a at even t, -// read _b; reversed at odd t. -// - Preferred path: cooperative launch + hardware whole-grid sync. -// - Fallback path: software 3-slot rotating grid barrier for devices/drivers -// that cannot do cooperative launch. -// -// 2026-04-16: grid_dim reduced from 28 to 16 after deadlock RCA. The previous -// cap of 28 relied on all blocks being concurrently resident on a 30-SM RTX -// 3060 Laptop. Under thermal throttling effective residency dropped to ~20-24, -// leaving scheduled blocks spinning on the software grid barrier waiting for -// peer blocks that would never run. 16 blocks is below any realistic residency -// floor and preserves enough warp parallelism (16*32 = 512 warps) to saturate -// memory bandwidth on the spatial-pooler stage. -// -// Kernel signature uses struct-by-value for pointers and config to stay -// inside cudarc's launch-arg count limit. - -#include -#include - -namespace cg = cooperative_groups; - -// Maximum columns owned per cluster-block in DSMEM. -// Supports n_columns up to COLS_PER_CLUSTER_BLOCK_MAX * cluster_size. -// At cluster_size=16: supports up to 256*16=4096 columns. -// Each array costs 256*4 = 1024 bytes; three arrays = 3072 bytes per SM — -// well under the 228 KB H200 shared-memory cap. -#define COLS_PER_CLUSTER_BLOCK_MAX 256u - -// Maximum input_bits supported by the TMA-multicast staging tile. -// At 32 KB this covers the production SDR width (16384 bits) with 2× headroom. -// Total shared per SM: 32768 (tile) + 3072 (DSMEM float arrays) = ~35 KB — -// well under the 228 KB H200 limit. -// -// Expected speedup from TMA multicast input staging (T9/T11): -// - Without staging: 16 SMs × T × (input_bits GMEM reads per timestep) -// - With staging: 1 TMA DMA per timestep, shared reads from L1 thereafter -// - Theoretical DRAM bandwidth reduction: ~16× on input reads -// - Wall-clock reduction estimate: -20 to -40 ms from reduced input fetch latency -#define INPUT_BITS_MAX 32768u - -extern "C" { - -struct FusedPtrs { - unsigned long long syn_bit; - unsigned long long syn_perm; - unsigned long long boost; - unsigned long long active_duty; - unsigned long long inhibition_threshold; - unsigned long long seg_cell_id; - unsigned long long seg_syn_count; - unsigned long long syn_presyn; - unsigned long long tm_syn_perm; - unsigned long long cell_seg_count; - unsigned long long cell_active_a; - unsigned long long cell_active_b; - unsigned long long cell_winner_a; - unsigned long long cell_winner_b; - unsigned long long inputs; - unsigned long long cols_out; - unsigned long long anom_out; - unsigned long long barrier_counters; - unsigned long long step_scratch; -}; - -struct FusedConfig { - // SP constants - unsigned int input_bits; - unsigned int n_columns; - unsigned int synapses_per_col; - float conn_thr; - float sp_inc; - float sp_dec; - float sparsity_target; - float duty_alpha; - float thr_adapt_rate; - // TM constants - unsigned int cells_per_column; - unsigned int n_cells; - unsigned int bits_words; - unsigned int max_segments_per_cell; - unsigned int synapses_per_segment; - unsigned int activation_threshold; - unsigned int learning_threshold; - unsigned int max_new_synapses; - int conn_thr_i16; - int perm_inc_i16; - int perm_dec_i16; - int predicted_seg_dec_i16; - int initial_perm_i16; - // Loop constants - unsigned int T; - unsigned int learn; - unsigned int iter_seed; - unsigned int cooperative_grid_sync; -}; - -// Hardware cluster barrier using Hopper sm_90a cooperative_groups::this_cluster().sync(). -// Replaces the former software Decoupled Look-Back (DLB) atomic-spin barrier. -// -// cluster::sync() is a single PTX instruction (barrier.cluster) that resolves -// in ~10-40 ns inside the cluster, with no device-level serialization. -// Multiple clusters (one per HTM region) run fully concurrently — bounded -// only by SM count (8 clusters × 16 SMs = 128 ≤ 132 on H200). -// -// The flags / expected / phase / cooperative_grid_sync parameters are kept -// in the signature for call-site compatibility but are unused. -__device__ static inline void fused_grid_barrier(cg::grid_group grid, - unsigned int * /* flags — unused */, - unsigned int /* expected — unused */, - unsigned int /* phase — unused */, - unsigned int /* cooperative_grid_sync — unused */) { -#if __CUDA_ARCH__ >= 900 - // Hopper+ : hardware cluster barrier (~10-40 ns) - auto cluster = cg::this_cluster(); - cluster.sync(); -#else - // Pre-Hopper (sm_80, sm_86, sm_89): grid-level cooperative sync. - // Requires cooperative kernel launch. ~us-ms range, adequate for HTM - // workload (kernel launch frequency is low). - grid.sync(); -#endif -} - -__device__ static inline unsigned int warp_sum_u32(unsigned int v) { - for (int off = 16; off > 0; off >>= 1) { - v += __shfl_down_sync(0xffffffffu, v, off); - } - return v; -} - -// Core kernel body — works for both single-region and batched launches. -// Single-region: caller passes the one FusedPtrs struct. -// Batched: each block reads its region's FusedPtrs via blockIdx.y before -// calling this. State is independent per region (each region owns its own -// GPU buffers); grid.sync() is the only cross-block primitive and it -// spans ALL blocks in the grid (harmless over-sync across regions). -__device__ static inline -void htm_fused_step_body(const FusedPtrs& P, const FusedConfig& cfg) { - cg::grid_group grid = cg::this_grid(); - // Cast pointers. - const unsigned int * __restrict__ syn_bit = (const unsigned int*)P.syn_bit; - float * __restrict__ syn_perm = (float*)P.syn_perm; - float * __restrict__ boost = (float*)P.boost; - float * __restrict__ active_duty = (float*)P.active_duty; - float * __restrict__ inhibition_threshold = (float*)P.inhibition_threshold; - unsigned int * __restrict__ seg_cell_id = (unsigned int*)P.seg_cell_id; - unsigned int * __restrict__ seg_syn_count = (unsigned int*)P.seg_syn_count; - unsigned int * __restrict__ syn_presyn = (unsigned int*)P.syn_presyn; - short * __restrict__ tm_syn_perm = (short*)P.tm_syn_perm; - unsigned int * __restrict__ cell_seg_count = (unsigned int*)P.cell_seg_count; - unsigned int * __restrict__ cell_active_a = (unsigned int*)P.cell_active_a; - unsigned int * __restrict__ cell_active_b = (unsigned int*)P.cell_active_b; - unsigned int * __restrict__ cell_winner_a = (unsigned int*)P.cell_winner_a; - unsigned int * __restrict__ cell_winner_b = (unsigned int*)P.cell_winner_b; - const unsigned char * __restrict__ inputs = (const unsigned char*)P.inputs; - unsigned char * __restrict__ cols_out = (unsigned char*)P.cols_out; - float * __restrict__ anom_out = (float*)P.anom_out; - unsigned int * __restrict__ barrier_counters = (unsigned int*)P.barrier_counters; - unsigned int * __restrict__ step_scratch = (unsigned int*)P.step_scratch; - - const unsigned int tid = threadIdx.x; - const unsigned int lane = tid & 31u; - const unsigned int warp = tid >> 5; - const unsigned int warps_per_block = blockDim.x >> 5; - const unsigned int gwarp = blockIdx.x * warps_per_block + warp; - const unsigned int n_warps = gridDim.x * warps_per_block; - - const unsigned int n_cols = cfg.n_columns; - const unsigned int col_lo = (gwarp * n_cols) / n_warps; - const unsigned int col_hi = ((gwarp + 1) * n_cols) / n_warps; - - unsigned int phase = 0u; - - // ========================================================= - // DSMEM: Cluster-distributed shared memory for hot per-column - // state (inhibition_threshold, boost, active_duty). - // - // On Hopper (sm_90+): Each block in the cluster owns a contiguous - // slice of columns in its own __shared__ arrays. Any block can - // peer-read another block's slice via cluster.map_shared_rank(). - // - // On Ampere (sm_86) and other pre-Hopper: No cluster support. - // Read/write directly from/to global memory (inhibition_threshold, - // boost, active_duty device pointers). Slightly higher latency but - // functionally correct. - // ========================================================= - -#if __CUDA_ARCH__ >= 900 - // Hopper+ cluster path - auto cluster = cg::this_cluster(); - const unsigned int cluster_block_rank = cluster.block_rank(); // 0..cluster_size-1 - const unsigned int cluster_sz = cluster.num_blocks(); // == gridDim.x (≤16) -#else - // Pre-Hopper: no cluster, each block is independent. - const unsigned int cluster_block_rank = blockIdx.x; - const unsigned int cluster_sz = gridDim.x; -#endif - - // Partition n_cols evenly across cluster blocks. - // Each block owns cols_per_block columns starting at my_col_start. - const unsigned int cols_per_block = - (n_cols + cluster_sz - 1u) / cluster_sz; // ceil div - const unsigned int my_col_start = - cluster_block_rank * cols_per_block; - const unsigned int my_col_end = - (my_col_start + cols_per_block < n_cols) - ? (my_col_start + cols_per_block) : n_cols; // clamp - -#if __CUDA_ARCH__ >= 900 - // Cluster-distributed shared memory arrays. - // Each block holds at most COLS_PER_CLUSTER_BLOCK_MAX floats per array. - // Peer blocks address into each other's smem via map_shared_rank. - __shared__ float s_inhib_thr [COLS_PER_CLUSTER_BLOCK_MAX]; - __shared__ float s_boost [COLS_PER_CLUSTER_BLOCK_MAX]; - __shared__ float s_active_duty[COLS_PER_CLUSTER_BLOCK_MAX]; -#endif - - // TMA multicast input staging tile (T9) — HOPPER ONLY. - // - // On Hopper: cg::memcpy_async with cluster scope multicasts input to all - // 16 SMs, reducing DRAM traffic by ~16×. - // On Ampere: 32 KB smem allocation exceeds per-block budget when - // cooperatively launched (48 KB total, registers eat the rest). Skip the - // tile entirely — Stage A reads from GMEM directly (original path). -#if __CUDA_ARCH__ >= 900 - __shared__ __align__(16) unsigned char s_input_tile[INPUT_BITS_MAX]; -#endif - -#if __CUDA_ARCH__ >= 900 - // Initial GMEM → smem load (reads state from previous forward call). - // Each block loads only its own slice; tid strides across the slice. - for (unsigned int c = my_col_start + tid; c < my_col_end; c += blockDim.x) { - const unsigned int off = c - my_col_start; - s_inhib_thr [off] = inhibition_threshold[c]; - s_boost [off] = boost[c]; - s_active_duty[off] = active_duty[c]; - } - - // All blocks in the cluster must finish loading before any block - // starts reading peer smem inside the T-loop. - cluster.sync(); -#else - // Pre-Hopper: no smem caching needed — reads go directly to GMEM. - // Grid sync ensures all blocks have completed Phase 0 init before T-loop. - grid.sync(); -#endif - - const unsigned int S = cfg.synapses_per_col; - const unsigned int cpc = cfg.cells_per_column; - const unsigned int SPS = cfg.synapses_per_segment; - const unsigned int MSC = cfg.max_segments_per_cell; - - // Main timestep loop. - for (unsigned int t = 0u; t < cfg.T; t++) { - const unsigned int inp_off = t * cfg.input_bits; - const unsigned int col_base_out = t * n_cols; - - unsigned int * curr_active = (t & 1u) ? cell_active_b : cell_active_a; - unsigned int * prev_active = (t & 1u) ? cell_active_a : cell_active_b; - unsigned int * curr_winner = (t & 1u) ? cell_winner_b : cell_winner_a; - unsigned int * prev_winner = (t & 1u) ? cell_winner_a : cell_winner_b; - - // ---- Phase 0: clear curr bitsets for my cell range ---- - const unsigned int my_cell_lo = col_lo * cpc; - const unsigned int my_cell_hi = col_hi * cpc; - if (cpc == 32u) { - // Fast path: one word per column. - for (unsigned int c = col_lo + lane; c < col_hi; c += 32u) { - curr_active[c] = 0u; - curr_winner[c] = 0u; - } - } else { - for (unsigned int cell = my_cell_lo + lane; cell < my_cell_hi; cell += 32u) { - unsigned int w = cell >> 5; - unsigned int m = 1u << (cell & 31u); - atomicAnd(&curr_active[w], ~m); - atomicAnd(&curr_winner[w], ~m); - } - } - - // Block 0, lane 0, warp 0 resets step-scratch counters. - if (blockIdx.x == 0u && tid == 0u) { - step_scratch[0] = 0u; - step_scratch[1] = 0u; - } - - // ---- BARRIER 1 ---- - // Fence: make the above clear-bitsets + scratch writes globally - // visible before peer blocks observe "barrier arrived". - __threadfence(); - fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); - - // ========================================================= - // T9: TMA MULTICAST INPUT STAGING - // - // Issue a single cluster-scope async DMA to broadcast this - // timestep's input slice into s_input_tile across all 16 SMs - // in the cluster simultaneously. On Hopper sm_90a, - // cg::memcpy_async with cluster scope maps to the TMA - // hardware unit (cp.async.bulk.tensor multicast), reducing - // DRAM input traffic by ~16× vs each block fetching its own - // copy from GMEM. - // - // The staging is gated on cfg.input_bits <= INPUT_BITS_MAX. - // If the tile is too small (custom large input_bits), we fall - // back to per-thread GMEM reads in Stage A (identical to the - // original path; use_input_tile==false). - // - // Ordering: BARRIER 1 completes before we issue the DMA. - // The DMA completes before Stage A reads s_input_tile. - // ========================================================= -#if __CUDA_ARCH__ >= 900 - const bool use_input_tile = (cfg.input_bits <= INPUT_BITS_MAX); - if (use_input_tile) { - auto tb = cg::this_thread_block(); - cg::memcpy_async(tb, s_input_tile, - inputs + inp_off, - cfg.input_bits); - cg::wait(tb); - cluster.sync(); - } -#else - const bool use_input_tile = false; -#endif - - // ========================================================= - // STAGE A: Spatial Pooler - // - // Hot per-column state (boost, inhibition_threshold, - // active_duty) is served from cluster DSMEM rather than - // GMEM for each of the T timesteps. GMEM is written on - // update so state persists across forward calls. - // ========================================================= - for (unsigned int c = col_lo; c < col_hi; c++) { - unsigned int base = c * S; - unsigned int local = 0u; - for (unsigned int s = lane; s < S; s += 32u) { - unsigned int b = syn_bit[base + s]; - float p = syn_perm[base + s]; - // T9: read from cluster-broadcast tile when available; - // fall back to direct GMEM when input_bits > INPUT_BITS_MAX. -#if __CUDA_ARCH__ >= 900 - unsigned int inp_byte = use_input_tile - ? (unsigned int)s_input_tile[b] - : (unsigned int)inputs[inp_off + b]; -#else - unsigned int inp_byte = (unsigned int)inputs[inp_off + b]; -#endif - unsigned int hit = ((inp_byte != 0u) && (p >= cfg.conn_thr)) ? 1u : 0u; - local += hit; - } - unsigned int overlap = warp_sum_u32(local); - overlap = __shfl_sync(0xffffffffu, overlap, 0); - - // Read boost + threshold for column c. -#if __CUDA_ARCH__ >= 900 - // Hopper: read from cluster-distributed shared memory. - const unsigned int owner_block = c / cols_per_block; - const unsigned int owner_offset = c - owner_block * cols_per_block; - float boost_val = cluster.map_shared_rank(s_boost, owner_block)[owner_offset]; - float thr = cluster.map_shared_rank(s_inhib_thr, owner_block)[owner_offset]; -#else - // Pre-Hopper: read directly from global memory. - float boost_val = boost[c]; - float thr = inhibition_threshold[c]; -#endif - - float boosted = (float)overlap * boost_val; - unsigned int is_active = (boosted > thr) ? 1u : 0u; - - if (lane == 0) { - cols_out[col_base_out + c] = (unsigned char)is_active; - if (is_active) { - atomicAdd(&step_scratch[0], 1u); - } - } - - // SP learn (Hebbian) on active columns. - // T9: use tile for input reads here too. - if (cfg.learn && is_active) { - for (unsigned int s = lane; s < S; s += 32u) { - unsigned int b = syn_bit[base + s]; - float p = syn_perm[base + s]; -#if __CUDA_ARCH__ >= 900 - unsigned int inp_byte = use_input_tile - ? (unsigned int)s_input_tile[b] - : (unsigned int)inputs[inp_off + b]; -#else - unsigned int inp_byte = (unsigned int)inputs[inp_off + b]; -#endif - if (inp_byte != 0u) { - p += cfg.sp_inc; - if (p > 1.0f) p = 1.0f; - } else { - p -= cfg.sp_dec; - if (p < 0.0f) p = 0.0f; - } - syn_perm[base + s] = p; - } - } - - // active_duty EMA + threshold adaptation. - // Writes go to both DSMEM (hot path, Hopper only) and GMEM (persistence). - if (lane == 0) { -#if __CUDA_ARCH__ >= 900 - float ad = cluster.map_shared_rank(s_active_duty, owner_block)[owner_offset]; -#else - float ad = active_duty[c]; -#endif - float sample = is_active ? 1.0f : 0.0f; - ad = (1.0f - cfg.duty_alpha) * ad + cfg.duty_alpha * sample; - -#if __CUDA_ARCH__ >= 900 - // Writeback: peer smem (for next timestep read) + GMEM (persistence). - cluster.map_shared_rank(s_active_duty, owner_block)[owner_offset] = ad; -#endif - active_duty[c] = ad; - - // Threshold steers toward target sparsity. - float err = ad - cfg.sparsity_target; - float new_thr = thr + cfg.thr_adapt_rate * err * 100.0f; - if (new_thr < 0.1f) new_thr = 0.1f; - if (new_thr > 1000.0f) new_thr = 1000.0f; - -#if __CUDA_ARCH__ >= 900 - // Writeback: peer smem (for next timestep read) + GMEM (persistence). - cluster.map_shared_rank(s_inhib_thr, owner_block)[owner_offset] = new_thr; -#endif - inhibition_threshold[c] = new_thr; - } - } - - // ---- DSMEM WRITEBACK SYNC: peer-smem writes must be visible cluster-wide ---- - // - // On Hopper: cluster.sync() ensures all peer smem writes from this - // timestep are visible to all blocks before Stage B / next t. - // On pre-Hopper: no smem peer writes occur (all state in GMEM), - // so no extra sync needed here — the grid barrier below suffices. -#if __CUDA_ARCH__ >= 900 - cluster.sync(); -#endif - - // ---- BARRIER 2: SP active_mask must be visible before TM reads ---- - // Fence: flush cols_out + active_duty + inhibition_threshold + step_scratch - // writes to global memory before peers advance past this barrier. - __threadfence(); - fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); - - // ========================================================= - // STAGE B: Temporal Memory - // ========================================================= - for (unsigned int c = col_lo; c < col_hi; c++) { - unsigned int col_active = cols_out[col_base_out + c]; - if (col_active == 0u) continue; - - unsigned int base_cell = c * cpc; - unsigned int any_predicted = 0u; - unsigned int best_seg_id_for_grow = 0xFFFFFFFFu; - unsigned int best_pot_count = 0u; - - for (unsigned int k = 0u; k < cpc; k++) { - unsigned int cell = base_cell + k; - unsigned int n_segs_here = cell_seg_count[cell]; - if (n_segs_here > MSC) n_segs_here = MSC; - if (n_segs_here == 0u) continue; - - unsigned int seg_base_id = cell * MSC; - unsigned int cell_is_predictive = 0u; - - for (unsigned int ls = 0u; ls < n_segs_here; ls++) { - unsigned int seg = seg_base_id + ls; - unsigned int n_syn = seg_syn_count[seg]; - if (n_syn == 0u) continue; - unsigned int syn_base = seg * SPS; - - unsigned int l_conn = 0u; - unsigned int l_pot = 0u; - for (unsigned int s = lane; s < n_syn; s += 32u) { - unsigned int presyn = syn_presyn[syn_base + s]; - unsigned int w = prev_active[presyn >> 5]; - unsigned int bit = (w >> (presyn & 31u)) & 1u; - if (bit) { - l_pot += 1u; - int p = (int)tm_syn_perm[syn_base + s]; - if (p >= cfg.conn_thr_i16) l_conn += 1u; - } - } - unsigned int tot_conn = warp_sum_u32(l_conn); - unsigned int tot_pot = warp_sum_u32(l_pot); - tot_conn = __shfl_sync(0xffffffffu, tot_conn, 0); - tot_pot = __shfl_sync(0xffffffffu, tot_pot, 0); - - if (tot_conn >= cfg.activation_threshold) cell_is_predictive = 1u; - if (tot_pot >= cfg.learning_threshold && tot_pot > best_pot_count) { - best_pot_count = tot_pot; - best_seg_id_for_grow = seg; - } - - // Reinforce predicted-and-correct segment. - if (cfg.learn && tot_conn >= cfg.activation_threshold) { - for (unsigned int s = lane; s < n_syn; s += 32u) { - unsigned int presyn = syn_presyn[syn_base + s]; - unsigned int w = prev_active[presyn >> 5]; - unsigned int bit = (w >> (presyn & 31u)) & 1u; - int p = (int)tm_syn_perm[syn_base + s]; - if (bit) { - int np = p + cfg.perm_inc_i16; - if (np > 32767) np = 32767; - tm_syn_perm[syn_base + s] = (short)np; - } else { - int np = p - cfg.perm_dec_i16; - if (np < 0) np = 0; - tm_syn_perm[syn_base + s] = (short)np; - } - } - } - } - - if (cell_is_predictive) { - any_predicted = 1u; - if (lane == 0) { - unsigned int w = cell >> 5; - unsigned int m = 1u << (cell & 31u); - atomicOr(&curr_active[w], m); - atomicOr(&curr_winner[w], m); - } - } - } - - // BURST if no predicted. - if (!any_predicted) { - if (lane == 0) { - for (unsigned int k = 0u; k < cpc; k++) { - unsigned int cell = base_cell + k; - unsigned int w = cell >> 5; - unsigned int m = 1u << (cell & 31u); - atomicOr(&curr_active[w], m); - } - unsigned int win = base_cell; - unsigned int ww = win >> 5; - unsigned int wm = 1u << (win & 31u); - atomicOr(&curr_winner[ww], wm); - atomicAdd(&step_scratch[1], 1u); - } - - if (cfg.learn) { - unsigned int target_seg; - unsigned int existing_syn; - if (best_seg_id_for_grow != 0xFFFFFFFFu) { - // Reuse best matching segment. - target_seg = best_seg_id_for_grow; - existing_syn = seg_syn_count[target_seg]; - target_seg = __shfl_sync(0xffffffffu, target_seg, 0); - existing_syn = __shfl_sync(0xffffffffu, existing_syn, 0); - - // Reinforce its existing synapses. - unsigned int syn_base = target_seg * SPS; - for (unsigned int s = lane; s < existing_syn; s += 32u) { - unsigned int presyn = syn_presyn[syn_base + s]; - unsigned int w = prev_active[presyn >> 5]; - unsigned int bit = (w >> (presyn & 31u)) & 1u; - int p = (int)tm_syn_perm[syn_base + s]; - if (bit) { - int np = p + cfg.perm_inc_i16; - if (np > 32767) np = 32767; - tm_syn_perm[syn_base + s] = (short)np; - } else { - int np = p - cfg.perm_dec_i16; - if (np < 0) np = 0; - tm_syn_perm[syn_base + s] = (short)np; - } - } - } else { - // Allocate new segment on winner cell (cell 0 of col). - unsigned int new_seg = 0u; - if (lane == 0) { - unsigned int winner_cell = base_cell; - unsigned int slot = atomicAdd(&cell_seg_count[winner_cell], 1u); - if (slot >= MSC) slot = slot % MSC; - new_seg = winner_cell * MSC + slot; - seg_cell_id[new_seg] = winner_cell; - seg_syn_count[new_seg] = 0u; - } - target_seg = __shfl_sync(0xffffffffu, new_seg, 0); - existing_syn = 0u; - } - - // Grow synapses to prev_winner cells — lane 0 serialized. - unsigned int room = (SPS > existing_syn) ? (SPS - existing_syn) : 0u; - unsigned int max_grow = (cfg.max_new_synapses < room) ? cfg.max_new_synapses : room; - if (lane == 0 && max_grow > 0u) { - unsigned int syn_base = target_seg * SPS; - unsigned int grown = 0u; - unsigned int start_off = (c * 2654435761u + cfg.iter_seed + t) % cfg.bits_words; - for (unsigned int w_off = 0u; - w_off < cfg.bits_words && grown < max_grow; - w_off++) { - unsigned int widx = (start_off + w_off) % cfg.bits_words; - unsigned int word = prev_winner[widx]; - while (word != 0u && grown < max_grow) { - unsigned int bit_pos = __ffs(word) - 1u; - word &= ~(1u << bit_pos); - unsigned int cell_id = widx * 32u + bit_pos; - if (cell_id >= cfg.n_cells) continue; - bool exists = false; - for (unsigned int es = 0u; es < existing_syn + grown; es++) { - if (syn_presyn[syn_base + es] == cell_id) { exists = true; break; } - } - if (exists) continue; - unsigned int write_idx = existing_syn + grown; - if (write_idx >= SPS) break; - syn_presyn[syn_base + write_idx] = cell_id; - tm_syn_perm[syn_base + write_idx] = (short)cfg.initial_perm_i16; - grown++; - } - } - if (grown > 0u) { - seg_syn_count[target_seg] = existing_syn + grown; - } - } - } - } - } - - // ---- BARRIER 3: TM writes complete before anomaly + next-step read ---- - // Fence: flush curr_active/curr_winner bitsets + tm_syn_perm + - // seg_syn_count + syn_presyn before peers advance and consume them as - // prev_active/prev_winner at t+1. - __threadfence(); - fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); - - // Write anomaly for step t. - if (blockIdx.x == 0u && tid == 0u) { - unsigned int total = step_scratch[0]; - unsigned int bad = step_scratch[1]; - float anom = (total > 0u) ? ((float)bad / (float)total) : 0.0f; - anom_out[t] = anom; - } - } -} - -// Single-region kernel (legacy call site). -__global__ __launch_bounds__(256, 2) -void htm_fused_step(FusedPtrs P, FusedConfig cfg) { - htm_fused_step_body(P, cfg); -} - -// Batched kernel: one cooperative launch for B regions. grid.y = B, -// grid.x = per-region block count. Each block reads its region's -// FusedPtrs from the device array via blockIdx.y. -__global__ __launch_bounds__(256, 2) -void htm_fused_step_batched(const FusedPtrs* __restrict__ P_arr, FusedConfig cfg) { - const FusedPtrs P = P_arr[blockIdx.y]; - htm_fused_step_body(P, cfg); -} - -} // extern "C" +// Fused HTM megakernel — SP + TM, all T timesteps in a single launch. +// +// Design rationale: +// - Global top-K column selection requires cross-block synchronization at +// every timestep (grid.sync is unreliable on WSL2/sm_86 without rdc=true). +// - Replace with per-column threshold activation using local lateral +// inhibition: column c activates if overlap[c]*boost[c] > threshold[c]. +// Threshold is a per-column running-EMA learned scalar that steers the +// column's long-run activation rate toward the global sparsity target. +// - This is biologically grounded (GABAergic local inhibition) and supported +// by HTM theory (duty-cycle boost already drives this loop; we just +// change which lever the EMA pulls). +// +// Launch shape: +// grid = min(device SM count, 16) // hard cap — see below +// block = 1024 threads = 32 warps +// Each warp of 32 owns a contiguous column slice (n_columns / total_warps). +// +// Cross-block coherence: +// - Ping-pong buffers for cell_active/cell_winner: write _a at even t, +// read _b; reversed at odd t. +// - Preferred path: cooperative launch + hardware whole-grid sync. +// - Fallback path: software 3-slot rotating grid barrier for devices/drivers +// that cannot do cooperative launch. +// +// 2026-04-16: grid_dim reduced from 28 to 16 after deadlock RCA. The previous +// cap of 28 relied on all blocks being concurrently resident on a 30-SM RTX +// 3060 Laptop. Under thermal throttling effective residency dropped to ~20-24, +// leaving scheduled blocks spinning on the software grid barrier waiting for +// peer blocks that would never run. 16 blocks is below any realistic residency +// floor and preserves enough warp parallelism (16*32 = 512 warps) to saturate +// memory bandwidth on the spatial-pooler stage. +// +// Kernel signature uses struct-by-value for pointers and config to stay +// inside cudarc's launch-arg count limit. + +#include +#include + +namespace cg = cooperative_groups; + +// Maximum columns owned per cluster-block in DSMEM. +// Supports n_columns up to COLS_PER_CLUSTER_BLOCK_MAX * cluster_size. +// At cluster_size=16: supports up to 256*16=4096 columns. +// Each array costs 256*4 = 1024 bytes; three arrays = 3072 bytes per SM — +// well under the 228 KB H200 shared-memory cap. +#define COLS_PER_CLUSTER_BLOCK_MAX 256u + +// Maximum input_bits supported by the TMA-multicast staging tile. +// At 32 KB this covers the production SDR width (16384 bits) with 2× headroom. +// Total shared per SM: 32768 (tile) + 3072 (DSMEM float arrays) = ~35 KB — +// well under the 228 KB H200 limit. +// +// Expected speedup from TMA multicast input staging (T9/T11): +// - Without staging: 16 SMs × T × (input_bits GMEM reads per timestep) +// - With staging: 1 TMA DMA per timestep, shared reads from L1 thereafter +// - Theoretical DRAM bandwidth reduction: ~16× on input reads +// - Wall-clock reduction estimate: -20 to -40 ms from reduced input fetch latency +#define INPUT_BITS_MAX 32768u + +extern "C" { + +struct FusedPtrs { + unsigned long long syn_bit; + unsigned long long syn_perm; + unsigned long long boost; + unsigned long long active_duty; + unsigned long long inhibition_threshold; + unsigned long long seg_cell_id; + unsigned long long seg_syn_count; + unsigned long long syn_presyn; + unsigned long long tm_syn_perm; + unsigned long long cell_seg_count; + unsigned long long cell_active_a; + unsigned long long cell_active_b; + unsigned long long cell_winner_a; + unsigned long long cell_winner_b; + unsigned long long inputs; + unsigned long long cols_out; + unsigned long long anom_out; + unsigned long long barrier_counters; + unsigned long long step_scratch; +}; + +struct FusedConfig { + // SP constants + unsigned int input_bits; + unsigned int n_columns; + unsigned int synapses_per_col; + float conn_thr; + float sp_inc; + float sp_dec; + float sparsity_target; + float duty_alpha; + float thr_adapt_rate; + // TM constants + unsigned int cells_per_column; + unsigned int n_cells; + unsigned int bits_words; + unsigned int max_segments_per_cell; + unsigned int synapses_per_segment; + unsigned int activation_threshold; + unsigned int learning_threshold; + unsigned int max_new_synapses; + int conn_thr_i16; + int perm_inc_i16; + int perm_dec_i16; + int predicted_seg_dec_i16; + int initial_perm_i16; + // Loop constants + unsigned int T; + unsigned int learn; + unsigned int iter_seed; + unsigned int cooperative_grid_sync; +}; + +// Hardware cluster barrier using Hopper sm_90a cooperative_groups::this_cluster().sync(). +// Replaces the former software Decoupled Look-Back (DLB) atomic-spin barrier. +// +// cluster::sync() is a single PTX instruction (barrier.cluster) that resolves +// in ~10-40 ns inside the cluster, with no device-level serialization. +// Multiple clusters (one per HTM region) run fully concurrently — bounded +// only by SM count (8 clusters × 16 SMs = 128 ≤ 132 on H200). +// +// The flags / expected / phase / cooperative_grid_sync parameters are kept +// in the signature for call-site compatibility but are unused. +__device__ static inline void fused_grid_barrier(cg::grid_group grid, + unsigned int * /* flags — unused */, + unsigned int /* expected — unused */, + unsigned int /* phase — unused */, + unsigned int /* cooperative_grid_sync — unused */) { +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Hopper+ : hardware cluster barrier (~10-40 ns) + auto cluster = cg::this_cluster(); + cluster.sync(); +#else + // Pre-Hopper (sm_80, sm_86, sm_89): grid-level cooperative sync. + // Requires cooperative kernel launch. ~us-ms range, adequate for HTM + // workload (kernel launch frequency is low). + grid.sync(); +#endif +} + +__device__ static inline unsigned int warp_sum_u32(unsigned int v) { + for (int off = 16; off > 0; off >>= 1) { + v += __shfl_down_sync(0xffffffffu, v, off); + } + return v; +} + +// Core kernel body — works for both single-region and batched launches. +// Single-region: caller passes the one FusedPtrs struct. +// Batched: each block reads its region's FusedPtrs via blockIdx.y before +// calling this. State is independent per region (each region owns its own +// GPU buffers); grid.sync() is the only cross-block primitive and it +// spans ALL blocks in the grid (harmless over-sync across regions). +__device__ static inline +void htm_fused_step_body(const FusedPtrs& P, const FusedConfig& cfg) { + cg::grid_group grid = cg::this_grid(); + // Cast pointers. + const unsigned int * __restrict__ syn_bit = (const unsigned int*)P.syn_bit; + float * __restrict__ syn_perm = (float*)P.syn_perm; + float * __restrict__ boost = (float*)P.boost; + float * __restrict__ active_duty = (float*)P.active_duty; + float * __restrict__ inhibition_threshold = (float*)P.inhibition_threshold; + unsigned int * __restrict__ seg_cell_id = (unsigned int*)P.seg_cell_id; + unsigned int * __restrict__ seg_syn_count = (unsigned int*)P.seg_syn_count; + unsigned int * __restrict__ syn_presyn = (unsigned int*)P.syn_presyn; + short * __restrict__ tm_syn_perm = (short*)P.tm_syn_perm; + unsigned int * __restrict__ cell_seg_count = (unsigned int*)P.cell_seg_count; + unsigned int * __restrict__ cell_active_a = (unsigned int*)P.cell_active_a; + unsigned int * __restrict__ cell_active_b = (unsigned int*)P.cell_active_b; + unsigned int * __restrict__ cell_winner_a = (unsigned int*)P.cell_winner_a; + unsigned int * __restrict__ cell_winner_b = (unsigned int*)P.cell_winner_b; + const unsigned char * __restrict__ inputs = (const unsigned char*)P.inputs; + unsigned char * __restrict__ cols_out = (unsigned char*)P.cols_out; + float * __restrict__ anom_out = (float*)P.anom_out; + unsigned int * __restrict__ barrier_counters = (unsigned int*)P.barrier_counters; + unsigned int * __restrict__ step_scratch = (unsigned int*)P.step_scratch; + + const unsigned int tid = threadIdx.x; + const unsigned int lane = tid & 31u; + const unsigned int warp = tid >> 5; + const unsigned int warps_per_block = blockDim.x >> 5; + const unsigned int gwarp = blockIdx.x * warps_per_block + warp; + const unsigned int n_warps = gridDim.x * warps_per_block; + + const unsigned int n_cols = cfg.n_columns; + const unsigned int col_lo = (gwarp * n_cols) / n_warps; + const unsigned int col_hi = ((gwarp + 1) * n_cols) / n_warps; + + unsigned int phase = 0u; + + // ========================================================= + // DSMEM: Cluster-distributed shared memory for hot per-column + // state (inhibition_threshold, boost, active_duty). + // + // On Hopper (sm_90+): Each block in the cluster owns a contiguous + // slice of columns in its own __shared__ arrays. Any block can + // peer-read another block's slice via cluster.map_shared_rank(). + // + // On Ampere (sm_86) and other pre-Hopper: No cluster support. + // Read/write directly from/to global memory (inhibition_threshold, + // boost, active_duty device pointers). Slightly higher latency but + // functionally correct. + // ========================================================= + +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Hopper+ cluster path + auto cluster = cg::this_cluster(); + const unsigned int cluster_block_rank = cluster.block_rank(); // 0..cluster_size-1 + const unsigned int cluster_sz = cluster.num_blocks(); // == gridDim.x (≤16) +#else + // Pre-Hopper: no cluster, each block is independent. + const unsigned int cluster_block_rank = blockIdx.x; + const unsigned int cluster_sz = gridDim.x; +#endif + + // Partition n_cols evenly across cluster blocks. + // Each block owns cols_per_block columns starting at my_col_start. + const unsigned int cols_per_block = + (n_cols + cluster_sz - 1u) / cluster_sz; // ceil div + const unsigned int my_col_start = + cluster_block_rank * cols_per_block; + const unsigned int my_col_end = + (my_col_start + cols_per_block < n_cols) + ? (my_col_start + cols_per_block) : n_cols; // clamp + +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Cluster-distributed shared memory arrays. + // Each block holds at most COLS_PER_CLUSTER_BLOCK_MAX floats per array. + // Peer blocks address into each other's smem via map_shared_rank. + __shared__ float s_inhib_thr [COLS_PER_CLUSTER_BLOCK_MAX]; + __shared__ float s_boost [COLS_PER_CLUSTER_BLOCK_MAX]; + __shared__ float s_active_duty[COLS_PER_CLUSTER_BLOCK_MAX]; +#endif + + // TMA multicast input staging tile (T9) — HOPPER ONLY. + // + // On Hopper: cg::memcpy_async with cluster scope multicasts input to all + // 16 SMs, reducing DRAM traffic by ~16×. + // On Ampere: 32 KB smem allocation exceeds per-block budget when + // cooperatively launched (48 KB total, registers eat the rest). Skip the + // tile entirely — Stage A reads from GMEM directly (original path). +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + __shared__ __align__(16) unsigned char s_input_tile[INPUT_BITS_MAX]; +#endif + +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Initial GMEM → smem load (reads state from previous forward call). + // Each block loads only its own slice; tid strides across the slice. + for (unsigned int c = my_col_start + tid; c < my_col_end; c += blockDim.x) { + const unsigned int off = c - my_col_start; + s_inhib_thr [off] = inhibition_threshold[c]; + s_boost [off] = boost[c]; + s_active_duty[off] = active_duty[c]; + } + + // All blocks in the cluster must finish loading before any block + // starts reading peer smem inside the T-loop. + cluster.sync(); +#else + // Pre-Hopper: no smem caching needed — reads go directly to GMEM. + // Grid sync ensures all blocks have completed Phase 0 init before T-loop. + grid.sync(); +#endif + + const unsigned int S = cfg.synapses_per_col; + const unsigned int cpc = cfg.cells_per_column; + const unsigned int SPS = cfg.synapses_per_segment; + const unsigned int MSC = cfg.max_segments_per_cell; + + // Main timestep loop. + for (unsigned int t = 0u; t < cfg.T; t++) { + const unsigned int inp_off = t * cfg.input_bits; + const unsigned int col_base_out = t * n_cols; + + unsigned int * curr_active = (t & 1u) ? cell_active_b : cell_active_a; + unsigned int * prev_active = (t & 1u) ? cell_active_a : cell_active_b; + unsigned int * curr_winner = (t & 1u) ? cell_winner_b : cell_winner_a; + unsigned int * prev_winner = (t & 1u) ? cell_winner_a : cell_winner_b; + + // ---- Phase 0: clear curr bitsets for my cell range ---- + const unsigned int my_cell_lo = col_lo * cpc; + const unsigned int my_cell_hi = col_hi * cpc; + if (cpc == 32u) { + // Fast path: one word per column. + for (unsigned int c = col_lo + lane; c < col_hi; c += 32u) { + curr_active[c] = 0u; + curr_winner[c] = 0u; + } + } else { + for (unsigned int cell = my_cell_lo + lane; cell < my_cell_hi; cell += 32u) { + unsigned int w = cell >> 5; + unsigned int m = 1u << (cell & 31u); + atomicAnd(&curr_active[w], ~m); + atomicAnd(&curr_winner[w], ~m); + } + } + + // Block 0, lane 0, warp 0 resets step-scratch counters. + if (blockIdx.x == 0u && tid == 0u) { + step_scratch[0] = 0u; + step_scratch[1] = 0u; + } + + // ---- BARRIER 1 ---- + // Fence: make the above clear-bitsets + scratch writes globally + // visible before peer blocks observe "barrier arrived". + __threadfence(); + fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); + + // ========================================================= + // T9: TMA MULTICAST INPUT STAGING + // + // Issue a single cluster-scope async DMA to broadcast this + // timestep's input slice into s_input_tile across all 16 SMs + // in the cluster simultaneously. On Hopper sm_90a, + // cg::memcpy_async with cluster scope maps to the TMA + // hardware unit (cp.async.bulk.tensor multicast), reducing + // DRAM input traffic by ~16× vs each block fetching its own + // copy from GMEM. + // + // The staging is gated on cfg.input_bits <= INPUT_BITS_MAX. + // If the tile is too small (custom large input_bits), we fall + // back to per-thread GMEM reads in Stage A (identical to the + // original path; use_input_tile==false). + // + // Ordering: BARRIER 1 completes before we issue the DMA. + // The DMA completes before Stage A reads s_input_tile. + // ========================================================= +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + const bool use_input_tile = (cfg.input_bits <= INPUT_BITS_MAX); + if (use_input_tile) { + auto tb = cg::this_thread_block(); + cg::memcpy_async(tb, s_input_tile, + inputs + inp_off, + cfg.input_bits); + cg::wait(tb); + cluster.sync(); + } +#else + const bool use_input_tile = false; +#endif + + // ========================================================= + // STAGE A: Spatial Pooler + // + // Hot per-column state (boost, inhibition_threshold, + // active_duty) is served from cluster DSMEM rather than + // GMEM for each of the T timesteps. GMEM is written on + // update so state persists across forward calls. + // ========================================================= + for (unsigned int c = col_lo; c < col_hi; c++) { + unsigned int base = c * S; + unsigned int local = 0u; + for (unsigned int s = lane; s < S; s += 32u) { + unsigned int b = syn_bit[base + s]; + float p = syn_perm[base + s]; + // T9: read from cluster-broadcast tile when available; + // fall back to direct GMEM when input_bits > INPUT_BITS_MAX. +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + unsigned int inp_byte = use_input_tile + ? (unsigned int)s_input_tile[b] + : (unsigned int)inputs[inp_off + b]; +#else + unsigned int inp_byte = (unsigned int)inputs[inp_off + b]; +#endif + unsigned int hit = ((inp_byte != 0u) && (p >= cfg.conn_thr)) ? 1u : 0u; + local += hit; + } + unsigned int overlap = warp_sum_u32(local); + overlap = __shfl_sync(0xffffffffu, overlap, 0); + + // Read boost + threshold for column c. +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Hopper: read from cluster-distributed shared memory. + const unsigned int owner_block = c / cols_per_block; + const unsigned int owner_offset = c - owner_block * cols_per_block; + float boost_val = cluster.map_shared_rank(s_boost, owner_block)[owner_offset]; + float thr = cluster.map_shared_rank(s_inhib_thr, owner_block)[owner_offset]; +#else + // Pre-Hopper: read directly from global memory. + float boost_val = boost[c]; + float thr = inhibition_threshold[c]; +#endif + + float boosted = (float)overlap * boost_val; + unsigned int is_active = (boosted > thr) ? 1u : 0u; + + if (lane == 0) { + cols_out[col_base_out + c] = (unsigned char)is_active; + if (is_active) { + atomicAdd(&step_scratch[0], 1u); + } + } + + // SP learn (Hebbian) on active columns. + // T9: use tile for input reads here too. + if (cfg.learn && is_active) { + for (unsigned int s = lane; s < S; s += 32u) { + unsigned int b = syn_bit[base + s]; + float p = syn_perm[base + s]; +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + unsigned int inp_byte = use_input_tile + ? (unsigned int)s_input_tile[b] + : (unsigned int)inputs[inp_off + b]; +#else + unsigned int inp_byte = (unsigned int)inputs[inp_off + b]; +#endif + if (inp_byte != 0u) { + p += cfg.sp_inc; + if (p > 1.0f) p = 1.0f; + } else { + p -= cfg.sp_dec; + if (p < 0.0f) p = 0.0f; + } + syn_perm[base + s] = p; + } + } + + // active_duty EMA + threshold adaptation. + // Writes go to both DSMEM (hot path, Hopper only) and GMEM (persistence). + if (lane == 0) { +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + float ad = cluster.map_shared_rank(s_active_duty, owner_block)[owner_offset]; +#else + float ad = active_duty[c]; +#endif + float sample = is_active ? 1.0f : 0.0f; + ad = (1.0f - cfg.duty_alpha) * ad + cfg.duty_alpha * sample; + +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Writeback: peer smem (for next timestep read) + GMEM (persistence). + cluster.map_shared_rank(s_active_duty, owner_block)[owner_offset] = ad; +#endif + active_duty[c] = ad; + + // Threshold steers toward target sparsity. + float err = ad - cfg.sparsity_target; + float new_thr = thr + cfg.thr_adapt_rate * err * 100.0f; + if (new_thr < 0.1f) new_thr = 0.1f; + if (new_thr > 1000.0f) new_thr = 1000.0f; + +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + // Writeback: peer smem (for next timestep read) + GMEM (persistence). + cluster.map_shared_rank(s_inhib_thr, owner_block)[owner_offset] = new_thr; +#endif + inhibition_threshold[c] = new_thr; + } + } + + // ---- DSMEM WRITEBACK SYNC: peer-smem writes must be visible cluster-wide ---- + // + // On Hopper: cluster.sync() ensures all peer smem writes from this + // timestep are visible to all blocks before Stage B / next t. + // On pre-Hopper: no smem peer writes occur (all state in GMEM), + // so no extra sync needed here — the grid barrier below suffices. +#if !defined(HTM_DISABLE_CLUSTER) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) + cluster.sync(); +#endif + + // ---- BARRIER 2: SP active_mask must be visible before TM reads ---- + // Fence: flush cols_out + active_duty + inhibition_threshold + step_scratch + // writes to global memory before peers advance past this barrier. + __threadfence(); + fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); + + // ========================================================= + // STAGE B: Temporal Memory + // ========================================================= + for (unsigned int c = col_lo; c < col_hi; c++) { + unsigned int col_active = cols_out[col_base_out + c]; + if (col_active == 0u) continue; + + unsigned int base_cell = c * cpc; + unsigned int any_predicted = 0u; + unsigned int best_seg_id_for_grow = 0xFFFFFFFFu; + unsigned int best_pot_count = 0u; + + for (unsigned int k = 0u; k < cpc; k++) { + unsigned int cell = base_cell + k; + unsigned int n_segs_here = cell_seg_count[cell]; + if (n_segs_here > MSC) n_segs_here = MSC; + if (n_segs_here == 0u) continue; + + unsigned int seg_base_id = cell * MSC; + unsigned int cell_is_predictive = 0u; + + for (unsigned int ls = 0u; ls < n_segs_here; ls++) { + unsigned int seg = seg_base_id + ls; + unsigned int n_syn = seg_syn_count[seg]; + if (n_syn == 0u) continue; + unsigned int syn_base = seg * SPS; + + unsigned int l_conn = 0u; + unsigned int l_pot = 0u; + for (unsigned int s = lane; s < n_syn; s += 32u) { + unsigned int presyn = syn_presyn[syn_base + s]; + unsigned int w = prev_active[presyn >> 5]; + unsigned int bit = (w >> (presyn & 31u)) & 1u; + if (bit) { + l_pot += 1u; + int p = (int)tm_syn_perm[syn_base + s]; + if (p >= cfg.conn_thr_i16) l_conn += 1u; + } + } + unsigned int tot_conn = warp_sum_u32(l_conn); + unsigned int tot_pot = warp_sum_u32(l_pot); + tot_conn = __shfl_sync(0xffffffffu, tot_conn, 0); + tot_pot = __shfl_sync(0xffffffffu, tot_pot, 0); + + if (tot_conn >= cfg.activation_threshold) cell_is_predictive = 1u; + if (tot_pot >= cfg.learning_threshold && tot_pot > best_pot_count) { + best_pot_count = tot_pot; + best_seg_id_for_grow = seg; + } + + // Reinforce predicted-and-correct segment. + if (cfg.learn && tot_conn >= cfg.activation_threshold) { + for (unsigned int s = lane; s < n_syn; s += 32u) { + unsigned int presyn = syn_presyn[syn_base + s]; + unsigned int w = prev_active[presyn >> 5]; + unsigned int bit = (w >> (presyn & 31u)) & 1u; + int p = (int)tm_syn_perm[syn_base + s]; + if (bit) { + int np = p + cfg.perm_inc_i16; + if (np > 32767) np = 32767; + tm_syn_perm[syn_base + s] = (short)np; + } else { + int np = p - cfg.perm_dec_i16; + if (np < 0) np = 0; + tm_syn_perm[syn_base + s] = (short)np; + } + } + } + } + + if (cell_is_predictive) { + any_predicted = 1u; + if (lane == 0) { + unsigned int w = cell >> 5; + unsigned int m = 1u << (cell & 31u); + atomicOr(&curr_active[w], m); + atomicOr(&curr_winner[w], m); + } + } + } + + // BURST if no predicted. + if (!any_predicted) { + if (lane == 0) { + for (unsigned int k = 0u; k < cpc; k++) { + unsigned int cell = base_cell + k; + unsigned int w = cell >> 5; + unsigned int m = 1u << (cell & 31u); + atomicOr(&curr_active[w], m); + } + unsigned int win = base_cell; + unsigned int ww = win >> 5; + unsigned int wm = 1u << (win & 31u); + atomicOr(&curr_winner[ww], wm); + atomicAdd(&step_scratch[1], 1u); + } + + if (cfg.learn) { + unsigned int target_seg; + unsigned int existing_syn; + if (best_seg_id_for_grow != 0xFFFFFFFFu) { + // Reuse best matching segment. + target_seg = best_seg_id_for_grow; + existing_syn = seg_syn_count[target_seg]; + target_seg = __shfl_sync(0xffffffffu, target_seg, 0); + existing_syn = __shfl_sync(0xffffffffu, existing_syn, 0); + + // Reinforce its existing synapses. + unsigned int syn_base = target_seg * SPS; + for (unsigned int s = lane; s < existing_syn; s += 32u) { + unsigned int presyn = syn_presyn[syn_base + s]; + unsigned int w = prev_active[presyn >> 5]; + unsigned int bit = (w >> (presyn & 31u)) & 1u; + int p = (int)tm_syn_perm[syn_base + s]; + if (bit) { + int np = p + cfg.perm_inc_i16; + if (np > 32767) np = 32767; + tm_syn_perm[syn_base + s] = (short)np; + } else { + int np = p - cfg.perm_dec_i16; + if (np < 0) np = 0; + tm_syn_perm[syn_base + s] = (short)np; + } + } + } else { + // Allocate new segment on winner cell (cell 0 of col). + unsigned int new_seg = 0u; + if (lane == 0) { + unsigned int winner_cell = base_cell; + unsigned int slot = atomicAdd(&cell_seg_count[winner_cell], 1u); + if (slot >= MSC) slot = slot % MSC; + new_seg = winner_cell * MSC + slot; + seg_cell_id[new_seg] = winner_cell; + seg_syn_count[new_seg] = 0u; + } + target_seg = __shfl_sync(0xffffffffu, new_seg, 0); + existing_syn = 0u; + } + + // Grow synapses to prev_winner cells — lane 0 serialized. + unsigned int room = (SPS > existing_syn) ? (SPS - existing_syn) : 0u; + unsigned int max_grow = (cfg.max_new_synapses < room) ? cfg.max_new_synapses : room; + if (lane == 0 && max_grow > 0u) { + unsigned int syn_base = target_seg * SPS; + unsigned int grown = 0u; + unsigned int start_off = (c * 2654435761u + cfg.iter_seed + t) % cfg.bits_words; + for (unsigned int w_off = 0u; + w_off < cfg.bits_words && grown < max_grow; + w_off++) { + unsigned int widx = (start_off + w_off) % cfg.bits_words; + unsigned int word = prev_winner[widx]; + while (word != 0u && grown < max_grow) { + unsigned int bit_pos = __ffs(word) - 1u; + word &= ~(1u << bit_pos); + unsigned int cell_id = widx * 32u + bit_pos; + if (cell_id >= cfg.n_cells) continue; + bool exists = false; + for (unsigned int es = 0u; es < existing_syn + grown; es++) { + if (syn_presyn[syn_base + es] == cell_id) { exists = true; break; } + } + if (exists) continue; + unsigned int write_idx = existing_syn + grown; + if (write_idx >= SPS) break; + syn_presyn[syn_base + write_idx] = cell_id; + tm_syn_perm[syn_base + write_idx] = (short)cfg.initial_perm_i16; + grown++; + } + } + if (grown > 0u) { + seg_syn_count[target_seg] = existing_syn + grown; + } + } + } + } + } + + // ---- BARRIER 3: TM writes complete before anomaly + next-step read ---- + // Fence: flush curr_active/curr_winner bitsets + tm_syn_perm + + // seg_syn_count + syn_presyn before peers advance and consume them as + // prev_active/prev_winner at t+1. + __threadfence(); + fused_grid_barrier(grid, barrier_counters, 0u, phase++, cfg.cooperative_grid_sync); + + // Write anomaly for step t. + if (blockIdx.x == 0u && tid == 0u) { + unsigned int total = step_scratch[0]; + unsigned int bad = step_scratch[1]; + float anom = (total > 0u) ? ((float)bad / (float)total) : 0.0f; + anom_out[t] = anom; + } + } +} + +// Single-region kernel (legacy call site). +__global__ __launch_bounds__(256, 2) +void htm_fused_step(FusedPtrs P, FusedConfig cfg) { + htm_fused_step_body(P, cfg); +} + +// Batched kernel: one cooperative launch for B regions. grid.y = B, +// grid.x = per-region block count. Each block reads its region's +// FusedPtrs from the device array via blockIdx.y. +__global__ __launch_bounds__(256, 2) +void htm_fused_step_batched(const FusedPtrs* __restrict__ P_arr, FusedConfig cfg) { + const FusedPtrs P = P_arr[blockIdx.y]; + htm_fused_step_body(P, cfg); +} + +} // extern "C" diff --git a/overlay/htm_rust/src/gpu/tests.rs b/overlay/htm_rust/src/gpu/tests.rs index 5ecb63b9d07a9e0c3f7d89bbfc35eb6a2a0bc5e5..ce08e523602d31fe0001cf7cfe24c636e2b5999b 100644 --- a/overlay/htm_rust/src/gpu/tests.rs +++ b/overlay/htm_rust/src/gpu/tests.rs @@ -1,643 +1,663 @@ -//! Parity tests: GPU SP vs CPU SP reference. -//! -//! With matching seeds the two should produce bit-identical active-column sets -//! when `learn=false`, and remain bit-identical over repeated `learn=true` -//! steps because the Hebbian update is deterministic (no RNG once initialised). -//! -//! Run with: cargo test --release --features gpu - -#![cfg(test)] -#![cfg(feature = "gpu")] - -use crate::sp::{SpatialPooler, SpatialPoolerConfig}; -use crate::gpu::sp_gpu::SpatialPoolerGpu; -use crate::gpu::tm_gpu::TemporalMemoryGpu; -use crate::gpu::fused::{ - launch_fused, plan_fused_launch, FusedState, -}; -use cudarc::driver::CudaSlice; -use rand::{Rng, SeedableRng}; -use rand_xoshiro::Xoshiro256PlusPlus; - -fn make_sdr(rng: &mut Xoshiro256PlusPlus, bits: usize, sparsity: f32) -> Vec { - let on = ((sparsity * bits as f32) as usize).max(1); - let mut v = vec![0u8; bits]; - let mut placed = 0; - while placed < on { - let i = rng.gen_range(0..bits); - if v[i] == 0 { - v[i] = 1; - placed += 1; - } - } - v -} - -#[test] -fn gpu_sp_matches_cpu_no_learn() { - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let mut cpu = SpatialPooler::new( - SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, - 1234, - ); - let cpu_for_gpu = SpatialPooler::new( - SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, - 1234, - ); - let mut gpu = SpatialPoolerGpu::from_cpu(&cpu_for_gpu) - .expect("gpu init (CUDA device available)"); - gpu.set_strict_parity(true); - - let mut rng = Xoshiro256PlusPlus::seed_from_u64(99); - for step in 0..20 { - let sdr_u8 = make_sdr(&mut rng, bits, 0.02); - let sdr_bool: Vec = sdr_u8.iter().map(|&x| x != 0).collect(); - - let cpu_active: Vec = cpu.compute(&sdr_bool, false); - let gpu_active: Vec = gpu.compute(&sdr_u8, false).expect("gpu compute"); - - assert_eq!( - cpu_active, gpu_active, - "mismatch at step {step}: len cpu={} gpu={}", - cpu_active.len(), gpu_active.len() - ); - } -} - -#[test] -fn gpu_sp_matches_cpu_with_learn() { - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let mut cpu = SpatialPooler::new( - SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, - 5678, - ); - let cpu_for_gpu = SpatialPooler::new( - SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, - 5678, - ); - let mut gpu = SpatialPoolerGpu::from_cpu(&cpu_for_gpu).expect("gpu init"); - gpu.set_strict_parity(true); - - let mut rng = Xoshiro256PlusPlus::seed_from_u64(42); - for step in 0..50 { - let sdr_u8 = make_sdr(&mut rng, bits, 0.02); - let sdr_bool: Vec = sdr_u8.iter().map(|&x| x != 0).collect(); - - let cpu_active = cpu.compute(&sdr_bool, true); - let gpu_active = gpu.compute(&sdr_u8, true).expect("gpu compute"); - - assert_eq!( - cpu_active, gpu_active, - "mismatch at step {step} with learning" - ); - } -} - -#[test] -fn gpu_tm_anomaly_decays_on_repeating_sequence() { - // End-to-end GPU pipeline: SP feeds TM; repeating SDR sequence should drive - // anomaly down over time. - use crate::gpu::HTMRegionGpu; // not pyclass methods; use internal constructor via Rust - // Easier: replicate the pipeline directly with SP + TM. - - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let n_cols = cfg.n_columns; - let cells_per_col = 32usize; - - let cpu_for_gpu = SpatialPooler::new(SpatialPoolerConfig::default(), 314); - let mut sp = SpatialPoolerGpu::from_cpu(&cpu_for_gpu).expect("gpu init"); - let dev = sp.dev_ref().clone(); - let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col) - .expect("gpu tm init"); - tm.reset().expect("tm reset"); - - // Build 3 fixed SDRs, feed them in a repeating sequence. - let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); - let make = |rng: &mut Xoshiro256PlusPlus| make_sdr(rng, bits, 0.02); - let seqs = [make(&mut rng), make(&mut rng), make(&mut rng)]; - - // Warm up SP so columns are stable per symbol. - for _ in 0..100 { - for s in &seqs { - let _ = sp.compute(s, true).expect("sp compute"); - } - } - - // Build a long input buffer: 100 repetitions of [A,B,C] = 300 steps. - let repeats = 100usize; - let t = repeats * 3; - let mut inputs_flat = vec![0u8; t * bits]; - for r in 0..repeats { - for (i, s) in seqs.iter().enumerate() { - let off = (r * 3 + i) * bits; - inputs_flat[off..off + bits].copy_from_slice(s); - } - } - let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs_flat).expect("htod"); - - let mut cols_dev = dev.alloc_zeros::(t * n_cols).expect("alloc cols"); - let mut anom_dev = dev.alloc_zeros::(t).expect("alloc anom"); - - sp.step_batch_with_tm( - &inputs_dev, - t, - bits, - true, - &mut cols_dev, - &mut anom_dev, - &mut tm, - ).expect("step_batch_with_tm"); - - let anom: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); - let cols: Vec = dev.dtoh_sync_copy(&cols_dev).expect("d2h cols"); - - // Active column count per step must equal k for every step. - let k = ((cfg.sparsity * n_cols as f32).round() as usize).max(1); - for ti in 0..t { - let step_slice = &cols[ti * n_cols..(ti + 1) * n_cols]; - let n_on = step_slice.iter().filter(|&&b| b != 0).count(); - assert_eq!(n_on, k, "step {ti} has {n_on} active cols, expected {k}"); - } - - // First repetition: anomaly should be near 1.0 (nothing predicted). - let early_avg: f32 = anom[3..9].iter().sum::() / 6.0; - // Last repetitions: anomaly should be noticeably lower. - let late_avg: f32 = anom[(t - 9)..t].iter().sum::() / 9.0; - eprintln!("gpu tm: early anomaly = {early_avg:.3}, late = {late_avg:.3}"); - assert!( - late_avg < early_avg, - "GPU TM should reduce anomaly on repeating sequence: early={early_avg:.3}, late={late_avg:.3}" - ); -} - -/// Cluster-sync smoke test: verifies that the fused megakernel (which relies on -/// hardware `cluster::sync()` / grid-barrier on H100/H200 Hopper) completes -/// without deadlock when called with real HTM state, and that output shapes are -/// sane (no NaN / Inf in anomaly scores, active-column count in plausible range). -/// -/// This is an *integration* test, not a synthetic micro-benchmark: it exercises -/// exactly the same `launch_fused` code path used in production, so any -/// deadlock in the cooperative-grid or DLB barrier would surface here. -/// -/// Skips gracefully (with an eprintln) when no GPU is available — the test -/// binary returns exit-code 0 in that case so CI still passes. -#[test] -fn cluster_sync_smoke_test() { - // Build a tiny HTM region (1024 inputs, 256 columns, 4 cells/column). - // This keeps VRAM usage minimal while still exercising all kernel paths. - let input_bits = 1024usize; - let n_columns = 256usize; - let cells_per_col = 4usize; - - // Probe cooperative launch attribute before doing any real work. - // CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH = 223 (added in CUDA 11.8 for Hopper). - // cudarc exposes raw attribute querying; we check cooperative launch (98) - // as the guard — cluster launch is a superset and not separately probed - // here since cudarc doesn't expose attribute 223 symbolically yet. - // On pre-Hopper hardware the DLB barrier path is used instead and the - // test still validates no deadlock on that path. - - let make_cfg = || SpatialPoolerConfig { - input_bits, - n_columns, - sparsity: 0.04, // ~10 active cols out of 256 - ..SpatialPoolerConfig::default() - }; - - let cpu_ref = SpatialPooler::new(make_cfg(), 42); - - let mut sp = match SpatialPoolerGpu::from_cpu(&cpu_ref) { - Ok(sp) => sp, - Err(e) => { - eprintln!("[cluster_sync_smoke_test] No GPU available ({e:?}) — skipping"); - return; - } - }; - - let dev = sp.dev_ref().clone(); - - // Check cooperative launch support; skip with a clear message if absent. - let cooperative_ok = matches!( - dev.attribute(cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH), - Ok(v) if v > 0 - ); - if !cooperative_ok { - eprintln!("[cluster_sync_smoke_test] CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH=0 — DLB path only, still running test"); - // We continue — the DLB path is the production fallback and must not deadlock either. - } - - let mut tm = match TemporalMemoryGpu::new(dev.clone(), n_columns, cells_per_col) { - Ok(tm) => tm, - Err(e) => { - eprintln!("[cluster_sync_smoke_test] TemporalMemoryGpu::new failed ({e:?}) — skipping"); - return; - } - }; - tm.reset().expect("tm reset"); - - let mut fused_st: FusedState = match FusedState::new( - dev.clone(), - n_columns, - cells_per_col, - sp.initial_threshold_estimate(), - ) { - Ok(f) => f, - Err(e) => { - eprintln!("[cluster_sync_smoke_test] FusedState::new failed ({e:?}) — skipping"); - return; - } - }; - fused_st.reset().expect("fused reset"); - - // Build T=4 timesteps of all-zero input SDRs. - let t = 4usize; - let inputs_flat = vec![0u8; t * input_bits]; - let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs_flat).expect("htod inputs"); - - let mut cols_dev = dev.alloc_zeros::(t * n_columns).expect("alloc cols"); - let mut anom_dev = dev.alloc_zeros::(t).expect("alloc anom"); - - // Execute with a 2-second timeout guard via a thread. If the kernel - // deadlocks, the parent test process times out and the CI job reports - // failure — we can't cancel a live CUDA kernel from Rust, but the - // launch_fused call itself must return within this window on any sane GPU. - // - // We run the kernel inline (not in a separate thread) because CUDA contexts - // are not safely shareable across threads without explicit multi-threading - // setup. The 2-second bound is enforced implicitly: if the kernel deadlocks, - // the test binary will hang and the CI timeout (typically 5 min) will kill it. - // For local dev, the deadlock would be immediately obvious. - - launch_fused( - &mut sp, - &mut tm, - &mut fused_st, - &inputs_dev, - &mut cols_dev, - &mut anom_dev, - t, - input_bits, - false, // learn=false for determinism - ).expect("launch_fused (cluster_sync_smoke_test): deadlock or CUDA error"); - - dev.synchronize().expect("device sync after launch_fused"); - - // --- Correctness assertions --- - - let cols_host: Vec = dev.dtoh_sync_copy(&cols_dev).expect("d2h cols"); - let anom_host: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); - - // Output buffers must be exactly the right size. - assert_eq!(cols_host.len(), t * n_columns, "cols buffer size mismatch"); - assert_eq!(anom_host.len(), t, "anom buffer size mismatch"); - - // Anomaly scores must be finite (NaN/Inf indicates numerical blow-up). - for (i, &a) in anom_host.iter().enumerate() { - assert!(a.is_finite(), "anomaly[{i}] is not finite: {a}"); - assert!(a >= 0.0 && a <= 1.0, "anomaly[{i}] out of [0,1]: {a}"); - } - - // Active-column count per step: threshold-based inhibition, so 0 is - // possible on cold start (before thresholds calibrate), but we assert - // <= n_columns to catch buffer overruns or completely wrong output. - for ti in 0..t { - let n_on = cols_host[ti * n_columns..(ti + 1) * n_columns] - .iter() - .filter(|&&b| b != 0) - .count(); - assert!( - n_on <= n_columns, - "step {ti}: active columns {n_on} > n_columns {n_columns} (buffer overrun?)" - ); - } - - eprintln!( - "[cluster_sync_smoke_test] PASSED: T={t}, n_cols={n_columns}, \ - input_bits={input_bits}, cooperative_supported={cooperative_ok}, \ - anom={anom_host:?}" - ); -} - -/// Parity check: the CAI zero-copy path (`step_many_cuda`) must produce -/// bit-identical outputs to the numpy H2D/D2H path (`step_batch_with_tm`), -/// since the kernel pipeline is the same — only the I/O wrapping changes. -/// We skip the PyO3 CAI dict plumbing here and test the underlying -/// ManuallyDrop + upgrade_device_ptr pattern directly. -#[test] -fn gpu_cuda_vs_numpy_parity() { - use std::mem::ManuallyDrop; - - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let n_cols = cfg.n_columns; - let cells_per_col = 32usize; - - // Build two identical (SP, TM) pairs from the same seed. - let build = || -> (SpatialPoolerGpu, TemporalMemoryGpu) { - let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 271828); - let sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu init"); - let dev = sp.dev_ref().clone(); - let mut tm = TemporalMemoryGpu::new(dev, n_cols, cells_per_col).expect("tm init"); - tm.reset().expect("tm reset"); - (sp, tm) - }; - - // Deterministic SDR sequence. - let mut rng = Xoshiro256PlusPlus::seed_from_u64(31337); - let t = 32usize; - let mut inputs_flat = vec![0u8; t * bits]; - for i in 0..t { - let sdr = make_sdr(&mut rng, bits, 0.02); - inputs_flat[i * bits..(i + 1) * bits].copy_from_slice(&sdr); - } - - // ---- Path A: owned CudaSlice (numpy-equivalent path) ---- - let (mut sp_a, mut tm_a) = build(); - let dev_a = sp_a.dev_ref().clone(); - let inputs_a: CudaSlice = dev_a.htod_sync_copy(&inputs_flat).expect("htod"); - let mut cols_a = dev_a.alloc_zeros::(t * n_cols).expect("alloc cols_a"); - let mut anom_a = dev_a.alloc_zeros::(t).expect("alloc anom_a"); - sp_a.step_batch_with_tm(&inputs_a, t, bits, false, &mut cols_a, &mut anom_a, &mut tm_a) - .expect("owned step_batch_with_tm"); - dev_a.synchronize().expect("sync a"); - let cols_a_host: Vec = dev_a.dtoh_sync_copy(&cols_a).expect("d2h cols_a"); - let anom_a_host: Vec = dev_a.dtoh_sync_copy(&anom_a).expect("d2h anom_a"); - - // ---- Path B: borrowed device pointers via upgrade_device_ptr ---- - // We allocate fresh owned CudaSlices on a fresh device, then take their - // raw ptrs and re-wrap as ManuallyDrop borrowed views — mimicking what - // `step_many_cuda` does with torch-owned CUDA memory. - let (mut sp_b, mut tm_b) = build(); - let dev_b = sp_b.dev_ref().clone(); - let inputs_b_owned: CudaSlice = dev_b.htod_sync_copy(&inputs_flat).expect("htod"); - let cols_b_owned = dev_b.alloc_zeros::(t * n_cols).expect("alloc cols_b"); - let anom_b_owned = dev_b.alloc_zeros::(t).expect("alloc anom_b"); - - // Extract raw CUdeviceptrs (and leak the owners so their Drop doesn't free). - let inputs_ptr = inputs_b_owned.leak(); - let cols_ptr = cols_b_owned.leak(); - let anom_ptr = anom_b_owned.leak(); - - // Re-wrap as borrowed views. - let inputs_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(inputs_ptr, t * bits) }); - let mut cols_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(cols_ptr, t * n_cols) }); - let mut anom_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(anom_ptr, t) }); - - sp_b.step_batch_with_tm(&inputs_b, t, bits, false, &mut cols_b, &mut anom_b, &mut tm_b) - .expect("borrowed step_batch_with_tm"); - dev_b.synchronize().expect("sync b"); - // `ManuallyDrop` doesn't auto-coerce to `&CudaSlice` for the DevicePtr - // trait bound on `dtoh_sync_copy`; explicit deref. - let cols_b_host: Vec = dev_b.dtoh_sync_copy(&*cols_b).expect("d2h cols_b"); - let anom_b_host: Vec = dev_b.dtoh_sync_copy(&*anom_b).expect("d2h anom_b"); - - // Re-own so Drop actually frees (we leaked above). - let _inputs_owned_again = unsafe { dev_b.upgrade_device_ptr::(inputs_ptr, t * bits) }; - let _cols_owned_again = unsafe { dev_b.upgrade_device_ptr::(cols_ptr, t * n_cols) }; - let _anom_owned_again = unsafe { dev_b.upgrade_device_ptr::(anom_ptr, t) }; - - assert_eq!(cols_a_host, cols_b_host, "active-column mask diverges between numpy and CAI paths"); - assert_eq!(anom_a_host.len(), anom_b_host.len()); - for (i, (a, b)) in anom_a_host.iter().zip(anom_b_host.iter()).enumerate() { - // Anomaly is a pure division of integer counts — bit-exact expected. - assert!((a - b).abs() < 1e-7, "anomaly mismatch at step {i}: a={a} b={b}"); - } -} - -/// Fused kernel: threshold activation should converge to near target sparsity -/// after a short warmup. Acceptance: mean activation rate per step lands in -/// [0.3*target, 2.5*target] after 500-step warmup. Because the threshold -/// starts conservative (=2.0) and the per-column adaptation rate is slow -/// (0.001), we allow a generous band — the test asserts directional -/// convergence toward the target, not tight matching. -#[test] -fn gpu_threshold_converges_to_sparsity() { - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let n_cols = cfg.n_columns; - let cells_per_col = 32usize; - let target = cfg.sparsity; // 0.02 = 40 cols expected - - let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 111); - let mut sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); - let dev = sp.dev_ref().clone(); - let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col).expect("tm init"); - let mut fused = FusedState::new( - dev.clone(), - n_cols, - cells_per_col, - sp.initial_threshold_estimate(), - ).expect("fused init"); - tm.reset().expect("tm reset"); - fused.reset().expect("fused reset"); - - // Warmup: 1000 random 2%-sparse SDRs. - let mut rng = Xoshiro256PlusPlus::seed_from_u64(31337); - let t_warm = 1000usize; - let mut inputs = vec![0u8; t_warm * bits]; - for ti in 0..t_warm { - let sdr = make_sdr(&mut rng, bits, 0.02); - inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); - } - let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs).expect("htod"); - let mut cols_dev = dev.alloc_zeros::(t_warm * n_cols).expect("alloc cols"); - let mut anom_dev = dev.alloc_zeros::(t_warm).expect("alloc anom"); - launch_fused( - &mut sp, &mut tm, &mut fused, - &inputs_dev, &mut cols_dev, &mut anom_dev, - t_warm, bits, true, - ).expect("warmup launch"); - dev.synchronize().expect("sync"); - - // Measurement pass: another 200 steps, measure mean activation. - let t_meas = 200usize; - let mut meas_inputs = vec![0u8; t_meas * bits]; - for ti in 0..t_meas { - let sdr = make_sdr(&mut rng, bits, 0.02); - meas_inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); - } - let meas_dev: CudaSlice = dev.htod_sync_copy(&meas_inputs).expect("htod meas"); - let mut meas_cols = dev.alloc_zeros::(t_meas * n_cols).expect("alloc meas cols"); - let mut meas_anom = dev.alloc_zeros::(t_meas).expect("alloc meas anom"); - launch_fused( - &mut sp, &mut tm, &mut fused, - &meas_dev, &mut meas_cols, &mut meas_anom, - t_meas, bits, true, - ).expect("meas launch"); - dev.synchronize().expect("sync meas"); - - let cols_host: Vec = dev.dtoh_sync_copy(&meas_cols).expect("d2h"); - let mut step_counts = Vec::with_capacity(t_meas); - for ti in 0..t_meas { - let n_on = cols_host[ti*n_cols..(ti+1)*n_cols] - .iter().filter(|&&b| b != 0).count(); - step_counts.push(n_on); - } - let mean_active: f64 = step_counts.iter().map(|&c| c as f64).sum::() - / (t_meas as f64); - let target_active = target as f64 * n_cols as f64; - eprintln!( - "threshold-activation convergence: mean_active/step = {mean_active:.1} \ - (target = {target_active:.1})" - ); - // Very generous band — we just want to confirm the threshold loop is - // functioning (not diverged to 0 or to all-active). - assert!( - mean_active >= 0.25 * target_active && mean_active <= 4.0 * target_active, - "mean active {mean_active:.1} outside [0.25x, 4x] of target {target_active:.1}" - ); -} - -/// Fused kernel: TM should learn a repeating sequence — anomaly decays. -#[test] -fn gpu_fused_tm_anomaly_decays_on_repeating_sequence() { - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let n_cols = cfg.n_columns; - let cells_per_col = 32usize; - - let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 271); - let mut sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); - let dev = sp.dev_ref().clone(); - let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col).expect("tm init"); - let mut fused = FusedState::new( - dev.clone(), - n_cols, - cells_per_col, - sp.initial_threshold_estimate(), - ).expect("fused init"); - tm.reset().expect("tm reset"); - fused.reset().expect("fused reset"); - - let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); - let make = |rng: &mut Xoshiro256PlusPlus| make_sdr(rng, bits, 0.02); - let seqs = [make(&mut rng), make(&mut rng), make(&mut rng)]; - - // Warmup SP threshold calibration with random SDRs first. - let warm = 300usize; - let mut warm_inputs = vec![0u8; warm * bits]; - for ti in 0..warm { - let sdr = make_sdr(&mut rng, bits, 0.02); - warm_inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); - } - let warm_dev: CudaSlice = dev.htod_sync_copy(&warm_inputs).expect("htod warm"); - let mut warm_cols = dev.alloc_zeros::(warm * n_cols).expect("alloc warm cols"); - let mut warm_anom = dev.alloc_zeros::(warm).expect("alloc warm anom"); - launch_fused( - &mut sp, &mut tm, &mut fused, - &warm_dev, &mut warm_cols, &mut warm_anom, - warm, bits, true, - ).expect("warm launch"); - dev.synchronize().expect("sync warm"); - - // Feed repeating A,B,C sequence for 100 reps. - let repeats = 100usize; - let t = repeats * 3; - let mut inputs = vec![0u8; t * bits]; - for r in 0..repeats { - for (i, s) in seqs.iter().enumerate() { - let off = (r*3 + i) * bits; - inputs[off..off+bits].copy_from_slice(s); - } - } - let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs).expect("htod rep"); - let mut cols_dev = dev.alloc_zeros::(t * n_cols).expect("alloc rep cols"); - let mut anom_dev = dev.alloc_zeros::(t).expect("alloc rep anom"); - launch_fused( - &mut sp, &mut tm, &mut fused, - &inputs_dev, &mut cols_dev, &mut anom_dev, - t, bits, true, - ).expect("rep launch"); - dev.synchronize().expect("sync rep"); - - let anom: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); - let early_avg: f32 = anom[3..12].iter().sum::() / 9.0; - let late_avg: f32 = anom[(t-9)..t].iter().sum::() / 9.0; - eprintln!("fused TM anomaly: early={early_avg:.3} late={late_avg:.3}"); - assert!( - late_avg < early_avg, - "anomaly must decay: early={early_avg:.3} late={late_avg:.3}" - ); - assert!( - late_avg < 0.5, - "late anomaly must be < 0.5 (got {late_avg:.3})" - ); -} - -#[test] -fn gpu_sp_yields_k_winners() { - let cfg = SpatialPoolerConfig::default(); - let bits = cfg.input_bits; - let n = cfg.n_columns; - let expected_k = ((cfg.sparsity * n as f32).round() as usize).max(1); - let cpu = SpatialPooler::new(SpatialPoolerConfig::default(), 7); - let mut gpu = SpatialPoolerGpu::from_cpu(&cpu).expect("gpu init"); - - let mut rng = Xoshiro256PlusPlus::seed_from_u64(1); - for _ in 0..10 { - let sdr_u8 = make_sdr(&mut rng, bits, 0.02); - let active = gpu.compute(&sdr_u8, false).expect("gpu compute"); - assert_eq!(active.len(), expected_k); - // Ensure sorted + unique. - for w in active.windows(2) { - assert!(w[0] < w[1], "duplicate or out-of-order winner indices"); - } - } -} - -#[test] -fn fused_launch_plan_uses_cooperative_grid_sync() { - let plan = plan_fused_launch(30, true, 30, None).expect("cooperative supported"); - assert_eq!(plan.grid_dim_x, 16); - assert_eq!(plan.cooperative_grid_limit, 30); -} - -#[test] -fn fused_launch_plan_scales_to_big_gpu() { - // H200-like: 132 SMs, high cooperative_grid_limit. Cap still applies. - let plan = plan_fused_launch(132, true, 1000, None).expect("cooperative supported"); - assert_eq!(plan.grid_dim_x, 16); // capped by default override - let plan = plan_fused_launch(132, true, 1000, Some(64)).expect("cooperative supported"); - assert_eq!(plan.grid_dim_x, 64); // override raises the cap -} - -#[test] -fn fused_launch_plan_refuses_non_cooperative_devices() { - // The slow path was removed. Devices without cooperative launch fail fast. - let err = plan_fused_launch(30, false, 0, None).unwrap_err(); - assert!(err.contains("cooperative launch")); -} - -#[test] -fn fused_grid_cap_env_override_is_honored() { - let cfg = SpatialPoolerConfig::default(); - let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 5252); - let sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); - let dev = sp.dev_ref().clone(); - - unsafe { std::env::set_var("HTM_FUSED_GRID_CAP", "12"); } - let fused = FusedState::new( - dev.clone(), - cfg.n_columns, - 32usize, - sp.initial_threshold_estimate(), - ).expect("fused init"); - unsafe { std::env::remove_var("HTM_FUSED_GRID_CAP"); } - - let sm_count = match dev.attribute( - cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, - ) { - Ok(v) => v as u32, - Err(_) => 16u32, - }; - let expected = sm_count.max(1).min(12); - assert_eq!( - fused.grid_dim_x, - expected, - "fused grid cap env override ignored: expected min(sm_count, 12) = {expected}, got {}", - fused.grid_dim_x, - ); -} +//! Parity tests: GPU SP vs CPU SP reference. +//! +//! With matching seeds the two should produce bit-identical active-column sets +//! when `learn=false`, and remain bit-identical over repeated `learn=true` +//! steps because the Hebbian update is deterministic (no RNG once initialised). +//! +//! Run with: cargo test --release --features gpu + +#![cfg(test)] +#![cfg(feature = "gpu")] + +use crate::sp::{SpatialPooler, SpatialPoolerConfig}; +use crate::gpu::sp_gpu::SpatialPoolerGpu; +use crate::gpu::tm_gpu::TemporalMemoryGpu; +use crate::gpu::fused::{ + launch_fused, plan_batched_grid_dim, plan_fused_launch, FusedState, +}; +use cudarc::driver::CudaSlice; +use rand::{Rng, SeedableRng}; +use rand_xoshiro::Xoshiro256PlusPlus; + +fn make_sdr(rng: &mut Xoshiro256PlusPlus, bits: usize, sparsity: f32) -> Vec { + let on = ((sparsity * bits as f32) as usize).max(1); + let mut v = vec![0u8; bits]; + let mut placed = 0; + while placed < on { + let i = rng.gen_range(0..bits); + if v[i] == 0 { + v[i] = 1; + placed += 1; + } + } + v +} + +#[test] +fn gpu_sp_matches_cpu_no_learn() { + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let mut cpu = SpatialPooler::new( + SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, + 1234, + ); + let cpu_for_gpu = SpatialPooler::new( + SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, + 1234, + ); + let mut gpu = SpatialPoolerGpu::from_cpu(&cpu_for_gpu) + .expect("gpu init (CUDA device available)"); + gpu.set_strict_parity(true); + + let mut rng = Xoshiro256PlusPlus::seed_from_u64(99); + for step in 0..20 { + let sdr_u8 = make_sdr(&mut rng, bits, 0.02); + let sdr_bool: Vec = sdr_u8.iter().map(|&x| x != 0).collect(); + + let cpu_active: Vec = cpu.compute(&sdr_bool, false); + let gpu_active: Vec = gpu.compute(&sdr_u8, false).expect("gpu compute"); + + assert_eq!( + cpu_active, gpu_active, + "mismatch at step {step}: len cpu={} gpu={}", + cpu_active.len(), gpu_active.len() + ); + } +} + +#[test] +fn gpu_sp_matches_cpu_with_learn() { + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let mut cpu = SpatialPooler::new( + SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, + 5678, + ); + let cpu_for_gpu = SpatialPooler::new( + SpatialPoolerConfig { ..SpatialPoolerConfig::default() }, + 5678, + ); + let mut gpu = SpatialPoolerGpu::from_cpu(&cpu_for_gpu).expect("gpu init"); + gpu.set_strict_parity(true); + + let mut rng = Xoshiro256PlusPlus::seed_from_u64(42); + for step in 0..50 { + let sdr_u8 = make_sdr(&mut rng, bits, 0.02); + let sdr_bool: Vec = sdr_u8.iter().map(|&x| x != 0).collect(); + + let cpu_active = cpu.compute(&sdr_bool, true); + let gpu_active = gpu.compute(&sdr_u8, true).expect("gpu compute"); + + assert_eq!( + cpu_active, gpu_active, + "mismatch at step {step} with learning" + ); + } +} + +#[test] +fn gpu_tm_anomaly_decays_on_repeating_sequence() { + // End-to-end GPU pipeline: SP feeds TM; repeating SDR sequence should drive + // anomaly down over time. + use crate::gpu::HTMRegionGpu; // not pyclass methods; use internal constructor via Rust + // Easier: replicate the pipeline directly with SP + TM. + + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let n_cols = cfg.n_columns; + let cells_per_col = 32usize; + + let cpu_for_gpu = SpatialPooler::new(SpatialPoolerConfig::default(), 314); + let mut sp = SpatialPoolerGpu::from_cpu(&cpu_for_gpu).expect("gpu init"); + let dev = sp.dev_ref().clone(); + let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col) + .expect("gpu tm init"); + tm.reset().expect("tm reset"); + + // Build 3 fixed SDRs, feed them in a repeating sequence. + let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); + let make = |rng: &mut Xoshiro256PlusPlus| make_sdr(rng, bits, 0.02); + let seqs = [make(&mut rng), make(&mut rng), make(&mut rng)]; + + // Warm up SP so columns are stable per symbol. + for _ in 0..100 { + for s in &seqs { + let _ = sp.compute(s, true).expect("sp compute"); + } + } + + // Build a long input buffer: 100 repetitions of [A,B,C] = 300 steps. + let repeats = 100usize; + let t = repeats * 3; + let mut inputs_flat = vec![0u8; t * bits]; + for r in 0..repeats { + for (i, s) in seqs.iter().enumerate() { + let off = (r * 3 + i) * bits; + inputs_flat[off..off + bits].copy_from_slice(s); + } + } + let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs_flat).expect("htod"); + + let mut cols_dev = dev.alloc_zeros::(t * n_cols).expect("alloc cols"); + let mut anom_dev = dev.alloc_zeros::(t).expect("alloc anom"); + + sp.step_batch_with_tm( + &inputs_dev, + t, + bits, + true, + &mut cols_dev, + &mut anom_dev, + &mut tm, + ).expect("step_batch_with_tm"); + + let anom: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); + let cols: Vec = dev.dtoh_sync_copy(&cols_dev).expect("d2h cols"); + + // Active column count per step must equal k for every step. + let k = ((cfg.sparsity * n_cols as f32).round() as usize).max(1); + for ti in 0..t { + let step_slice = &cols[ti * n_cols..(ti + 1) * n_cols]; + let n_on = step_slice.iter().filter(|&&b| b != 0).count(); + assert_eq!(n_on, k, "step {ti} has {n_on} active cols, expected {k}"); + } + + // First repetition: anomaly should be near 1.0 (nothing predicted). + let early_avg: f32 = anom[3..9].iter().sum::() / 6.0; + // Last repetitions: anomaly should be noticeably lower. + let late_avg: f32 = anom[(t - 9)..t].iter().sum::() / 9.0; + eprintln!("gpu tm: early anomaly = {early_avg:.3}, late = {late_avg:.3}"); + assert!( + late_avg < early_avg, + "GPU TM should reduce anomaly on repeating sequence: early={early_avg:.3}, late={late_avg:.3}" + ); +} + +/// Cluster-sync smoke test: verifies that the fused megakernel (which relies on +/// hardware `cluster::sync()` / grid-barrier on H100/H200 Hopper) completes +/// without deadlock when called with real HTM state, and that output shapes are +/// sane (no NaN / Inf in anomaly scores, active-column count in plausible range). +/// +/// This is an *integration* test, not a synthetic micro-benchmark: it exercises +/// exactly the same `launch_fused` code path used in production, so any +/// deadlock in the cooperative-grid or DLB barrier would surface here. +/// +/// Skips gracefully (with an eprintln) when no GPU is available — the test +/// binary returns exit-code 0 in that case so CI still passes. +#[test] +fn cluster_sync_smoke_test() { + // Build a tiny HTM region (1024 inputs, 256 columns, 4 cells/column). + // This keeps VRAM usage minimal while still exercising all kernel paths. + let input_bits = 1024usize; + let n_columns = 256usize; + let cells_per_col = 4usize; + + // Probe cooperative launch attribute before doing any real work. + // CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH = 223 (added in CUDA 11.8 for Hopper). + // cudarc exposes raw attribute querying; we check cooperative launch (98) + // as the guard — cluster launch is a superset and not separately probed + // here since cudarc doesn't expose attribute 223 symbolically yet. + // On pre-Hopper hardware the DLB barrier path is used instead and the + // test still validates no deadlock on that path. + + let make_cfg = || SpatialPoolerConfig { + input_bits, + n_columns, + sparsity: 0.04, // ~10 active cols out of 256 + ..SpatialPoolerConfig::default() + }; + + let cpu_ref = SpatialPooler::new(make_cfg(), 42); + + let mut sp = match SpatialPoolerGpu::from_cpu(&cpu_ref) { + Ok(sp) => sp, + Err(e) => { + eprintln!("[cluster_sync_smoke_test] No GPU available ({e:?}) — skipping"); + return; + } + }; + + let dev = sp.dev_ref().clone(); + + // Check cooperative launch support; skip with a clear message if absent. + let cooperative_ok = matches!( + dev.attribute(cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH), + Ok(v) if v > 0 + ); + if !cooperative_ok { + eprintln!("[cluster_sync_smoke_test] CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH=0 — DLB path only, still running test"); + // We continue — the DLB path is the production fallback and must not deadlock either. + } + + let mut tm = match TemporalMemoryGpu::new(dev.clone(), n_columns, cells_per_col) { + Ok(tm) => tm, + Err(e) => { + eprintln!("[cluster_sync_smoke_test] TemporalMemoryGpu::new failed ({e:?}) — skipping"); + return; + } + }; + tm.reset().expect("tm reset"); + + let mut fused_st: FusedState = match FusedState::new( + dev.clone(), + n_columns, + cells_per_col, + sp.initial_threshold_estimate(), + ) { + Ok(f) => f, + Err(e) => { + eprintln!("[cluster_sync_smoke_test] FusedState::new failed ({e:?}) — skipping"); + return; + } + }; + fused_st.reset().expect("fused reset"); + + // Build T=4 timesteps of all-zero input SDRs. + let t = 4usize; + let inputs_flat = vec![0u8; t * input_bits]; + let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs_flat).expect("htod inputs"); + + let mut cols_dev = dev.alloc_zeros::(t * n_columns).expect("alloc cols"); + let mut anom_dev = dev.alloc_zeros::(t).expect("alloc anom"); + + // Execute with a 2-second timeout guard via a thread. If the kernel + // deadlocks, the parent test process times out and the CI job reports + // failure — we can't cancel a live CUDA kernel from Rust, but the + // launch_fused call itself must return within this window on any sane GPU. + // + // We run the kernel inline (not in a separate thread) because CUDA contexts + // are not safely shareable across threads without explicit multi-threading + // setup. The 2-second bound is enforced implicitly: if the kernel deadlocks, + // the test binary will hang and the CI timeout (typically 5 min) will kill it. + // For local dev, the deadlock would be immediately obvious. + + launch_fused( + &mut sp, + &mut tm, + &mut fused_st, + &inputs_dev, + &mut cols_dev, + &mut anom_dev, + t, + input_bits, + false, // learn=false for determinism + ).expect("launch_fused (cluster_sync_smoke_test): deadlock or CUDA error"); + + dev.synchronize().expect("device sync after launch_fused"); + + // --- Correctness assertions --- + + let cols_host: Vec = dev.dtoh_sync_copy(&cols_dev).expect("d2h cols"); + let anom_host: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); + + // Output buffers must be exactly the right size. + assert_eq!(cols_host.len(), t * n_columns, "cols buffer size mismatch"); + assert_eq!(anom_host.len(), t, "anom buffer size mismatch"); + + // Anomaly scores must be finite (NaN/Inf indicates numerical blow-up). + for (i, &a) in anom_host.iter().enumerate() { + assert!(a.is_finite(), "anomaly[{i}] is not finite: {a}"); + assert!(a >= 0.0 && a <= 1.0, "anomaly[{i}] out of [0,1]: {a}"); + } + + // Active-column count per step: threshold-based inhibition, so 0 is + // possible on cold start (before thresholds calibrate), but we assert + // <= n_columns to catch buffer overruns or completely wrong output. + for ti in 0..t { + let n_on = cols_host[ti * n_columns..(ti + 1) * n_columns] + .iter() + .filter(|&&b| b != 0) + .count(); + assert!( + n_on <= n_columns, + "step {ti}: active columns {n_on} > n_columns {n_columns} (buffer overrun?)" + ); + } + + eprintln!( + "[cluster_sync_smoke_test] PASSED: T={t}, n_cols={n_columns}, \ + input_bits={input_bits}, cooperative_supported={cooperative_ok}, \ + anom={anom_host:?}" + ); +} + +/// Parity check: the CAI zero-copy path (`step_many_cuda`) must produce +/// bit-identical outputs to the numpy H2D/D2H path (`step_batch_with_tm`), +/// since the kernel pipeline is the same — only the I/O wrapping changes. +/// We skip the PyO3 CAI dict plumbing here and test the underlying +/// ManuallyDrop + upgrade_device_ptr pattern directly. +#[test] +fn gpu_cuda_vs_numpy_parity() { + use std::mem::ManuallyDrop; + + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let n_cols = cfg.n_columns; + let cells_per_col = 32usize; + + // Build two identical (SP, TM) pairs from the same seed. + let build = || -> (SpatialPoolerGpu, TemporalMemoryGpu) { + let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 271828); + let sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu init"); + let dev = sp.dev_ref().clone(); + let mut tm = TemporalMemoryGpu::new(dev, n_cols, cells_per_col).expect("tm init"); + tm.reset().expect("tm reset"); + (sp, tm) + }; + + // Deterministic SDR sequence. + let mut rng = Xoshiro256PlusPlus::seed_from_u64(31337); + let t = 32usize; + let mut inputs_flat = vec![0u8; t * bits]; + for i in 0..t { + let sdr = make_sdr(&mut rng, bits, 0.02); + inputs_flat[i * bits..(i + 1) * bits].copy_from_slice(&sdr); + } + + // ---- Path A: owned CudaSlice (numpy-equivalent path) ---- + let (mut sp_a, mut tm_a) = build(); + let dev_a = sp_a.dev_ref().clone(); + let inputs_a: CudaSlice = dev_a.htod_sync_copy(&inputs_flat).expect("htod"); + let mut cols_a = dev_a.alloc_zeros::(t * n_cols).expect("alloc cols_a"); + let mut anom_a = dev_a.alloc_zeros::(t).expect("alloc anom_a"); + sp_a.step_batch_with_tm(&inputs_a, t, bits, false, &mut cols_a, &mut anom_a, &mut tm_a) + .expect("owned step_batch_with_tm"); + dev_a.synchronize().expect("sync a"); + let cols_a_host: Vec = dev_a.dtoh_sync_copy(&cols_a).expect("d2h cols_a"); + let anom_a_host: Vec = dev_a.dtoh_sync_copy(&anom_a).expect("d2h anom_a"); + + // ---- Path B: borrowed device pointers via upgrade_device_ptr ---- + // We allocate fresh owned CudaSlices on a fresh device, then take their + // raw ptrs and re-wrap as ManuallyDrop borrowed views — mimicking what + // `step_many_cuda` does with torch-owned CUDA memory. + let (mut sp_b, mut tm_b) = build(); + let dev_b = sp_b.dev_ref().clone(); + let inputs_b_owned: CudaSlice = dev_b.htod_sync_copy(&inputs_flat).expect("htod"); + let cols_b_owned = dev_b.alloc_zeros::(t * n_cols).expect("alloc cols_b"); + let anom_b_owned = dev_b.alloc_zeros::(t).expect("alloc anom_b"); + + // Extract raw CUdeviceptrs (and leak the owners so their Drop doesn't free). + let inputs_ptr = inputs_b_owned.leak(); + let cols_ptr = cols_b_owned.leak(); + let anom_ptr = anom_b_owned.leak(); + + // Re-wrap as borrowed views. + let inputs_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(inputs_ptr, t * bits) }); + let mut cols_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(cols_ptr, t * n_cols) }); + let mut anom_b = ManuallyDrop::new(unsafe { dev_b.upgrade_device_ptr::(anom_ptr, t) }); + + sp_b.step_batch_with_tm(&inputs_b, t, bits, false, &mut cols_b, &mut anom_b, &mut tm_b) + .expect("borrowed step_batch_with_tm"); + dev_b.synchronize().expect("sync b"); + // `ManuallyDrop` doesn't auto-coerce to `&CudaSlice` for the DevicePtr + // trait bound on `dtoh_sync_copy`; explicit deref. + let cols_b_host: Vec = dev_b.dtoh_sync_copy(&*cols_b).expect("d2h cols_b"); + let anom_b_host: Vec = dev_b.dtoh_sync_copy(&*anom_b).expect("d2h anom_b"); + + // Re-own so Drop actually frees (we leaked above). + let _inputs_owned_again = unsafe { dev_b.upgrade_device_ptr::(inputs_ptr, t * bits) }; + let _cols_owned_again = unsafe { dev_b.upgrade_device_ptr::(cols_ptr, t * n_cols) }; + let _anom_owned_again = unsafe { dev_b.upgrade_device_ptr::(anom_ptr, t) }; + + assert_eq!(cols_a_host, cols_b_host, "active-column mask diverges between numpy and CAI paths"); + assert_eq!(anom_a_host.len(), anom_b_host.len()); + for (i, (a, b)) in anom_a_host.iter().zip(anom_b_host.iter()).enumerate() { + // Anomaly is a pure division of integer counts — bit-exact expected. + assert!((a - b).abs() < 1e-7, "anomaly mismatch at step {i}: a={a} b={b}"); + } +} + +/// Fused kernel: threshold activation should converge to near target sparsity +/// after a short warmup. Acceptance: mean activation rate per step lands in +/// [0.3*target, 2.5*target] after 500-step warmup. Because the threshold +/// starts conservative (=2.0) and the per-column adaptation rate is slow +/// (0.001), we allow a generous band — the test asserts directional +/// convergence toward the target, not tight matching. +#[test] +fn gpu_threshold_converges_to_sparsity() { + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let n_cols = cfg.n_columns; + let cells_per_col = 32usize; + let target = cfg.sparsity; // 0.02 = 40 cols expected + + let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 111); + let mut sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); + let dev = sp.dev_ref().clone(); + let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col).expect("tm init"); + let mut fused = FusedState::new( + dev.clone(), + n_cols, + cells_per_col, + sp.initial_threshold_estimate(), + ).expect("fused init"); + tm.reset().expect("tm reset"); + fused.reset().expect("fused reset"); + + // Warmup: 1000 random 2%-sparse SDRs. + let mut rng = Xoshiro256PlusPlus::seed_from_u64(31337); + let t_warm = 1000usize; + let mut inputs = vec![0u8; t_warm * bits]; + for ti in 0..t_warm { + let sdr = make_sdr(&mut rng, bits, 0.02); + inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); + } + let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs).expect("htod"); + let mut cols_dev = dev.alloc_zeros::(t_warm * n_cols).expect("alloc cols"); + let mut anom_dev = dev.alloc_zeros::(t_warm).expect("alloc anom"); + launch_fused( + &mut sp, &mut tm, &mut fused, + &inputs_dev, &mut cols_dev, &mut anom_dev, + t_warm, bits, true, + ).expect("warmup launch"); + dev.synchronize().expect("sync"); + + // Measurement pass: another 200 steps, measure mean activation. + let t_meas = 200usize; + let mut meas_inputs = vec![0u8; t_meas * bits]; + for ti in 0..t_meas { + let sdr = make_sdr(&mut rng, bits, 0.02); + meas_inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); + } + let meas_dev: CudaSlice = dev.htod_sync_copy(&meas_inputs).expect("htod meas"); + let mut meas_cols = dev.alloc_zeros::(t_meas * n_cols).expect("alloc meas cols"); + let mut meas_anom = dev.alloc_zeros::(t_meas).expect("alloc meas anom"); + launch_fused( + &mut sp, &mut tm, &mut fused, + &meas_dev, &mut meas_cols, &mut meas_anom, + t_meas, bits, true, + ).expect("meas launch"); + dev.synchronize().expect("sync meas"); + + let cols_host: Vec = dev.dtoh_sync_copy(&meas_cols).expect("d2h"); + let mut step_counts = Vec::with_capacity(t_meas); + for ti in 0..t_meas { + let n_on = cols_host[ti*n_cols..(ti+1)*n_cols] + .iter().filter(|&&b| b != 0).count(); + step_counts.push(n_on); + } + let mean_active: f64 = step_counts.iter().map(|&c| c as f64).sum::() + / (t_meas as f64); + let target_active = target as f64 * n_cols as f64; + eprintln!( + "threshold-activation convergence: mean_active/step = {mean_active:.1} \ + (target = {target_active:.1})" + ); + // Very generous band — we just want to confirm the threshold loop is + // functioning (not diverged to 0 or to all-active). + assert!( + mean_active >= 0.25 * target_active && mean_active <= 4.0 * target_active, + "mean active {mean_active:.1} outside [0.25x, 4x] of target {target_active:.1}" + ); +} + +/// Fused kernel: TM should learn a repeating sequence — anomaly decays. +#[test] +fn gpu_fused_tm_anomaly_decays_on_repeating_sequence() { + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let n_cols = cfg.n_columns; + let cells_per_col = 32usize; + + let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 271); + let mut sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); + let dev = sp.dev_ref().clone(); + let mut tm = TemporalMemoryGpu::new(dev.clone(), n_cols, cells_per_col).expect("tm init"); + let mut fused = FusedState::new( + dev.clone(), + n_cols, + cells_per_col, + sp.initial_threshold_estimate(), + ).expect("fused init"); + tm.reset().expect("tm reset"); + fused.reset().expect("fused reset"); + + let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); + let make = |rng: &mut Xoshiro256PlusPlus| make_sdr(rng, bits, 0.02); + let seqs = [make(&mut rng), make(&mut rng), make(&mut rng)]; + + // Warmup SP threshold calibration with random SDRs first. + let warm = 300usize; + let mut warm_inputs = vec![0u8; warm * bits]; + for ti in 0..warm { + let sdr = make_sdr(&mut rng, bits, 0.02); + warm_inputs[ti*bits..(ti+1)*bits].copy_from_slice(&sdr); + } + let warm_dev: CudaSlice = dev.htod_sync_copy(&warm_inputs).expect("htod warm"); + let mut warm_cols = dev.alloc_zeros::(warm * n_cols).expect("alloc warm cols"); + let mut warm_anom = dev.alloc_zeros::(warm).expect("alloc warm anom"); + launch_fused( + &mut sp, &mut tm, &mut fused, + &warm_dev, &mut warm_cols, &mut warm_anom, + warm, bits, true, + ).expect("warm launch"); + dev.synchronize().expect("sync warm"); + + // Feed repeating A,B,C sequence for 100 reps. + let repeats = 100usize; + let t = repeats * 3; + let mut inputs = vec![0u8; t * bits]; + for r in 0..repeats { + for (i, s) in seqs.iter().enumerate() { + let off = (r*3 + i) * bits; + inputs[off..off+bits].copy_from_slice(s); + } + } + let inputs_dev: CudaSlice = dev.htod_sync_copy(&inputs).expect("htod rep"); + let mut cols_dev = dev.alloc_zeros::(t * n_cols).expect("alloc rep cols"); + let mut anom_dev = dev.alloc_zeros::(t).expect("alloc rep anom"); + launch_fused( + &mut sp, &mut tm, &mut fused, + &inputs_dev, &mut cols_dev, &mut anom_dev, + t, bits, true, + ).expect("rep launch"); + dev.synchronize().expect("sync rep"); + + let anom: Vec = dev.dtoh_sync_copy(&anom_dev).expect("d2h anom"); + let early_avg: f32 = anom[3..12].iter().sum::() / 9.0; + let late_avg: f32 = anom[(t-9)..t].iter().sum::() / 9.0; + eprintln!("fused TM anomaly: early={early_avg:.3} late={late_avg:.3}"); + assert!( + late_avg < early_avg, + "anomaly must decay: early={early_avg:.3} late={late_avg:.3}" + ); + assert!( + late_avg < 0.5, + "late anomaly must be < 0.5 (got {late_avg:.3})" + ); +} + +#[test] +fn gpu_sp_yields_k_winners() { + let cfg = SpatialPoolerConfig::default(); + let bits = cfg.input_bits; + let n = cfg.n_columns; + let expected_k = ((cfg.sparsity * n as f32).round() as usize).max(1); + let cpu = SpatialPooler::new(SpatialPoolerConfig::default(), 7); + let mut gpu = SpatialPoolerGpu::from_cpu(&cpu).expect("gpu init"); + + let mut rng = Xoshiro256PlusPlus::seed_from_u64(1); + for _ in 0..10 { + let sdr_u8 = make_sdr(&mut rng, bits, 0.02); + let active = gpu.compute(&sdr_u8, false).expect("gpu compute"); + assert_eq!(active.len(), expected_k); + // Ensure sorted + unique. + for w in active.windows(2) { + assert!(w[0] < w[1], "duplicate or out-of-order winner indices"); + } + } +} + +#[test] +fn fused_launch_plan_uses_cooperative_grid_sync() { + let plan = plan_fused_launch(30, true, 30, None).expect("cooperative supported"); + assert_eq!(plan.grid_dim_x, 16); + assert_eq!(plan.cooperative_grid_limit, 30); +} + +#[test] +fn fused_launch_plan_scales_to_big_gpu() { + // H200-like: 132 SMs, high cooperative_grid_limit. Cap still applies. + let plan = plan_fused_launch(132, true, 1000, None).expect("cooperative supported"); + assert_eq!(plan.grid_dim_x, 16); // capped by default override + let plan = plan_fused_launch(132, true, 1000, Some(64)).expect("cooperative supported"); + assert_eq!(plan.grid_dim_x, 64); // override raises the cap +} + +#[test] +fn fused_launch_plan_refuses_non_cooperative_devices() { + // The slow path was removed. Devices without cooperative launch fail fast. + let err = plan_fused_launch(30, false, 0, None).unwrap_err(); + assert!(err.contains("cooperative launch")); +} + +#[test] +fn fused_grid_cap_env_override_is_honored() { + let cfg = SpatialPoolerConfig::default(); + let cpu_ref = SpatialPooler::new(SpatialPoolerConfig::default(), 5252); + let sp = SpatialPoolerGpu::from_cpu(&cpu_ref).expect("gpu sp init"); + let dev = sp.dev_ref().clone(); + + unsafe { std::env::set_var("HTM_FUSED_GRID_CAP", "12"); } + let fused = FusedState::new( + dev.clone(), + cfg.n_columns, + 32usize, + sp.initial_threshold_estimate(), + ).expect("fused init"); + unsafe { std::env::remove_var("HTM_FUSED_GRID_CAP"); } + + let sm_count = match dev.attribute( + cudarc::driver::sys::CUdevice_attribute::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, + ) { + Ok(v) => v as u32, + Err(_) => 16u32, + }; + let expected = sm_count.max(1).min(12); + assert_eq!( + fused.grid_dim_x, + expected, + "fused grid cap env override ignored: expected min(sm_count, 12) = {expected}, got {}", + fused.grid_dim_x, + ); +} + +#[test] +fn batched_grid_plan_clamps_a10g_batch32_under_cooperative_limit() { + // A10G observed in HF Jobs: cooperative_grid_limit=400, B=32. + // grid_x=16 requests 512 cooperative blocks and fails; clamp to 12. + let grid_x = plan_batched_grid_dim(16, 400, 32, false).expect("fits after clamp"); + assert_eq!(grid_x, 12); +} + +#[test] +fn batched_grid_plan_reports_oversized_batch() { + let err = plan_batched_grid_dim(16, 31, 32, false).unwrap_err(); + assert!(err.contains("COOPERATIVE_LAUNCH_TOO_LARGE")); +} + +#[test] +fn batched_grid_plan_does_not_clamp_cluster_launches() { + let grid_x = plan_batched_grid_dim(16, 31, 32, true).expect("cluster path bypasses cooperative limit"); + assert_eq!(grid_x, 16); +} diff --git a/overlay/htm_rust/src/lib.rs b/overlay/htm_rust/src/lib.rs index b60773d9fac871879260012110984992bfff7001..3c67947ab147ccba26fd4458823f1656a2099c3d 100644 --- a/overlay/htm_rust/src/lib.rs +++ b/overlay/htm_rust/src/lib.rs @@ -1,198 +1,198 @@ -//! pyo3 bindings for HTMRegion (Numenta BAMI-spec HTM). -//! -//! Exposed class: -//! HTMRegion(input_bits, n_columns, cells_per_column, seed) -> HTMRegion -//! .step(input_sdr: np.ndarray[bool; input_bits], learn: bool = True) -//! -> (active_columns: np.ndarray[bool; n_columns], -//! active_cells: np.ndarray[bool; n_columns*cells_per_column], -//! predicted_cells:np.ndarray[bool; n_columns*cells_per_column], -//! anomaly: float) -//! .reset() -//! .n_columns -> int -//! .cells_per_column -> int -//! .input_bits -> int -//! -//! GIL is dropped during the heavy compute via `py.allow_threads(...)` so the -//! region is effectively `Send` for Python-side threading. - -// pyo3 0.22 `#[pymethods]` expansion inserts an implicit `.into()` on the -// returned `Result` to normalise the error type, which clippy reports as -// `useless_conversion` when our methods already return `PyErr`. The emitted -// code sits outside the user-written impl, so item-level allows don't reach -// it; the module-wide allow is the documented workaround. -#![allow(clippy::useless_conversion)] - -mod region; -mod sp; -mod tm; - -#[cfg(feature = "gpu")] -mod gpu; - -use numpy::{ - IntoPyArray, PyArray1, PyArray2, PyArrayMethods, PyReadonlyArray1, PyReadonlyArray2, - PyUntypedArrayMethods, -}; -use pyo3::prelude::*; - -use crate::region::HTMRegionCore; - -/// Result of one HTM step: (active_columns, active_cells, predicted_cells, anomaly). -type StepOutput<'py> = ( - Bound<'py, PyArray1>, - Bound<'py, PyArray1>, - Bound<'py, PyArray1>, - f32, -); - -#[pyclass(module = "htm_rust")] -pub struct HTMRegion { - core: HTMRegionCore, -} - -#[pymethods] -impl HTMRegion { - /// Create a new HTM region. - /// - /// Args: - /// input_bits: length of binary input SDR - /// n_columns: number of mini-columns in the SP (e.g. 2048) - /// cells_per_column: cells per column in the TM (e.g. 32) - /// seed: RNG seed for reproducibility - #[new] - #[pyo3(signature = (input_bits, n_columns, cells_per_column, seed=42))] - fn new( - input_bits: usize, - n_columns: usize, - cells_per_column: usize, - seed: u64, - ) -> PyResult { - if input_bits == 0 { - return Err(pyo3::exceptions::PyValueError::new_err( - "input_bits must be > 0", - )); - } - if n_columns == 0 { - return Err(pyo3::exceptions::PyValueError::new_err( - "n_columns must be > 0", - )); - } - if cells_per_column == 0 { - return Err(pyo3::exceptions::PyValueError::new_err( - "cells_per_column must be > 0", - )); - } - Ok(Self { - core: HTMRegionCore::new(input_bits, n_columns, cells_per_column, seed), - }) - } - - #[getter] - fn input_bits(&self) -> usize { self.core.sp.cfg.input_bits } - - #[getter] - fn n_columns(&self) -> usize { self.core.sp.cfg.n_columns } - - #[getter] - fn cells_per_column(&self) -> usize { self.core.tm.cfg.cells_per_column } - - /// Process one timestep. - /// - /// Args: - /// input_sdr: 1-D numpy boolean array of length `input_bits`. - /// learn: if True, update SP permanences and TM synapses. - /// - /// Returns: - /// (active_columns, active_cells, predicted_cells, anomaly) - #[pyo3(signature = (input_sdr, learn=true))] - fn step<'py>( - &mut self, - py: Python<'py>, - input_sdr: PyReadonlyArray1<'py, bool>, - learn: bool, - ) -> PyResult> { - let expected = self.core.sp.cfg.input_bits; - let slice = input_sdr.as_slice()?; - let got = slice.len(); - if got != expected { - return Err(pyo3::exceptions::PyValueError::new_err(format!( - "input_sdr length {got} != expected input_bits {expected}", - ))); - } - - // Copy input to an owned Vec so we can drop the GIL. - let input_vec: Vec = slice.to_vec(); - - let (active_cols, active_cells, predicted_cells, anomaly) = - py.allow_threads(|| self.core.step(&input_vec, learn)); - - let a: Bound<'py, PyArray1> = active_cols.into_pyarray_bound(py); - let c: Bound<'py, PyArray1> = active_cells.into_pyarray_bound(py); - let p: Bound<'py, PyArray1> = predicted_cells.into_pyarray_bound(py); - Ok((a, c, p, anomaly)) - } - - /// Clear TM predictive state. Does NOT unlearn synapses. - fn reset(&mut self) { self.core.reset(); } - - /// Process T timesteps from a `(T, input_bits)` bool ndarray. - /// - /// Returns: - /// cols: (T, n_columns) float32 0/1 active-column mask - /// anom: (T,) float32 anomaly scores - /// - /// Single GIL release for the whole pass, avoiding T × Python-call overhead. - #[pyo3(signature = (inputs, learn=true))] - fn step_many<'py>( - &mut self, - py: Python<'py>, - inputs: PyReadonlyArray2<'py, bool>, - learn: bool, - ) -> PyResult<(Bound<'py, PyArray2>, Bound<'py, PyArray1>)> { - let shape = inputs.shape(); - if shape.len() != 2 { - return Err(pyo3::exceptions::PyValueError::new_err( - "inputs must be 2-D (T, input_bits)", - )); - } - let t = shape[0]; - let bits = shape[1]; - let expected = self.core.sp.cfg.input_bits; - if bits != expected { - return Err(pyo3::exceptions::PyValueError::new_err(format!( - "inputs last dim {bits} != expected input_bits {expected}", - ))); - } - let slice = inputs.as_slice()?; - let n_cols = self.core.sp.cfg.n_columns; - - // Own the input buffer so we can drop the GIL. - let input_vec: Vec = slice.to_vec(); - - let (cols_u8, anom) = - py.allow_threads(|| self.core.step_many(&input_vec, bits, t, learn)); - - // Convert u8 mask to f32 for direct numpy consumption. - let cols_f32: Vec = cols_u8.iter().map(|&b| b as f32).collect(); - - // Build (T, n_cols) and (T,) arrays. - let cols_arr = - numpy::PyArray1::from_vec_bound(py, cols_f32) - .reshape([t, n_cols]) - .map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?; - let anom_arr = numpy::PyArray1::from_vec_bound(py, anom); - Ok((cols_arr, anom_arr)) - } -} - -/// Python module entry point. -#[pymodule] -fn htm_rust(m: &Bound<'_, PyModule>) -> PyResult<()> { - m.add_class::()?; - #[cfg(feature = "gpu")] - { - gpu::register(m)?; - } - m.add("__version__", env!("CARGO_PKG_VERSION"))?; - Ok(()) -} +//! pyo3 bindings for HTMRegion (Numenta BAMI-spec HTM). +//! +//! Exposed class: +//! HTMRegion(input_bits, n_columns, cells_per_column, seed) -> HTMRegion +//! .step(input_sdr: np.ndarray[bool; input_bits], learn: bool = True) +//! -> (active_columns: np.ndarray[bool; n_columns], +//! active_cells: np.ndarray[bool; n_columns*cells_per_column], +//! predicted_cells:np.ndarray[bool; n_columns*cells_per_column], +//! anomaly: float) +//! .reset() +//! .n_columns -> int +//! .cells_per_column -> int +//! .input_bits -> int +//! +//! GIL is dropped during the heavy compute via `py.allow_threads(...)` so the +//! region is effectively `Send` for Python-side threading. + +// pyo3 0.22 `#[pymethods]` expansion inserts an implicit `.into()` on the +// returned `Result` to normalise the error type, which clippy reports as +// `useless_conversion` when our methods already return `PyErr`. The emitted +// code sits outside the user-written impl, so item-level allows don't reach +// it; the module-wide allow is the documented workaround. +#![allow(clippy::useless_conversion)] + +mod region; +mod sp; +mod tm; + +#[cfg(feature = "gpu")] +mod gpu; + +use numpy::{ + IntoPyArray, PyArray1, PyArray2, PyArrayMethods, PyReadonlyArray1, PyReadonlyArray2, + PyUntypedArrayMethods, +}; +use pyo3::prelude::*; + +use crate::region::HTMRegionCore; + +/// Result of one HTM step: (active_columns, active_cells, predicted_cells, anomaly). +type StepOutput<'py> = ( + Bound<'py, PyArray1>, + Bound<'py, PyArray1>, + Bound<'py, PyArray1>, + f32, +); + +#[pyclass(module = "htm_rust")] +pub struct HTMRegion { + core: HTMRegionCore, +} + +#[pymethods] +impl HTMRegion { + /// Create a new HTM region. + /// + /// Args: + /// input_bits: length of binary input SDR + /// n_columns: number of mini-columns in the SP (e.g. 2048) + /// cells_per_column: cells per column in the TM (e.g. 32) + /// seed: RNG seed for reproducibility + #[new] + #[pyo3(signature = (input_bits, n_columns, cells_per_column, seed=42))] + fn new( + input_bits: usize, + n_columns: usize, + cells_per_column: usize, + seed: u64, + ) -> PyResult { + if input_bits == 0 { + return Err(pyo3::exceptions::PyValueError::new_err( + "input_bits must be > 0", + )); + } + if n_columns == 0 { + return Err(pyo3::exceptions::PyValueError::new_err( + "n_columns must be > 0", + )); + } + if cells_per_column == 0 { + return Err(pyo3::exceptions::PyValueError::new_err( + "cells_per_column must be > 0", + )); + } + Ok(Self { + core: HTMRegionCore::new(input_bits, n_columns, cells_per_column, seed), + }) + } + + #[getter] + fn input_bits(&self) -> usize { self.core.sp.cfg.input_bits } + + #[getter] + fn n_columns(&self) -> usize { self.core.sp.cfg.n_columns } + + #[getter] + fn cells_per_column(&self) -> usize { self.core.tm.cfg.cells_per_column } + + /// Process one timestep. + /// + /// Args: + /// input_sdr: 1-D numpy boolean array of length `input_bits`. + /// learn: if True, update SP permanences and TM synapses. + /// + /// Returns: + /// (active_columns, active_cells, predicted_cells, anomaly) + #[pyo3(signature = (input_sdr, learn=true))] + fn step<'py>( + &mut self, + py: Python<'py>, + input_sdr: PyReadonlyArray1<'py, bool>, + learn: bool, + ) -> PyResult> { + let expected = self.core.sp.cfg.input_bits; + let slice = input_sdr.as_slice()?; + let got = slice.len(); + if got != expected { + return Err(pyo3::exceptions::PyValueError::new_err(format!( + "input_sdr length {got} != expected input_bits {expected}", + ))); + } + + // Copy input to an owned Vec so we can drop the GIL. + let input_vec: Vec = slice.to_vec(); + + let (active_cols, active_cells, predicted_cells, anomaly) = + py.allow_threads(|| self.core.step(&input_vec, learn)); + + let a: Bound<'py, PyArray1> = active_cols.into_pyarray_bound(py); + let c: Bound<'py, PyArray1> = active_cells.into_pyarray_bound(py); + let p: Bound<'py, PyArray1> = predicted_cells.into_pyarray_bound(py); + Ok((a, c, p, anomaly)) + } + + /// Clear TM predictive state. Does NOT unlearn synapses. + fn reset(&mut self) { self.core.reset(); } + + /// Process T timesteps from a `(T, input_bits)` bool ndarray. + /// + /// Returns: + /// cols: (T, n_columns) float32 0/1 active-column mask + /// anom: (T,) float32 anomaly scores + /// + /// Single GIL release for the whole pass, avoiding T × Python-call overhead. + #[pyo3(signature = (inputs, learn=true))] + fn step_many<'py>( + &mut self, + py: Python<'py>, + inputs: PyReadonlyArray2<'py, bool>, + learn: bool, + ) -> PyResult<(Bound<'py, PyArray2>, Bound<'py, PyArray1>)> { + let shape = inputs.shape(); + if shape.len() != 2 { + return Err(pyo3::exceptions::PyValueError::new_err( + "inputs must be 2-D (T, input_bits)", + )); + } + let t = shape[0]; + let bits = shape[1]; + let expected = self.core.sp.cfg.input_bits; + if bits != expected { + return Err(pyo3::exceptions::PyValueError::new_err(format!( + "inputs last dim {bits} != expected input_bits {expected}", + ))); + } + let slice = inputs.as_slice()?; + let n_cols = self.core.sp.cfg.n_columns; + + // Own the input buffer so we can drop the GIL. + let input_vec: Vec = slice.to_vec(); + + let (cols_u8, anom) = + py.allow_threads(|| self.core.step_many(&input_vec, bits, t, learn)); + + // Convert u8 mask to f32 for direct numpy consumption. + let cols_f32: Vec = cols_u8.iter().map(|&b| b as f32).collect(); + + // Build (T, n_cols) and (T,) arrays. + let cols_arr = + numpy::PyArray1::from_vec_bound(py, cols_f32) + .reshape([t, n_cols]) + .map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(format!("{e}")))?; + let anom_arr = numpy::PyArray1::from_vec_bound(py, anom); + Ok((cols_arr, anom_arr)) + } +} + +/// Python module entry point. +#[pymodule] +fn htm_rust(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + #[cfg(feature = "gpu")] + { + gpu::register(m)?; + } + m.add("__version__", env!("CARGO_PKG_VERSION"))?; + Ok(()) +} diff --git a/overlay/htm_rust/src/region.rs b/overlay/htm_rust/src/region.rs index d9516db727cb0f7e0358912fd9c36d479a5c0363..8f33f88a917fd146ac5218530dcb22f9182f3627 100644 --- a/overlay/htm_rust/src/region.rs +++ b/overlay/htm_rust/src/region.rs @@ -1,94 +1,94 @@ -//! HTMRegion: compose SpatialPooler + TemporalMemory into a single step(). - -use crate::sp::{SpatialPooler, SpatialPoolerConfig}; -use crate::tm::{TemporalMemory, TemporalMemoryConfig}; - -pub struct HTMRegionCore { - pub sp: SpatialPooler, - pub tm: TemporalMemory, -} - -impl HTMRegionCore { - pub fn new( - input_bits: usize, - n_columns: usize, - cells_per_column: usize, - seed: u64, - ) -> Self { - let defaults = SpatialPoolerConfig::default(); - let sp_cfg = SpatialPoolerConfig { - input_bits, - n_columns, - // Scale potential_radius to at most the input size. - potential_radius: defaults.potential_radius.min(input_bits), - ..defaults - }; - - let tm_cfg = TemporalMemoryConfig { - n_columns, - cells_per_column, - ..TemporalMemoryConfig::default() - }; - - Self { - sp: SpatialPooler::new(sp_cfg, seed), - tm: TemporalMemory::new(tm_cfg, seed.wrapping_add(0x9E3779B97F4A7C15)), - } - } - - /// Process one timestep. Returns (active_columns_mask, - /// active_cells_mask, predicted_cells_mask, anomaly). - pub fn step( - &mut self, - input_sdr: &[bool], - learn: bool, - ) -> (Vec, Vec, Vec, f32) { - let active_cols = self.sp.compute(input_sdr, learn); - - let mut active_cols_mask = vec![false; self.sp.cfg.n_columns]; - for &c in &active_cols { - active_cols_mask[c as usize] = true; - } - - let anomaly = self.tm.compute(&active_cols, learn); - - // active_cells and predictive_cells are stored as Vec already. - let active_cells_mask = self.tm.active_cells.clone(); - let predicted_cells_mask = self.tm.predictive_cells.clone(); - - (active_cols_mask, active_cells_mask, predicted_cells_mask, anomaly) - } - - pub fn reset(&mut self) { - self.tm.reset(); - } - - /// Process T timesteps in one call. Returns flat `(T*n_columns)` active-column - /// mask (u8 0/1) and `(T,)` anomaly scores. - /// - /// Amortises the per-step Python round-trip for training: one GIL release, - /// one copy-out. Used by `HTMLayer.step_many`. - pub fn step_many( - &mut self, - inputs_flat: &[bool], - input_bits: usize, - t: usize, - learn: bool, - ) -> (Vec, Vec) { - let n_cols = self.sp.cfg.n_columns; - debug_assert_eq!(inputs_flat.len(), t * input_bits); - let mut cols = vec![0u8; t * n_cols]; - let mut anom = vec![0f32; t]; - for ti in 0..t { - let off = ti * input_bits; - let input = &inputs_flat[off..off + input_bits]; - let active_cols = self.sp.compute(input, learn); - let co = ti * n_cols; - for &c in &active_cols { - cols[co + c as usize] = 1; - } - anom[ti] = self.tm.compute(&active_cols, learn); - } - (cols, anom) - } -} +//! HTMRegion: compose SpatialPooler + TemporalMemory into a single step(). + +use crate::sp::{SpatialPooler, SpatialPoolerConfig}; +use crate::tm::{TemporalMemory, TemporalMemoryConfig}; + +pub struct HTMRegionCore { + pub sp: SpatialPooler, + pub tm: TemporalMemory, +} + +impl HTMRegionCore { + pub fn new( + input_bits: usize, + n_columns: usize, + cells_per_column: usize, + seed: u64, + ) -> Self { + let defaults = SpatialPoolerConfig::default(); + let sp_cfg = SpatialPoolerConfig { + input_bits, + n_columns, + // Scale potential_radius to at most the input size. + potential_radius: defaults.potential_radius.min(input_bits), + ..defaults + }; + + let tm_cfg = TemporalMemoryConfig { + n_columns, + cells_per_column, + ..TemporalMemoryConfig::default() + }; + + Self { + sp: SpatialPooler::new(sp_cfg, seed), + tm: TemporalMemory::new(tm_cfg, seed.wrapping_add(0x9E3779B97F4A7C15)), + } + } + + /// Process one timestep. Returns (active_columns_mask, + /// active_cells_mask, predicted_cells_mask, anomaly). + pub fn step( + &mut self, + input_sdr: &[bool], + learn: bool, + ) -> (Vec, Vec, Vec, f32) { + let active_cols = self.sp.compute(input_sdr, learn); + + let mut active_cols_mask = vec![false; self.sp.cfg.n_columns]; + for &c in &active_cols { + active_cols_mask[c as usize] = true; + } + + let anomaly = self.tm.compute(&active_cols, learn); + + // active_cells and predictive_cells are stored as Vec already. + let active_cells_mask = self.tm.active_cells.clone(); + let predicted_cells_mask = self.tm.predictive_cells.clone(); + + (active_cols_mask, active_cells_mask, predicted_cells_mask, anomaly) + } + + pub fn reset(&mut self) { + self.tm.reset(); + } + + /// Process T timesteps in one call. Returns flat `(T*n_columns)` active-column + /// mask (u8 0/1) and `(T,)` anomaly scores. + /// + /// Amortises the per-step Python round-trip for training: one GIL release, + /// one copy-out. Used by `HTMLayer.step_many`. + pub fn step_many( + &mut self, + inputs_flat: &[bool], + input_bits: usize, + t: usize, + learn: bool, + ) -> (Vec, Vec) { + let n_cols = self.sp.cfg.n_columns; + debug_assert_eq!(inputs_flat.len(), t * input_bits); + let mut cols = vec![0u8; t * n_cols]; + let mut anom = vec![0f32; t]; + for ti in 0..t { + let off = ti * input_bits; + let input = &inputs_flat[off..off + input_bits]; + let active_cols = self.sp.compute(input, learn); + let co = ti * n_cols; + for &c in &active_cols { + cols[co + c as usize] = 1; + } + anom[ti] = self.tm.compute(&active_cols, learn); + } + (cols, anom) + } +} diff --git a/overlay/htm_rust/src/sp.rs b/overlay/htm_rust/src/sp.rs index f63bc616ea6d4d9019a7a9c7d879960ee632d660..b9a90de84a7b9518ab8b80cfe09958ff549752e8 100644 --- a/overlay/htm_rust/src/sp.rs +++ b/overlay/htm_rust/src/sp.rs @@ -1,302 +1,302 @@ -//! Numenta BAMI-spec Spatial Pooler. -//! -//! Implements: -//! - 2048 (configurable) mini-columns with proximal dendrites -//! - `potential_synapses` (default 40) synapses per column sampled from -//! `potential_radius` (default 1024) random input bits -//! - Permanence in [0.0, 1.0] (f32), connected_threshold = 0.5 -//! - syn_perm_active_inc = +0.04, syn_perm_inactive_dec = -0.008 -//! - Global k-WTA inhibition (top `sparsity` fraction of columns) -//! - Boost factor with exponential duty-cycle tracking (Numenta formula) -//! -//! Reference: BAMI "Spatial Pooling Algorithm Details" (Numenta, 2017). - -use rand::Rng; -use rand::SeedableRng; -use rand::seq::SliceRandom; -use rand_xoshiro::Xoshiro256PlusPlus; - -/// A single proximal dendrite: a sparse set of potential synapses onto -/// specific input bit indices, with per-synapse permanence values. -#[derive(Clone)] -pub struct ProximalDendrite { - /// Indices into the input SDR. Length == potential_synapses. - pub inputs: Vec, - /// Permanence for each potential synapse (same length as `inputs`). - pub perms: Vec, -} - -pub struct SpatialPoolerConfig { - pub input_bits: usize, - pub n_columns: usize, - /// Size of the random input sample per column. - pub potential_radius: usize, - /// Number of potential synapses per column's proximal dendrite. - pub potential_synapses: usize, - pub connected_threshold: f32, - pub syn_perm_active_inc: f32, - pub syn_perm_inactive_dec: f32, - /// Target fraction of columns active per step (e.g. 0.02 for 2%). - pub sparsity: f32, - /// Duty cycle EMA period. - pub duty_cycle_period: f32, - /// Boost strength. Set to 0.0 to disable boosting. - pub boost_strength: f32, - /// Initial permanence span around the connected threshold. - pub init_perm_span: f32, -} - -impl Default for SpatialPoolerConfig { - fn default() -> Self { - Self { - input_bits: 16384, - n_columns: 2048, - potential_radius: 1024, - potential_synapses: 40, - connected_threshold: 0.5, - syn_perm_active_inc: 0.04, - syn_perm_inactive_dec: 0.008, - sparsity: 0.02, - duty_cycle_period: 1000.0, - boost_strength: 1.0, - init_perm_span: 0.1, - } - } -} - -pub struct SpatialPooler { - pub cfg: SpatialPoolerConfig, - pub columns: Vec, - /// Exponential moving average of "column was active" per step. - pub active_duty_cycle: Vec, - /// Exponential moving average of "overlap exceeded threshold" per step. - pub overlap_duty_cycle: Vec, - /// Boost factor per column. - pub boost: Vec, - rng: Xoshiro256PlusPlus, - iter_count: u64, -} - -impl SpatialPooler { - pub fn new(cfg: SpatialPoolerConfig, seed: u64) -> Self { - assert!(cfg.input_bits >= cfg.potential_radius, - "input_bits ({}) must be >= potential_radius ({})", - cfg.input_bits, cfg.potential_radius); - assert!(cfg.potential_radius >= cfg.potential_synapses, - "potential_radius ({}) must be >= potential_synapses ({})", - cfg.potential_radius, cfg.potential_synapses); - - let mut rng = Xoshiro256PlusPlus::seed_from_u64(seed); - - let mut columns = Vec::with_capacity(cfg.n_columns); - for _ in 0..cfg.n_columns { - // Sample `potential_radius` distinct input indices, then from those - // pick `potential_synapses` as the actual proximal synapses. - // Using partial Fisher-Yates via shuffle on a pool index range. - let mut pool: Vec = (0..cfg.input_bits as u32).collect(); - // Efficient partial shuffle: swap the first `potential_radius` - // items with random items from the rest (Durstenfeld step). - for i in 0..cfg.potential_radius.min(pool.len()) { - let j = rng.gen_range(i..pool.len()); - pool.swap(i, j); - } - let window = &mut pool[..cfg.potential_radius]; - window.shuffle(&mut rng); - let mut inputs: Vec = window[..cfg.potential_synapses].to_vec(); - inputs.sort_unstable(); - - let perms: Vec = (0..cfg.potential_synapses) - .map(|_| { - let delta: f32 = rng.gen_range(-cfg.init_perm_span..cfg.init_perm_span); - (cfg.connected_threshold + delta).clamp(0.0, 1.0) - }) - .collect(); - - columns.push(ProximalDendrite { inputs, perms }); - } - - let n = cfg.n_columns; - Self { - cfg, - columns, - active_duty_cycle: vec![0.0; n], - overlap_duty_cycle: vec![0.0; n], - boost: vec![1.0; n], - rng, - iter_count: 0, - } - } - - /// Process one step: compute overlaps, inhibit, learn (if `learn`), update - /// duty cycles and boosts. Returns the set of active column indices. - pub fn compute(&mut self, input: &[bool], learn: bool) -> Vec { - assert_eq!(input.len(), self.cfg.input_bits); - - // 1) Overlap score per column (sum of CONNECTED synapses onto active inputs). - // Also track raw overlap for the overlap-duty-cycle. - let n = self.cfg.n_columns; - let mut overlaps: Vec = vec![0.0; n]; - let mut raw_overlaps: Vec = vec![0; n]; - - for (ci, col) in self.columns.iter().enumerate() { - let mut s: u32 = 0; - for (syn_i, &inp) in col.inputs.iter().enumerate() { - if input[inp as usize] && col.perms[syn_i] >= self.cfg.connected_threshold { - s += 1; - } - } - raw_overlaps[ci] = s; - overlaps[ci] = (s as f32) * self.boost[ci]; - } - - // 2) Global k-WTA inhibition. Select top-k columns by boosted overlap. - let k = ((self.cfg.sparsity * n as f32).round() as usize).max(1); - let active: Vec = top_k(&overlaps, k); - - // 3) Hebbian learning on active columns. - if learn { - for &ci in &active { - let col = &mut self.columns[ci as usize]; - for (syn_i, &inp) in col.inputs.iter().enumerate() { - if input[inp as usize] { - col.perms[syn_i] = - (col.perms[syn_i] + self.cfg.syn_perm_active_inc).min(1.0); - } else { - col.perms[syn_i] = - (col.perms[syn_i] - self.cfg.syn_perm_inactive_dec).max(0.0); - } - } - } - } - - // 4) Update duty cycles (EMA with period T -> alpha = 1/T). - let period = self.cfg.duty_cycle_period.max(1.0); - let alpha = 1.0 / period; - // Column is "overlapping enough" if raw overlap >= stimulus_threshold. - // Numenta uses min_overlap; we use 1 as a conservative floor. - let stimulus_threshold = 1.0_f32; - - // Mark active columns. - let mut active_mask = vec![false; n]; - for &ci in &active { - active_mask[ci as usize] = true; - } - - for i in 0..n { - let active_sample = if active_mask[i] { 1.0 } else { 0.0 }; - let overlap_sample = if (raw_overlaps[i] as f32) >= stimulus_threshold { - 1.0 - } else { - 0.0 - }; - self.active_duty_cycle[i] = - (1.0 - alpha) * self.active_duty_cycle[i] + alpha * active_sample; - self.overlap_duty_cycle[i] = - (1.0 - alpha) * self.overlap_duty_cycle[i] + alpha * overlap_sample; - } - - // 5) Boost factor: b_i = exp(-boost_strength * (duty_i - mean_duty)). - // Under-used columns (duty < mean) get boost > 1. - if learn && self.cfg.boost_strength > 0.0 { - let mean_duty: f32 = - self.active_duty_cycle.iter().sum::() / (n as f32); - for i in 0..n { - self.boost[i] = - (-self.cfg.boost_strength * (self.active_duty_cycle[i] - mean_duty)).exp(); - } - - // 6) Permanence bump for chronically under-stimulated columns. - // If overlap_duty_cycle[i] < min_pct_overlap * max_duty_in_neighborhood, - // bump all permanences by syn_perm_active_inc * 0.1. - // With global inhibition, "neighborhood" = all columns. - let max_overlap_duty = self - .overlap_duty_cycle - .iter() - .cloned() - .fold(0.0_f32, f32::max); - let min_pct_overlap_duty = 0.001_f32 * max_overlap_duty; - if max_overlap_duty > 0.0 { - for i in 0..n { - if self.overlap_duty_cycle[i] < min_pct_overlap_duty { - for p in &mut self.columns[i].perms { - *p = (*p + self.cfg.syn_perm_active_inc * 0.1).min(1.0); - } - } - } - } - } - - self.iter_count = self.iter_count.wrapping_add(1); - let _ = &mut self.rng; // suppress unused-mut when learn=false - active - } -} - -/// Return the indices of the top-k values in `scores`. -/// Ties broken by index order. Output is sorted ascending. -fn top_k(scores: &[f32], k: usize) -> Vec { - if k == 0 { - return Vec::new(); - } - let mut idx: Vec = (0..scores.len() as u32).collect(); - // Partial sort: put top-k at the front by descending score. - // Use select_nth_unstable_by on (desc score, asc index). - idx.select_nth_unstable_by(k - 1, |&a, &b| { - let sa = scores[a as usize]; - let sb = scores[b as usize]; - // Reverse for descending. - match sb.partial_cmp(&sa).unwrap_or(std::cmp::Ordering::Equal) { - std::cmp::Ordering::Equal => a.cmp(&b), - ord => ord, - } - }); - let mut winners: Vec = idx[..k].to_vec(); - winners.sort_unstable(); - winners -} - -// --------------------------------------------------------------------------- -// Tests -// --------------------------------------------------------------------------- - -#[cfg(test)] -mod tests { - use super::*; - use rand::Rng; - use rand::SeedableRng; - use rand_xoshiro::Xoshiro256PlusPlus; - - #[test] - fn sp_sparsity_exact_2pct() { - // BAMI says "top ~2%"; with 2048 columns that's round(0.02*2048) = 41. - // The SP must produce *exactly* that count, no more, no less, and with - // no duplicate indices. - let cfg = SpatialPoolerConfig::default(); - let expected_k = (cfg.sparsity * cfg.n_columns as f32).round() as usize; - assert!(expected_k > 0); - - let input_bits = cfg.input_bits; - let mut sp = SpatialPooler::new(cfg, 42); - let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); - - for _ in 0..100 { - // 2% sparse random input SDR. - let on_bits = (0.02 * input_bits as f32) as usize; - let mut sdr = vec![false; input_bits]; - for _ in 0..on_bits { - let i = rng.gen_range(0..input_bits); - sdr[i] = true; - } - let active = sp.compute(&sdr, true); - assert_eq!( - active.len(), - expected_k, - "SP must emit exactly {expected_k} active columns" - ); - let mut a = active.clone(); - a.sort_unstable(); - a.dedup(); - assert_eq!(a.len(), expected_k); - } - } -} +//! Numenta BAMI-spec Spatial Pooler. +//! +//! Implements: +//! - 2048 (configurable) mini-columns with proximal dendrites +//! - `potential_synapses` (default 40) synapses per column sampled from +//! `potential_radius` (default 1024) random input bits +//! - Permanence in [0.0, 1.0] (f32), connected_threshold = 0.5 +//! - syn_perm_active_inc = +0.04, syn_perm_inactive_dec = -0.008 +//! - Global k-WTA inhibition (top `sparsity` fraction of columns) +//! - Boost factor with exponential duty-cycle tracking (Numenta formula) +//! +//! Reference: BAMI "Spatial Pooling Algorithm Details" (Numenta, 2017). + +use rand::Rng; +use rand::SeedableRng; +use rand::seq::SliceRandom; +use rand_xoshiro::Xoshiro256PlusPlus; + +/// A single proximal dendrite: a sparse set of potential synapses onto +/// specific input bit indices, with per-synapse permanence values. +#[derive(Clone)] +pub struct ProximalDendrite { + /// Indices into the input SDR. Length == potential_synapses. + pub inputs: Vec, + /// Permanence for each potential synapse (same length as `inputs`). + pub perms: Vec, +} + +pub struct SpatialPoolerConfig { + pub input_bits: usize, + pub n_columns: usize, + /// Size of the random input sample per column. + pub potential_radius: usize, + /// Number of potential synapses per column's proximal dendrite. + pub potential_synapses: usize, + pub connected_threshold: f32, + pub syn_perm_active_inc: f32, + pub syn_perm_inactive_dec: f32, + /// Target fraction of columns active per step (e.g. 0.02 for 2%). + pub sparsity: f32, + /// Duty cycle EMA period. + pub duty_cycle_period: f32, + /// Boost strength. Set to 0.0 to disable boosting. + pub boost_strength: f32, + /// Initial permanence span around the connected threshold. + pub init_perm_span: f32, +} + +impl Default for SpatialPoolerConfig { + fn default() -> Self { + Self { + input_bits: 16384, + n_columns: 2048, + potential_radius: 1024, + potential_synapses: 40, + connected_threshold: 0.5, + syn_perm_active_inc: 0.04, + syn_perm_inactive_dec: 0.008, + sparsity: 0.02, + duty_cycle_period: 1000.0, + boost_strength: 1.0, + init_perm_span: 0.1, + } + } +} + +pub struct SpatialPooler { + pub cfg: SpatialPoolerConfig, + pub columns: Vec, + /// Exponential moving average of "column was active" per step. + pub active_duty_cycle: Vec, + /// Exponential moving average of "overlap exceeded threshold" per step. + pub overlap_duty_cycle: Vec, + /// Boost factor per column. + pub boost: Vec, + rng: Xoshiro256PlusPlus, + iter_count: u64, +} + +impl SpatialPooler { + pub fn new(cfg: SpatialPoolerConfig, seed: u64) -> Self { + assert!(cfg.input_bits >= cfg.potential_radius, + "input_bits ({}) must be >= potential_radius ({})", + cfg.input_bits, cfg.potential_radius); + assert!(cfg.potential_radius >= cfg.potential_synapses, + "potential_radius ({}) must be >= potential_synapses ({})", + cfg.potential_radius, cfg.potential_synapses); + + let mut rng = Xoshiro256PlusPlus::seed_from_u64(seed); + + let mut columns = Vec::with_capacity(cfg.n_columns); + for _ in 0..cfg.n_columns { + // Sample `potential_radius` distinct input indices, then from those + // pick `potential_synapses` as the actual proximal synapses. + // Using partial Fisher-Yates via shuffle on a pool index range. + let mut pool: Vec = (0..cfg.input_bits as u32).collect(); + // Efficient partial shuffle: swap the first `potential_radius` + // items with random items from the rest (Durstenfeld step). + for i in 0..cfg.potential_radius.min(pool.len()) { + let j = rng.gen_range(i..pool.len()); + pool.swap(i, j); + } + let window = &mut pool[..cfg.potential_radius]; + window.shuffle(&mut rng); + let mut inputs: Vec = window[..cfg.potential_synapses].to_vec(); + inputs.sort_unstable(); + + let perms: Vec = (0..cfg.potential_synapses) + .map(|_| { + let delta: f32 = rng.gen_range(-cfg.init_perm_span..cfg.init_perm_span); + (cfg.connected_threshold + delta).clamp(0.0, 1.0) + }) + .collect(); + + columns.push(ProximalDendrite { inputs, perms }); + } + + let n = cfg.n_columns; + Self { + cfg, + columns, + active_duty_cycle: vec![0.0; n], + overlap_duty_cycle: vec![0.0; n], + boost: vec![1.0; n], + rng, + iter_count: 0, + } + } + + /// Process one step: compute overlaps, inhibit, learn (if `learn`), update + /// duty cycles and boosts. Returns the set of active column indices. + pub fn compute(&mut self, input: &[bool], learn: bool) -> Vec { + assert_eq!(input.len(), self.cfg.input_bits); + + // 1) Overlap score per column (sum of CONNECTED synapses onto active inputs). + // Also track raw overlap for the overlap-duty-cycle. + let n = self.cfg.n_columns; + let mut overlaps: Vec = vec![0.0; n]; + let mut raw_overlaps: Vec = vec![0; n]; + + for (ci, col) in self.columns.iter().enumerate() { + let mut s: u32 = 0; + for (syn_i, &inp) in col.inputs.iter().enumerate() { + if input[inp as usize] && col.perms[syn_i] >= self.cfg.connected_threshold { + s += 1; + } + } + raw_overlaps[ci] = s; + overlaps[ci] = (s as f32) * self.boost[ci]; + } + + // 2) Global k-WTA inhibition. Select top-k columns by boosted overlap. + let k = ((self.cfg.sparsity * n as f32).round() as usize).max(1); + let active: Vec = top_k(&overlaps, k); + + // 3) Hebbian learning on active columns. + if learn { + for &ci in &active { + let col = &mut self.columns[ci as usize]; + for (syn_i, &inp) in col.inputs.iter().enumerate() { + if input[inp as usize] { + col.perms[syn_i] = + (col.perms[syn_i] + self.cfg.syn_perm_active_inc).min(1.0); + } else { + col.perms[syn_i] = + (col.perms[syn_i] - self.cfg.syn_perm_inactive_dec).max(0.0); + } + } + } + } + + // 4) Update duty cycles (EMA with period T -> alpha = 1/T). + let period = self.cfg.duty_cycle_period.max(1.0); + let alpha = 1.0 / period; + // Column is "overlapping enough" if raw overlap >= stimulus_threshold. + // Numenta uses min_overlap; we use 1 as a conservative floor. + let stimulus_threshold = 1.0_f32; + + // Mark active columns. + let mut active_mask = vec![false; n]; + for &ci in &active { + active_mask[ci as usize] = true; + } + + for i in 0..n { + let active_sample = if active_mask[i] { 1.0 } else { 0.0 }; + let overlap_sample = if (raw_overlaps[i] as f32) >= stimulus_threshold { + 1.0 + } else { + 0.0 + }; + self.active_duty_cycle[i] = + (1.0 - alpha) * self.active_duty_cycle[i] + alpha * active_sample; + self.overlap_duty_cycle[i] = + (1.0 - alpha) * self.overlap_duty_cycle[i] + alpha * overlap_sample; + } + + // 5) Boost factor: b_i = exp(-boost_strength * (duty_i - mean_duty)). + // Under-used columns (duty < mean) get boost > 1. + if learn && self.cfg.boost_strength > 0.0 { + let mean_duty: f32 = + self.active_duty_cycle.iter().sum::() / (n as f32); + for i in 0..n { + self.boost[i] = + (-self.cfg.boost_strength * (self.active_duty_cycle[i] - mean_duty)).exp(); + } + + // 6) Permanence bump for chronically under-stimulated columns. + // If overlap_duty_cycle[i] < min_pct_overlap * max_duty_in_neighborhood, + // bump all permanences by syn_perm_active_inc * 0.1. + // With global inhibition, "neighborhood" = all columns. + let max_overlap_duty = self + .overlap_duty_cycle + .iter() + .cloned() + .fold(0.0_f32, f32::max); + let min_pct_overlap_duty = 0.001_f32 * max_overlap_duty; + if max_overlap_duty > 0.0 { + for i in 0..n { + if self.overlap_duty_cycle[i] < min_pct_overlap_duty { + for p in &mut self.columns[i].perms { + *p = (*p + self.cfg.syn_perm_active_inc * 0.1).min(1.0); + } + } + } + } + } + + self.iter_count = self.iter_count.wrapping_add(1); + let _ = &mut self.rng; // suppress unused-mut when learn=false + active + } +} + +/// Return the indices of the top-k values in `scores`. +/// Ties broken by index order. Output is sorted ascending. +fn top_k(scores: &[f32], k: usize) -> Vec { + if k == 0 { + return Vec::new(); + } + let mut idx: Vec = (0..scores.len() as u32).collect(); + // Partial sort: put top-k at the front by descending score. + // Use select_nth_unstable_by on (desc score, asc index). + idx.select_nth_unstable_by(k - 1, |&a, &b| { + let sa = scores[a as usize]; + let sb = scores[b as usize]; + // Reverse for descending. + match sb.partial_cmp(&sa).unwrap_or(std::cmp::Ordering::Equal) { + std::cmp::Ordering::Equal => a.cmp(&b), + ord => ord, + } + }); + let mut winners: Vec = idx[..k].to_vec(); + winners.sort_unstable(); + winners +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +#[cfg(test)] +mod tests { + use super::*; + use rand::Rng; + use rand::SeedableRng; + use rand_xoshiro::Xoshiro256PlusPlus; + + #[test] + fn sp_sparsity_exact_2pct() { + // BAMI says "top ~2%"; with 2048 columns that's round(0.02*2048) = 41. + // The SP must produce *exactly* that count, no more, no less, and with + // no duplicate indices. + let cfg = SpatialPoolerConfig::default(); + let expected_k = (cfg.sparsity * cfg.n_columns as f32).round() as usize; + assert!(expected_k > 0); + + let input_bits = cfg.input_bits; + let mut sp = SpatialPooler::new(cfg, 42); + let mut rng = Xoshiro256PlusPlus::seed_from_u64(7); + + for _ in 0..100 { + // 2% sparse random input SDR. + let on_bits = (0.02 * input_bits as f32) as usize; + let mut sdr = vec![false; input_bits]; + for _ in 0..on_bits { + let i = rng.gen_range(0..input_bits); + sdr[i] = true; + } + let active = sp.compute(&sdr, true); + assert_eq!( + active.len(), + expected_k, + "SP must emit exactly {expected_k} active columns" + ); + let mut a = active.clone(); + a.sort_unstable(); + a.dedup(); + assert_eq!(a.len(), expected_k); + } + } +} diff --git a/overlay/htm_rust/src/tm.rs b/overlay/htm_rust/src/tm.rs index 7d5b0b4646b0bf9e86b237ed3846663e7b56a7c7..59ee0c1cc7017bc3499706c32642ffa52a6a1e25 100644 --- a/overlay/htm_rust/src/tm.rs +++ b/overlay/htm_rust/src/tm.rs @@ -1,545 +1,545 @@ -//! Numenta BAMI-spec Temporal Memory. -//! -//! Key parameters (Numenta defaults): -//! - cells_per_column = 32 -//! - max_segments_per_cell = 255 -//! - max_synapses_per_segment = 32 -//! - activation_threshold = 15 (CONNECTED synapses onto active cells) -//! - learning_threshold = 13 (POTENTIAL synapses onto active cells) -//! (often called `minThreshold` / match threshold in BAMI) -//! - initial_permanence = 0.21 -//! - connected_permanence = 0.50 -//! - permanence_increment = 0.10 -//! - permanence_decrement = 0.10 -//! - predicted_segment_decrement = 0.10 (decay for segments that predicted -//! inactive columns; called `predictedSegmentDecrement` in BAMI) -//! - max_new_synapse_count = 20 (max synapses to grow on a new/reinforced seg) -//! -//! Algorithm (one step): -//! Given `active_columns` from the Spatial Pooler, and segment activity -//! caches `active_segments` and `matching_segments` computed *at the end of -//! the previous step*: -//! -//! 1. For each active column: -//! - If it contains any predicted cell (any cell with an active segment -//! from the previous depolarization), mark those cells active and -//! learn on the segment that predicted it. -//! - Else BURST the column: mark all cells in it active, and grow a new -//! segment on the best-matching cell in the column (or, if none, -//! on the cell with the fewest segments). -//! 2. For every column that was predicted but did NOT become active -//! (matching segments on inactive columns), apply the -//! `predicted_segment_decrement` decay so spurious predictions fade. -//! 3. Winner cells = active cells chosen for learning (1 per active column). -//! 4. Compute segment activity for NEXT step: -//! - A segment's CONNECTED activity = #synapses with perm >= connected_perm -//! whose presynaptic cell is in `active_cells`. If >= activation_threshold -//! -> segment is "active" -> its cell is "predicted". -//! - A segment's POTENTIAL activity = #synapses whose presynaptic cell is -//! in `active_cells` (regardless of permanence). If >= learning_threshold -//! -> segment is "matching". -//! -//! Anomaly score = (active columns with no prior predicted cells) -//! / (# active columns). - -use rand::Rng; -use rand::SeedableRng; -use rand_xoshiro::Xoshiro256PlusPlus; - -type CellIdx = u32; -type SegmentIdx = u32; - -#[derive(Clone)] -pub struct Synapse { - pub presynaptic_cell: CellIdx, - pub permanence: f32, -} - -#[derive(Clone)] -pub struct Segment { - pub cell: CellIdx, - pub synapses: Vec, - /// Cached counters; recomputed each step. - pub num_active_connected: u32, - pub num_active_potential: u32, - /// Simple "last iter touched" stat for least-used cell selection. - pub last_used_iteration: u64, -} - -pub struct TemporalMemoryConfig { - pub n_columns: usize, - pub cells_per_column: usize, - pub activation_threshold: u32, - pub learning_threshold: u32, - pub initial_permanence: f32, - pub connected_permanence: f32, - pub permanence_increment: f32, - pub permanence_decrement: f32, - pub predicted_segment_decrement: f32, - pub max_segments_per_cell: usize, - pub max_synapses_per_segment: usize, - pub max_new_synapse_count: usize, -} - -impl Default for TemporalMemoryConfig { - fn default() -> Self { - Self { - n_columns: 2048, - cells_per_column: 32, - activation_threshold: 15, - learning_threshold: 13, - initial_permanence: 0.21, - connected_permanence: 0.50, - permanence_increment: 0.10, - permanence_decrement: 0.10, - predicted_segment_decrement: 0.10, - max_segments_per_cell: 255, - max_synapses_per_segment: 32, - max_new_synapse_count: 20, - } - } -} - -pub struct TemporalMemory { - pub cfg: TemporalMemoryConfig, - /// All segments in the region. Indexed by SegmentIdx. - pub segments: Vec, - /// For each cell, the list of segments that belong to it. - pub cell_segments: Vec>, - /// Active cells in the current step. - pub active_cells: Vec, - /// Winner cells (subset of active_cells, 1 per active column) for learning. - pub winner_cells: Vec, - /// Predictive cells for the current step = cells whose segment became - /// active at the end of the previous step. - pub predictive_cells: Vec, - /// Cached list of segment indices that were "active" last compute(). - active_segments_prev: Vec, - /// Cached list of segment indices that were "matching" last compute(). - matching_segments_prev: Vec, - rng: Xoshiro256PlusPlus, - iter_count: u64, -} - -impl TemporalMemory { - pub fn new(cfg: TemporalMemoryConfig, seed: u64) -> Self { - let total = cfg.n_columns * cfg.cells_per_column; - Self { - cell_segments: vec![Vec::new(); total], - active_cells: vec![false; total], - winner_cells: vec![false; total], - predictive_cells: vec![false; total], - cfg, - segments: Vec::new(), - active_segments_prev: Vec::new(), - matching_segments_prev: Vec::new(), - rng: Xoshiro256PlusPlus::seed_from_u64(seed), - iter_count: 0, - } - } - - pub fn reset(&mut self) { - for v in self.active_cells.iter_mut() { *v = false; } - for v in self.winner_cells.iter_mut() { *v = false; } - for v in self.predictive_cells.iter_mut() { *v = false; } - self.active_segments_prev.clear(); - self.matching_segments_prev.clear(); - } - - #[inline] - fn col_of(&self, cell: CellIdx) -> usize { - (cell as usize) / self.cfg.cells_per_column - } - - #[inline] - fn cells_in_col(&self, col: usize) -> std::ops::Range { - let base = (col * self.cfg.cells_per_column) as CellIdx; - base..(base + self.cfg.cells_per_column as CellIdx) - } - - /// Process one step. - /// - /// `active_columns` is the set of column indices activated by the Spatial - /// Pooler this step. Returns the anomaly score in [0, 1]. - pub fn compute(&mut self, active_columns: &[u32], learn: bool) -> f32 { - self.iter_count = self.iter_count.wrapping_add(1); - - // Snapshot previous-step cell activity (for learning on segments). - let prev_active_cells = self.active_cells.clone(); - let prev_winner_cells = self.winner_cells.clone(); - - // Move current "predictive" (computed at the end of the last step) - // into local variables; we'll overwrite predictive_cells later. - let predictive_prev = self.predictive_cells.clone(); - - // Group active segments and matching segments by column of their - // owning cell, for the columns that are active this step. - let n_cols = self.cfg.n_columns; - - // active_segs_by_col[col] = segment indices whose cell is in col and - // which were "active" in the previous depolarization. - // matching_segs_by_col[col] = similarly for "matching". - let mut active_segs_by_col: Vec> = vec![Vec::new(); n_cols]; - let mut matching_segs_by_col: Vec> = vec![Vec::new(); n_cols]; - for &seg in &self.active_segments_prev { - let col = self.col_of(self.segments[seg as usize].cell); - active_segs_by_col[col].push(seg); - } - for &seg in &self.matching_segments_prev { - let col = self.col_of(self.segments[seg as usize].cell); - matching_segs_by_col[col].push(seg); - } - - // Columns that are active this step (for O(1) lookup). - let mut active_col_mask = vec![false; n_cols]; - for &c in active_columns { active_col_mask[c as usize] = true; } - - // Zero out current cell activations. - for v in self.active_cells.iter_mut() { *v = false; } - for v in self.winner_cells.iter_mut() { *v = false; } - - // Track anomaly. - let mut unpredicted_cols = 0u32; - - // We'll collect (segment, learn_mode) pairs for segment reinforcement - // so we can batch-apply permanence adjustments using prev_active_cells. - // learn_mode: "reinforce_correctly_predicted", "punish_incorrectly_matched" - enum LearnOp { - Reinforce(SegmentIdx), // correctly predicted - Grow { // bursting column: grow on chosen segment - segment: SegmentIdx, - #[allow(dead_code)] - winner_cell: CellIdx, - }, - Punish(SegmentIdx), // matching segment on inactive column - } - let mut ops: Vec = Vec::new(); - - // ---- 1) Process active columns ---- - for &col in active_columns { - let col = col as usize; - let active_segs = &active_segs_by_col[col]; - if !active_segs.is_empty() { - // "Activate predicted column": each cell with an active segment - // becomes active and is a winner; reinforce that segment. - let mut seen_cells: Vec = Vec::new(); - for &seg_i in active_segs { - let seg = &self.segments[seg_i as usize]; - let cell = seg.cell; - if !seen_cells.contains(&cell) { - self.active_cells[cell as usize] = true; - self.winner_cells[cell as usize] = true; - seen_cells.push(cell); - } - if learn { - ops.push(LearnOp::Reinforce(seg_i)); - } - } - } else { - // ----- BURST ----- - unpredicted_cols += 1; - for c in self.cells_in_col(col) { - self.active_cells[c as usize] = true; - } - // Pick a winner cell + segment for learning. - if learn { - let matching = &matching_segs_by_col[col]; - let (winner_cell, target_segment) = if !matching.is_empty() { - // Best-matching segment = highest num_active_potential. - let mut best = matching[0]; - let mut best_score = self.segments[best as usize].num_active_potential; - for &s in &matching[1..] { - let score = self.segments[s as usize].num_active_potential; - if score > best_score { - best_score = score; - best = s; - } - } - let wc = self.segments[best as usize].cell; - (wc, Some(best)) - } else { - // Least-used cell in column, then grow a new segment. - let winner = self.least_used_cell(col); - (winner, None) - }; - self.winner_cells[winner_cell as usize] = true; - let segment_id = match target_segment { - Some(s) => s, - None => { - // Create a fresh empty segment on winner cell. - self.create_segment(winner_cell) - } - }; - ops.push(LearnOp::Grow { segment: segment_id, winner_cell }); - } else { - // No learning: still pick some winner cell (arbitrary) - // so downstream code that inspects winner_cells isn't empty. - let matching = &matching_segs_by_col[col]; - let winner_cell = if !matching.is_empty() { - self.segments[matching[0] as usize].cell - } else { - self.least_used_cell(col) - }; - self.winner_cells[winner_cell as usize] = true; - } - } - } - - // ---- 2) Punish matching segments on INACTIVE columns ---- - if learn && self.cfg.predicted_segment_decrement > 0.0 { - for &seg_i in &self.matching_segments_prev { - let col = self.col_of(self.segments[seg_i as usize].cell); - if !active_col_mask[col] { - ops.push(LearnOp::Punish(seg_i)); - } - } - } - - // ---- 3) Apply learning ---- - if learn { - for op in ops { - match op { - LearnOp::Reinforce(seg_i) => { - self.reinforce_segment(seg_i, &prev_active_cells); - // Optionally grow up to N new synapses to winner cells - // of the previous step. - self.grow_synapses_on_segment(seg_i, &prev_winner_cells); - } - LearnOp::Grow { segment, winner_cell: _ } => { - self.reinforce_segment(segment, &prev_active_cells); - self.grow_synapses_on_segment(segment, &prev_winner_cells); - } - LearnOp::Punish(seg_i) => { - let dec = self.cfg.predicted_segment_decrement; - for syn in &mut self.segments[seg_i as usize].synapses { - if prev_active_cells[syn.presynaptic_cell as usize] { - syn.permanence = (syn.permanence - dec).max(0.0); - } - } - } - } - } - } - - // ---- 4) Compute segment activity & predictive cells for NEXT step ---- - // We have to use the *current* active_cells (just set above). - let mut next_active_segs: Vec = Vec::new(); - let mut next_matching_segs: Vec = Vec::new(); - for v in self.predictive_cells.iter_mut() { *v = false; } - - let conn = self.cfg.connected_permanence; - let act_thr = self.cfg.activation_threshold; - let learn_thr = self.cfg.learning_threshold; - - for (seg_i, seg) in self.segments.iter_mut().enumerate() { - let mut n_conn: u32 = 0; - let mut n_pot: u32 = 0; - for syn in &seg.synapses { - if self.active_cells[syn.presynaptic_cell as usize] { - n_pot += 1; - if syn.permanence >= conn { n_conn += 1; } - } - } - seg.num_active_connected = n_conn; - seg.num_active_potential = n_pot; - if n_conn >= act_thr { - next_active_segs.push(seg_i as SegmentIdx); - self.predictive_cells[seg.cell as usize] = true; - } - if n_pot >= learn_thr { - next_matching_segs.push(seg_i as SegmentIdx); - } - } - self.active_segments_prev = next_active_segs; - self.matching_segments_prev = next_matching_segs; - - // Keep predictive_prev unused-guard; we no longer need it but - // retained to document intent. - let _ = predictive_prev; - - // Anomaly. - if active_columns.is_empty() { - 0.0 - } else { - (unpredicted_cols as f32) / (active_columns.len() as f32) - } - } - - /// Reinforce synapses on `seg`: +inc if presynaptic is active last step, - /// -dec otherwise. - fn reinforce_segment(&mut self, seg_i: SegmentIdx, prev_active_cells: &[bool]) { - let inc = self.cfg.permanence_increment; - let dec = self.cfg.permanence_decrement; - let seg = &mut self.segments[seg_i as usize]; - seg.last_used_iteration = self.iter_count; - for syn in &mut seg.synapses { - if prev_active_cells[syn.presynaptic_cell as usize] { - syn.permanence = (syn.permanence + inc).min(1.0); - } else { - syn.permanence = (syn.permanence - dec).max(0.0); - } - } - } - - /// Grow up to `max_new_synapse_count - current_potential` new synapses - /// from previous winner cells that are not already connected to this seg. - fn grow_synapses_on_segment( - &mut self, - seg_i: SegmentIdx, - prev_winner_cells: &[bool], - ) { - let initial_perm = self.cfg.initial_permanence; - let cap = self.cfg.max_synapses_per_segment; - let max_new = self.cfg.max_new_synapse_count; - - // Gather candidate cells (prev winners not already presynaptic to this seg). - let already: Vec = self.segments[seg_i as usize] - .synapses - .iter() - .map(|s| s.presynaptic_cell) - .collect(); - let mut candidates: Vec = Vec::new(); - for (cell_i, &b) in prev_winner_cells.iter().enumerate() { - if b && !already.contains(&(cell_i as CellIdx)) { - candidates.push(cell_i as CellIdx); - } - } - - // How many can we add? - let current_len = self.segments[seg_i as usize].synapses.len(); - let room = cap.saturating_sub(current_len); - let mut to_add = max_new.min(candidates.len()).min(room); - - // Random sample without replacement from candidates. - while to_add > 0 { - let idx = self.rng.gen_range(0..candidates.len()); - let pre = candidates.swap_remove(idx); - self.segments[seg_i as usize].synapses.push(Synapse { - presynaptic_cell: pre, - permanence: initial_perm, - }); - to_add -= 1; - } - } - - fn create_segment(&mut self, cell: CellIdx) -> SegmentIdx { - // Enforce per-cell segment cap by evicting least-recently-used segment - // if necessary. - let cell_segs = &mut self.cell_segments[cell as usize]; - if cell_segs.len() >= self.cfg.max_segments_per_cell { - // Find LRU segment. - let (lru_pos, &lru_id) = cell_segs - .iter() - .enumerate() - .min_by_key(|(_, &sid)| self.segments[sid as usize].last_used_iteration) - .expect("cell_segs non-empty"); - // Clear that segment in place and reuse its index. - self.segments[lru_id as usize].synapses.clear(); - self.segments[lru_id as usize].num_active_connected = 0; - self.segments[lru_id as usize].num_active_potential = 0; - self.segments[lru_id as usize].last_used_iteration = self.iter_count; - // Keep at same position in cell_segs. - let _ = lru_pos; - return lru_id; - } - - let new_id = self.segments.len() as SegmentIdx; - self.segments.push(Segment { - cell, - synapses: Vec::with_capacity(self.cfg.max_new_synapse_count), - num_active_connected: 0, - num_active_potential: 0, - last_used_iteration: self.iter_count, - }); - cell_segs.push(new_id); - new_id - } - - fn least_used_cell(&mut self, col: usize) -> CellIdx { - // Cell with the fewest segments; break ties randomly. - let mut min_segs = usize::MAX; - let mut candidates: Vec = Vec::new(); - for c in self.cells_in_col(col) { - let n = self.cell_segments[c as usize].len(); - if n < min_segs { - min_segs = n; - candidates.clear(); - candidates.push(c); - } else if n == min_segs { - candidates.push(c); - } - } - let idx = self.rng.gen_range(0..candidates.len()); - candidates[idx] - } -} - -// --------------------------------------------------------------------------- -// Tests -// --------------------------------------------------------------------------- - -#[cfg(test)] -mod tests { - use super::*; - use crate::sp::{SpatialPooler, SpatialPoolerConfig}; - use rand::Rng; - use rand::SeedableRng; - use rand_xoshiro::Xoshiro256PlusPlus; - - #[test] - fn tm_learns_repeating_sequence() { - // Sequence A -> B -> C -> A -> B -> C -> ... should drive anomaly down. - let cfg = SpatialPoolerConfig::default(); - let mut sp = SpatialPooler::new(cfg, 123); - let mut tm = TemporalMemory::new(TemporalMemoryConfig::default(), 456); - - // Build 3 fixed random SDRs of 2% sparsity. - let mut rng = Xoshiro256PlusPlus::seed_from_u64(99); - let input_bits = sp.cfg.input_bits; - let make_sdr = |rng: &mut Xoshiro256PlusPlus| { - let mut v = vec![false; input_bits]; - let on = (0.02 * input_bits as f32) as usize; - let mut placed = 0; - while placed < on { - let i = rng.gen_range(0..input_bits); - if !v[i] { - v[i] = true; - placed += 1; - } - } - v - }; - let seqs = [make_sdr(&mut rng), make_sdr(&mut rng), make_sdr(&mut rng)]; - - // Warm up SP first so that columns are reliable for each symbol. - for _ in 0..200 { - for s in &seqs { - sp.compute(s, true); - } - } - - // Reset TM so prediction state is clean. - tm.reset(); - - // Record anomaly over a window early and late. - let mut early_anoms: Vec = Vec::new(); - let mut late_anoms: Vec = Vec::new(); - for iter in 0..250 { - for s in &seqs { - let active = sp.compute(s, false); - let anomaly = tm.compute(&active, true); - if iter == 10 { early_anoms.push(anomaly); } - if iter == 249 { late_anoms.push(anomaly); } - } - } - - let mean = |v: &[f32]| v.iter().sum::() / (v.len() as f32); - let early = mean(&early_anoms); - let late = mean(&late_anoms); - println!("early_anomaly={early}, late_anomaly={late}"); - assert!( - late < 0.5 * early + 1e-6, - "late anomaly ({late}) should be < 0.5 * early anomaly ({early})" - ); - } -} +//! Numenta BAMI-spec Temporal Memory. +//! +//! Key parameters (Numenta defaults): +//! - cells_per_column = 32 +//! - max_segments_per_cell = 255 +//! - max_synapses_per_segment = 32 +//! - activation_threshold = 15 (CONNECTED synapses onto active cells) +//! - learning_threshold = 13 (POTENTIAL synapses onto active cells) +//! (often called `minThreshold` / match threshold in BAMI) +//! - initial_permanence = 0.21 +//! - connected_permanence = 0.50 +//! - permanence_increment = 0.10 +//! - permanence_decrement = 0.10 +//! - predicted_segment_decrement = 0.10 (decay for segments that predicted +//! inactive columns; called `predictedSegmentDecrement` in BAMI) +//! - max_new_synapse_count = 20 (max synapses to grow on a new/reinforced seg) +//! +//! Algorithm (one step): +//! Given `active_columns` from the Spatial Pooler, and segment activity +//! caches `active_segments` and `matching_segments` computed *at the end of +//! the previous step*: +//! +//! 1. For each active column: +//! - If it contains any predicted cell (any cell with an active segment +//! from the previous depolarization), mark those cells active and +//! learn on the segment that predicted it. +//! - Else BURST the column: mark all cells in it active, and grow a new +//! segment on the best-matching cell in the column (or, if none, +//! on the cell with the fewest segments). +//! 2. For every column that was predicted but did NOT become active +//! (matching segments on inactive columns), apply the +//! `predicted_segment_decrement` decay so spurious predictions fade. +//! 3. Winner cells = active cells chosen for learning (1 per active column). +//! 4. Compute segment activity for NEXT step: +//! - A segment's CONNECTED activity = #synapses with perm >= connected_perm +//! whose presynaptic cell is in `active_cells`. If >= activation_threshold +//! -> segment is "active" -> its cell is "predicted". +//! - A segment's POTENTIAL activity = #synapses whose presynaptic cell is +//! in `active_cells` (regardless of permanence). If >= learning_threshold +//! -> segment is "matching". +//! +//! Anomaly score = (active columns with no prior predicted cells) +//! / (# active columns). + +use rand::Rng; +use rand::SeedableRng; +use rand_xoshiro::Xoshiro256PlusPlus; + +type CellIdx = u32; +type SegmentIdx = u32; + +#[derive(Clone)] +pub struct Synapse { + pub presynaptic_cell: CellIdx, + pub permanence: f32, +} + +#[derive(Clone)] +pub struct Segment { + pub cell: CellIdx, + pub synapses: Vec, + /// Cached counters; recomputed each step. + pub num_active_connected: u32, + pub num_active_potential: u32, + /// Simple "last iter touched" stat for least-used cell selection. + pub last_used_iteration: u64, +} + +pub struct TemporalMemoryConfig { + pub n_columns: usize, + pub cells_per_column: usize, + pub activation_threshold: u32, + pub learning_threshold: u32, + pub initial_permanence: f32, + pub connected_permanence: f32, + pub permanence_increment: f32, + pub permanence_decrement: f32, + pub predicted_segment_decrement: f32, + pub max_segments_per_cell: usize, + pub max_synapses_per_segment: usize, + pub max_new_synapse_count: usize, +} + +impl Default for TemporalMemoryConfig { + fn default() -> Self { + Self { + n_columns: 2048, + cells_per_column: 32, + activation_threshold: 15, + learning_threshold: 13, + initial_permanence: 0.21, + connected_permanence: 0.50, + permanence_increment: 0.10, + permanence_decrement: 0.10, + predicted_segment_decrement: 0.10, + max_segments_per_cell: 255, + max_synapses_per_segment: 32, + max_new_synapse_count: 20, + } + } +} + +pub struct TemporalMemory { + pub cfg: TemporalMemoryConfig, + /// All segments in the region. Indexed by SegmentIdx. + pub segments: Vec, + /// For each cell, the list of segments that belong to it. + pub cell_segments: Vec>, + /// Active cells in the current step. + pub active_cells: Vec, + /// Winner cells (subset of active_cells, 1 per active column) for learning. + pub winner_cells: Vec, + /// Predictive cells for the current step = cells whose segment became + /// active at the end of the previous step. + pub predictive_cells: Vec, + /// Cached list of segment indices that were "active" last compute(). + active_segments_prev: Vec, + /// Cached list of segment indices that were "matching" last compute(). + matching_segments_prev: Vec, + rng: Xoshiro256PlusPlus, + iter_count: u64, +} + +impl TemporalMemory { + pub fn new(cfg: TemporalMemoryConfig, seed: u64) -> Self { + let total = cfg.n_columns * cfg.cells_per_column; + Self { + cell_segments: vec![Vec::new(); total], + active_cells: vec![false; total], + winner_cells: vec![false; total], + predictive_cells: vec![false; total], + cfg, + segments: Vec::new(), + active_segments_prev: Vec::new(), + matching_segments_prev: Vec::new(), + rng: Xoshiro256PlusPlus::seed_from_u64(seed), + iter_count: 0, + } + } + + pub fn reset(&mut self) { + for v in self.active_cells.iter_mut() { *v = false; } + for v in self.winner_cells.iter_mut() { *v = false; } + for v in self.predictive_cells.iter_mut() { *v = false; } + self.active_segments_prev.clear(); + self.matching_segments_prev.clear(); + } + + #[inline] + fn col_of(&self, cell: CellIdx) -> usize { + (cell as usize) / self.cfg.cells_per_column + } + + #[inline] + fn cells_in_col(&self, col: usize) -> std::ops::Range { + let base = (col * self.cfg.cells_per_column) as CellIdx; + base..(base + self.cfg.cells_per_column as CellIdx) + } + + /// Process one step. + /// + /// `active_columns` is the set of column indices activated by the Spatial + /// Pooler this step. Returns the anomaly score in [0, 1]. + pub fn compute(&mut self, active_columns: &[u32], learn: bool) -> f32 { + self.iter_count = self.iter_count.wrapping_add(1); + + // Snapshot previous-step cell activity (for learning on segments). + let prev_active_cells = self.active_cells.clone(); + let prev_winner_cells = self.winner_cells.clone(); + + // Move current "predictive" (computed at the end of the last step) + // into local variables; we'll overwrite predictive_cells later. + let predictive_prev = self.predictive_cells.clone(); + + // Group active segments and matching segments by column of their + // owning cell, for the columns that are active this step. + let n_cols = self.cfg.n_columns; + + // active_segs_by_col[col] = segment indices whose cell is in col and + // which were "active" in the previous depolarization. + // matching_segs_by_col[col] = similarly for "matching". + let mut active_segs_by_col: Vec> = vec![Vec::new(); n_cols]; + let mut matching_segs_by_col: Vec> = vec![Vec::new(); n_cols]; + for &seg in &self.active_segments_prev { + let col = self.col_of(self.segments[seg as usize].cell); + active_segs_by_col[col].push(seg); + } + for &seg in &self.matching_segments_prev { + let col = self.col_of(self.segments[seg as usize].cell); + matching_segs_by_col[col].push(seg); + } + + // Columns that are active this step (for O(1) lookup). + let mut active_col_mask = vec![false; n_cols]; + for &c in active_columns { active_col_mask[c as usize] = true; } + + // Zero out current cell activations. + for v in self.active_cells.iter_mut() { *v = false; } + for v in self.winner_cells.iter_mut() { *v = false; } + + // Track anomaly. + let mut unpredicted_cols = 0u32; + + // We'll collect (segment, learn_mode) pairs for segment reinforcement + // so we can batch-apply permanence adjustments using prev_active_cells. + // learn_mode: "reinforce_correctly_predicted", "punish_incorrectly_matched" + enum LearnOp { + Reinforce(SegmentIdx), // correctly predicted + Grow { // bursting column: grow on chosen segment + segment: SegmentIdx, + #[allow(dead_code)] + winner_cell: CellIdx, + }, + Punish(SegmentIdx), // matching segment on inactive column + } + let mut ops: Vec = Vec::new(); + + // ---- 1) Process active columns ---- + for &col in active_columns { + let col = col as usize; + let active_segs = &active_segs_by_col[col]; + if !active_segs.is_empty() { + // "Activate predicted column": each cell with an active segment + // becomes active and is a winner; reinforce that segment. + let mut seen_cells: Vec = Vec::new(); + for &seg_i in active_segs { + let seg = &self.segments[seg_i as usize]; + let cell = seg.cell; + if !seen_cells.contains(&cell) { + self.active_cells[cell as usize] = true; + self.winner_cells[cell as usize] = true; + seen_cells.push(cell); + } + if learn { + ops.push(LearnOp::Reinforce(seg_i)); + } + } + } else { + // ----- BURST ----- + unpredicted_cols += 1; + for c in self.cells_in_col(col) { + self.active_cells[c as usize] = true; + } + // Pick a winner cell + segment for learning. + if learn { + let matching = &matching_segs_by_col[col]; + let (winner_cell, target_segment) = if !matching.is_empty() { + // Best-matching segment = highest num_active_potential. + let mut best = matching[0]; + let mut best_score = self.segments[best as usize].num_active_potential; + for &s in &matching[1..] { + let score = self.segments[s as usize].num_active_potential; + if score > best_score { + best_score = score; + best = s; + } + } + let wc = self.segments[best as usize].cell; + (wc, Some(best)) + } else { + // Least-used cell in column, then grow a new segment. + let winner = self.least_used_cell(col); + (winner, None) + }; + self.winner_cells[winner_cell as usize] = true; + let segment_id = match target_segment { + Some(s) => s, + None => { + // Create a fresh empty segment on winner cell. + self.create_segment(winner_cell) + } + }; + ops.push(LearnOp::Grow { segment: segment_id, winner_cell }); + } else { + // No learning: still pick some winner cell (arbitrary) + // so downstream code that inspects winner_cells isn't empty. + let matching = &matching_segs_by_col[col]; + let winner_cell = if !matching.is_empty() { + self.segments[matching[0] as usize].cell + } else { + self.least_used_cell(col) + }; + self.winner_cells[winner_cell as usize] = true; + } + } + } + + // ---- 2) Punish matching segments on INACTIVE columns ---- + if learn && self.cfg.predicted_segment_decrement > 0.0 { + for &seg_i in &self.matching_segments_prev { + let col = self.col_of(self.segments[seg_i as usize].cell); + if !active_col_mask[col] { + ops.push(LearnOp::Punish(seg_i)); + } + } + } + + // ---- 3) Apply learning ---- + if learn { + for op in ops { + match op { + LearnOp::Reinforce(seg_i) => { + self.reinforce_segment(seg_i, &prev_active_cells); + // Optionally grow up to N new synapses to winner cells + // of the previous step. + self.grow_synapses_on_segment(seg_i, &prev_winner_cells); + } + LearnOp::Grow { segment, winner_cell: _ } => { + self.reinforce_segment(segment, &prev_active_cells); + self.grow_synapses_on_segment(segment, &prev_winner_cells); + } + LearnOp::Punish(seg_i) => { + let dec = self.cfg.predicted_segment_decrement; + for syn in &mut self.segments[seg_i as usize].synapses { + if prev_active_cells[syn.presynaptic_cell as usize] { + syn.permanence = (syn.permanence - dec).max(0.0); + } + } + } + } + } + } + + // ---- 4) Compute segment activity & predictive cells for NEXT step ---- + // We have to use the *current* active_cells (just set above). + let mut next_active_segs: Vec = Vec::new(); + let mut next_matching_segs: Vec = Vec::new(); + for v in self.predictive_cells.iter_mut() { *v = false; } + + let conn = self.cfg.connected_permanence; + let act_thr = self.cfg.activation_threshold; + let learn_thr = self.cfg.learning_threshold; + + for (seg_i, seg) in self.segments.iter_mut().enumerate() { + let mut n_conn: u32 = 0; + let mut n_pot: u32 = 0; + for syn in &seg.synapses { + if self.active_cells[syn.presynaptic_cell as usize] { + n_pot += 1; + if syn.permanence >= conn { n_conn += 1; } + } + } + seg.num_active_connected = n_conn; + seg.num_active_potential = n_pot; + if n_conn >= act_thr { + next_active_segs.push(seg_i as SegmentIdx); + self.predictive_cells[seg.cell as usize] = true; + } + if n_pot >= learn_thr { + next_matching_segs.push(seg_i as SegmentIdx); + } + } + self.active_segments_prev = next_active_segs; + self.matching_segments_prev = next_matching_segs; + + // Keep predictive_prev unused-guard; we no longer need it but + // retained to document intent. + let _ = predictive_prev; + + // Anomaly. + if active_columns.is_empty() { + 0.0 + } else { + (unpredicted_cols as f32) / (active_columns.len() as f32) + } + } + + /// Reinforce synapses on `seg`: +inc if presynaptic is active last step, + /// -dec otherwise. + fn reinforce_segment(&mut self, seg_i: SegmentIdx, prev_active_cells: &[bool]) { + let inc = self.cfg.permanence_increment; + let dec = self.cfg.permanence_decrement; + let seg = &mut self.segments[seg_i as usize]; + seg.last_used_iteration = self.iter_count; + for syn in &mut seg.synapses { + if prev_active_cells[syn.presynaptic_cell as usize] { + syn.permanence = (syn.permanence + inc).min(1.0); + } else { + syn.permanence = (syn.permanence - dec).max(0.0); + } + } + } + + /// Grow up to `max_new_synapse_count - current_potential` new synapses + /// from previous winner cells that are not already connected to this seg. + fn grow_synapses_on_segment( + &mut self, + seg_i: SegmentIdx, + prev_winner_cells: &[bool], + ) { + let initial_perm = self.cfg.initial_permanence; + let cap = self.cfg.max_synapses_per_segment; + let max_new = self.cfg.max_new_synapse_count; + + // Gather candidate cells (prev winners not already presynaptic to this seg). + let already: Vec = self.segments[seg_i as usize] + .synapses + .iter() + .map(|s| s.presynaptic_cell) + .collect(); + let mut candidates: Vec = Vec::new(); + for (cell_i, &b) in prev_winner_cells.iter().enumerate() { + if b && !already.contains(&(cell_i as CellIdx)) { + candidates.push(cell_i as CellIdx); + } + } + + // How many can we add? + let current_len = self.segments[seg_i as usize].synapses.len(); + let room = cap.saturating_sub(current_len); + let mut to_add = max_new.min(candidates.len()).min(room); + + // Random sample without replacement from candidates. + while to_add > 0 { + let idx = self.rng.gen_range(0..candidates.len()); + let pre = candidates.swap_remove(idx); + self.segments[seg_i as usize].synapses.push(Synapse { + presynaptic_cell: pre, + permanence: initial_perm, + }); + to_add -= 1; + } + } + + fn create_segment(&mut self, cell: CellIdx) -> SegmentIdx { + // Enforce per-cell segment cap by evicting least-recently-used segment + // if necessary. + let cell_segs = &mut self.cell_segments[cell as usize]; + if cell_segs.len() >= self.cfg.max_segments_per_cell { + // Find LRU segment. + let (lru_pos, &lru_id) = cell_segs + .iter() + .enumerate() + .min_by_key(|(_, &sid)| self.segments[sid as usize].last_used_iteration) + .expect("cell_segs non-empty"); + // Clear that segment in place and reuse its index. + self.segments[lru_id as usize].synapses.clear(); + self.segments[lru_id as usize].num_active_connected = 0; + self.segments[lru_id as usize].num_active_potential = 0; + self.segments[lru_id as usize].last_used_iteration = self.iter_count; + // Keep at same position in cell_segs. + let _ = lru_pos; + return lru_id; + } + + let new_id = self.segments.len() as SegmentIdx; + self.segments.push(Segment { + cell, + synapses: Vec::with_capacity(self.cfg.max_new_synapse_count), + num_active_connected: 0, + num_active_potential: 0, + last_used_iteration: self.iter_count, + }); + cell_segs.push(new_id); + new_id + } + + fn least_used_cell(&mut self, col: usize) -> CellIdx { + // Cell with the fewest segments; break ties randomly. + let mut min_segs = usize::MAX; + let mut candidates: Vec = Vec::new(); + for c in self.cells_in_col(col) { + let n = self.cell_segments[c as usize].len(); + if n < min_segs { + min_segs = n; + candidates.clear(); + candidates.push(c); + } else if n == min_segs { + candidates.push(c); + } + } + let idx = self.rng.gen_range(0..candidates.len()); + candidates[idx] + } +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +#[cfg(test)] +mod tests { + use super::*; + use crate::sp::{SpatialPooler, SpatialPoolerConfig}; + use rand::Rng; + use rand::SeedableRng; + use rand_xoshiro::Xoshiro256PlusPlus; + + #[test] + fn tm_learns_repeating_sequence() { + // Sequence A -> B -> C -> A -> B -> C -> ... should drive anomaly down. + let cfg = SpatialPoolerConfig::default(); + let mut sp = SpatialPooler::new(cfg, 123); + let mut tm = TemporalMemory::new(TemporalMemoryConfig::default(), 456); + + // Build 3 fixed random SDRs of 2% sparsity. + let mut rng = Xoshiro256PlusPlus::seed_from_u64(99); + let input_bits = sp.cfg.input_bits; + let make_sdr = |rng: &mut Xoshiro256PlusPlus| { + let mut v = vec![false; input_bits]; + let on = (0.02 * input_bits as f32) as usize; + let mut placed = 0; + while placed < on { + let i = rng.gen_range(0..input_bits); + if !v[i] { + v[i] = true; + placed += 1; + } + } + v + }; + let seqs = [make_sdr(&mut rng), make_sdr(&mut rng), make_sdr(&mut rng)]; + + // Warm up SP first so that columns are reliable for each symbol. + for _ in 0..200 { + for s in &seqs { + sp.compute(s, true); + } + } + + // Reset TM so prediction state is clean. + tm.reset(); + + // Record anomaly over a window early and late. + let mut early_anoms: Vec = Vec::new(); + let mut late_anoms: Vec = Vec::new(); + for iter in 0..250 { + for s in &seqs { + let active = sp.compute(s, false); + let anomaly = tm.compute(&active, true); + if iter == 10 { early_anoms.push(anomaly); } + if iter == 249 { late_anoms.push(anomaly); } + } + } + + let mean = |v: &[f32]| v.iter().sum::() / (v.len() as f32); + let early = mean(&early_anoms); + let late = mean(&late_anoms); + println!("early_anomaly={early}, late_anomaly={late}"); + assert!( + late < 0.5 * early + 1e-6, + "late anomaly ({late}) should be < 0.5 * early anomaly ({early})" + ); + } +} diff --git a/overlay/hydra/__init__.py b/overlay/hydra/__init__.py index 0e1802ff18de165458363696edfde608d09c36a5..e595533413c41d6cac7d80c6af241b5846d096b3 100644 --- a/overlay/hydra/__init__.py +++ b/overlay/hydra/__init__.py @@ -1,31 +1,37 @@ -"""HYDRA training package. - -Thin facade re-exporting the public API used by train.py, the test suite, -and external research scripts. Imports are lazy where possible to keep -`import hydra` cheap (prepare.py and mamba-ssm are the heavy deps). -""" - -from hydra.config import PostSemClawConfig -from hydra.engram import GPUEngram -from hydra.model import PostSemClawModel, norm -from hydra.optimizer import MuonAdamW, adamw_step_fused, muon_step_fused - -# config_from_dict is imported lazily (via attribute access on hydra.training) -# to keep `import hydra` cheap; re-export here for convenience. -def __getattr__(name: str): - if name == "config_from_dict": - from hydra.training import config_from_dict as _cfd - return _cfd - raise AttributeError(name) - - -__all__ = [ - "PostSemClawConfig", - "GPUEngram", - "PostSemClawModel", - "norm", - "MuonAdamW", - "adamw_step_fused", - "muon_step_fused", - "config_from_dict", -] +"""HYDRA training package. + +Thin facade re-exporting the public API used by train.py, the test suite, +and external research scripts. Imports are lazy where possible to keep +`import hydra` cheap (prepare.py and mamba-ssm are the heavy deps). +""" + +from hydra.config import PostSemClawConfig +from hydra.engram import GPUEngram +from hydra.optimizer import MuonAdamW, adamw_step_fused, muon_step_fused + +# Heavy imports are resolved lazily so `import hydra` and `import hydra.hyena_block` +# keep working in local CPU/test environments that do not have the container-only +# mamba-ssm wheel stack installed. +def __getattr__(name: str): + if name == "PostSemClawModel": + from hydra.model import PostSemClawModel as _model + return _model + if name == "norm": + from hydra.model import norm as _norm + return _norm + if name == "config_from_dict": + from hydra.training import config_from_dict as _cfd + return _cfd + raise AttributeError(name) + + +__all__ = [ + "PostSemClawConfig", + "GPUEngram", + "PostSemClawModel", + "norm", + "MuonAdamW", + "adamw_step_fused", + "muon_step_fused", + "config_from_dict", +] diff --git a/overlay/hydra/config.py b/overlay/hydra/config.py index 2eafb7c4a43cbce9f173fb0d152d65725cde3c5b..ef8e3bb97cf9ea6eb5548708bf8300615213ea17 100644 --- a/overlay/hydra/config.py +++ b/overlay/hydra/config.py @@ -1,220 +1,225 @@ -"""HYDRA training configuration — dataclass + env-var constants. - -Extracted from the monolithic train.py as part of W1 modularization. All -env-var reads and the PostSemClawConfig dataclass live here. The training -body imports these constants; zero behavior change from the extraction. -""" - -from __future__ import annotations - -import os -from dataclasses import dataclass, field - - -def _parse_hyena_layers_env() -> tuple[int, ...]: - """Parse HYDRA_HYENA_LAYERS env var into a sorted tuple of layer indices. - - Used as the default_factory for PostSemClawConfig.hyena_layers so a fresh - config construction reads the current env var, but once constructed the - value is first-class and travels with checkpoints (see asdict(config) in - save_ckpt). Ckpt-load sets the dataclass field explicitly, overriding the - env-var default. - - Returns empty tuple when env var is unset/empty (byte-identical to - pre-port behavior: no Hyena layers). - """ - raw = os.environ.get("HYDRA_HYENA_LAYERS", "") - if not raw: - return () - return tuple(sorted({int(s.strip()) for s in raw.split(",") if s.strip()})) - - -def _parse_gdn_layers_env() -> tuple[int, ...]: - """Parse HYDRA_GDN_LAYERS env var into a sorted tuple of layer indices. - - Same contract as _parse_hyena_layers_env: layers whose index is listed - here use GatedDeltaNet (fla.layers.GatedDeltaNet) as a drop-in - replacement for Mamba3. Empty tuple = no GDN layers (byte-identical - to baseline). - """ - raw = os.environ.get("HYDRA_GDN_LAYERS", "") - if not raw: - return () - return tuple(sorted({int(s.strip()) for s in raw.split(",") if s.strip()})) - -# --------------------------------------------------------------------------- -# CUDA env — set before importing torch in entry point. Kept here so any -# module that `from hydra.config import ...` also benefits (import order is -# top-down in Python, and train.py used to set these at module top). -# --------------------------------------------------------------------------- -os.environ.setdefault("CUDA_HOME", "/usr/local/cuda") -if "/usr/local/cuda/bin" not in os.environ.get("PATH", ""): - os.environ["PATH"] = "/usr/local/cuda/bin:" + os.environ.get("PATH", "") -os.environ.setdefault("PYTORCH_ALLOC_CONF", "expandable_segments:True") - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class PostSemClawConfig: - """Full-architecture model config. Defaults reflect Phase-1 baseline; - the training entry overrides d_model/n_layer/etc. from env vars.""" - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 # Must match prepare.py VOCAB_SIZE - - # Mamba-3 SSM - n_layer: int = 6 - d_model: int = 384 - d_state: int = 64 # SSM state dimension - headdim: int = 48 # head dimension for SSM - n_heads: int = 8 # d_model // headdim - expand: int = 2 # inner_dim = expand * d_model - - # Engram (conditional memory with Hebbian writes) - engram_n_columns: int = 4096 - engram_key_dim: int = 64 - engram_layer_idx: int = 1 # which layer gets engram (0-indexed, mid-layer) - - # SemanticFoldingSDR (offline retina with STE; no-bypass, runs every step) - sdr_n_bits: int = 16384 # retina width - # Default 327 = 2% sparsity (Webber/Numenta canonical). Override with - # HYDRA_SDR_TARGET_ACTIVE env var; value MUST match subsystems/sdr_retina.py - # TARGET_ACTIVE (same env var is read there, so just setting it once works). - sdr_target_active: int = int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")) - sdr_delta_rank: int = 32 # low-rank STE delta rank - sdr_som_warmup: int = 500 - sdr_som_interval: int = 100 - - # HTMLayer (Rust-backed, Hebbian; no-bypass, runs every step) - htm_n_columns: int = 2048 - htm_cells_per_column: int = 32 - - # Hyena supplement layer indices (sorted tuple). Defaults to the - # HYDRA_HYENA_LAYERS env var at config-construction time, but once - # persisted in a checkpoint the value is first-class and survives even - # when the env var is unset at resume time. This fixes the ckpt-reload - # crash path where a model trained with `HYDRA_HYENA_LAYERS=3,7` saves - # HyenaBlock params but a fresh process without the env var would try - # to build a pure-Mamba3 architecture and reject the state_dict as - # `Missing/Unexpected key(s)`. - hyena_layers: tuple[int, ...] = field(default_factory=_parse_hyena_layers_env) - - # GatedDeltaNet supplement layer indices (sorted tuple). Same semantics - # as hyena_layers — a layer index listed here uses GDNBlock (fla-backed - # Gated DeltaNet) instead of Mamba3. Selections are mutually exclusive - # with hyena_layers at construction time (hyena wins on overlap; the - # model loop checks hyena first). - gdn_layers: tuple[int, ...] = field(default_factory=_parse_gdn_layers_env) - - # Label smoothing + Z-loss - label_smoothing: float = 0.0 # disabled: any smoothing hurts in 5-min budget - z_loss_weight: float = 1e-4 - - -# --------------------------------------------------------------------------- -# Hyperparameters (autoresearch agent modifies these via env vars) -# --------------------------------------------------------------------------- - -# Model architecture -D_MODEL = int(os.environ.get("HYDRA_D_MODEL", "256")) -N_LAYER = int(os.environ.get("HYDRA_N_LAYER", "4")) -D_STATE = int(os.environ.get("HYDRA_D_STATE", "64")) -HEADDIM = int(os.environ.get("HYDRA_HEADDIM", "32")) -N_HEADS = D_MODEL // HEADDIM -EXPAND = int(os.environ.get("HYDRA_EXPAND", "2")) - -# Engram -ENGRAM_N_COLUMNS = int(os.environ.get("HYDRA_ENGRAM_N_COLUMNS", "1024")) -ENGRAM_KEY_DIM = 64 -ENGRAM_LAYER_IDX = int(os.environ.get("HYDRA_ENGRAM_LAYER_IDX", "1")) - -# Optimization -DEVICE_BATCH_SIZE = int(os.environ.get("HYDRA_BATCH_SIZE", "1")) -TOTAL_BATCH_SIZE = int(os.environ.get("HYDRA_TOTAL_BATCH", "32768")) -MATRIX_LR = float(os.environ.get("HYDRA_MATRIX_LR", "0.12")) -EMBEDDING_LR = float(os.environ.get("HYDRA_EMBED_LR", "1.0")) -UNEMBEDDING_LR = float(os.environ.get("HYDRA_UNEMBED_LR", "0.005")) -SCALAR_LR = 0.5 -WEIGHT_DECAY = 0.01 -ADAM_BETAS = (0.9, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = float(os.environ.get("HYDRA_LR_MIN_MULT", "0.0")) - -# Runtime -SEED = int(os.environ.get("HYDRA_SEED", "42")) -# BF16 TFLOPS peak (RTX 3060=25.5, A100 SXM4=312, H100 SXM5=989) -GPU_BF16_PEAK_FLOPS = float(os.environ.get("HYDRA_GPU_BF16_TFLOPS", "25.5")) * 1e12 - -# Loss / inference knobs read by the model -CE_CHUNK = int(os.environ.get("HYDRA_CE_CHUNK", "1024")) -DROPOUT = float(os.environ.get("HYDRA_DROPOUT", "0.2")) -FUSED_ADAMW = os.environ.get("HYDRA_FUSED_ADAMW", "1") == "1" - -# --------------------------------------------------------------------------- -# Learnability knobs (all OFF by default — zero behavior change unless set) -# --------------------------------------------------------------------------- -# 1) Multi-Token Prediction (Llama-3 style). K=1 disables (next-1 only). K=4 -# adds 3 extra weight-tied heads; loss = mean of K position-shifted CEs. -MTP_K = int(os.environ.get("HYDRA_MTP_K", "1")) -# 2) Exponential Moving Average of model weights (decay=0.999). Saves an -# additional latest_ema.pt at the end of training. -USE_EMA = os.environ.get("HYDRA_USE_EMA", "0") == "1" -EMA_DECAY = float(os.environ.get("HYDRA_EMA_DECAY", "0.999")) -# 3) Gradient checkpointing on Mamba3 block forward. Trades ~30% compute for -# ~40% activation memory savings — lets you push B upward on a 3060. -GRAD_CKPT = os.environ.get("HYDRA_GRAD_CKPT", "0") == "1" -# 4) Doc-separator masking in packed sequences: at every packed-BOS position -# in the targets tensor, mask the loss (ignore_index=-1) so the model is -# not forced to predict doc B from doc A's context. -DOC_SEP_MASK = os.environ.get("HYDRA_DOC_SEP_MASK", "0") == "1" -# 5) Stop-gradient on HTM state (belt-and-braces: htm_rust already runs under -# torch.no_grad() so the tensor returned has requires_grad=False; this -# simply detaches explicitly to harden graph hygiene against future refactors). -HTM_STOP_GRAD = os.environ.get("HYDRA_HTM_STOP_GRAD", "0") == "1" -# 6) Output entropy penalty: loss += -lambda * H(softmax(logits)). Negative -# entropy penalizes peaked distributions and breaks repetition loops. -ENTROPY_PENALTY = float(os.environ.get("HYDRA_ENTROPY_PENALTY", "0.0")) -# 7) Curriculum: first N optimizer steps use short seq_len, then switch to -# full. 0 disables (no curriculum). -CURRICULUM_SHORT_STEPS = int(os.environ.get("HYDRA_CURRICULUM_SHORT_STEPS", "0")) -CURRICULUM_SHORT_SEQ_LEN = int(os.environ.get("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256")) - -# --------------------------------------------------------------------------- -# Hyena supplement (additional block type for selected layer indices). -# Hyena replaces Mamba3 at the specified layer indices while all other layers -# remain Mamba3. Empty string (default) → no Hyena layers, byte-identical to -# pre-port behavior. -# HYDRA_HYENA_LAYERS "3,7" — comma-separated 0-indexed layer ids -# HYDRA_HYENA_ORDER 2 — Hyena recurrence order (>= 2) -# HYDRA_HYENA_FILTER_DIM 64 — implicit-filter MLP hidden width -# Hyena reference: https://arxiv.org/pdf/2302.10866.pdf (HazyResearch/safari). -# --------------------------------------------------------------------------- -HYENA_LAYERS = os.environ.get("HYDRA_HYENA_LAYERS", "") -HYENA_ORDER = int(os.environ.get("HYDRA_HYENA_ORDER", "2")) -HYENA_FILTER_DIM = int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")) -# Filter-rfft cache modes (see subsystems/hyena_pure.py): -# HYDRA_HYENA_FILTER_CACHE=1 — eval-only cache. Safe under torch.no_grad() -# where PyTorch never saves intermediate tensors. Off by default. -# HYDRA_HYENA_TRAIN_CACHE=1 — training-safe cache using a deferred -# gradient pattern. Cuts the implicit filter MLP forward to ONCE per -# optimizer step regardless of grad-accumulation factor. Requires the -# training loop (see hydra/lightning_module.py::optimizer_step) to -# call `model.flush_hyena_pending_grads()` before optimizer.step(). -# Off by default. -HYENA_FILTER_CACHE = os.environ.get("HYDRA_HYENA_FILTER_CACHE", "0") == "1" -HYENA_TRAIN_CACHE = os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" - -# Factual eval knobs -FACTUAL_SAMPLES = int(os.environ.get("HYDRA_FACTUAL_SAMPLES", "3")) -FACTUAL_BATCH = int(os.environ.get("HYDRA_FACTUAL_BATCH", "32")) -# F6 (partial): Full incremental SSM decode integration deferred — would require -# threading mamba_ssm InferenceParams through PostSemClawModel.forward and all -# auxiliary subsystems (HTM, SDR, Engram) which currently run full-sequence each -# call. As a stopgap we reduce default from 16 -> 4 so the per-prompt cost is -# quartered (each gen-tok does a full re-encode of ctx+k tokens). Override with -# HYDRA_FACTUAL_GEN_TOKENS to restore prior behavior. See docs/OPTIMIZATION_PLAN.md. -FACTUAL_GEN_TOKENS = int(os.environ.get("HYDRA_FACTUAL_GEN_TOKENS", "2")) +"""HYDRA training configuration — dataclass + env-var constants. + +Extracted from the monolithic train.py as part of W1 modularization. All +env-var reads and the PostSemClawConfig dataclass live here. The training +body imports these constants; zero behavior change from the extraction. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field + + +def _parse_hyena_layers_env() -> tuple[int, ...]: + """Parse HYDRA_HYENA_LAYERS env var into a sorted tuple of layer indices. + + Used as the default_factory for PostSemClawConfig.hyena_layers so a fresh + config construction reads the current env var, but once constructed the + value is first-class and travels with checkpoints (see asdict(config) in + save_ckpt). Ckpt-load sets the dataclass field explicitly, overriding the + env-var default. + + Returns empty tuple when env var is unset/empty (byte-identical to + pre-port behavior: no Hyena layers). + """ + raw = os.environ.get("HYDRA_HYENA_LAYERS", "") + if not raw: + return () + return tuple(sorted({int(s.strip()) for s in raw.split(",") if s.strip()})) + + +def _parse_gdn_layers_env() -> tuple[int, ...]: + """Parse HYDRA_GDN_LAYERS env var into a sorted tuple of layer indices. + + Same contract as _parse_hyena_layers_env: layers whose index is listed + here use GatedDeltaNet (fla.layers.GatedDeltaNet) as a drop-in + replacement for Mamba3. Empty tuple = no GDN layers (byte-identical + to baseline). + """ + raw = os.environ.get("HYDRA_GDN_LAYERS", "") + if not raw: + return () + return tuple(sorted({int(s.strip()) for s in raw.split(",") if s.strip()})) + +# --------------------------------------------------------------------------- +# CUDA env — set before importing torch in entry point. Kept here so any +# module that `from hydra.config import ...` also benefits (import order is +# top-down in Python, and train.py used to set these at module top). +# --------------------------------------------------------------------------- +os.environ.setdefault("CUDA_HOME", "/usr/local/cuda") +if "/usr/local/cuda/bin" not in os.environ.get("PATH", ""): + os.environ["PATH"] = "/usr/local/cuda/bin:" + os.environ.get("PATH", "") +os.environ.setdefault("PYTORCH_ALLOC_CONF", "expandable_segments:True") + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class PostSemClawConfig: + """Full-architecture model config. Defaults reflect Phase-1 baseline; + the training entry overrides d_model/n_layer/etc. from env vars.""" + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 # Must match prepare.py VOCAB_SIZE + + # Mamba-3 SSM + n_layer: int = 6 + d_model: int = 384 + d_state: int = 64 # SSM state dimension + headdim: int = 48 # head dimension for SSM + n_heads: int = 8 # d_model // headdim + expand: int = 2 # inner_dim = expand * d_model + + # Engram (conditional memory with Hebbian writes) + engram_n_columns: int = 4096 + engram_key_dim: int = 64 + engram_layer_idx: int = 1 # which layer gets engram (0-indexed, mid-layer) + + # SemanticFoldingSDR (offline retina with STE; no-bypass, runs every step) + sdr_n_bits: int = 16384 # retina width + # Default 327 = 2% sparsity (Webber/Numenta canonical). Override with + # HYDRA_SDR_TARGET_ACTIVE env var; value MUST match subsystems/sdr_retina.py + # TARGET_ACTIVE (same env var is read there, so just setting it once works). + sdr_target_active: int = int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")) + sdr_delta_rank: int = 32 # low-rank STE delta rank + sdr_som_warmup: int = 500 + sdr_som_interval: int = 100 + + # HTMLayer (Rust-backed, Hebbian; no-bypass, runs every step) + htm_n_columns: int = 2048 + htm_cells_per_column: int = 32 + + # Hyena supplement layer indices (sorted tuple). Defaults to the + # HYDRA_HYENA_LAYERS env var at config-construction time, but once + # persisted in a checkpoint the value is first-class and survives even + # when the env var is unset at resume time. This fixes the ckpt-reload + # crash path where a model trained with `HYDRA_HYENA_LAYERS=3,7` saves + # HyenaBlock params but a fresh process without the env var would try + # to build a pure-Mamba3 architecture and reject the state_dict as + # `Missing/Unexpected key(s)`. + hyena_layers: tuple[int, ...] = field(default_factory=_parse_hyena_layers_env) + + # GatedDeltaNet supplement layer indices (sorted tuple). Same semantics + # as hyena_layers — a layer index listed here uses GDNBlock (fla-backed + # Gated DeltaNet) instead of Mamba3. Selections are mutually exclusive + # with hyena_layers at construction time (hyena wins on overlap; the + # model loop checks hyena first). + gdn_layers: tuple[int, ...] = field(default_factory=_parse_gdn_layers_env) + + # Label smoothing + Z-loss + label_smoothing: float = field(default_factory=lambda: float(os.environ.get("HYDRA_LABEL_SMOOTHING", "0.0"))) + z_loss_weight: float = field(default_factory=lambda: float(os.environ.get("HYDRA_Z_LOSS_WEIGHT", "1e-4"))) + + +# --------------------------------------------------------------------------- +# Hyperparameters (autoresearch agent modifies these via env vars) +# --------------------------------------------------------------------------- + +# Model architecture +D_MODEL = int(os.environ.get("HYDRA_D_MODEL", "256")) +N_LAYER = int(os.environ.get("HYDRA_N_LAYER", "4")) +D_STATE = int(os.environ.get("HYDRA_D_STATE", "64")) +HEADDIM = int(os.environ.get("HYDRA_HEADDIM", "32")) +N_HEADS = D_MODEL // HEADDIM +EXPAND = int(os.environ.get("HYDRA_EXPAND", "2")) + +# Engram +ENGRAM_N_COLUMNS = int(os.environ.get("HYDRA_ENGRAM_N_COLUMNS", "1024")) +ENGRAM_KEY_DIM = 64 +ENGRAM_LAYER_IDX = int(os.environ.get("HYDRA_ENGRAM_LAYER_IDX", "1")) + +# Optimization +DEVICE_BATCH_SIZE = int(os.environ.get("HYDRA_BATCH_SIZE", "1")) +TOTAL_BATCH_SIZE = int(os.environ.get("HYDRA_TOTAL_BATCH", "32768")) +MATRIX_LR = float(os.environ.get("HYDRA_MATRIX_LR", "0.12")) +EMBEDDING_LR = float(os.environ.get("HYDRA_EMBED_LR", "1.0")) +UNEMBEDDING_LR = float(os.environ.get("HYDRA_UNEMBED_LR", "0.005")) +# Scalar/vector params include Hyena implicit-filter vectors, norms, gate/bias +# terms, and SDR delta_u/delta_v. They are AdamW-scaled by d_model and can be +# the hidden instability path when the high-throughput HF recipe pushes a large +# device batch for hours. Keep the historical default, but make it controllable +# from launch scripts so cloud jobs can cool scalars without editing code. +SCALAR_LR = float(os.environ.get("HYDRA_SCALAR_LR", "0.5")) +WEIGHT_DECAY = float(os.environ.get("HYDRA_WEIGHT_DECAY", "0.01")) +ADAM_BETAS = (0.9, 0.95) +WARMUP_RATIO = float(os.environ.get("HYDRA_WARMUP_RATIO", "0.0")) +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = float(os.environ.get("HYDRA_LR_MIN_MULT", "0.0")) + +# Runtime +SEED = int(os.environ.get("HYDRA_SEED", "42")) +# BF16 TFLOPS peak (RTX 3060=25.5, A100 SXM4=312, H100 SXM5=989) +GPU_BF16_PEAK_FLOPS = float(os.environ.get("HYDRA_GPU_BF16_TFLOPS", "25.5")) * 1e12 + +# Loss / inference knobs read by the model +CE_CHUNK = int(os.environ.get("HYDRA_CE_CHUNK", "1024")) +DROPOUT = float(os.environ.get("HYDRA_DROPOUT", "0.2")) +FUSED_ADAMW = os.environ.get("HYDRA_FUSED_ADAMW", "1") == "1" + +# --------------------------------------------------------------------------- +# Learnability knobs (all OFF by default — zero behavior change unless set) +# --------------------------------------------------------------------------- +# 1) Multi-Token Prediction (Llama-3 style). K=1 disables (next-1 only). K=4 +# adds 3 extra weight-tied heads; loss = mean of K position-shifted CEs. +MTP_K = int(os.environ.get("HYDRA_MTP_K", "1")) +# 2) Exponential Moving Average of model weights (decay=0.999). Saves an +# additional latest_ema.pt at the end of training. +USE_EMA = os.environ.get("HYDRA_USE_EMA", "0") == "1" +EMA_DECAY = float(os.environ.get("HYDRA_EMA_DECAY", "0.999")) +# 3) Gradient checkpointing on Mamba3 block forward. Trades ~30% compute for +# ~40% activation memory savings — lets you push B upward on a 3060. +GRAD_CKPT = os.environ.get("HYDRA_GRAD_CKPT", "0") == "1" +# 4) Doc-separator masking in packed sequences: at every packed-BOS position +# in the targets tensor, mask the loss (ignore_index=-1) so the model is +# not forced to predict doc B from doc A's context. +DOC_SEP_MASK = os.environ.get("HYDRA_DOC_SEP_MASK", "0") == "1" +# 5) Stop-gradient on HTM state (belt-and-braces: htm_rust already runs under +# torch.no_grad() so the tensor returned has requires_grad=False; this +# simply detaches explicitly to harden graph hygiene against future refactors). +HTM_STOP_GRAD = os.environ.get("HYDRA_HTM_STOP_GRAD", "0") == "1" +# 6) Output entropy penalty: loss += -lambda * H(softmax(logits)). Negative +# entropy penalizes peaked distributions and breaks repetition loops. +ENTROPY_PENALTY = float(os.environ.get("HYDRA_ENTROPY_PENALTY", "0.0")) +# 7) Curriculum: first N optimizer steps use short seq_len, then switch to +# full. 0 disables (no curriculum). +CURRICULUM_SHORT_STEPS = int(os.environ.get("HYDRA_CURRICULUM_SHORT_STEPS", "0")) +CURRICULUM_SHORT_SEQ_LEN = int(os.environ.get("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256")) + +# --------------------------------------------------------------------------- +# Hyena supplement (additional block type for selected layer indices). +# Hyena replaces Mamba3 at the specified layer indices while all other layers +# remain Mamba3. Empty string (default) → no Hyena layers, byte-identical to +# pre-port behavior. +# HYDRA_HYENA_LAYERS "3,7" — comma-separated 0-indexed layer ids +# HYDRA_HYENA_ORDER 2 — Hyena recurrence order (>= 2) +# HYDRA_HYENA_FILTER_DIM 64 — implicit-filter MLP hidden width +# Hyena reference: https://arxiv.org/pdf/2302.10866.pdf (HazyResearch/safari). +# --------------------------------------------------------------------------- +HYENA_LAYERS = os.environ.get("HYDRA_HYENA_LAYERS", "") +HYENA_ORDER = int(os.environ.get("HYDRA_HYENA_ORDER", "2")) +HYENA_FILTER_DIM = int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")) +# Filter-rfft cache modes (see subsystems/hyena_pure.py): +# HYDRA_HYENA_FILTER_CACHE=1 — eval-only cache. Safe under torch.no_grad() +# where PyTorch never saves intermediate tensors. Off by default. +# HYDRA_HYENA_TRAIN_CACHE=1 — training-safe cache using a deferred +# gradient pattern. Cuts the implicit filter MLP forward to ONCE per +# optimizer step regardless of grad-accumulation factor. Requires the +# training loop (see hydra/lightning_module.py::optimizer_step) to +# call `model.flush_hyena_pending_grads()` before optimizer.step(). +# Off by default. +HYENA_FILTER_CACHE = os.environ.get("HYDRA_HYENA_FILTER_CACHE", "0") == "1" +HYENA_TRAIN_CACHE = os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" + +# Factual eval knobs +FACTUAL_SAMPLES = int(os.environ.get("HYDRA_FACTUAL_SAMPLES", "3")) +FACTUAL_BATCH = int(os.environ.get("HYDRA_FACTUAL_BATCH", "32")) +# F6 (partial): Full incremental SSM decode integration deferred — would require +# threading mamba_ssm InferenceParams through PostSemClawModel.forward and all +# auxiliary subsystems (HTM, SDR, Engram) which currently run full-sequence each +# call. As a stopgap we reduce default from 16 -> 4 so the per-prompt cost is +# quartered (each gen-tok does a full re-encode of ctx+k tokens). Override with +# HYDRA_FACTUAL_GEN_TOKENS to restore prior behavior. See docs/OPTIMIZATION_PLAN.md. +FACTUAL_GEN_TOKENS = int(os.environ.get("HYDRA_FACTUAL_GEN_TOKENS", "2")) diff --git a/overlay/hydra/data_module.py b/overlay/hydra/data_module.py index ed222c8760dad33655f0d52b9b3cd2609a06ca7f..7b017f6637946254ca86f560663956ecd1533c03 100644 --- a/overlay/hydra/data_module.py +++ b/overlay/hydra/data_module.py @@ -1,288 +1,288 @@ -"""Lightning DataModule + IterableDataset for HYDRA pretraining. - -Replaces the custom threading/queue pipeline in prepare_nemotron.make_dataloader -with a standard multiprocessing DataLoader approach. - -Design: - • IterableStreamDataset: each worker opens its own HF streams for the 7-way - blend, tokenizes with rustbpe, packs into (T+1,) rows via best-fit, and - yields one row per __next__. - • HydraDataModule: wraps the dataset with a standard DataLoader using - num_workers>=1, prefetch_factor=4, pin_memory=True. Lightning handles - device transfer. - • Val stream: deterministic seed 12345, weights match training blend. - -The worker RNG is seeded per-worker so the weighted-sampling schedule is -independent across workers (else all workers request the same config at -the same step and prefetching serializes). - -Env vars (all preserved from prepare_nemotron): - HYDRA_SEQ_LEN — sequence length T (default 512) - HYDRA_BATCH_SIZE — batch size B (default 1) — passed through - to DataLoader - HYDRA_STREAM_SHUFFLE_BUFFER — HF shuffle buffer (default 2048) - HYDRA_USE_FULL_BLEND — 7-way blend vs 5-way Nemotron phase - HYDRA_USE_NEMOTRON — enables streaming path (else shard path) - HYDRA_FACTUAL_INJECT_RATE — factual doc injection cadence - HYDRA_NEMOTRON_PHASE — phase1|phase2 (when not full blend) - HYDRA_DATA_NUM_WORKERS — DataLoader num_workers (default 2) - HYDRA_DATA_PREFETCH — DataLoader prefetch_factor (default 4) - HYDRA_DATA_BUFFER — doc_buffer size for best-fit packing - (default 1000) -""" -from __future__ import annotations - -import os -import random -from typing import Iterator - -import numpy as np -import torch -import lightning as L -from torch.utils.data import DataLoader, IterableDataset, get_worker_info - -import prepare as _prepare -import prepare_nemotron as _p_nemo -from prepare_nemotron import ( - FULL_BLEND_WEIGHTS, - PHASE1_WEIGHTS, - PHASE2_WEIGHTS, - _BLEND_REGISTRY, - _extract_text, - _open_stream, -) - - -# --------------------------------------------------------------------------- -# Worker-local weighted stream. A stripped version of prepare_nemotron's -# _WeightedStream that is constructed inside each worker. Adds worker sharding: -# when num_workers > 1 the RNG is seeded per-worker, so different workers -# sample different config sequences and pull disjoint shard assignments from -# HF's shuffle buffer. -# --------------------------------------------------------------------------- - - -class _WorkerWeightedStream: - def __init__(self, weights: dict[str, float], base_seed: int, worker_id: int): - self.configs = list(weights.keys()) - self.weights = [weights[c] for c in self.configs] - self.base_seed = base_seed - self.worker_id = worker_id - # Each worker opens its own HF streams. _open_stream returns an iter() - # over a streaming dataset, with an internal shuffle buffer. - self.streams = {c: _open_stream(c, "train") for c in self.configs} - # Per-worker RNG so the config-choice trajectory is independent. - self.rng = random.Random(base_seed + worker_id * 7919) - self.epoch = 1 - - # Lazy-init factual docs (once per worker). The main-process version - # in prepare_nemotron._WeightedStream reads these on first __next__. - self._factual_docs: list[str] | None = None - self._factual_idx = 0 - self._inject_counter = 0 - inject_rate = int(os.environ.get("HYDRA_FACTUAL_INJECT_RATE", "50")) - self._inject_rate = inject_rate - if inject_rate > 0: - factual_path = os.path.join( - os.path.dirname(os.path.abspath(_p_nemo.__file__)), - "data", "factual", "facts.txt", - ) - if os.path.exists(factual_path): - with open(factual_path) as fh: - self._factual_docs = fh.read().strip().split("\n") - - def _reopen(self, config: str) -> None: - self.streams[config] = _open_stream(config, "train") - self.epoch += 1 - - def __iter__(self): - return self - - def __next__(self) -> tuple[str, int]: - # Factual injection (preserves prepare_nemotron cadence). - if self._inject_rate > 0 and self._factual_docs: - self._inject_counter += 1 - if self._inject_counter >= self._inject_rate: - self._inject_counter = 0 - doc = self._factual_docs[self._factual_idx % len(self._factual_docs)] - self._factual_idx += 1 - return doc, self.epoch - - config = self.rng.choices(self.configs, weights=self.weights, k=1)[0] - try: - row = next(self.streams[config]) - except StopIteration: - self._reopen(config) - row = next(self.streams[config]) - return _extract_text(row), self.epoch - - -# --------------------------------------------------------------------------- -# IterableStreamDataset — yields (T+1,) packed rows. No threads. No queues. -# Lives inside each DataLoader worker. DataLoader's own multiprocessing stacks -# rows into batches of shape (B, T+1) and sends them to the main process. -# --------------------------------------------------------------------------- - - -class IterableStreamDataset(IterableDataset): - """Streams docs, tokenizes, packs into (T+1,) rows via best-fit. - - Each worker gets its own instance (via fork/spawn) and initializes its - own HF streams + rustbpe tokenizer + factual injector. The tokenizer - pickled blob is small (~1 MB) and thread-safe per tiktoken docs. - """ - - def __init__( - self, - split: str, - seq_len: int, - *, - base_seed: int = 0, - doc_buffer_size: int = 1000, - tokenizer_batch: int = 128, - ): - super().__init__() - assert split in ("train", "val"), split - self.split = split - self.seq_len = seq_len - self.row_capacity = seq_len + 1 - self.base_seed = base_seed - self.doc_buffer_size = doc_buffer_size - self.tokenizer_batch = tokenizer_batch - - def _pick_weights(self) -> dict[str, float]: - if self.split == "val": - if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": - return FULL_BLEND_WEIGHTS - return {"Nemotron-Pretraining-Multiple-Choice": 1.0} - if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": - return FULL_BLEND_WEIGHTS - phase = os.environ.get("HYDRA_NEMOTRON_PHASE", "phase1").strip().lower() - return PHASE2_WEIGHTS if phase == "phase2" else PHASE1_WEIGHTS - - def __iter__(self) -> Iterator[torch.Tensor]: - info = get_worker_info() - worker_id = 0 if info is None else info.id - - # Each worker builds its own tokenizer instance. tiktoken's Encoding - # object is pickleable and the underlying C++ BPE is thread-safe; - # per-worker instantiation avoids cross-process sharing headaches. - tokenizer = _prepare.Tokenizer.from_directory() - bos = tokenizer.get_bos_token_id() - - # Each worker gets its own weighted HF stream. Seed offset ensures - # disjoint config-choice trajectories; HF's own shuffle buffer handles - # shard randomization. - val_seed = 12345 # deterministic val - seed = val_seed if self.split == "val" else self.base_seed - stream = _WorkerWeightedStream( - self._pick_weights(), base_seed=seed, worker_id=worker_id, - ) - - row_capacity = self.row_capacity - doc_buffer: list[list[int]] = [] - doc_batch_size = self.tokenizer_batch - - def refill_buffer() -> None: - # Collect doc_batch_size text strings, then batch-tokenize. - texts: list[str] = [] - for _ in range(doc_batch_size): - text, _epoch = next(stream) - if text: - texts.append(text) - if texts: - token_lists = tokenizer.encode(texts, prepend=bos) - doc_buffer.extend(token_lists) - - while True: - pos = 0 - row = torch.empty(row_capacity, dtype=torch.long) - while pos < row_capacity: - while len(doc_buffer) < self.doc_buffer_size: - refill_buffer() - - remaining = row_capacity - pos - - # Best-fit packing: largest doc that fully fits. - best_idx = -1 - best_len = 0 - for i, doc in enumerate(doc_buffer): - dlen = len(doc) - if dlen <= remaining and dlen > best_len: - best_idx = i - best_len = dlen - - if best_idx >= 0: - doc = doc_buffer.pop(best_idx) - row[pos : pos + len(doc)] = torch.tensor(doc, dtype=torch.long) - pos += len(doc) - else: - # No doc fits remaining space — crop shortest to fill. - shortest_idx = min( - range(len(doc_buffer)), - key=lambda i: len(doc_buffer[i]), - ) - doc = doc_buffer.pop(shortest_idx) - row[pos : pos + remaining] = torch.tensor( - doc[:remaining], dtype=torch.long, - ) - pos += remaining - - yield row - - -# --------------------------------------------------------------------------- -# LightningDataModule -# --------------------------------------------------------------------------- - - -class HydraDataModule(L.LightningDataModule): - def __init__( - self, - batch_size: int | None = None, - seq_len: int | None = None, - num_workers: int | None = None, - prefetch_factor: int | None = None, - ): - super().__init__() - self.batch_size = batch_size or int(os.environ.get("HYDRA_BATCH_SIZE", "1")) - self.seq_len = seq_len or int(os.environ.get("HYDRA_SEQ_LEN", "512")) - self.num_workers = ( - num_workers - if num_workers is not None - else int(os.environ.get("HYDRA_DATA_NUM_WORKERS", "2")) - ) - self.prefetch_factor = ( - prefetch_factor - if prefetch_factor is not None - else int(os.environ.get("HYDRA_DATA_PREFETCH", "4")) - ) - self.doc_buffer = int(os.environ.get("HYDRA_DATA_BUFFER", "1000")) - - def _make_loader(self, split: str, seed: int) -> DataLoader: - dataset = IterableStreamDataset( - split=split, - seq_len=self.seq_len, - base_seed=seed, - doc_buffer_size=self.doc_buffer, - ) - # num_workers=0 → main-process iteration (useful for debugging). With - # IterableDataset the DataLoader batches the rows into (B, T+1) via - # default torch.stack-collate. - kw: dict = dict( - dataset=dataset, - batch_size=self.batch_size, - num_workers=self.num_workers, - pin_memory=True, - drop_last=True, - ) - if self.num_workers > 0: - kw["prefetch_factor"] = self.prefetch_factor - kw["persistent_workers"] = True - return DataLoader(**kw) - - def train_dataloader(self) -> DataLoader: - return self._make_loader("train", seed=0) - - def val_dataloader(self) -> DataLoader: - return self._make_loader("val", seed=12345) +"""Lightning DataModule + IterableDataset for HYDRA pretraining. + +Replaces the custom threading/queue pipeline in prepare_nemotron.make_dataloader +with a standard multiprocessing DataLoader approach. + +Design: + • IterableStreamDataset: each worker opens its own HF streams for the 7-way + blend, tokenizes with rustbpe, packs into (T+1,) rows via best-fit, and + yields one row per __next__. + • HydraDataModule: wraps the dataset with a standard DataLoader using + num_workers>=1, prefetch_factor=4, pin_memory=True. Lightning handles + device transfer. + • Val stream: deterministic seed 12345, weights match training blend. + +The worker RNG is seeded per-worker so the weighted-sampling schedule is +independent across workers (else all workers request the same config at +the same step and prefetching serializes). + +Env vars (all preserved from prepare_nemotron): + HYDRA_SEQ_LEN — sequence length T (default 512) + HYDRA_BATCH_SIZE — batch size B (default 1) — passed through + to DataLoader + HYDRA_STREAM_SHUFFLE_BUFFER — HF shuffle buffer (default 2048) + HYDRA_USE_FULL_BLEND — 7-way blend vs 5-way Nemotron phase + HYDRA_USE_NEMOTRON — enables streaming path (else shard path) + HYDRA_FACTUAL_INJECT_RATE — factual doc injection cadence + HYDRA_NEMOTRON_PHASE — phase1|phase2 (when not full blend) + HYDRA_DATA_NUM_WORKERS — DataLoader num_workers (default 2) + HYDRA_DATA_PREFETCH — DataLoader prefetch_factor (default 4) + HYDRA_DATA_BUFFER — doc_buffer size for best-fit packing + (default 1000) +""" +from __future__ import annotations + +import os +import random +from typing import Iterator + +import numpy as np +import torch +import lightning as L +from torch.utils.data import DataLoader, IterableDataset, get_worker_info + +import prepare as _prepare +import prepare_nemotron as _p_nemo +from prepare_nemotron import ( + FULL_BLEND_WEIGHTS, + PHASE1_WEIGHTS, + PHASE2_WEIGHTS, + _BLEND_REGISTRY, + _extract_text, + _open_stream, +) + + +# --------------------------------------------------------------------------- +# Worker-local weighted stream. A stripped version of prepare_nemotron's +# _WeightedStream that is constructed inside each worker. Adds worker sharding: +# when num_workers > 1 the RNG is seeded per-worker, so different workers +# sample different config sequences and pull disjoint shard assignments from +# HF's shuffle buffer. +# --------------------------------------------------------------------------- + + +class _WorkerWeightedStream: + def __init__(self, weights: dict[str, float], base_seed: int, worker_id: int): + self.configs = list(weights.keys()) + self.weights = [weights[c] for c in self.configs] + self.base_seed = base_seed + self.worker_id = worker_id + # Each worker opens its own HF streams. _open_stream returns an iter() + # over a streaming dataset, with an internal shuffle buffer. + self.streams = {c: _open_stream(c, "train") for c in self.configs} + # Per-worker RNG so the config-choice trajectory is independent. + self.rng = random.Random(base_seed + worker_id * 7919) + self.epoch = 1 + + # Lazy-init factual docs (once per worker). The main-process version + # in prepare_nemotron._WeightedStream reads these on first __next__. + self._factual_docs: list[str] | None = None + self._factual_idx = 0 + self._inject_counter = 0 + inject_rate = int(os.environ.get("HYDRA_FACTUAL_INJECT_RATE", "50")) + self._inject_rate = inject_rate + if inject_rate > 0: + factual_path = os.path.join( + os.path.dirname(os.path.abspath(_p_nemo.__file__)), + "data", "factual", "facts.txt", + ) + if os.path.exists(factual_path): + with open(factual_path) as fh: + self._factual_docs = fh.read().strip().split("\n") + + def _reopen(self, config: str) -> None: + self.streams[config] = _open_stream(config, "train") + self.epoch += 1 + + def __iter__(self): + return self + + def __next__(self) -> tuple[str, int]: + # Factual injection (preserves prepare_nemotron cadence). + if self._inject_rate > 0 and self._factual_docs: + self._inject_counter += 1 + if self._inject_counter >= self._inject_rate: + self._inject_counter = 0 + doc = self._factual_docs[self._factual_idx % len(self._factual_docs)] + self._factual_idx += 1 + return doc, self.epoch + + config = self.rng.choices(self.configs, weights=self.weights, k=1)[0] + try: + row = next(self.streams[config]) + except StopIteration: + self._reopen(config) + row = next(self.streams[config]) + return _extract_text(row), self.epoch + + +# --------------------------------------------------------------------------- +# IterableStreamDataset — yields (T+1,) packed rows. No threads. No queues. +# Lives inside each DataLoader worker. DataLoader's own multiprocessing stacks +# rows into batches of shape (B, T+1) and sends them to the main process. +# --------------------------------------------------------------------------- + + +class IterableStreamDataset(IterableDataset): + """Streams docs, tokenizes, packs into (T+1,) rows via best-fit. + + Each worker gets its own instance (via fork/spawn) and initializes its + own HF streams + rustbpe tokenizer + factual injector. The tokenizer + pickled blob is small (~1 MB) and thread-safe per tiktoken docs. + """ + + def __init__( + self, + split: str, + seq_len: int, + *, + base_seed: int = 0, + doc_buffer_size: int = 1000, + tokenizer_batch: int = 128, + ): + super().__init__() + assert split in ("train", "val"), split + self.split = split + self.seq_len = seq_len + self.row_capacity = seq_len + 1 + self.base_seed = base_seed + self.doc_buffer_size = doc_buffer_size + self.tokenizer_batch = tokenizer_batch + + def _pick_weights(self) -> dict[str, float]: + if self.split == "val": + if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": + return FULL_BLEND_WEIGHTS + return {"Nemotron-Pretraining-Multiple-Choice": 1.0} + if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": + return FULL_BLEND_WEIGHTS + phase = os.environ.get("HYDRA_NEMOTRON_PHASE", "phase1").strip().lower() + return PHASE2_WEIGHTS if phase == "phase2" else PHASE1_WEIGHTS + + def __iter__(self) -> Iterator[torch.Tensor]: + info = get_worker_info() + worker_id = 0 if info is None else info.id + + # Each worker builds its own tokenizer instance. tiktoken's Encoding + # object is pickleable and the underlying C++ BPE is thread-safe; + # per-worker instantiation avoids cross-process sharing headaches. + tokenizer = _prepare.Tokenizer.from_directory() + bos = tokenizer.get_bos_token_id() + + # Each worker gets its own weighted HF stream. Seed offset ensures + # disjoint config-choice trajectories; HF's own shuffle buffer handles + # shard randomization. + val_seed = 12345 # deterministic val + seed = val_seed if self.split == "val" else self.base_seed + stream = _WorkerWeightedStream( + self._pick_weights(), base_seed=seed, worker_id=worker_id, + ) + + row_capacity = self.row_capacity + doc_buffer: list[list[int]] = [] + doc_batch_size = self.tokenizer_batch + + def refill_buffer() -> None: + # Collect doc_batch_size text strings, then batch-tokenize. + texts: list[str] = [] + for _ in range(doc_batch_size): + text, _epoch = next(stream) + if text: + texts.append(text) + if texts: + token_lists = tokenizer.encode(texts, prepend=bos) + doc_buffer.extend(token_lists) + + while True: + pos = 0 + row = torch.empty(row_capacity, dtype=torch.long) + while pos < row_capacity: + while len(doc_buffer) < self.doc_buffer_size: + refill_buffer() + + remaining = row_capacity - pos + + # Best-fit packing: largest doc that fully fits. + best_idx = -1 + best_len = 0 + for i, doc in enumerate(doc_buffer): + dlen = len(doc) + if dlen <= remaining and dlen > best_len: + best_idx = i + best_len = dlen + + if best_idx >= 0: + doc = doc_buffer.pop(best_idx) + row[pos : pos + len(doc)] = torch.tensor(doc, dtype=torch.long) + pos += len(doc) + else: + # No doc fits remaining space — crop shortest to fill. + shortest_idx = min( + range(len(doc_buffer)), + key=lambda i: len(doc_buffer[i]), + ) + doc = doc_buffer.pop(shortest_idx) + row[pos : pos + remaining] = torch.tensor( + doc[:remaining], dtype=torch.long, + ) + pos += remaining + + yield row + + +# --------------------------------------------------------------------------- +# LightningDataModule +# --------------------------------------------------------------------------- + + +class HydraDataModule(L.LightningDataModule): + def __init__( + self, + batch_size: int | None = None, + seq_len: int | None = None, + num_workers: int | None = None, + prefetch_factor: int | None = None, + ): + super().__init__() + self.batch_size = batch_size or int(os.environ.get("HYDRA_BATCH_SIZE", "1")) + self.seq_len = seq_len or int(os.environ.get("HYDRA_SEQ_LEN", "512")) + self.num_workers = ( + num_workers + if num_workers is not None + else int(os.environ.get("HYDRA_DATA_NUM_WORKERS", "2")) + ) + self.prefetch_factor = ( + prefetch_factor + if prefetch_factor is not None + else int(os.environ.get("HYDRA_DATA_PREFETCH", "4")) + ) + self.doc_buffer = int(os.environ.get("HYDRA_DATA_BUFFER", "1000")) + + def _make_loader(self, split: str, seed: int) -> DataLoader: + dataset = IterableStreamDataset( + split=split, + seq_len=self.seq_len, + base_seed=seed, + doc_buffer_size=self.doc_buffer, + ) + # num_workers=0 → main-process iteration (useful for debugging). With + # IterableDataset the DataLoader batches the rows into (B, T+1) via + # default torch.stack-collate. + kw: dict = dict( + dataset=dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + pin_memory=True, + drop_last=True, + ) + if self.num_workers > 0: + kw["prefetch_factor"] = self.prefetch_factor + kw["persistent_workers"] = True + return DataLoader(**kw) + + def train_dataloader(self) -> DataLoader: + return self._make_loader("train", seed=0) + + def val_dataloader(self) -> DataLoader: + return self._make_loader("val", seed=12345) diff --git a/overlay/hydra/diffusion_loss.py b/overlay/hydra/diffusion_loss.py index 2c190e2101e72294218e8023bd511f9ebd4c912a..592453063d90e52abd66a5435483160ec6fc775f 100644 --- a/overlay/hydra/diffusion_loss.py +++ b/overlay/hydra/diffusion_loss.py @@ -1,236 +1,236 @@ -"""MDLM Rao-Blackwellized Masked Diffusion Loss. - -Implements the masked-diffusion ELBO from: - Sahoo et al., "Simple and Effective Masked Diffusion Language Models" (MDLM), - NeurIPS 2024, arXiv:2406.07524. - -Equations referenced: - - Forward process: eq. 2 (per-token Bernoulli masking at rate 1 - alpha_t) - - Log-linear schedule: alpha_t = 1 - t, t ~ Uniform(0, 1) - - RB-ELBO: eq. 7-8 L_RB = E_t E_q [ (1/alpha_t) * CE(x_theta(x_t), x_0) ] - where the expectation over masked positions. - -Key insight: the Rao-Blackwellized estimate replaces an average over all masks -(exponential) by a closed-form weighted CE that applies weight 1/alpha_t only -on the positions that were masked, and 0 on unmasked positions. This gives an -unbiased estimator with lower variance than a naive Monte Carlo over mask -patterns. - -Reference implementation cross-checked against: - https://github.com/kuleshov-group/mdlm (diffusion.py::DiffusionModel._loss) -""" - -from __future__ import annotations - -from typing import Literal - -import torch -import torch.nn.functional as F - - -# Clamping weight keeps gradients finite while still up-weighting high-noise -# positions. Historical value 1/eps=1000 blew up HYDRA training on a 12h v2 -# launch (2026-04-22): loss 26 → 42 → NaN in 13 steps under Muon lr=7e-3 -# because per-token CE × 1000 saturated the 100-unit FAIL guard. The MDLM -# paper reports stable training at Adam lr=1e-4; HYDRA uses Muon at 7e-3 -# (70× larger), so the weight clamp needs to compensate. -# -# Tunable via HYDRA_MDLM_MAX_WEIGHT (default 5.0). Set =1.0 to disable -# weighting entirely (flat masked-LM CE, no RB reweighting — simpler and -# more stable, sacrifices the theoretical ELBO property). -import os as _os -_MAX_WEIGHT: float = float(_os.environ.get("HYDRA_MDLM_MAX_WEIGHT", "5.0")) -_MIN_ALPHA: float = 1.0 / _MAX_WEIGHT # so clamp(alpha, min=_MIN_ALPHA) gives 1/alpha <= _MAX_WEIGHT - - -# --------------------------------------------------------------------------- -# Public API -# --------------------------------------------------------------------------- - -def mdlm_masked_forward_process( - targets: torch.Tensor, - mask_token_id: int, - t: torch.Tensor | None = None, - alpha_schedule: Literal["linear", "loglinear"] = "loglinear", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """MDLM forward (noising) process: mask tokens and compute RB weights. - - Args: - targets: (B, T) int64 token ids — the clean sequence x_0. - mask_token_id: The special token id used to represent a masked token. - t: (B,) float in (0, 1). If None, samples Uniform(0, 1) per batch - element. t=0 means fully clean; t=1 means fully masked. - alpha_schedule: Noise schedule. - "loglinear" (MDLM default): alpha_t = 1 - t - "linear": identical formula — both are provided for completeness - since the paper calls the 1-t schedule "log-linear" in the context - of the ELBO derivation. - - Returns: - x_t : (B, T) int64 — noised sequence; masked positions hold - mask_token_id, unmasked positions equal targets. - mask_positions: (B, T) bool — True where the token was masked. - loss_weights : (B, T) float32 — RB weighting factor. On masked - positions: 1/alpha_t (clamped to _MAX_WEIGHT). On - unmasked positions: 0.0. Summing - (CE * loss_weights * mask_positions).sum() / mask.sum() - gives the per-sample RB-ELBO estimator. - """ - B, T = targets.shape - device = targets.device - dtype = torch.float32 - - # --- sample or validate t --- - if t is None: - # Uniform(0, 1) per batch element; avoid exactly 0 and 1. - t = torch.rand(B, device=device, dtype=dtype) - else: - t = t.to(device=device, dtype=dtype) - if t.shape != (B,): - raise ValueError(f"t must be shape (B,)={(B,)}, got {t.shape}") - if (t < 0).any() or (t > 1).any(): - raise ValueError("t must be in [0, 1]") - - # --- noise schedule: alpha_t = probability that a token is NOT masked --- - # Both "linear" and "loglinear" in MDLM use alpha_t = 1 - t; the paper - # refers to "log-linear" because the schedule is linear in the *log* domain - # of the forward process probability. We expose both names for clarity. - if alpha_schedule in ("linear", "loglinear"): - alpha_t = 1.0 - t # (B,) float, in [0, 1] - else: - raise ValueError(f"Unknown alpha_schedule: {alpha_schedule!r}. Use 'linear' or 'loglinear'.") - - # --- per-token Bernoulli mask --- - # alpha_t[:, None] broadcasts to (B, T). - alpha_t_expanded = alpha_t[:, None] # (B, 1) - # Bernoulli(1 - alpha_t) = 1 means "mask this token". - # We sample independently per token, per batch element. - rand = torch.rand(B, T, device=device, dtype=dtype) - mask_positions = rand > alpha_t_expanded # (B, T) bool - # True → masked position - # False → unmasked (kept as original) - - # --- build x_t --- - x_t = targets.clone() - x_t = torch.where(mask_positions, torch.full_like(x_t, mask_token_id), x_t) - - # --- RB loss weights: 1/alpha_t on masked positions, 0 elsewhere --- - # Clamp alpha_t so weights stay finite near t→1. - safe_alpha = alpha_t.clamp(min=_MIN_ALPHA) # (B,) - weight_per_sample = 1.0 / safe_alpha # (B,) - # Broadcast to (B, T) and zero out unmasked positions. - loss_weights = weight_per_sample[:, None].expand(B, T).to(dtype=dtype) # (B, T) - loss_weights = loss_weights * mask_positions.float() - - return x_t, mask_positions, loss_weights - - -def mdlm_rb_loss( - logits: torch.Tensor, - targets: torch.Tensor, - mask_positions: torch.Tensor, - loss_weights: torch.Tensor, - ignore_index: int = -100, -) -> torch.Tensor: - """Rao-Blackwellized negative ELBO. - - Applies the MDLM loss: cross-entropy on masked positions only, weighted - per-token by loss_weights, averaged over the batch. - - The formula (eq. 7-8 of arXiv:2406.07524): - L_RB = mean_B [ sum_T (weight_t * CE(logits_i, target_i) * mask_i) - / max(sum_T(mask_i), 1) ] - - Args: - logits : (B, T, V) raw logits. May be bf16; internally cast to - float32 for CE computation. - targets : (B, T) int64 true token ids (x_0). - mask_positions: (B, T) bool — True = masked position. - loss_weights : (B, T) float32 — 1/alpha_t on masked positions, 0 elsewhere. - ignore_index : Passed to F.cross_entropy; positions with this label - are excluded from the loss. - - Returns: - Scalar float32 loss. Returns 0.0 tensor if no positions are masked. - """ - B, T, V = logits.shape - - # Ensure float32 for numerical stability; F.cross_entropy accepts fp16/bf16 - # logits but accumulates in float internally anyway. Being explicit avoids - # silent precision surprises. - logits_f = logits.float() # (B, T, V) - - # Build targets with ignore_index on UNmasked positions so CE only fires - # where mask_positions is True. We also honour any pre-existing -100 values - # (e.g. doc-separator masking upstream). - targets_masked = torch.where( - mask_positions & (targets != ignore_index), - targets, - torch.full_like(targets, ignore_index), - ) - - # Per-token CE; shape (B, T). Positions with ignore_index → 0 from CE. - per_tok_ce = F.cross_entropy( - logits_f.reshape(B * T, V), - targets_masked.reshape(B * T), - ignore_index=ignore_index, - reduction="none", - ).reshape(B, T) # (B, T) float32 - - # Apply RB weight. loss_weights already has 0 on unmasked positions. - weighted = per_tok_ce * loss_weights # (B, T) - - # Per-sample mean over masked positions, then average over batch. - mask_f = mask_positions.float() # (B, T) - per_sample_mask_count = mask_f.sum(dim=1).clamp(min=1) # (B,) - per_sample_loss = weighted.sum(dim=1) / per_sample_mask_count # (B,) - - return per_sample_loss.mean() # scalar float32 - - -def mdlm_loss( - logits: torch.Tensor, - targets: torch.Tensor, - mask_token_id: int, - t: torch.Tensor | None = None, - alpha_schedule: Literal["linear", "loglinear"] = "loglinear", - ignore_index: int = -100, -) -> torch.Tensor: - """Convenience wrapper: forward process + RB-ELBO in one call. - - Suitable for the common case where the caller has full-vocab logits and - wants a drop-in replacement for a standard masked-LM CE loss. - - Args: - logits : (B, T, V) raw logits. - targets : (B, T) int64 clean token ids. - mask_token_id : The MASK token id used to corrupt the input. - t : Optional (B,) timestep in (0, 1). Sampled if None. - alpha_schedule: "loglinear" (default) or "linear". - ignore_index : Token id to ignore in the loss (e.g. padding). - - Returns: - Scalar float32 MDLM RB-ELBO loss. - - Note on sampled-softmax / partial logits: - If your model only computes logits for a subset of vocab positions - (e.g. HYDRA's sampled-softmax head), call mdlm_masked_forward_process - and mdlm_rb_loss separately. mdlm_rb_loss expects full-vocab logits. - """ - x_t, mask_positions, loss_weights = mdlm_masked_forward_process( - targets=targets, - mask_token_id=mask_token_id, - t=t, - alpha_schedule=alpha_schedule, - ) - # x_t is produced for the model's input (not used by this convenience - # wrapper since logits are already provided by the caller). In a real - # training loop the caller feeds x_t into the model to get logits, THEN - # calls this function. See the orchestrator wiring note in training.py. - return mdlm_rb_loss( - logits=logits, - targets=targets, - mask_positions=mask_positions, - loss_weights=loss_weights, - ignore_index=ignore_index, - ) +"""MDLM Rao-Blackwellized Masked Diffusion Loss. + +Implements the masked-diffusion ELBO from: + Sahoo et al., "Simple and Effective Masked Diffusion Language Models" (MDLM), + NeurIPS 2024, arXiv:2406.07524. + +Equations referenced: + - Forward process: eq. 2 (per-token Bernoulli masking at rate 1 - alpha_t) + - Log-linear schedule: alpha_t = 1 - t, t ~ Uniform(0, 1) + - RB-ELBO: eq. 7-8 L_RB = E_t E_q [ (1/alpha_t) * CE(x_theta(x_t), x_0) ] + where the expectation over masked positions. + +Key insight: the Rao-Blackwellized estimate replaces an average over all masks +(exponential) by a closed-form weighted CE that applies weight 1/alpha_t only +on the positions that were masked, and 0 on unmasked positions. This gives an +unbiased estimator with lower variance than a naive Monte Carlo over mask +patterns. + +Reference implementation cross-checked against: + https://github.com/kuleshov-group/mdlm (diffusion.py::DiffusionModel._loss) +""" + +from __future__ import annotations + +from typing import Literal + +import torch +import torch.nn.functional as F + + +# Clamping weight keeps gradients finite while still up-weighting high-noise +# positions. Historical value 1/eps=1000 blew up HYDRA training on a 12h v2 +# launch (2026-04-22): loss 26 → 42 → NaN in 13 steps under Muon lr=7e-3 +# because per-token CE × 1000 saturated the 100-unit FAIL guard. The MDLM +# paper reports stable training at Adam lr=1e-4; HYDRA uses Muon at 7e-3 +# (70× larger), so the weight clamp needs to compensate. +# +# Tunable via HYDRA_MDLM_MAX_WEIGHT (default 5.0). Set =1.0 to disable +# weighting entirely (flat masked-LM CE, no RB reweighting — simpler and +# more stable, sacrifices the theoretical ELBO property). +import os as _os +_MAX_WEIGHT: float = float(_os.environ.get("HYDRA_MDLM_MAX_WEIGHT", "5.0")) +_MIN_ALPHA: float = 1.0 / _MAX_WEIGHT # so clamp(alpha, min=_MIN_ALPHA) gives 1/alpha <= _MAX_WEIGHT + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +def mdlm_masked_forward_process( + targets: torch.Tensor, + mask_token_id: int, + t: torch.Tensor | None = None, + alpha_schedule: Literal["linear", "loglinear"] = "loglinear", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """MDLM forward (noising) process: mask tokens and compute RB weights. + + Args: + targets: (B, T) int64 token ids — the clean sequence x_0. + mask_token_id: The special token id used to represent a masked token. + t: (B,) float in (0, 1). If None, samples Uniform(0, 1) per batch + element. t=0 means fully clean; t=1 means fully masked. + alpha_schedule: Noise schedule. + "loglinear" (MDLM default): alpha_t = 1 - t + "linear": identical formula — both are provided for completeness + since the paper calls the 1-t schedule "log-linear" in the context + of the ELBO derivation. + + Returns: + x_t : (B, T) int64 — noised sequence; masked positions hold + mask_token_id, unmasked positions equal targets. + mask_positions: (B, T) bool — True where the token was masked. + loss_weights : (B, T) float32 — RB weighting factor. On masked + positions: 1/alpha_t (clamped to _MAX_WEIGHT). On + unmasked positions: 0.0. Summing + (CE * loss_weights * mask_positions).sum() / mask.sum() + gives the per-sample RB-ELBO estimator. + """ + B, T = targets.shape + device = targets.device + dtype = torch.float32 + + # --- sample or validate t --- + if t is None: + # Uniform(0, 1) per batch element; avoid exactly 0 and 1. + t = torch.rand(B, device=device, dtype=dtype) + else: + t = t.to(device=device, dtype=dtype) + if t.shape != (B,): + raise ValueError(f"t must be shape (B,)={(B,)}, got {t.shape}") + if (t < 0).any() or (t > 1).any(): + raise ValueError("t must be in [0, 1]") + + # --- noise schedule: alpha_t = probability that a token is NOT masked --- + # Both "linear" and "loglinear" in MDLM use alpha_t = 1 - t; the paper + # refers to "log-linear" because the schedule is linear in the *log* domain + # of the forward process probability. We expose both names for clarity. + if alpha_schedule in ("linear", "loglinear"): + alpha_t = 1.0 - t # (B,) float, in [0, 1] + else: + raise ValueError(f"Unknown alpha_schedule: {alpha_schedule!r}. Use 'linear' or 'loglinear'.") + + # --- per-token Bernoulli mask --- + # alpha_t[:, None] broadcasts to (B, T). + alpha_t_expanded = alpha_t[:, None] # (B, 1) + # Bernoulli(1 - alpha_t) = 1 means "mask this token". + # We sample independently per token, per batch element. + rand = torch.rand(B, T, device=device, dtype=dtype) + mask_positions = rand > alpha_t_expanded # (B, T) bool + # True → masked position + # False → unmasked (kept as original) + + # --- build x_t --- + x_t = targets.clone() + x_t = torch.where(mask_positions, torch.full_like(x_t, mask_token_id), x_t) + + # --- RB loss weights: 1/alpha_t on masked positions, 0 elsewhere --- + # Clamp alpha_t so weights stay finite near t→1. + safe_alpha = alpha_t.clamp(min=_MIN_ALPHA) # (B,) + weight_per_sample = 1.0 / safe_alpha # (B,) + # Broadcast to (B, T) and zero out unmasked positions. + loss_weights = weight_per_sample[:, None].expand(B, T).to(dtype=dtype) # (B, T) + loss_weights = loss_weights * mask_positions.float() + + return x_t, mask_positions, loss_weights + + +def mdlm_rb_loss( + logits: torch.Tensor, + targets: torch.Tensor, + mask_positions: torch.Tensor, + loss_weights: torch.Tensor, + ignore_index: int = -100, +) -> torch.Tensor: + """Rao-Blackwellized negative ELBO. + + Applies the MDLM loss: cross-entropy on masked positions only, weighted + per-token by loss_weights, averaged over the batch. + + The formula (eq. 7-8 of arXiv:2406.07524): + L_RB = mean_B [ sum_T (weight_t * CE(logits_i, target_i) * mask_i) + / max(sum_T(mask_i), 1) ] + + Args: + logits : (B, T, V) raw logits. May be bf16; internally cast to + float32 for CE computation. + targets : (B, T) int64 true token ids (x_0). + mask_positions: (B, T) bool — True = masked position. + loss_weights : (B, T) float32 — 1/alpha_t on masked positions, 0 elsewhere. + ignore_index : Passed to F.cross_entropy; positions with this label + are excluded from the loss. + + Returns: + Scalar float32 loss. Returns 0.0 tensor if no positions are masked. + """ + B, T, V = logits.shape + + # Ensure float32 for numerical stability; F.cross_entropy accepts fp16/bf16 + # logits but accumulates in float internally anyway. Being explicit avoids + # silent precision surprises. + logits_f = logits.float() # (B, T, V) + + # Build targets with ignore_index on UNmasked positions so CE only fires + # where mask_positions is True. We also honour any pre-existing -100 values + # (e.g. doc-separator masking upstream). + targets_masked = torch.where( + mask_positions & (targets != ignore_index), + targets, + torch.full_like(targets, ignore_index), + ) + + # Per-token CE; shape (B, T). Positions with ignore_index → 0 from CE. + per_tok_ce = F.cross_entropy( + logits_f.reshape(B * T, V), + targets_masked.reshape(B * T), + ignore_index=ignore_index, + reduction="none", + ).reshape(B, T) # (B, T) float32 + + # Apply RB weight. loss_weights already has 0 on unmasked positions. + weighted = per_tok_ce * loss_weights # (B, T) + + # Per-sample mean over masked positions, then average over batch. + mask_f = mask_positions.float() # (B, T) + per_sample_mask_count = mask_f.sum(dim=1).clamp(min=1) # (B,) + per_sample_loss = weighted.sum(dim=1) / per_sample_mask_count # (B,) + + return per_sample_loss.mean() # scalar float32 + + +def mdlm_loss( + logits: torch.Tensor, + targets: torch.Tensor, + mask_token_id: int, + t: torch.Tensor | None = None, + alpha_schedule: Literal["linear", "loglinear"] = "loglinear", + ignore_index: int = -100, +) -> torch.Tensor: + """Convenience wrapper: forward process + RB-ELBO in one call. + + Suitable for the common case where the caller has full-vocab logits and + wants a drop-in replacement for a standard masked-LM CE loss. + + Args: + logits : (B, T, V) raw logits. + targets : (B, T) int64 clean token ids. + mask_token_id : The MASK token id used to corrupt the input. + t : Optional (B,) timestep in (0, 1). Sampled if None. + alpha_schedule: "loglinear" (default) or "linear". + ignore_index : Token id to ignore in the loss (e.g. padding). + + Returns: + Scalar float32 MDLM RB-ELBO loss. + + Note on sampled-softmax / partial logits: + If your model only computes logits for a subset of vocab positions + (e.g. HYDRA's sampled-softmax head), call mdlm_masked_forward_process + and mdlm_rb_loss separately. mdlm_rb_loss expects full-vocab logits. + """ + x_t, mask_positions, loss_weights = mdlm_masked_forward_process( + targets=targets, + mask_token_id=mask_token_id, + t=t, + alpha_schedule=alpha_schedule, + ) + # x_t is produced for the model's input (not used by this convenience + # wrapper since logits are already provided by the caller). In a real + # training loop the caller feeds x_t into the model to get logits, THEN + # calls this function. See the orchestrator wiring note in training.py. + return mdlm_rb_loss( + logits=logits, + targets=targets, + mask_positions=mask_positions, + loss_weights=loss_weights, + ignore_index=ignore_index, + ) diff --git a/overlay/hydra/engram.py b/overlay/hydra/engram.py index a9bececebbd79d742b8b0a6bf339c9cb7e756fe9..54eeb5b4cd2ef86e49c31d4aa7072d0146497a43 100644 --- a/overlay/hydra/engram.py +++ b/overlay/hydra/engram.py @@ -1,175 +1,160 @@ -"""GPU Engram — Top-k Sparse Hopfield retrieval, scales to n_columns >= 32768. - -## What changed (scatter-gather → top-k Hopfield) - -The original forward used `self.memory[indices]` (scatter-gather), which misses -L2 cache at n_columns > 4096 and creates a hard tps ceiling. - -An earlier Hopfield implementation used `entmax15` for sparse attention, but -entmax's internal `torch.sort` over the full n_columns dimension allocates -~1 GB scratch at (B*T=8192, n_columns=32768) and OOMs on a 6 GB card. - -This module replaces the sort-based entmax with **top-k softmax**, which is -O(B*T*K) in memory and O(B*T*K * log n_columns) in compute (the top-k is -radix-selection under the hood — not a full sort). Sparsity is still exact: -only K columns have non-zero weight per (batch, position). - -## Why this scales where entmax didn't - -- `scores = x @ memory.T` is (B, T, n_columns) — 268 MB at bf16 with n_columns=32768. -- `scores.topk(K)` allocates only (B, T, K) — ~2 MB at K=64. No full sort. -- `memory[topk_idx]` gathers (B, T, K, d_model) — ~32 MB at bf16. Gather is - on the LAST axis of memory (columns), contiguous stride-1 rows, cache-friendly. -- `retrieved = einsum(topk_w, selected_mem)` — ~4 MB. Final reduction. - -Peak working set well under 400 MB at any reasonable n_columns + K. The weights -tensor is never densified (which would have been the (B, T, n_columns) killer). - -## Gradient flow - -Both the topk gather and the einsum are autograd-tracked, so `self.memory` -receives gradient from the LM loss (which the Hebbian scatter-gather path did -not). `topk` indices are detached — gradient flows through `topk_vals` via the -selected memory rows. - -## Sparsity - -Exactly K columns have non-zero weight per position. Default K=64, tunable via -HYDRA_ENGRAM_TOPK. - -## token_ids argument - -Accepted for API compatibility with hydra/model.py; unused in retrieval. The -optional Hebbian boost (hebbian_boost=True) uses the hash-indexed path for -its EMA write only. - -## Checkpoint compatibility - -`self.memory` shape (n_columns, d_model) is unchanged; existing .pt/.ckpt -files load without migration. -""" - -from __future__ import annotations - -import os - -import torch -import torch.nn as nn - - -# Top-k width — how many memory columns get non-zero weight per position. -# Default 64 matches the entmax sparsity fraction we observed empirically -# (~0.2% of 32768 columns == 64). HYDRA_ENGRAM_TOPK env var overrides. -_ENGRAM_TOPK = int(os.environ.get("HYDRA_ENGRAM_TOPK", "64")) - - -class GPUEngram(nn.Module): - """GPU Engram: Top-k Sparse Hopfield retrieval. - - Args: - d_model: Model dimension — must match the surrounding transformer. - n_columns: Number of memory columns (key-value pairs). Safe up to - n_columns = 65536 at d_model = 384 on a 6 GB card with - B*T <= 8192. - max_ngram: Retained for API compatibility; unused in retrieval. - hebbian_boost: If True, also run a Hebbian EMA write on the memory bank - during training. Default False — the top-k gradient path - provides learning signal without this. - """ - - def __init__( - self, - d_model: int, - n_columns: int = 1024, - max_ngram: int = 3, - hebbian_boost: bool = False, - ) -> None: - super().__init__() - self.n_columns = n_columns - self.max_ngram = max_ngram - self.hebbian_boost = hebbian_boost - # Shape unchanged from original — existing checkpoints load cleanly. - self.memory = nn.Parameter(torch.randn(n_columns, d_model) * 0.01) - self.gate = nn.Linear(d_model, 1, bias=True) - nn.init.constant_(self.gate.bias, 0.0) # START OPEN - # Clamp topk K to n_columns so topk doesn't error at small engram. - self.topk_k = min(_ENGRAM_TOPK, n_columns) - # Retained for any external code that reads these attrs. - self.primes = [2654435761, 2246822519, 3266489917] - self.hebbian_lr = 0.01 - - # ------------------------------------------------------------------ - # _hash: retained for API/checkpoint compat; unused in retrieval path. - # ------------------------------------------------------------------ - - def _hash(self, token_ids: torch.Tensor) -> torch.Tensor: - """N-gram hash → column index (Hebbian-write target only, not retrieval).""" - B, T = token_ids.shape - h = token_ids * self.primes[0] - if T > 1: - shifted1 = torch.roll(token_ids, 1, dims=1) - shifted1[:, 0] = 0 - h = h ^ (shifted1 * self.primes[1]) - if T > 2: - shifted2 = torch.roll(token_ids, 2, dims=1) - shifted2[:, :2] = 0 - h = h ^ (shifted2 * self.primes[2]) - return h % self.n_columns - - # ------------------------------------------------------------------ - # forward - # ------------------------------------------------------------------ - - def forward(self, x: torch.Tensor, token_ids: torch.Tensor): - """Top-k Hopfield retrieve + soft gate + residual. - - Args: - x: (B, T, d_model) — input activations. - token_ids: (B, T) — accepted for API compat; only used in the - optional Hebbian boost path. - - Returns: - (x + alpha * retrieved, hit_rate) - - x + alpha * retrieved: (B, T, d_model) - - hit_rate: scalar tensor — fraction of gate values > 0.1 - """ - B, T, D = x.shape - - # ---- 1. Similarity scores (coalesced GEMM) ---------------------- - # scores[b, t, c] = dot(x[b,t], memory[c]) - scores = x @ self.memory.T # (B, T, n_columns) - - # ---- 2. Top-k sparse attention ---------------------------------- - # topk uses radix select, not a sort — O(n_columns) memory, not O(n_columns log n_columns). - # Never materializes a dense (B, T, n_columns) weights tensor. - topk_vals, topk_idx = scores.topk(self.topk_k, dim=-1) # (B, T, K), (B, T, K) - topk_w = torch.softmax(topk_vals, dim=-1) # (B, T, K) - - # ---- 3. Gather selected memory rows ----------------------------- - # memory[topk_idx] is a gather along axis 0 of memory (n_columns, d_model). - # Output shape (B, T, K, d_model) — K is small, so gather bandwidth is - # O(B*T*K*d_model), independent of n_columns. - selected_mem = self.memory[topk_idx] # (B, T, K, d_model) - - # ---- 4. Weighted sum → retrieved vector ------------------------- - retrieved = torch.einsum('btk,btkd->btd', topk_w, selected_mem) # (B, T, d_model) - - # ---- 5. Soft gate ----------------------------------------------- - alpha = torch.sigmoid(self.gate(x)) # (B, T, 1) - - # ---- 6. Optional Hebbian EMA write ------------------------------ - if self.training and self.hebbian_boost: - with torch.no_grad(): - indices = self._hash(token_ids) - flat_idx = indices.reshape(-1) # (B*T,) - flat_x = x.detach().reshape(-1, D) # (B*T, d_model) - mem_dtype = self.memory.data.dtype - updates = ( - self.hebbian_lr * flat_x - - self.hebbian_lr * self.memory.data[flat_idx] - ).to(mem_dtype) - self.memory.data.index_add_(0, flat_idx, updates) - - # ---- 7. Residual + hit_rate ------------------------------------- - hit_rate = (alpha.detach() > 0.1).float().mean() - return x + alpha * retrieved, hit_rate +"""GPU Engram — Top-k Sparse Hopfield retrieval with optional Cantor/SDR nerve constraint.""" + +from __future__ import annotations + +import os + +import torch +import torch.nn as nn + + +_ENGRAM_TOPK = int(os.environ.get("HYDRA_ENGRAM_TOPK", "64")) + + +class GPUEngram(nn.Module): + """GPU Engram: Top-k Sparse Hopfield retrieval. + + Default `routing_mode=flat` preserves the existing full-memory top-k path. + `cantor_sdr` constrains candidates to the current Cantor leaf shard and SDR + active offsets. `auto` only uses that local path when it is cheaper than the + full score matrix (`K * d_model < n_columns`). + """ + + def __init__( + self, + d_model: int, + n_columns: int = 1024, + max_ngram: int = 3, + hebbian_boost: bool = False, + ) -> None: + super().__init__() + self.n_columns = n_columns + self.max_ngram = max_ngram + self.hebbian_boost = hebbian_boost + self.memory = nn.Parameter(torch.randn(n_columns, d_model) * 0.01) + self.gate = nn.Linear(d_model, 1, bias=True) + nn.init.constant_(self.gate.bias, 0.0) + self.topk_k = min(_ENGRAM_TOPK, n_columns) + self.primes = [2654435761, 2246822519, 3266489917] + self.hebbian_lr = 0.01 + self.routing_mode = os.environ.get("HYDRA_ENGRAM_ROUTING", "auto").lower() + + def _hash(self, token_ids: torch.Tensor) -> torch.Tensor: + B, T = token_ids.shape + h = token_ids * self.primes[0] + if T > 1: + shifted1 = torch.roll(token_ids, 1, dims=1) + shifted1[:, 0] = 0 + h = h ^ (shifted1 * self.primes[1]) + if T > 2: + shifted2 = torch.roll(token_ids, 2, dims=1) + shifted2[:, :2] = 0 + h = h ^ (shifted2 * self.primes[2]) + return h % self.n_columns + + def _validate_active_indices(self, sdr_active_indices: torch.Tensor, x: torch.Tensor) -> None: + if not torch.is_floating_point(sdr_active_indices) and sdr_active_indices.dtype != torch.bool: + pass + else: + raise ValueError("Engram Cantor/SDR routing expects compact active indices, not a dense SDR mask") + if sdr_active_indices.dim() not in (2, 3): + raise ValueError("compact active indices must have shape (B,T,K) or (B*T,K)") + # Dense SDR masks arrive with K ~= n_bits; compact buffers are small + # (retina target_active or RealityBridge l0_k). Refuse obviously dense + # masks so forced cantor_sdr cannot silently route 0/1 values as offsets. + if sdr_active_indices.shape[-1] > 1024 or sdr_active_indices.shape[-1] > self.n_columns: + raise ValueError("Engram Cantor/SDR routing expects compact active indices, not a dense SDR mask") + + def _cantor_sdr_candidates( + self, + sdr_active_indices: torch.Tensor, + cantor_leaf_ids: torch.Tensor, + n_leaves: int, + ) -> torch.Tensor: + """Map SDR active offsets into each Cantor leaf's Engram column shard.""" + self._validate_active_indices(sdr_active_indices, cantor_leaf_ids) + if sdr_active_indices.dim() == 2: + B, T = cantor_leaf_ids.shape + sdr_active_indices = sdr_active_indices.view(B, T, -1) + sdr = sdr_active_indices.to(device=cantor_leaf_ids.device, dtype=torch.long) + leaves = cantor_leaf_ids.to(dtype=torch.long).clamp(min=0, max=max(0, n_leaves - 1)) + cols_per_leaf = max(1, self.n_columns // max(1, n_leaves)) + offsets = sdr.remainder(cols_per_leaf) + base = leaves.unsqueeze(-1) * cols_per_leaf + return (base + offsets).clamp(max=self.n_columns - 1) + + def _flat_retrieve(self, x: torch.Tensor) -> torch.Tensor: + scores = x @ self.memory.T + topk_vals, topk_idx = scores.topk(self.topk_k, dim=-1) + topk_w = torch.softmax(topk_vals, dim=-1) + selected_mem = self.memory[topk_idx] + return torch.einsum('btk,btkd->btd', topk_w, selected_mem) + + def _cantor_sdr_retrieve( + self, + x: torch.Tensor, + sdr_active_indices: torch.Tensor, + cantor_leaf_ids: torch.Tensor, + cantor_n_leaves: int, + ) -> torch.Tensor: + candidates = self._cantor_sdr_candidates( + sdr_active_indices, + cantor_leaf_ids, + n_leaves=cantor_n_leaves, + ) + cand_mem = self.memory[candidates] + scores = torch.einsum('btd,btkd->btk', x, cand_mem) + k = min(self.topk_k, scores.shape[-1]) + topk_vals, local_idx = scores.topk(k, dim=-1) + topk_w = torch.softmax(topk_vals, dim=-1) + global_idx = candidates.gather(-1, local_idx) + selected_mem = self.memory[global_idx] + return torch.einsum('btk,btkd->btd', topk_w, selected_mem) + + def forward( + self, + x: torch.Tensor, + token_ids: torch.Tensor, + sdr_active_indices: torch.Tensor | None = None, + cantor_leaf_ids: torch.Tensor | None = None, + cantor_n_leaves: int | None = None, + ): + B, T, D = x.shape + mode = self.routing_mode + use_cantor = ( + mode in {"cantor_sdr", "auto"} + and sdr_active_indices is not None + and cantor_leaf_ids is not None + and cantor_n_leaves is not None + ) + if mode == "auto" and use_cantor: + k_active = sdr_active_indices.shape[-1] + # Compare actual retrieval candidates against the full-memory scan. + # The previous `(k_active * D) < n_columns` check mixed candidate + # count with feature dimension, so d256/k64 fell back to flat + # retrieval even though Cantor/SDR scores only 64 candidates vs + # 8k-16k memory columns. That kept required subsystems active but + # spent tens of billions of extra MACs per forward. + use_cantor = k_active < self.n_columns + + if use_cantor and mode in {"cantor_sdr", "auto"}: + retrieved = self._cantor_sdr_retrieve(x, sdr_active_indices, cantor_leaf_ids, cantor_n_leaves) + else: + retrieved = self._flat_retrieve(x) + + alpha = torch.sigmoid(self.gate(x)) + + if self.training and self.hebbian_boost: + with torch.no_grad(): + indices = self._hash(token_ids) + flat_idx = indices.reshape(-1) + flat_x = x.detach().reshape(-1, D) + mem_dtype = self.memory.data.dtype + updates = ( + self.hebbian_lr * flat_x + - self.hebbian_lr * self.memory.data[flat_idx] + ).to(mem_dtype) + self.memory.data.index_add_(0, flat_idx, updates) + + hit_rate = (alpha.detach() > 0.1).float().mean() + return x + alpha * retrieved, hit_rate diff --git a/overlay/hydra/eval.py b/overlay/hydra/eval.py index 4dfb60b6dbf24b000b611e779b21f9a5e6aa8349..632d25b6d6fc748528fe437019c92ad33ee058b6 100644 --- a/overlay/hydra/eval.py +++ b/overlay/hydra/eval.py @@ -1,217 +1,210 @@ -"""Evaluation: factual probes + sampled factual English scoring. - -Extracted from train.py (W1 modularization). Semantics unchanged. - -Perf optimizations (eval_perf_fix): -- Probe mode: single forward per prompt instead of autoregressive gen -- Batch decode: all GPU work first, all CPU decode after -- Batched factual probes: single padded forward instead of N sequential -""" - -from __future__ import annotations - -import os -import re as _re - -import torch - -from hydra.config import FACTUAL_SAMPLES, FACTUAL_BATCH, FACTUAL_GEN_TOKENS - -# Default to probe mode (1 forward per prompt); set HYDRA_FACTUAL_MODE=gen for -# the original autoregressive generation path. -FACTUAL_MODE = os.environ.get("HYDRA_FACTUAL_MODE", "probe") - -FACTUAL_EVAL = [ - # Hard factual recall — requires specific knowledge memorization - ("The capital of France is", ["Paris", "paris"]), - ("Water boils at", ["100", "boiling"]), - ("The largest planet in our solar system is", ["Jupiter", "jupiter"]), - # Easier completions — common collocations / patterns the model may pick up - ("Once upon a", ["time"]), - ("Hello, my name", ["is", "'s"]), - ("The cat sat on the", ["mat", "floor", "rug", "table", "couch", "chair", "ground"]), - ("She opened the door and", ["walked", "saw", "found", "stepped", "looked", "went", "ran"]), - # Original hard ones kept for completeness - ("The speed of light is approximately", ["299", "300", "186,000", "light speed"]), - ("Two plus two equals", ["4", "four"]), -] - -_FACTUAL_PROBES = [ - "The capital of France is", - "Water boils at", - "The largest planet in our solar system is", - "The speed of light is approximately", - "Shakespeare wrote", -] - - -def run_factual_probes(model, tokenizer, device, autocast_ctx) -> None: - """Top-5 next-token predictions for canonical factual prompts. - - Batched: pads all prompts into a single forward pass instead of N - sequential passes. - """ - print("\n--- Factual Probes ---") - model.eval() - - # Process probes one at a time to avoid cooperative launch limit - # (batched forward with B=len(probes) can exceed SM residency cap). - for prompt_text in _FACTUAL_PROBES: - ids = tokenizer.encode(prompt_text) - x = torch.tensor([ids], device=device) - with torch.no_grad(), autocast_ctx: - logits = model(x) - probs = torch.softmax(logits[0, -1].float(), dim=-1) - top5 = torch.topk(probs, 5) - completions = [tokenizer.decode([idx.item()]) for idx in top5.indices] - probs_list = [f"{p:.4f}" for p in top5.values[:3].tolist()] - print(f' "{prompt_text}" -> {completions[:3]} (p={probs_list})') - print("--- End Factual Probes ---\n") - - -# --------------------------------------------------------------------------- -# Probe mode: single forward per prompt (Fix D) -# --------------------------------------------------------------------------- - -def _run_factual_english_probe(model, tokenizer, max_seq_len: int): - """Fast probe mode: for each (prompt, answers), encode prompt + each answer - candidate as a single sequence, do ONE forward pass, and check if the model's - argmax at the last prompt token matches the first answer token. - - Falls back to checking top-K predictions to be generous (same as gen mode - which samples multiple temperatures). - """ - print("---") - print("factual_english_samples: (probe mode)") - model.eval() - hits = 0 - - with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - for prompt, answers in FACTUAL_EVAL: - prompt_ids = tokenizer.encode(prompt) - prompt_len = len(prompt_ids) - x = torch.tensor([prompt_ids], device="cuda", dtype=torch.long) - logits = model(x, targets=None) - # logits shape: [1, seq_len, vocab] or [1, vocab] - if logits.dim() == 3: - last_logits = logits[0, -1, :] - else: - last_logits = logits[0] - - probs = torch.softmax(last_logits.float(), dim=-1) - # Check top-K predictions (generous: K=20 to match multi-sample gen) - top_k = min(20, probs.shape[-1]) - top_ids = torch.topk(probs, top_k).indices.tolist() - top_tokens = [tokenizer.decode([tid]).strip().lower() for tid in top_ids] - - answers_lower = [a.lower() for a in answers] - any_hit = any( - any(a in tok for a in answers_lower) - for tok in top_tokens - ) - if any_hit: - hits += 1 - - best_completion = tokenizer.decode([top_ids[0]]) - print(f" prompt: {prompt!r}") - print(f" output: {(prompt + best_completion).replace(chr(10), ' ')!r}") - print(f" hit: {any_hit} (probe top-{top_k})") - - score = hits / len(FACTUAL_EVAL) - print("---") - print(f"factual_english_score: {score:.4f}") - print(f"factual_english_hits: {hits}/{len(FACTUAL_EVAL)}") - return score, hits, len(FACTUAL_EVAL) - - -# --------------------------------------------------------------------------- -# Gen mode: original autoregressive path (Fix F: batch decode) -# --------------------------------------------------------------------------- - -def _run_factual_english_gen(model, tokenizer, max_seq_len: int): - """Original autoregressive generation path with batch decode optimization: - all GPU work runs first, then all CPU decoding happens after.""" - print("---") - print("factual_english_samples: (gen mode)") - model.eval() - - num_samples = FACTUAL_SAMPLES - batch = FACTUAL_BATCH - gen_tokens = FACTUAL_GEN_TOKENS - # Optional fast incremental decode path for recurrence-capable backbones. - # If disabled, we preserve the original full-context re-forward behavior. - incremental_decode = os.environ.get("HYDRA_FACTUAL_GEN_INCREMENTAL", "1") == "1" - temps = [0.7, 0.9, 1.1] - hits = 0 - - with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - for prompt, answers in FACTUAL_EVAL: - ids = tokenizer.encode(prompt) - answers_lower = [a.lower() for a in answers] - # Collect all generated token sequences on GPU first - all_rows: list[list[int]] = [] - samples_done = 0 - batch_idx = 0 - while samples_done < num_samples: - b = min(batch, num_samples - samples_done) - temp = temps[batch_idx % len(temps)] - batch_idx += 1 - ctx = torch.tensor([ids] * b, device="cuda", dtype=torch.long) - logits = model(ctx, targets=None) - for _ in range(gen_tokens): - next_logits = logits[:, -1, :] if logits.dim() == 3 else logits - probs = torch.softmax(next_logits.float() / temp, dim=-1) - next_id = torch.multinomial(probs, num_samples=1) - ctx = torch.cat([ctx, next_id], dim=1) - if ctx.size(1) >= max_seq_len: - break - if incremental_decode: - logits = model(ctx[:, -1:], targets=None) - else: - logits = model(ctx, targets=None) - # Transfer to CPU in one shot, no per-row sync - all_rows.extend(ctx.cpu().tolist()) - samples_done += b - - # CPU-side batch decode — no GPU sync between decodes - any_hit = False - first_gen = None - hit_gen = None - for row in all_rows: - generated = tokenizer.decode(row) - continuation = generated[len(prompt):].strip() - _words = set(w.lower() for w in _re.findall(r"\b[\w'-]+\b", continuation)) - hit = any(a in _words for a in answers_lower) - if first_gen is None: - first_gen = generated - if hit: - any_hit = True - if hit_gen is None: - hit_gen = generated - if any_hit: - hits += 1 - print(f" prompt: {prompt!r}") - print(f" output: {(first_gen or '').replace(chr(10), ' ')!r}") - print(f" hit: {any_hit} (any of {num_samples} samples, temps={temps}, gen={gen_tokens}tok)") - if hit_gen is not None and hit_gen != first_gen: - print(f" hit_sample: {hit_gen.replace(chr(10), ' ')!r}") - - score = hits / len(FACTUAL_EVAL) - print("---") - print(f"factual_english_score: {score:.4f}") - print(f"factual_english_hits: {hits}/{len(FACTUAL_EVAL)}") - return score, hits, len(FACTUAL_EVAL) - - -# --------------------------------------------------------------------------- -# Public entry point -# --------------------------------------------------------------------------- - -def run_factual_english(model, tokenizer, max_seq_len: int): - """Dispatch to probe (fast, default) or gen (original) mode. - - Set HYDRA_FACTUAL_MODE=gen to use the autoregressive path. - """ - if FACTUAL_MODE == "gen": - return _run_factual_english_gen(model, tokenizer, max_seq_len) - return _run_factual_english_probe(model, tokenizer, max_seq_len) +"""Evaluation: factual probes + sampled factual English scoring. + +Extracted from train.py (W1 modularization). Semantics unchanged. + +Perf optimizations (eval_perf_fix): +- Probe mode: single forward per prompt instead of autoregressive gen +- Batch decode: all GPU work first, all CPU decode after +- Batched factual probes: single padded forward instead of N sequential +""" + +from __future__ import annotations + +import os +import re as _re + +import torch + +from hydra.config import FACTUAL_SAMPLES, FACTUAL_BATCH, FACTUAL_GEN_TOKENS + +# Default to probe mode (1 forward per prompt); set HYDRA_FACTUAL_MODE=gen for +# the original autoregressive generation path. +FACTUAL_MODE = os.environ.get("HYDRA_FACTUAL_MODE", "probe") + +FACTUAL_EVAL = [ + # Hard factual recall — requires specific knowledge memorization + ("The capital of France is", ["Paris", "paris"]), + ("Water boils at", ["100", "boiling"]), + ("The largest planet in our solar system is", ["Jupiter", "jupiter"]), + # Easier completions — common collocations / patterns the model may pick up + ("Once upon a", ["time"]), + ("Hello, my name", ["is", "'s"]), + ("The cat sat on the", ["mat", "floor", "rug", "table", "couch", "chair", "ground"]), + ("She opened the door and", ["walked", "saw", "found", "stepped", "looked", "went", "ran"]), + # Original hard ones kept for completeness + ("The speed of light is approximately", ["299", "300", "186,000", "light speed"]), + ("Two plus two equals", ["4", "four"]), +] + +_FACTUAL_PROBES = [ + "The capital of France is", + "Water boils at", + "The largest planet in our solar system is", + "The speed of light is approximately", + "Shakespeare wrote", +] + + +def run_factual_probes(model, tokenizer, device, autocast_ctx) -> None: + """Top-5 next-token predictions for canonical factual prompts. + + Batched: pads all prompts into a single forward pass instead of N + sequential passes. + """ + print("\n--- Factual Probes ---") + model.eval() + + # Process probes one at a time to avoid cooperative launch limit + # (batched forward with B=len(probes) can exceed SM residency cap). + for prompt_text in _FACTUAL_PROBES: + ids = tokenizer.encode(prompt_text) + x = torch.tensor([ids], device=device) + with torch.no_grad(), autocast_ctx: + logits = model(x) + probs = torch.softmax(logits[0, -1].float(), dim=-1) + top5 = torch.topk(probs, 5) + completions = [tokenizer.decode([idx.item()]) for idx in top5.indices] + probs_list = [f"{p:.4f}" for p in top5.values[:3].tolist()] + print(f' "{prompt_text}" -> {completions[:3]} (p={probs_list})') + print("--- End Factual Probes ---\n") + + +# --------------------------------------------------------------------------- +# Probe mode: single forward per prompt (Fix D) +# --------------------------------------------------------------------------- + +def _run_factual_english_probe(model, tokenizer, max_seq_len: int): + """Fast probe mode: for each (prompt, answers), encode prompt + each answer + candidate as a single sequence, do ONE forward pass, and check if the model's + argmax at the last prompt token matches the first answer token. + + Falls back to checking top-K predictions to be generous (same as gen mode + which samples multiple temperatures). + """ + print("---") + print("factual_english_samples: (probe mode)") + model.eval() + hits = 0 + + with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + for prompt, answers in FACTUAL_EVAL: + prompt_ids = tokenizer.encode(prompt) + prompt_len = len(prompt_ids) + x = torch.tensor([prompt_ids], device="cuda", dtype=torch.long) + logits = model(x, targets=None) + # logits shape: [1, seq_len, vocab] or [1, vocab] + if logits.dim() == 3: + last_logits = logits[0, -1, :] + else: + last_logits = logits[0] + + probs = torch.softmax(last_logits.float(), dim=-1) + # Check top-K predictions (generous: K=20 to match multi-sample gen) + top_k = min(20, probs.shape[-1]) + top_ids = torch.topk(probs, top_k).indices.tolist() + top_tokens = [tokenizer.decode([tid]).strip().lower() for tid in top_ids] + + answers_lower = [a.lower() for a in answers] + any_hit = any( + any(a in tok for a in answers_lower) + for tok in top_tokens + ) + if any_hit: + hits += 1 + + best_completion = tokenizer.decode([top_ids[0]]) + print(f" prompt: {prompt!r}") + print(f" output: {(prompt + best_completion).replace(chr(10), ' ')!r}") + print(f" hit: {any_hit} (probe top-{top_k})") + + score = hits / len(FACTUAL_EVAL) + print("---") + print(f"factual_english_score: {score:.4f}") + print(f"factual_english_hits: {hits}/{len(FACTUAL_EVAL)}") + return score, hits, len(FACTUAL_EVAL) + + +# --------------------------------------------------------------------------- +# Gen mode: original autoregressive path (Fix F: batch decode) +# --------------------------------------------------------------------------- + +def _run_factual_english_gen(model, tokenizer, max_seq_len: int): + """Original autoregressive generation path with batch decode optimization: + all GPU work runs first, then all CPU decoding happens after.""" + print("---") + print("factual_english_samples: (gen mode)") + model.eval() + + num_samples = FACTUAL_SAMPLES + batch = FACTUAL_BATCH + gen_tokens = FACTUAL_GEN_TOKENS + temps = [0.7, 0.9, 1.1] + hits = 0 + + with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + for prompt, answers in FACTUAL_EVAL: + ids = tokenizer.encode(prompt) + answers_lower = [a.lower() for a in answers] + # Collect all generated token sequences on GPU first + all_rows: list[list[int]] = [] + samples_done = 0 + batch_idx = 0 + while samples_done < num_samples: + b = min(batch, num_samples - samples_done) + temp = temps[batch_idx % len(temps)] + batch_idx += 1 + ctx = torch.tensor([ids] * b, device="cuda", dtype=torch.long) + for _ in range(gen_tokens): + logits = model(ctx, targets=None) + next_logits = logits[:, -1, :] if logits.dim() == 3 else logits + probs = torch.softmax(next_logits.float() / temp, dim=-1) + next_id = torch.multinomial(probs, num_samples=1) + ctx = torch.cat([ctx, next_id], dim=1) + if ctx.size(1) >= max_seq_len: + break + # Transfer to CPU in one shot, no per-row sync + all_rows.extend(ctx.cpu().tolist()) + samples_done += b + + # CPU-side batch decode — no GPU sync between decodes + any_hit = False + first_gen = None + hit_gen = None + for row in all_rows: + generated = tokenizer.decode(row) + continuation = generated[len(prompt):].strip() + _words = set(w.lower() for w in _re.findall(r"\b[\w'-]+\b", continuation)) + hit = any(a in _words for a in answers_lower) + if first_gen is None: + first_gen = generated + if hit: + any_hit = True + if hit_gen is None: + hit_gen = generated + if any_hit: + hits += 1 + print(f" prompt: {prompt!r}") + print(f" output: {(first_gen or '').replace(chr(10), ' ')!r}") + print(f" hit: {any_hit} (any of {num_samples} samples, temps={temps}, gen={gen_tokens}tok)") + if hit_gen is not None and hit_gen != first_gen: + print(f" hit_sample: {hit_gen.replace(chr(10), ' ')!r}") + + score = hits / len(FACTUAL_EVAL) + print("---") + print(f"factual_english_score: {score:.4f}") + print(f"factual_english_hits: {hits}/{len(FACTUAL_EVAL)}") + return score, hits, len(FACTUAL_EVAL) + + +# --------------------------------------------------------------------------- +# Public entry point +# --------------------------------------------------------------------------- + +def run_factual_english(model, tokenizer, max_seq_len: int): + """Dispatch to probe (fast, default) or gen (original) mode. + + Set HYDRA_FACTUAL_MODE=gen to use the autoregressive path. + """ + if FACTUAL_MODE == "gen": + return _run_factual_english_gen(model, tokenizer, max_seq_len) + return _run_factual_english_probe(model, tokenizer, max_seq_len) diff --git a/overlay/hydra/gdn_block.py b/overlay/hydra/gdn_block.py index d6fe13d3c0c5566b574ffac3f8666314dd1783a1..c5b5fdbd19bbd3a842f2a53a67ec030402cbbe9e 100644 --- a/overlay/hydra/gdn_block.py +++ b/overlay/hydra/gdn_block.py @@ -1,126 +1,126 @@ -"""GDNBlock — Gated Delta Net block, drop-in shape-compatible with Mamba3Block and HyenaBlock. - -GatedDeltaNet (GDN) reference: arXiv:2412.06464 (ICLR 2025, NVLabs). -Implementation: flash-linear-attention (fla) library, Triton kernels, sm86-compatible. - -Interface contract (MUST match how Mamba3/Hyena are called in hydra/model.py): - block = GDNBlock(d_model, ...) - y = block(x) # x: [B, T, d_model] -> y: [B, T, d_model] - -The surrounding mHC layer does NOT pre-norm before calling this block (the -raw hidden state is passed in); the block itself applies no input normalization, -same as HyenaBlock. We return the raw operator output; the mHC layer adds it -as a residual stream contribution. - -NO attention, NO softmax-over-sequence-dim. All state is stateless between -.forward() calls by default (use_cache=False, past_key_values=None). -""" - -from __future__ import annotations - -try: - from fla.layers.gated_deltanet import GatedDeltaNet as _GatedDeltaNet -except ImportError as _fla_err: - raise ImportError( - "flash-linear-attention (fla) is required for GDNBlock but could not be imported. " - "Install it with:\n" - " pip install flash-linear-attention\n" - "or from source:\n" - " pip install git+https://github.com/fla-org/flash-linear-attention.git\n" - f"Original error: {_fla_err}" - ) from _fla_err - -import torch -import torch.nn as nn - - -class GDNBlock(nn.Module): - """Gated Delta Net block, drop-in shape-compatible with HYDRA's Mamba3Block and HyenaBlock. - - Wraps `fla.layers.GatedDeltaNet` with the same external API that - `hydra.hyena_block.HyenaBlock` exposes: - - forward(x: Tensor[B, T, d_model]) -> Tensor[B, T, d_model] - - Internal GatedDeltaNet.forward returns a 3-tuple - (hidden_states, attn_weights, past_key_values); we extract [0] and - return only the hidden states, keeping the residual stream unchanged. - - GDN outperforms Mamba-2 on in-context retrieval benchmarks (MQAR, etc.) - at equal or faster compute, making it a targeted fix for HYDRA's factual - plateau. - - Parameter counts are deliberately kept within 2x of a Mamba3 block at the - same d_model/n_heads to be drop-in affordable. - """ - - def __init__( - self, - d_model: int, - n_heads: int = 6, - mode: str = "chunk", # 'chunk' for training, 'fused_recurrent' for inference - expand_v: float = 2.0, # value-projection expansion; controls KV memory - use_short_conv: bool = True, - conv_size: int = 4, - ): - super().__init__() - self.d_model = d_model - self.n_heads = n_heads - self.mode = mode - - # head_dim must divide d_model. GDN uses separate q/k head_dim from v; - # we set head_dim for q/k such that n_heads * head_dim == d_model. - if d_model % n_heads != 0: - raise ValueError( - f"d_model={d_model} must be divisible by n_heads={n_heads} " - "so that head_dim = d_model // n_heads is an integer." - ) - head_dim = d_model // n_heads - - self.gdn = _GatedDeltaNet( - hidden_size=d_model, - expand_v=expand_v, - head_dim=head_dim, - num_heads=n_heads, - mode=mode, - use_gate=True, # gating is the key architectural feature of GDN - use_short_conv=use_short_conv, - conv_size=conv_size, - layer_idx=None, # no KV-cache layer indexing; we manage state ourselves - ) - - # ------------------------------------------------------------------ - # Forward - # ------------------------------------------------------------------ - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """x: [B, T, d_model] -> y: [B, T, d_model]. - - Passes through GatedDeltaNet with use_cache=False so no recurrent - state leaks between independent forward() calls (important for - gradient-accumulation loops and eval). - """ - # GatedDeltaNet.forward signature: - # (hidden_states, attention_mask=None, past_key_values=None, - # use_cache=False, output_attentions=False) - # Returns: tuple(hidden_states, attn_weights|None, past_kv|None) - out, _, _ = self.gdn( - hidden_states=x, - attention_mask=None, - past_key_values=None, - use_cache=False, - output_attentions=False, - ) - return out - - # ------------------------------------------------------------------ - # API parity with HyenaBlock and Mamba3Block - # ------------------------------------------------------------------ - - def invalidate_caches(self) -> None: - """No-op — GDNBlock holds no persistent filter cache. - - Provided for API parity with HyenaBlock, which invalidates its - Hyena filter cache here. Calling this is always safe. - """ - pass +"""GDNBlock — Gated Delta Net block, drop-in shape-compatible with Mamba3Block and HyenaBlock. + +GatedDeltaNet (GDN) reference: arXiv:2412.06464 (ICLR 2025, NVLabs). +Implementation: flash-linear-attention (fla) library, Triton kernels, sm86-compatible. + +Interface contract (MUST match how Mamba3/Hyena are called in hydra/model.py): + block = GDNBlock(d_model, ...) + y = block(x) # x: [B, T, d_model] -> y: [B, T, d_model] + +The surrounding mHC layer does NOT pre-norm before calling this block (the +raw hidden state is passed in); the block itself applies no input normalization, +same as HyenaBlock. We return the raw operator output; the mHC layer adds it +as a residual stream contribution. + +NO attention, NO softmax-over-sequence-dim. All state is stateless between +.forward() calls by default (use_cache=False, past_key_values=None). +""" + +from __future__ import annotations + +try: + from fla.layers.gated_deltanet import GatedDeltaNet as _GatedDeltaNet +except ImportError as _fla_err: + raise ImportError( + "flash-linear-attention (fla) is required for GDNBlock but could not be imported. " + "Install it with:\n" + " pip install flash-linear-attention\n" + "or from source:\n" + " pip install git+https://github.com/fla-org/flash-linear-attention.git\n" + f"Original error: {_fla_err}" + ) from _fla_err + +import torch +import torch.nn as nn + + +class GDNBlock(nn.Module): + """Gated Delta Net block, drop-in shape-compatible with HYDRA's Mamba3Block and HyenaBlock. + + Wraps `fla.layers.GatedDeltaNet` with the same external API that + `hydra.hyena_block.HyenaBlock` exposes: + + forward(x: Tensor[B, T, d_model]) -> Tensor[B, T, d_model] + + Internal GatedDeltaNet.forward returns a 3-tuple + (hidden_states, attn_weights, past_key_values); we extract [0] and + return only the hidden states, keeping the residual stream unchanged. + + GDN outperforms Mamba-2 on in-context retrieval benchmarks (MQAR, etc.) + at equal or faster compute, making it a targeted fix for HYDRA's factual + plateau. + + Parameter counts are deliberately kept within 2x of a Mamba3 block at the + same d_model/n_heads to be drop-in affordable. + """ + + def __init__( + self, + d_model: int, + n_heads: int = 6, + mode: str = "chunk", # 'chunk' for training, 'fused_recurrent' for inference + expand_v: float = 2.0, # value-projection expansion; controls KV memory + use_short_conv: bool = True, + conv_size: int = 4, + ): + super().__init__() + self.d_model = d_model + self.n_heads = n_heads + self.mode = mode + + # head_dim must divide d_model. GDN uses separate q/k head_dim from v; + # we set head_dim for q/k such that n_heads * head_dim == d_model. + if d_model % n_heads != 0: + raise ValueError( + f"d_model={d_model} must be divisible by n_heads={n_heads} " + "so that head_dim = d_model // n_heads is an integer." + ) + head_dim = d_model // n_heads + + self.gdn = _GatedDeltaNet( + hidden_size=d_model, + expand_v=expand_v, + head_dim=head_dim, + num_heads=n_heads, + mode=mode, + use_gate=True, # gating is the key architectural feature of GDN + use_short_conv=use_short_conv, + conv_size=conv_size, + layer_idx=None, # no KV-cache layer indexing; we manage state ourselves + ) + + # ------------------------------------------------------------------ + # Forward + # ------------------------------------------------------------------ + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """x: [B, T, d_model] -> y: [B, T, d_model]. + + Passes through GatedDeltaNet with use_cache=False so no recurrent + state leaks between independent forward() calls (important for + gradient-accumulation loops and eval). + """ + # GatedDeltaNet.forward signature: + # (hidden_states, attention_mask=None, past_key_values=None, + # use_cache=False, output_attentions=False) + # Returns: tuple(hidden_states, attn_weights|None, past_kv|None) + out, _, _ = self.gdn( + hidden_states=x, + attention_mask=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + ) + return out + + # ------------------------------------------------------------------ + # API parity with HyenaBlock and Mamba3Block + # ------------------------------------------------------------------ + + def invalidate_caches(self) -> None: + """No-op — GDNBlock holds no persistent filter cache. + + Provided for API parity with HyenaBlock, which invalidates its + Hyena filter cache here. Calling this is always safe. + """ + pass diff --git a/overlay/hydra/hyena_block.py b/overlay/hydra/hyena_block.py index 25182659263d8a8993d235c8cb8d1a165ff744ff..2ca2b8f4a2a64dd35a6461a2735c7e220d2ed1b0 100644 --- a/overlay/hydra/hyena_block.py +++ b/overlay/hydra/hyena_block.py @@ -1,68 +1,68 @@ -"""HyenaBlock — drop-in block for HYDRA, supplement to Mamba3. - -Wraps `subsystems.hyena_pure.HyenaOperator` with a pre-norm + residual scheme -consistent with how the mHC stack wraps Mamba3 in `hydra/model.py`. - -Interface contract (MUST match how Mamba3 is called in model.py): - block = HyenaBlock(d_model, seq_len) - y = block(x) # x: [B, T, d_model] -> y: [B, T, d_model] - -The surrounding mHC layer does the pre-norm (`norm(h)`) BEFORE calling the -block, so the block itself should NOT re-normalize at input — same as Mamba3 -in the current model. We return the raw operator output; the mHC layer then -adds it as a residual stream contribution. - -NO attention, NO softmax-over-sequence-dim, NO KV-cache. All forbidden -imports enumerated in tests/test_hyena.py (test #7) are absent. -""" - -from __future__ import annotations - -import os - -import torch -import torch.nn as nn - -from subsystems.hyena_pure import HyenaOperator - - -class HyenaBlock(nn.Module): - """Single Hyena block, shape-compatible with Mamba3 in HYDRA.""" - - def __init__( - self, - d_model: int, - seq_len: int, - order: int | None = None, - filter_order: int | None = None, - dropout: float = 0.0, - filter_dropout: float = 0.0, - short_filter_order: int = 3, - activation: str = "id", - ): - super().__init__() - # Env overrides (documented in hydra/config.py). - if order is None: - order = int(os.environ.get("HYDRA_HYENA_ORDER", "2")) - if filter_order is None: - filter_order = int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")) - - self.d_model = d_model - self.seq_len = seq_len - self.order = order - self.filter_order = filter_order - - self.operator = HyenaOperator( - d_model=d_model, - l_max=seq_len, - order=order, - filter_order=filter_order, - dropout=dropout, - filter_dropout=filter_dropout, - short_filter_order=short_filter_order, - activation=activation, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """x: [B, T, d_model] -> y: [B, T, d_model].""" - return self.operator(x) +"""HyenaBlock — drop-in block for HYDRA, supplement to Mamba3. + +Wraps `subsystems.hyena_pure.HyenaOperator` with a pre-norm + residual scheme +consistent with how the mHC stack wraps Mamba3 in `hydra/model.py`. + +Interface contract (MUST match how Mamba3 is called in model.py): + block = HyenaBlock(d_model, seq_len) + y = block(x) # x: [B, T, d_model] -> y: [B, T, d_model] + +The surrounding mHC layer does the pre-norm (`norm(h)`) BEFORE calling the +block, so the block itself should NOT re-normalize at input — same as Mamba3 +in the current model. We return the raw operator output; the mHC layer then +adds it as a residual stream contribution. + +NO attention, NO softmax-over-sequence-dim, NO KV-cache. All forbidden +imports enumerated in tests/test_hyena.py (test #7) are absent. +""" + +from __future__ import annotations + +import os + +import torch +import torch.nn as nn + +from subsystems.hyena_pure import HyenaOperator + + +class HyenaBlock(nn.Module): + """Single Hyena block, shape-compatible with Mamba3 in HYDRA.""" + + def __init__( + self, + d_model: int, + seq_len: int, + order: int | None = None, + filter_order: int | None = None, + dropout: float = 0.0, + filter_dropout: float = 0.0, + short_filter_order: int = 3, + activation: str = "id", + ): + super().__init__() + # Env overrides (documented in hydra/config.py). + if order is None: + order = int(os.environ.get("HYDRA_HYENA_ORDER", "2")) + if filter_order is None: + filter_order = int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")) + + self.d_model = d_model + self.seq_len = seq_len + self.order = order + self.filter_order = filter_order + + self.operator = HyenaOperator( + d_model=d_model, + l_max=seq_len, + order=order, + filter_order=filter_order, + dropout=dropout, + filter_dropout=filter_dropout, + short_filter_order=short_filter_order, + activation=activation, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """x: [B, T, d_model] -> y: [B, T, d_model].""" + return self.operator(x) diff --git a/overlay/hydra/lightning_module.py b/overlay/hydra/lightning_module.py index 65724c0d5605dc2e7a8abb7b6de8e1451b22d862..322215ea079ef569b42f895e74564a3dbf7f49e0 100644 --- a/overlay/hydra/lightning_module.py +++ b/overlay/hydra/lightning_module.py @@ -1,326 +1,326 @@ -"""LightningModule wrapping PostSemClawModel. - -Thin adapter. The model and the MuonAdamW optimizer are unchanged. This -module implements: - - • configure_optimizers — returns the existing MuonAdamW (subclass of - torch.optim.Optimizer) built by model.setup_optimizer. Lightning accepts - this directly. - • training_step — splits (B, T+1) batches into (x, y), forwards through - the model, logs loss / bpb / tps / mfu / vram. Preserves the - sampled-softmax path inside PostSemClawModel (no changes there). - • optimizer_step — before each step we update LR + muon momentum + WD - using the same time-progress schedule as hydra/training.py - (get_lr_multiplier / get_muon_momentum / get_weight_decay). Lightning - handles grad accumulation via Trainer(accumulate_grad_batches=N). - -The SDR SOM update and Hestia QAT snap are called at the same cadence as -the legacy loop, but inline on the main thread (Lightning provides its own -callbacks for async work if we need to extract them later — keeping it -simple for now). - -Env vars respected: - HYDRA_TIME_BUDGET — wall-clock budget (s) used for LR schedule - and as Trainer max_time - HYDRA_HESTIA_INTERVAL — steps between Hestia snaps (default 100) - HYDRA_BATCH_SIZE — device batch size (for throughput calc) - HYDRA_SEQ_LEN — sequence length (for throughput calc) -""" -from __future__ import annotations - -import math -import os -import time - -import torch -import lightning as L - -from hydra.config import ( - ADAM_BETAS, - EMBEDDING_LR, - FINAL_LR_FRAC, - GPU_BF16_PEAK_FLOPS, - MATRIX_LR, - SCALAR_LR, - UNEMBEDDING_LR, - WARMUP_RATIO, - WEIGHT_DECAY, - PostSemClawConfig, -) -from hydra.model import PostSemClawModel - - -# --------------------------------------------------------------------------- -# LR / momentum / wd schedules — verbatim copy of hydra/training.py so the -# curves match exactly. Kept here to avoid import cycles. -# --------------------------------------------------------------------------- - - -def _lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - decay_progress = (progress - WARMUP_RATIO) / max(1.0 - WARMUP_RATIO, 1e-9) - return FINAL_LR_FRAC + 0.5 * (1.0 - FINAL_LR_FRAC) * ( - 1 + math.cos(math.pi * decay_progress) - ) - - -def _muon_momentum(step: int) -> float: - frac = min(step / 300.0, 1.0) - return (1 - frac) * 0.85 + frac * 0.95 - - -def _weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- - - -class HydraLightningModule(L.LightningModule): - """Lightning wrapper. Public attrs: self.model, self.config.""" - - def __init__(self, config: PostSemClawConfig): - super().__init__() - self.config = config - self.model = PostSemClawModel(config) - # Model weights init must be deferred to the correct device; done by - # caller after construction (to match the meta-device + to_empty() - # pattern used in the legacy loop). - - # Time-based progress tracks the legacy loop's semantics: LR cosine - # is driven by wall-clock, not step count. We capture training start - # in on_train_start and TIME_BUDGET from env. - self.time_budget = float( - int(os.environ.get("HYDRA_TIME_BUDGET", "300")) - ) - self._train_start_time: float | None = None - self._total_training_time = 0.0 - self._last_step_end: float | None = None - self._hestia_interval = int(os.environ.get("HYDRA_HESTIA_INTERVAL", "100")) - self._flops_per_token = 0 - self._tokens_per_step = 0 - - # Smoothed loss for the header-line log (matches legacy format). - self._ema_beta = 0.9 - self._smooth_loss = 0.0 - self._bpt_ema = 0.0 - self._token_bytes: torch.Tensor | None = None - - # ------------------------------------------------------------------ - # Lifecycle - # ------------------------------------------------------------------ - - def on_train_start(self) -> None: - self._train_start_time = time.time() - self._last_step_end = self._train_start_time - self._flops_per_token = self.model.estimate_flops() - # Tokens processed per optimizer step (pre-accum). - B = int(os.environ.get("HYDRA_BATCH_SIZE", "1")) - T = int(os.environ.get("HYDRA_SEQ_LEN", "512")) - self._tokens_per_step = B * T - - # Build/cache token_bytes LUT (for bits-per-byte live metric). - import prepare as _p - self._token_bytes = _p.get_token_bytes(device=self.device) - - def configure_optimizers(self): - optimizer = self.model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, - ) - return optimizer - - # ------------------------------------------------------------------ - # Training step. Lightning auto-handles: autocast (via precision flag - # on Trainer), backward, grad-accum, zero_grad. We only: - # - split batch into (x, y) - # - forward through model (autocast is established by Trainer) - # - return loss (grads flow from return) - # ------------------------------------------------------------------ - - def training_step(self, batch: torch.Tensor, batch_idx: int): - # DataLoader produces (B, T+1) rows; split into input/target. - # Lightning's default collate already moved batch to self.device via - # the accelerator callback when pin_memory=True and device != cpu. - if batch.dim() != 2: - raise RuntimeError(f"Expected (B, T+1) batch, got shape {tuple(batch.shape)}") - x = batch[:, :-1].contiguous() - y = batch[:, 1:].contiguous() - - loss = self.model(x, y) - # Lightning applies the grad-accum divisor automatically; we just - # return the raw loss. loss.detach() is stored for logging. - self._log_step(loss.detach(), y) - return loss - - # ------------------------------------------------------------------ - # Optimizer step hook: update LR / momentum / WD using time-progress. - # Runs once per optimizer step (after all accum micro-batches). - # ------------------------------------------------------------------ - - def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure): - # Update schedules from wall-clock progress. - now = time.time() - if self._train_start_time is None: - self._train_start_time = now - self._last_step_end = now - progress = min(self._total_training_time / max(self.time_budget, 1.0), 1.0) - - step = self.global_step - lrm = _lr_multiplier(progress) - mom = _muon_momentum(step) - wd = _weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group.get("kind") == "muon": - group["momentum"] = mom - group["weight_decay"] = wd - - # Grad clip (matches legacy loop). Lightning provides this via - # Trainer(gradient_clip_val=1.0) but we want the exact call-site. - torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1.0) - - # Hyena train-cache: we must flush accumulated micro-batch grads BACK - # into the filter MLP params AFTER the accum-backward closure has run - # but BEFORE the optimizer actually consumes the grads. Lightning - # composes these so the closure runs inside optimizer.step(). We wrap - # the closure to insert our flush at the exact right moment. - # - # Ordering within the wrapped closure: - # 1. optimizer_closure() — runs all micro-batch forwards + backwards. - # Each Hyena micro-batch backward accumulates into _k_leaf.grad. - # 2. flush_hyena_pending_grads() — one-shot - # torch.autograd.backward(_k_graph, _k_leaf.grad) per HyenaFilter. - # Now filter MLP / pos_emb / bias params have their correct grads. - # - # No-op when HYDRA_HYENA_TRAIN_CACHE=0 or no Hyena blocks exist. - _has_flush = hasattr(self.model, "flush_hyena_pending_grads") - if _has_flush: - _orig_closure = optimizer_closure - - def _wrapped_closure(): - result = _orig_closure() - self.model.flush_hyena_pending_grads() - return result - - effective_closure = _wrapped_closure - else: - effective_closure = optimizer_closure - - # Run the step (this is what Lightning would have done for us). - optimizer.step(closure=effective_closure) - self.model.zero_grad(set_to_none=True) - - # Hyena filter-rfft cache invalidation. No-op if: - # (a) no Hyena layers are in the model, or - # (b) HYDRA_HYENA_FILTER_CACHE=0 and HYDRA_HYENA_TRAIN_CACHE=0 - # (the operators never populated either cache) - # In either case this is a handful of Python attribute resets. - if hasattr(self.model, "invalidate_hyena_caches"): - self.model.invalidate_hyena_caches() - - # Hestia QAT snap every N steps. Temperature anneals every step. - progress_now = (now - self._train_start_time) / max(self.time_budget, 1.0) - self.model.hestia.anneal_temperature(progress_now) - if self._hestia_interval > 0 and step % self._hestia_interval == 0: - self.model.hestia.apply_to(self.model) - - # SDR SOM update when the model stashed an sdr in the last forward. - _last_sdr = getattr(self.model, "_last_sdr", None) - if _last_sdr is not None and hasattr(self.model.sdr_semantic, "maybe_som_update"): - # x from the last training_step is not available here without - # captured state; the legacy loop passed (x, _last_sdr). To keep - # the interface clean we pass the last batch's x via a buffer. - # Since _last_sdr is derived from idx, we reuse self._last_x. - if getattr(self, "_last_x", None) is not None: - self.model.sdr_semantic.maybe_som_update(self._last_x, _last_sdr) - - # Advance the wall-clock counter for LR schedule (matches legacy - # behavior which incremented only after the first warm-up step). - dt = now - (self._last_step_end or now) - self._last_step_end = now - if step > 10: - self._total_training_time += dt - - # ------------------------------------------------------------------ - # Logging — mirrors the step=NNNNN line format of the legacy loop so - # grep/tee pipelines keep working. - # ------------------------------------------------------------------ - - def _log_step(self, loss: torch.Tensor, y: torch.Tensor) -> None: - # Stash the current x so optimizer_step can drive SOM update. - self._last_x = None # reset; we will set it below. - # We don't have x here (already discarded); emit a None marker that - # the SOM hook will silently skip if absent. - - loss_f = float(loss.item()) - if not math.isfinite(loss_f) or loss_f > 100: - # Let Lightning raise / the trainer callbacks handle this. - self.log("train_loss_nan", 1.0) - return - - step = self.global_step - self._smooth_loss = ( - self._ema_beta * self._smooth_loss + (1 - self._ema_beta) * loss_f - ) - debiased = self._smooth_loss / max(1 - self._ema_beta ** (step + 1), 1e-9) - dt = max(time.time() - (self._last_step_end or time.time()), 1e-6) - tps = int(self._tokens_per_step / dt) if dt > 0 else 0 - mfu = ( - 100.0 - * self._flops_per_token - * self._tokens_per_step - / dt - / GPU_BF16_PEAK_FLOPS - if dt > 0 - else 0.0 - ) - - # bpb live: y flat -> token_bytes LUT -> avg bytes/token - bpt = debiased / math.log(2) - if self._token_bytes is not None: - with torch.no_grad(): - y_flat = y.reshape(-1) - nbytes = self._token_bytes[y_flat] - mask = nbytes > 0 - denom = mask.sum().clamp(min=1).float() - avg_bpt = (nbytes.float() * mask.float()).sum() / denom - bpt_batch = float(avg_bpt.item()) - if step == 0 or self._bpt_ema <= 0.0: - self._bpt_ema = bpt_batch - else: - self._bpt_ema = 0.98 * self._bpt_ema + 0.02 * bpt_batch - bpb = bpt / max(self._bpt_ema, 1e-6) - vram = ( - torch.cuda.memory_allocated() / 1024 / 1024 - if torch.cuda.is_available() - else 0.0 - ) - - self.log_dict( - { - "train/loss": debiased, - "train/bpb": bpb, - "train/bpt": bpt, - "train/tps": float(tps), - "train/mfu": float(mfu), - "train/vram_mib": float(vram), - }, - prog_bar=False, - on_step=True, - on_epoch=False, - ) - - # Match legacy one-line format: "step=NNNNN loss=x bpb=y tps=z ..." - print( - f"step={step:05d} loss={debiased:.4f} bpb={bpb:.4f} " - f"bpt={bpt:.3f} bpt_div={self._bpt_ema:.2f} " - f"tps={tps} dt_ms={dt*1000:.0f} mfu={mfu:.1f} " - f"vram={vram:.0f}MiB", - flush=True, - ) +"""LightningModule wrapping PostSemClawModel. + +Thin adapter. The model and the MuonAdamW optimizer are unchanged. This +module implements: + + • configure_optimizers — returns the existing MuonAdamW (subclass of + torch.optim.Optimizer) built by model.setup_optimizer. Lightning accepts + this directly. + • training_step — splits (B, T+1) batches into (x, y), forwards through + the model, logs loss / bpb / tps / mfu / vram. Preserves the + sampled-softmax path inside PostSemClawModel (no changes there). + • optimizer_step — before each step we update LR + muon momentum + WD + using the same time-progress schedule as hydra/training.py + (get_lr_multiplier / get_muon_momentum / get_weight_decay). Lightning + handles grad accumulation via Trainer(accumulate_grad_batches=N). + +The SDR SOM update and Hestia QAT snap are called at the same cadence as +the legacy loop, but inline on the main thread (Lightning provides its own +callbacks for async work if we need to extract them later — keeping it +simple for now). + +Env vars respected: + HYDRA_TIME_BUDGET — wall-clock budget (s) used for LR schedule + and as Trainer max_time + HYDRA_HESTIA_INTERVAL — steps between Hestia snaps (default 100) + HYDRA_BATCH_SIZE — device batch size (for throughput calc) + HYDRA_SEQ_LEN — sequence length (for throughput calc) +""" +from __future__ import annotations + +import math +import os +import time + +import torch +import lightning as L + +from hydra.config import ( + ADAM_BETAS, + EMBEDDING_LR, + FINAL_LR_FRAC, + GPU_BF16_PEAK_FLOPS, + MATRIX_LR, + SCALAR_LR, + UNEMBEDDING_LR, + WARMUP_RATIO, + WEIGHT_DECAY, + PostSemClawConfig, +) +from hydra.model import PostSemClawModel + + +# --------------------------------------------------------------------------- +# LR / momentum / wd schedules — verbatim copy of hydra/training.py so the +# curves match exactly. Kept here to avoid import cycles. +# --------------------------------------------------------------------------- + + +def _lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + decay_progress = (progress - WARMUP_RATIO) / max(1.0 - WARMUP_RATIO, 1e-9) + return FINAL_LR_FRAC + 0.5 * (1.0 - FINAL_LR_FRAC) * ( + 1 + math.cos(math.pi * decay_progress) + ) + + +def _muon_momentum(step: int) -> float: + frac = min(step / 300.0, 1.0) + return (1 - frac) * 0.85 + frac * 0.95 + + +def _weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- + + +class HydraLightningModule(L.LightningModule): + """Lightning wrapper. Public attrs: self.model, self.config.""" + + def __init__(self, config: PostSemClawConfig): + super().__init__() + self.config = config + self.model = PostSemClawModel(config) + # Model weights init must be deferred to the correct device; done by + # caller after construction (to match the meta-device + to_empty() + # pattern used in the legacy loop). + + # Time-based progress tracks the legacy loop's semantics: LR cosine + # is driven by wall-clock, not step count. We capture training start + # in on_train_start and TIME_BUDGET from env. + self.time_budget = float( + int(os.environ.get("HYDRA_TIME_BUDGET", "300")) + ) + self._train_start_time: float | None = None + self._total_training_time = 0.0 + self._last_step_end: float | None = None + self._hestia_interval = int(os.environ.get("HYDRA_HESTIA_INTERVAL", "100")) + self._flops_per_token = 0 + self._tokens_per_step = 0 + + # Smoothed loss for the header-line log (matches legacy format). + self._ema_beta = 0.9 + self._smooth_loss = 0.0 + self._bpt_ema = 0.0 + self._token_bytes: torch.Tensor | None = None + + # ------------------------------------------------------------------ + # Lifecycle + # ------------------------------------------------------------------ + + def on_train_start(self) -> None: + self._train_start_time = time.time() + self._last_step_end = self._train_start_time + self._flops_per_token = self.model.estimate_flops() + # Tokens processed per optimizer step (pre-accum). + B = int(os.environ.get("HYDRA_BATCH_SIZE", "1")) + T = int(os.environ.get("HYDRA_SEQ_LEN", "512")) + self._tokens_per_step = B * T + + # Build/cache token_bytes LUT (for bits-per-byte live metric). + import prepare as _p + self._token_bytes = _p.get_token_bytes(device=self.device) + + def configure_optimizers(self): + optimizer = self.model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, + ) + return optimizer + + # ------------------------------------------------------------------ + # Training step. Lightning auto-handles: autocast (via precision flag + # on Trainer), backward, grad-accum, zero_grad. We only: + # - split batch into (x, y) + # - forward through model (autocast is established by Trainer) + # - return loss (grads flow from return) + # ------------------------------------------------------------------ + + def training_step(self, batch: torch.Tensor, batch_idx: int): + # DataLoader produces (B, T+1) rows; split into input/target. + # Lightning's default collate already moved batch to self.device via + # the accelerator callback when pin_memory=True and device != cpu. + if batch.dim() != 2: + raise RuntimeError(f"Expected (B, T+1) batch, got shape {tuple(batch.shape)}") + x = batch[:, :-1].contiguous() + y = batch[:, 1:].contiguous() + + loss = self.model(x, y) + # Lightning applies the grad-accum divisor automatically; we just + # return the raw loss. loss.detach() is stored for logging. + self._log_step(loss.detach(), y) + return loss + + # ------------------------------------------------------------------ + # Optimizer step hook: update LR / momentum / WD using time-progress. + # Runs once per optimizer step (after all accum micro-batches). + # ------------------------------------------------------------------ + + def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure): + # Update schedules from wall-clock progress. + now = time.time() + if self._train_start_time is None: + self._train_start_time = now + self._last_step_end = now + progress = min(self._total_training_time / max(self.time_budget, 1.0), 1.0) + + step = self.global_step + lrm = _lr_multiplier(progress) + mom = _muon_momentum(step) + wd = _weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group.get("kind") == "muon": + group["momentum"] = mom + group["weight_decay"] = wd + + # Grad clip (matches legacy loop). Lightning provides this via + # Trainer(gradient_clip_val=1.0) but we want the exact call-site. + torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1.0) + + # Hyena train-cache: we must flush accumulated micro-batch grads BACK + # into the filter MLP params AFTER the accum-backward closure has run + # but BEFORE the optimizer actually consumes the grads. Lightning + # composes these so the closure runs inside optimizer.step(). We wrap + # the closure to insert our flush at the exact right moment. + # + # Ordering within the wrapped closure: + # 1. optimizer_closure() — runs all micro-batch forwards + backwards. + # Each Hyena micro-batch backward accumulates into _k_leaf.grad. + # 2. flush_hyena_pending_grads() — one-shot + # torch.autograd.backward(_k_graph, _k_leaf.grad) per HyenaFilter. + # Now filter MLP / pos_emb / bias params have their correct grads. + # + # No-op when HYDRA_HYENA_TRAIN_CACHE=0 or no Hyena blocks exist. + _has_flush = hasattr(self.model, "flush_hyena_pending_grads") + if _has_flush: + _orig_closure = optimizer_closure + + def _wrapped_closure(): + result = _orig_closure() + self.model.flush_hyena_pending_grads() + return result + + effective_closure = _wrapped_closure + else: + effective_closure = optimizer_closure + + # Run the step (this is what Lightning would have done for us). + optimizer.step(closure=effective_closure) + self.model.zero_grad(set_to_none=True) + + # Hyena filter-rfft cache invalidation. No-op if: + # (a) no Hyena layers are in the model, or + # (b) HYDRA_HYENA_FILTER_CACHE=0 and HYDRA_HYENA_TRAIN_CACHE=0 + # (the operators never populated either cache) + # In either case this is a handful of Python attribute resets. + if hasattr(self.model, "invalidate_hyena_caches"): + self.model.invalidate_hyena_caches() + + # Hestia QAT snap every N steps. Temperature anneals every step. + progress_now = (now - self._train_start_time) / max(self.time_budget, 1.0) + self.model.hestia.anneal_temperature(progress_now) + if self._hestia_interval > 0 and step % self._hestia_interval == 0: + self.model.hestia.apply_to(self.model) + + # SDR SOM update when the model stashed an sdr in the last forward. + _last_sdr = getattr(self.model, "_last_sdr", None) + if _last_sdr is not None and hasattr(self.model.sdr_semantic, "maybe_som_update"): + # x from the last training_step is not available here without + # captured state; the legacy loop passed (x, _last_sdr). To keep + # the interface clean we pass the last batch's x via a buffer. + # Since _last_sdr is derived from idx, we reuse self._last_x. + if getattr(self, "_last_x", None) is not None: + self.model.sdr_semantic.maybe_som_update(self._last_x, _last_sdr) + + # Advance the wall-clock counter for LR schedule (matches legacy + # behavior which incremented only after the first warm-up step). + dt = now - (self._last_step_end or now) + self._last_step_end = now + if step > 10: + self._total_training_time += dt + + # ------------------------------------------------------------------ + # Logging — mirrors the step=NNNNN line format of the legacy loop so + # grep/tee pipelines keep working. + # ------------------------------------------------------------------ + + def _log_step(self, loss: torch.Tensor, y: torch.Tensor) -> None: + # Stash the current x so optimizer_step can drive SOM update. + self._last_x = None # reset; we will set it below. + # We don't have x here (already discarded); emit a None marker that + # the SOM hook will silently skip if absent. + + loss_f = float(loss.item()) + if not math.isfinite(loss_f) or loss_f > 100: + # Let Lightning raise / the trainer callbacks handle this. + self.log("train_loss_nan", 1.0) + return + + step = self.global_step + self._smooth_loss = ( + self._ema_beta * self._smooth_loss + (1 - self._ema_beta) * loss_f + ) + debiased = self._smooth_loss / max(1 - self._ema_beta ** (step + 1), 1e-9) + dt = max(time.time() - (self._last_step_end or time.time()), 1e-6) + tps = int(self._tokens_per_step / dt) if dt > 0 else 0 + mfu = ( + 100.0 + * self._flops_per_token + * self._tokens_per_step + / dt + / GPU_BF16_PEAK_FLOPS + if dt > 0 + else 0.0 + ) + + # bpb live: y flat -> token_bytes LUT -> avg bytes/token + bpt = debiased / math.log(2) + if self._token_bytes is not None: + with torch.no_grad(): + y_flat = y.reshape(-1) + nbytes = self._token_bytes[y_flat] + mask = nbytes > 0 + denom = mask.sum().clamp(min=1).float() + avg_bpt = (nbytes.float() * mask.float()).sum() / denom + bpt_batch = float(avg_bpt.item()) + if step == 0 or self._bpt_ema <= 0.0: + self._bpt_ema = bpt_batch + else: + self._bpt_ema = 0.98 * self._bpt_ema + 0.02 * bpt_batch + bpb = bpt / max(self._bpt_ema, 1e-6) + vram = ( + torch.cuda.memory_allocated() / 1024 / 1024 + if torch.cuda.is_available() + else 0.0 + ) + + self.log_dict( + { + "train/loss": debiased, + "train/bpb": bpb, + "train/bpt": bpt, + "train/tps": float(tps), + "train/mfu": float(mfu), + "train/vram_mib": float(vram), + }, + prog_bar=False, + on_step=True, + on_epoch=False, + ) + + # Match legacy one-line format: "step=NNNNN loss=x bpb=y tps=z ..." + print( + f"step={step:05d} loss={debiased:.4f} bpb={bpb:.4f} " + f"bpt={bpt:.3f} bpt_div={self._bpt_ema:.2f} " + f"tps={tps} dt_ms={dt*1000:.0f} mfu={mfu:.1f} " + f"vram={vram:.0f}MiB", + flush=True, + ) diff --git a/overlay/hydra/model.py b/overlay/hydra/model.py index bccb034ac343d876b48960bbd2c724ecf148e7e3..0741074b7bd7a925aefcb89814e3bcb409408eb5 100644 --- a/overlay/hydra/model.py +++ b/overlay/hydra/model.py @@ -1,934 +1,1229 @@ -"""PostSemClawModel — full-architecture model assembly. - -Extracted from the monolithic train.py (W1 modularization). Semantics -unchanged. Imports `GPUEngram` from `hydra.engram` and `MuonAdamW` from -`hydra.optimizer`. - -Triton kernel integration status (Phase 2): - HYDRA_FUSED_BCNORM — DEFERRED. The bcnorm_fused Triton kernel fuses - LayerNorm + RoPE on B/C projections. However, mamba-ssm's Mamba3 block - uses RMSNormGated (not LayerNorm) for B/C, and RoPE is applied inside - the mamba3_siso_combined CUDA kernel via the Angles parameter. Replacing - would require either (a) monkey-patching RMSNormGated + intercepting the - fused CUDA scan — invasive, 50+ lines, high breakage risk — or (b) a - full custom Mamba3Block reimplementation. Both are out of scope for - Phase 2. The kernel is validated standalone; integration deferred to - Phase 3 when HYDRA moves to a custom SSM block. - - HYDRA_FUSED_SSD — DEFERRED. The ssd_exp_trap Triton kernel implements - exponential-trapezoidal discretization as a sequential scan. mamba-ssm's - Mamba3 block delegates the entire scan + gating + output projection to - mamba3_siso_combined (a compiled CUDA kernel with tilelang). Replacing - it would require decomposing the combined kernel into constituent ops - and substituting only the scan — not feasible without a custom block. - Same Phase 3 gate as above. - -Both env vars are accepted but currently no-ops (gates read, logged, but -the code path is unchanged). This avoids silent regression if someone -sets them expecting a speedup. -""" - -from __future__ import annotations - -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from mamba_ssm import Mamba3 - - -def _ensure_triton_cuda_backend_registered() -> None: - """Ensure Triton sees exactly one CUDA backend in HF Jobs A10 runtime. - - In some Triton 3.5.1 environments, `triton.compiler.compiler.backends` - and `triton.runtime.driver.backends` are empty even though - `triton.backends.nvidia` is available and CUDA is active. When that - happens, Mamba3 layernorm path crashes at first forward with - "0 compatible backends for target (cuda)". +"""PostSemClawModel — full-architecture model assembly. + +Extracted from the monolithic train.py (W1 modularization). Semantics +unchanged. Imports `GPUEngram` from `hydra.engram` and `MuonAdamW` from +`hydra.optimizer`. + +Triton kernel integration status (Phase 2): + HYDRA_FUSED_BCNORM — DEFERRED. The bcnorm_fused Triton kernel fuses + LayerNorm + RoPE on B/C projections. However, mamba-ssm's Mamba3 block + uses RMSNormGated (not LayerNorm) for B/C, and RoPE is applied inside + the mamba3_siso_combined CUDA kernel via the Angles parameter. Replacing + would require either (a) monkey-patching RMSNormGated + intercepting the + fused CUDA scan — invasive, 50+ lines, high breakage risk — or (b) a + full custom Mamba3Block reimplementation. Both are out of scope for + Phase 2. The kernel is validated standalone; integration deferred to + Phase 3 when HYDRA moves to a custom SSM block. + + HYDRA_FUSED_SSD — DEFERRED. The ssd_exp_trap Triton kernel implements + exponential-trapezoidal discretization as a sequential scan. mamba-ssm's + Mamba3 block delegates the entire scan + gating + output projection to + mamba3_siso_combined (a compiled CUDA kernel with tilelang). Replacing + it would require decomposing the combined kernel into constituent ops + and substituting only the scan — not feasible without a custom block. + Same Phase 3 gate as above. + +Both env vars are accepted but currently no-ops (gates read, logged, but +the code path is unchanged). This avoids silent regression if someone +sets them expecting a speedup. +""" + +from __future__ import annotations + +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F + +try: + from mamba_ssm import Mamba3 +except ModuleNotFoundError: # local CPU tests may run outside the HF image wheel stack + Mamba3 = None + +from subsystems.hestia_mini import HestiaQAT +from subsystems.htm import HTMLayer +from subsystems.mhc_mini import ManifoldHyperConnection +from subsystems.sdr_semantic import SemanticFoldingSDR +from subsystems.fused_sdr_project import FusedSDRProject + +from subsystems.cantor_router import CantorRouter + +from hydra.engram import GPUEngram +from hydra.reality_bridge import RealityPoincareBridge +from hydra.hyena_block import HyenaBlock +# GDNBlock is imported lazily inside __init__ so the `fla` dependency is +# only required when HYDRA_GDN_LAYERS is actually non-empty. Baseline +# pure-Mamba3 runs continue to work without flash-linear-attention installed. +from hydra.optimizer import MuonAdamW + + +FLOAT32_BYTES = torch.finfo(torch.float32).bits // 8 + + +def norm(x: torch.Tensor) -> torch.Tensor: + """RMSNorm over the last dim — stateless, autocast-friendly.""" + return F.rms_norm(x, (x.size(-1),)) + + +def semantic_gaussian_mollify( + x: torch.Tensor, + std: float, + training: bool, + eval_enabled: bool = False, +) -> torch.Tensor: + """Tiny Gaussian semantic smoothing gate for SDR/Engram queries. + + Default identity; train-only unless explicitly enabled for eval. This acts + as local mollification around the discrete SDR/Cantor seam without changing + checkpoint shapes. + """ + if std <= 0.0 or (not training and not eval_enabled): + return x + return x + torch.randn_like(x) * std + + +def paired_slow_fast_orthogonality(w: torch.Tensor) -> torch.Tensor: + """Cheap W_slow ⊕ W_fast row-pair orthogonality proxy.""" + if w.dim() != 2 or w.shape[0] < 2: + return w.new_zeros(()) + slow = w[0::2].float() + fast = w[1::2].float() + n = min(slow.shape[0], fast.shape[0]) + if n == 0: + return w.new_zeros(()) + slow = F.normalize(slow[:n], dim=-1, eps=1e-6) + fast = F.normalize(fast[:n], dim=-1, eps=1e-6) + return (slow * fast).sum(dim=-1).square().mean() + + +class PostSemClawModel(nn.Module): + """Full Post-SEM-Claw model assembly. + + Architecture: + Token Embedding -> [Mamba3 + residual] x n_layer + -> SDR + Engram (at configured layer) -> norm -> LM head + + Interface (must match prepare.py evaluate_bpb): + model(x, y, reduction='none').view(-1) -> per-token losses + model(x, y, reduction='mean') -> scalar loss """ - try: - import triton.compiler.compiler as cc - import triton.runtime.driver as rd - from triton.backends import Backend - from triton.backends.nvidia.compiler import CUDABackend - from triton.backends.nvidia.driver import CudaDriver - - if hasattr(rd, "backends") and isinstance(rd.backends, dict) and not rd.backends: - rd.backends["nvidia"] = Backend(compiler=CUDABackend, driver=CudaDriver) - - if hasattr(cc, "backends") and isinstance(cc.backends, dict) and not cc.backends: - cc.backends["nvidia"] = Backend(compiler=CUDABackend, driver=CudaDriver) - except Exception: - # Keep model construction resilient; runtime will raise explicit Triton - # errors later if backend setup is still invalid. - pass - - -_ensure_triton_cuda_backend_registered() - -from subsystems.hestia_mini import HestiaQAT -from subsystems.htm import HTMLayer -from subsystems.mhc_mini import ManifoldHyperConnection -from subsystems.sdr_semantic import SemanticFoldingSDR - -from hydra.engram import GPUEngram -from hydra.hyena_block import HyenaBlock -# GDNBlock is imported lazily inside __init__ so the `fla` dependency is -# only required when HYDRA_GDN_LAYERS is actually non-empty. Baseline -# pure-Mamba3 runs continue to work without flash-linear-attention installed. -from hydra.optimizer import MuonAdamW - - -def norm(x: torch.Tensor) -> torch.Tensor: - """RMSNorm over the last dim — stateless, autocast-friendly.""" - return F.rms_norm(x, (x.size(-1),)) - - -class PostSemClawModel(nn.Module): - """Full Post-SEM-Claw model assembly. - - Architecture: - Token Embedding -> [Mamba3 + residual] x n_layer - -> SDR + Engram (at configured layer) -> norm -> LM head - - Interface (must match prepare.py evaluate_bpb): - model(x, y, reduction='none').view(-1) -> per-token losses - model(x, y, reduction='mean') -> scalar loss - """ - + def __init__(self, config): super().__init__() - _ensure_triton_cuda_backend_registered() self.config = config - self._throughput_mode = os.environ.get("HYDRA_THROUGHPUT_MODE", "0") == "1" - - # Token embedding - self.wte = nn.Embedding(config.vocab_size, config.d_model) - - # Mamba-3 blocks — official mamba-ssm fused CUDA kernel. No fallbacks. - # RoPE is applied internally by the Mamba3 CUDA kernel via the Angles - # parameter; external cos/sin buffers are not needed. - # - # Hyena supplement: layers whose index appears in `config.hyena_layers` - # are instantiated as HyenaBlock instead of Mamba3. The config field - # is populated from HYDRA_HYENA_LAYERS at construction time and then - # persisted to checkpoints, so resume is safe even when the env var - # is unset. Empty tuple → all-Mamba3, byte-identical to pre-port. - _hyena_layer_set = set(getattr(config, "hyena_layers", ()) or ()) - _gdn_layer_set = set(getattr(config, "gdn_layers", ()) or ()) - # Hyena wins on overlap; conflict is logged at construction time. - _both = _hyena_layer_set & _gdn_layer_set - if _both: - print(f"[WARN] layers in both hyena_layers and gdn_layers; using Hyena: {sorted(_both)}", flush=True) - _gdn_layer_set -= _hyena_layer_set - - if _gdn_layer_set: - from hydra.gdn_block import GDNBlock # requires `fla` package - - def _build_block(i: int) -> nn.Module: - if i in _hyena_layer_set: - return HyenaBlock( - d_model=config.d_model, - seq_len=config.sequence_len, - order=int(os.environ.get("HYDRA_HYENA_ORDER", "2")), - filter_order=int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")), - ) - if i in _gdn_layer_set: - return GDNBlock( - d_model=config.d_model, - n_heads=config.n_heads, - ) - return Mamba3( - d_model=config.d_model, - d_state=config.d_state, - expand=config.expand, - headdim=config.headdim, - is_mimo=False, # SISO path uses stable mamba3_siso_combined kernel - chunk_size=int(os.environ.get("HYDRA_MAMBA3_CHUNK", "64")), # 64 is the validated default; 128 tripped a Triton autotune hang (>8min, no progress) - is_outproj_norm=False, - dtype=torch.bfloat16, - ) - - self.blocks = nn.ModuleList([_build_block(i) for i in range(config.n_layer)]) - - # Full-architecture SDR: offline semantic retina + STE (no-bypass). - if self._throughput_mode: - self.sdr_semantic = None - self.htm = None - self.htm_proj = None - self.htm_anom_proj = None - self.engram = None - self.engram_layer_idx = -1 - else: - self.sdr_semantic = SemanticFoldingSDR( - vocab_size=config.vocab_size, - n_bits=config.sdr_n_bits, - target_active=config.sdr_target_active, - delta_rank=config.sdr_delta_rank, - som_warmup_steps=config.sdr_som_warmup, - som_update_interval=config.sdr_som_interval, - ) - # HTM spatial pooler + temporal memory (Rust, Hebbian). - self.htm = HTMLayer( - input_bits=config.sdr_n_bits, - n_columns=config.htm_n_columns, - cells_per_column=config.htm_cells_per_column, - batch_size=1, - seed=42, - learn=True, - reset_each_forward=True, + # Token embedding + self.wte = nn.Embedding(config.vocab_size, config.d_model) + + # Mamba-3 blocks — official mamba-ssm fused CUDA kernel. No fallbacks. + # RoPE is applied internally by the Mamba3 CUDA kernel via the Angles + # parameter; external cos/sin buffers are not needed. + # + # Hyena supplement: layers whose index appears in `config.hyena_layers` + # are instantiated as HyenaBlock instead of Mamba3. The config field + # is populated from HYDRA_HYENA_LAYERS at construction time and then + # persisted to checkpoints, so resume is safe even when the env var + # is unset. Empty tuple → all-Mamba3, byte-identical to pre-port. + _hyena_layer_set = set(getattr(config, "hyena_layers", ()) or ()) + _gdn_layer_set = set(getattr(config, "gdn_layers", ()) or ()) + # Hyena wins on overlap; conflict is logged at construction time. + _both = _hyena_layer_set & _gdn_layer_set + if _both: + print(f"[WARN] layers in both hyena_layers and gdn_layers; using Hyena: {sorted(_both)}", flush=True) + _gdn_layer_set -= _hyena_layer_set + + if _gdn_layer_set: + from hydra.gdn_block import GDNBlock # requires `fla` package + + def _build_block(i: int) -> nn.Module: + if i in _hyena_layer_set: + return HyenaBlock( + d_model=config.d_model, + seq_len=config.sequence_len, + order=int(os.environ.get("HYDRA_HYENA_ORDER", "2")), + filter_order=int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "64")), + ) + if i in _gdn_layer_set: + return GDNBlock( + d_model=config.d_model, + n_heads=config.n_heads, + ) + if Mamba3 is None: + raise RuntimeError( + "mamba_ssm is required for Mamba3 layers; set hyena_layers/gdn_layers " + "to cover every layer or run inside the HF runtime image." + ) + block = Mamba3( + d_model=config.d_model, + d_state=config.d_state, + expand=config.expand, + headdim=config.headdim, + is_mimo=False, # SISO path uses stable mamba3_siso_combined kernel + chunk_size=int(os.environ.get("HYDRA_MAMBA3_CHUNK", "64")), # 64 is the validated default; 128 tripped a Triton autotune hang (>8min, no progress) + is_outproj_norm=False, + dtype=torch.bfloat16, ) + # Fix dt_bias gradient starvation: Mamba3 init samples dt uniformly + # in log-space from [0.001, 0.1], giving dt_bias in [-6.9, -2.25]. + # softplus'(dt_bias) = sigmoid(dt_bias). At -6.9: 0.1% grad survives; + # at -2.25: 9.5% survives. Shift up so sigmoid is 2-68% instead. + # The SSM can still learn to make dt_bias more negative if finer + # temporal resolution is needed — now the gradient will survive to + # do so. + with torch.no_grad(): + block.dt_bias.add_(3.0) + return block + + self.blocks = nn.ModuleList([_build_block(i) for i in range(config.n_layer)]) + + # Full-architecture SDR: offline semantic retina + STE (no-bypass). + self.sdr_semantic = SemanticFoldingSDR( + vocab_size=config.vocab_size, + n_bits=config.sdr_n_bits, + target_active=config.sdr_target_active, + delta_rank=config.sdr_delta_rank, + som_warmup_steps=config.sdr_som_warmup, + som_update_interval=config.sdr_som_interval, + ) + + # HTM spatial pooler + temporal memory (Rust, Hebbian). + self.htm = HTMLayer( + input_bits=config.sdr_n_bits, + n_columns=config.htm_n_columns, + cells_per_column=config.htm_cells_per_column, + batch_size=1, # grows lazily to actual B on first forward + seed=42, + learn=True, + reset_each_forward=True, + ) + + # Gradient bridge: (n_columns + anomaly) -> d_model. + self.htm_proj = nn.Linear(config.htm_n_columns + 1, config.d_model, bias=False) + + # GPU Engram with Hebbian writes — runs EVERY step. + self.engram = GPUEngram( + d_model=config.d_model, + n_columns=config.engram_n_columns, + max_ngram=3, + ) + self.engram_layer_idx = config.engram_layer_idx - self.htm_proj = nn.Linear(config.htm_n_columns, config.d_model, bias=False) - self.htm_anom_proj = nn.Linear(1, config.d_model, bias=False) + # Cantor router: gradient-free topological routing engine. + # Partitions query space into 2^depth leaves (default 128). + # Each leaf constrains which Engram columns are eligible for + # retrieval — replacing the flat top-k with a geometric partition. + # Phase 1: static branching vectors, zero learnable parameters. + _cantor_depth = int(os.environ.get("HYDRA_CANTOR_DEPTH", "7")) + self.cantor = CantorRouter( + depth=_cantor_depth, + d_query=config.d_model, + device="cuda" if torch.cuda.is_available() else "cpu", + ) + self._cantor_enabled = os.environ.get("HYDRA_CANTOR_DISABLE", "0") != "1" - self.engram = GPUEngram( + # SDR-to-d_model projection: routes differentiable SDR signal (STE) + # into the engram's residual stream. One bf16 matmul per forward step + # at (B*T, n_bits) @ (n_bits, d_model) — ~2.6M MACs vs 5B total, + # <0.05% overhead. Zero extra memory (no intermediates materialized + # beyond the matmul output). This single projection backpropagates LM + # loss gradients through sdr_semantic.delta_u/delta_v, finally giving + # the curated semantic retina real learning signal. + self.sdr_proj = nn.Linear(config.sdr_n_bits, config.d_model, bias=False) + + # SEM-Claw Reality/Poincare bridge. Enabled by default: emits a + # compact int16 L0 active-index buffer for Engram/Cantor routing and a + # differentiable Poincare coordinate for metrics/regularizers. Set + # HYDRA_REALITY_BRIDGE=0 to fall back to retina active indices only. + self._reality_bridge_enabled = os.environ.get("HYDRA_REALITY_BRIDGE", "1") != "0" + if self._reality_bridge_enabled: + self.reality_bridge = RealityPoincareBridge( d_model=config.d_model, - n_columns=config.engram_n_columns, - max_ngram=3, + d_reality=int(os.environ.get("HYDRA_REALITY_DIM", "133")), + l0_k=int(os.environ.get("HYDRA_REALITY_L0_K", "64")), + ) + else: + self.reality_bridge = None + + # Manifold-Constrained Hyper-Connections (one per Mamba-3 block). + self.mhc = nn.ModuleList([ + ManifoldHyperConnection(d_model=config.d_model, n_streams=2, sinkhorn_iters=3) + for _ in range(config.n_layer) + ]) + + # Hestia QAT — ternary weight quantization applied post-optimizer-step. + self.hestia = HestiaQAT(enabled=True, bits=1.58) + + # LM head + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + + # Learnability knob 1: Multi-Token Prediction (Llama-3 style). + # MTP_K=1 -> standard next-token. MTP_K>1 -> extra heads predict + # tokens at positions t+1, t+2, ..., t+K. Heads are weight-tied to + # lm_head (we share Parameters), so the only extra compute is + # additional CE losses; no new params. Activated via HYDRA_MTP_K. + self._mtp_k = max(1, int(os.environ.get("HYDRA_MTP_K", "1"))) + + # Learnability knob 3: gradient checkpointing on Mamba3 blocks. + self._grad_ckpt = os.environ.get("HYDRA_GRAD_CKPT", "0") == "1" + + # Learnability knob 4: doc-separator BOS masking in packed sequences. + self._doc_sep_mask = os.environ.get("HYDRA_DOC_SEP_MASK", "0") == "1" + # BOS token id is looked up lazily on first forward (requires tokenizer + # load); -1 means uninitialized. + self._bos_token_id = -1 + + # Learnability knob 5: explicit stop-grad on HTM tensor (htm_rust + # outputs already have requires_grad=False; this is defense-in-depth). + self._htm_stop_grad = os.environ.get("HYDRA_HTM_STOP_GRAD", "0") == "1" + + # Learnability knob 6: entropy penalty coefficient on LM logits. + self._entropy_penalty = float(os.environ.get("HYDRA_ENTROPY_PENALTY", "0.0")) + + # SEM-Claw upgrades: Gaussian query mollification and W_slow ⊕ W_fast + # orthogonality probe/regularizer are enabled by default. Set the env + # values to 0 to disable during ablations. + self._semantic_smooth_std = float(os.environ.get("HYDRA_SEMANTIC_SMOOTH_STD", "0.01")) + self._semantic_smooth_eval = os.environ.get("HYDRA_SEMANTIC_SMOOTH_EVAL", "0") == "1" + self._sf_ortho_lambda = float(os.environ.get("HYDRA_SLOW_FAST_ORTHO_LAMBDA", "1e-4")) + self._sf_ortho_metrics = os.environ.get("HYDRA_SLOW_FAST_ORTHO_METRICS", "1") != "0" + self._sf_ortho_every = max(1, int(os.environ.get("HYDRA_SLOW_FAST_ORTHO_EVERY", "100"))) + self._sf_ortho_step = 0 + self._sf_ortho_targets = tuple( + s.strip() for s in os.environ.get( + "HYDRA_SLOW_FAST_ORTHO_TARGETS", + "cantor,engram,block_out_proj", + ).split(",") if s.strip() + ) + + # Residual dropout + self.drop = nn.Dropout(float(os.environ.get("HYDRA_DROPOUT", "0.2"))) + + # Logits soft-capping + self.softcap = 15.0 + + # Secondary metrics storage + self._metrics = {} + # Cantor leaf utilization is an in-training fidelity metric. The final + # run summary can execute tiny validation/factual-probe forwards after + # training, so a single last-forward leaf count can falsely look + # collapsed (e.g. 2/128 leaves from a handful of probe prompts). Track + # the maximum seen during training separately from the instantaneous + # last-forward value. + self._cantor_active_leaves_train_max = 0 + # Engram hit-rate has the same last-forward overwrite hazard: final + # validation/factual forwards can be tiny or distribution-shifted, so + # preserve training-window max/mean alongside the instantaneous value. + self._engram_hit_rate_train_max = 0.0 + self._engram_hit_rate_train_sum = 0.0 + self._engram_hit_rate_train_count = 0 + + # Per-layer diagnostic panel. Env-gated; zero overhead when off. + # Emits residual-contribution (delta_ratio), feature std, effective rank, + # gradient norm per layer; used to identify minimum viable n_layer + find + # entropy leakage / dead layers. See docs/depth-sweep.md. + self._diag_enabled = os.environ.get("HYDRA_LAYER_DIAGNOSTICS", "0") == "1" + self._diag_step = 0 + self._diag_svd_every = int(os.environ.get("HYDRA_LAYER_DIAG_SVD_EVERY", "100")) + if self._diag_enabled: + # Gradient-norm backward hooks on each Mamba3 block output. + for _i, _block in enumerate(self.blocks): + def _mk_grad_hook(_layer_idx): + def _hook(module, grad_input, grad_output): + if grad_output and grad_output[0] is not None: + g = grad_output[0].detach() + self._metrics[f'layer_{_layer_idx}_grad_norm'] = float( + g.pow(2).mean().sqrt().item() + ) + return _hook + _block.register_full_backward_hook(_mk_grad_hook(_i)) + + # Forward hooks on each Mamba3 block capture the block's OUTPUT + # directly. This is the clean measurement: unlike merge_streams() + # sampling which sees (streams + M*block_output) in bf16 — where + # small block contributions round to zero against unit-norm + # residuals — this captures `block_output` itself as produced. + # Reports both its absolute RMS norm and its ratio to the block + # INPUT's RMS norm (contribution magnitude relative to the + # residual it's added to). + for _i, _block in enumerate(self.blocks): + def _mk_fwd_hook(_layer_idx): + def _hook(module, inputs, output): + with torch.no_grad(): + inp = inputs[0].detach().float() if inputs else None + out = output.detach().float() if isinstance(output, torch.Tensor) else None + if out is not None: + out_rms = out.pow(2).mean().sqrt().item() + self._metrics[f'layer_{_layer_idx}_block_out_rms'] = float(out_rms) + if inp is not None: + in_rms = inp.pow(2).mean().sqrt().item() + self._metrics[f'layer_{_layer_idx}_block_in_rms'] = float(in_rms) + self._metrics[f'layer_{_layer_idx}_contrib_ratio'] = float( + out_rms / (in_rms + 1e-8) + ) + return _hook + _block.register_forward_hook(_mk_fwd_hook(_i)) + + # Triton kernel integration gates (Phase 2 — deferred, see module docstring). + self._fused_bcnorm = os.environ.get("HYDRA_FUSED_BCNORM", "0") == "1" + self._fused_ssd = os.environ.get("HYDRA_FUSED_SSD", "0") == "1" + if self._fused_bcnorm or self._fused_ssd: + import sys + _active = [] + if self._fused_bcnorm: + _active.append("HYDRA_FUSED_BCNORM") + if self._fused_ssd: + _active.append("HYDRA_FUSED_SSD") + print( + f"[HYDRA] Triton kernel gates set: {', '.join(_active)}. " + f"NOTE: Both are DEFERRED (mamba-ssm Mamba3 uses internal " + f"CUDA kernels). Gates accepted but currently no-ops.", + file=sys.stderr, + ) + + # R6 optional torch.compile on the impl forward. Gated (default OFF). + if os.environ.get("HYDRA_MODEL_COMPILE", "0") == "1": + self._forward_impl = torch.compile( + self._forward_impl, + fullgraph=False, + dynamic=True, + mode="default", ) - self.engram_layer_idx = config.engram_layer_idx - - # Manifold-Constrained Hyper-Connections (one per Mamba-3 block). - self.mhc = nn.ModuleList([ - ManifoldHyperConnection(d_model=config.d_model, n_streams=2, sinkhorn_iters=3) - for _ in range(config.n_layer) - ]) - - # Hestia QAT — ternary weight quantization applied post-optimizer-step. - self.hestia = HestiaQAT(enabled=True, bits=1.58) - - # LM head - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - - # Learnability knob 1: Multi-Token Prediction (Llama-3 style). - # MTP_K=1 -> standard next-token. MTP_K>1 -> extra heads predict - # tokens at positions t+1, t+2, ..., t+K. Heads are weight-tied to - # lm_head (we share Parameters), so the only extra compute is - # additional CE losses; no new params. Activated via HYDRA_MTP_K. - self._mtp_k = max(1, int(os.environ.get("HYDRA_MTP_K", "1"))) - - # Learnability knob 3: gradient checkpointing on Mamba3 blocks. - self._grad_ckpt = os.environ.get("HYDRA_GRAD_CKPT", "0") == "1" - - # Learnability knob 4: doc-separator BOS masking in packed sequences. - self._doc_sep_mask = os.environ.get("HYDRA_DOC_SEP_MASK", "0") == "1" - # BOS token id is looked up lazily on first forward (requires tokenizer - # load); -1 means uninitialized. - self._bos_token_id = -1 - - # Learnability knob 5: explicit stop-grad on HTM tensor (htm_rust - # outputs already have requires_grad=False; this is defense-in-depth). - self._htm_stop_grad = os.environ.get("HYDRA_HTM_STOP_GRAD", "0") == "1" - - # Learnability knob 6: entropy penalty coefficient on LM logits. - self._entropy_penalty = float(os.environ.get("HYDRA_ENTROPY_PENALTY", "0.0")) - - # Residual dropout - self.drop = nn.Dropout(float(os.environ.get("HYDRA_DROPOUT", "0.2"))) - - # Logits soft-capping - self.softcap = 15.0 - - # Secondary metrics storage - self._metrics = {} - - # Per-layer diagnostic panel. Env-gated; zero overhead when off. - # Emits residual-contribution (delta_ratio), feature std, effective rank, - # gradient norm per layer; used to identify minimum viable n_layer + find - # entropy leakage / dead layers. See docs/depth-sweep.md. - self._diag_enabled = os.environ.get("HYDRA_LAYER_DIAGNOSTICS", "0") == "1" - self._diag_step = 0 - self._diag_svd_every = int(os.environ.get("HYDRA_LAYER_DIAG_SVD_EVERY", "100")) - if self._diag_enabled: - # Gradient-norm backward hooks on each Mamba3 block output. - for _i, _block in enumerate(self.blocks): - def _mk_grad_hook(_layer_idx): - def _hook(module, grad_input, grad_output): - if grad_output and grad_output[0] is not None: - g = grad_output[0].detach() - self._metrics[f'layer_{_layer_idx}_grad_norm'] = float( - g.pow(2).mean().sqrt().item() - ) - return _hook - _block.register_full_backward_hook(_mk_grad_hook(_i)) - - # Forward hooks on each Mamba3 block capture the block's OUTPUT - # directly. This is the clean measurement: unlike merge_streams() - # sampling which sees (streams + M*block_output) in bf16 — where - # small block contributions round to zero against unit-norm - # residuals — this captures `block_output` itself as produced. - # Reports both its absolute RMS norm and its ratio to the block - # INPUT's RMS norm (contribution magnitude relative to the - # residual it's added to). - for _i, _block in enumerate(self.blocks): - def _mk_fwd_hook(_layer_idx): - def _hook(module, inputs, output): - with torch.no_grad(): - inp = inputs[0].detach().float() if inputs else None - out = output.detach().float() if isinstance(output, torch.Tensor) else None - if out is not None: - out_rms = out.pow(2).mean().sqrt().item() - self._metrics[f'layer_{_layer_idx}_block_out_rms'] = float(out_rms) - if inp is not None: - in_rms = inp.pow(2).mean().sqrt().item() - self._metrics[f'layer_{_layer_idx}_block_in_rms'] = float(in_rms) - self._metrics[f'layer_{_layer_idx}_contrib_ratio'] = float( - out_rms / (in_rms + 1e-8) - ) - return _hook - _block.register_forward_hook(_mk_fwd_hook(_i)) - - # Triton kernel integration gates (Phase 2 — deferred, see module docstring). - self._fused_bcnorm = os.environ.get("HYDRA_FUSED_BCNORM", "0") == "1" - self._fused_ssd = os.environ.get("HYDRA_FUSED_SSD", "0") == "1" - if self._fused_bcnorm or self._fused_ssd: - import sys - _active = [] - if self._fused_bcnorm: - _active.append("HYDRA_FUSED_BCNORM") - if self._fused_ssd: - _active.append("HYDRA_FUSED_SSD") - print( - f"[HYDRA] Triton kernel gates set: {', '.join(_active)}. " - f"NOTE: Both are DEFERRED (mamba-ssm Mamba3 uses internal " - f"CUDA kernels). Gates accepted but currently no-ops.", - file=sys.stderr, - ) - - # R6 optional torch.compile on the impl forward. Gated (default OFF). - if os.environ.get("HYDRA_MODEL_COMPILE", "0") == "1": - self._forward_impl = torch.compile( - self._forward_impl, - fullgraph=False, - dynamic=True, - mode="default", - ) - - @torch.no_grad() - def init_weights(self) -> None: - s = 3 ** 0.5 * self.config.d_model ** -0.5 - - # Move SDR retina indices (plain attribute, not buffer) to same device as params. - # Required because to_empty() only moves params/buffers, and _retina_indices - # is loaded from numpy (always CPU) by SemanticFoldingSDR.__init__. - device = self.wte.weight.device - if self.sdr_semantic is not None and hasattr(self.sdr_semantic, '_retina_indices'): + + @torch.no_grad() + def init_weights(self) -> None: + s = 3 ** 0.5 * self.config.d_model ** -0.5 + + # Move SDR retina indices (plain attribute, not buffer) to same device as params. + # Required because to_empty() only moves params/buffers, and _retina_indices + # is loaded from numpy (always CPU) by SemanticFoldingSDR.__init__. + device = self.wte.weight.device + if hasattr(self.sdr_semantic, '_retina_indices'): self.sdr_semantic._retina_indices = self.sdr_semantic._retina_indices.to(device) - - # Embedding init: GPT-2 / LLaMA convention. std=1.0 was chosen for - # vocab=8192; at larger vocabs, smaller std prevents logit blowup. - # Use std = 1/sqrt(d_model) which scales sensibly with model width. - import math as _math - _d_model = self.wte.weight.shape[1] - wte_std = float(os.environ.get("HYDRA_WTE_STD", str(1.0 / _math.sqrt(_d_model)))) - nn.init.normal_(self.wte.weight, mean=0.0, std=wte_std) - # LM head init: was std=0.001 — PATHOLOGICAL at vocab>=32k because - # logits collapse to zero, loss locks at log(V)~=11, gradient through - # head ∝ 1/V is too small to escape. GPT-2 uses std=0.02; LLaMA uses - # std=1/sqrt(d_model). Pick 0.02 as robust default, env-overridable. - lm_head_std = float(os.environ.get("HYDRA_LM_HEAD_STD", "0.02")) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=lm_head_std) - # F8 (NOT APPLIED): Weight tying would save V*D params but current LR - # groups have embedding_lr=1.0 and unembedding_lr=0.005 × d_model_scale - # — tying forces the shared tensor under a single LR group and either - # the embeddings learn 200x too slow (under unembed LR) or the LM head - # becomes unstable (under embed LR). Short 15-step smoke with tying + - # embed-group update showed initial loss jump 9 -> 20. Deferred until - # LR groups are re-tuned; see docs/OPTIMIZATION_PLAN.md Post-plan. - - for li, block in enumerate(self.blocks): - if hasattr(block, 'in_proj') and hasattr(block.in_proj, 'weight'): - nn.init.uniform_(block.in_proj.weight, -s, s) - if hasattr(block, 'out_proj') and hasattr(block.out_proj, 'weight'): - # GPT-2 residual init: std = 0.02 / sqrt(2 * n_layer). - # NOT zeros — zero init makes the block a permanent pass-through - # (block_out_rms=0, zero gradient flow to SSM internals). - # With non-zero init the block contributes to the residual stream - # from step 1, giving the SSM scan actual gradient signal. - n_layer = self.config.n_layer - out_std = float(os.environ.get( - "HYDRA_OUT_PROJ_STD", - str(0.02 / (2 * n_layer) ** 0.5), - )) - nn.init.normal_(block.out_proj.weight, mean=0.0, std=out_std) - - if self.htm_proj is not None: - nn.init.normal_(self.htm_proj.weight, mean=0.0, std=s) - if self.htm_anom_proj is not None: - nn.init.normal_(self.htm_anom_proj.weight, mean=0.0, std=s) - - # Cast to bf16 to match Mamba3 dtype; Muon groups by shape so mixed - # dtypes in the same shape group would break lerp_ dtype checks. - self.wte.to(dtype=torch.bfloat16) - if self.htm_proj is not None: - self.htm_proj.to(dtype=torch.bfloat16) - if self.htm_anom_proj is not None: - self.htm_anom_proj.to(dtype=torch.bfloat16) - if self.engram is not None: - self.engram.to(dtype=torch.bfloat16) - - def set_bos_token_id(self, bos_id: int) -> None: - """Inform the model of the tokenizer's BOS id so doc-separator - masking (learnability #4) knows which positions to skip. Called from - training setup once the tokenizer is loaded.""" - self._bos_token_id = int(bos_id) - - def invalidate_hyena_caches(self) -> None: - """Invalidate filter-rfft caches on all Hyena blocks. - - MUST be called after each `optimizer.step()` when - `HYDRA_HYENA_FILTER_CACHE=1` is set, otherwise cached rfft values - will be reused with stale filter parameters. - - No-op for blocks that are not HyenaBlock (Mamba3, etc.). - """ - for block in self.blocks: - if hasattr(block, "operator") and hasattr(block.operator, "invalidate_filter_cache"): - block.operator.invalidate_filter_cache() - - def flush_hyena_pending_grads(self) -> None: - """Push pending train-cache filter gradients into filter params. - - Used ONLY when HYDRA_HYENA_TRAIN_CACHE=1. Must be called exactly once - per optimizer step, BEFORE `optimizer.step()` and BEFORE - `invalidate_hyena_caches()`. The lightning_module wires this in - `optimizer_step` around the existing optimizer.step() call. - - No-op if: - * No HyenaBlocks are in the model, OR - * No micro-batch ever ran with grad enabled (e.g. all-eval step). - """ - for block in self.blocks: - if hasattr(block, "operator") and hasattr(block.operator, "flush_pending_filter_grads"): - block.operator.flush_pending_filter_grads() - - def estimate_flops(self) -> int: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - sdr = sum(p.numel() for p in self.sdr_semantic.parameters()) if self.sdr_semantic is not None else 0 - htm_proj = sum(p.numel() for p in self.htm_proj.parameters()) if self.htm_proj is not None else 0 - htm_anom_proj = sum(p.numel() for p in self.htm_anom_proj.parameters()) if self.htm_anom_proj is not None else 0 - engram = sum(p.numel() for p in self.engram.parameters()) if self.engram is not None else 0 - total = sum(p.numel() for p in self.parameters()) - return { - 'wte': wte, 'lm_head': lm_head, 'blocks': blocks, - 'sdr_semantic': sdr, 'htm_proj': htm_proj, - 'htm_anom_proj': htm_anom_proj, - 'engram': engram, 'total': total, - } - - def get_secondary_metrics(self) -> dict: - """Flush any lingering CUDA tensors to host (single sync).""" - flushed = {} - for k, v in self._metrics.items(): - if hasattr(v, 'item'): - try: - flushed[k] = float(v.item()) - except Exception: - flushed[k] = v - else: - flushed[k] = v - return flushed - - def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.6, matrix_lr=0.04, - weight_decay=0.2, adam_betas=(0.8, 0.95), scalar_lr=0.5): - """Setup MuonAdamW optimizer with per-component LR groups.""" - model_dim = self.config.d_model - - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - # Muon routing guard: 2D parameters are NOT automatically matrices. - # Exclude: - # (a) params whose name ends in `.freq` — Sin frequency vectors used - # by Hyena's implicit filter MLP. Shape (1, dim) is nominally 2D - # but semantically a per-dim scalar. Muon's polar-express - # orthogonalization would force it toward an orthogonal matrix, - # destroying the learned modulation frequencies. - # (b) 2-D params with min(shape) < MUON_MIN_DIM. Tiny projections - # (e.g. HyenaFilter.implicit_filter.0.weight of shape (64, 3)) - # get collapsed toward near-identity by orthogonalization on the - # narrow axis, damaging expressivity. These belong in AdamW. - # These exclusions route the params into the AdamW scalar/vector group. - MUON_MIN_DIM = 8 - - def _muon_eligible(name: str, p: torch.Tensor) -> bool: - if p.dim() != 2: - return False - if name.endswith(".freq"): - return False - if min(p.shape) < MUON_MIN_DIM: - return False - return True - - # Matrix params -> Muon (2D weight matrices passing the routing guard). - matrix_params = [] - for name, p in self.blocks.named_parameters(): - if _muon_eligible(name, p): - matrix_params.append(p) - # NOTE (W1 audit REG-2): SemanticFoldingSDR.delta_u / delta_v are - # currently GRADIENT-DEAD. The forward path uses `binary_only(idx)` for - # HTM and stores it as `self._last_sdr`, but does NOT route the STE - # output through any downstream op. Including them in the Muon group - # burns compute (stack + orthogonalize + lerp) on zero-grad params - # every step. Excluded here; a later W5 pass can reconnect STE via a - # gated residual if the SDR signal is wanted back in-graph. The - # parameters still exist, so no state_dict break. - # for p in self.sdr_semantic.parameters(): - # if p.dim() == 2: - # matrix_params.append(p) - if self.htm_proj is not None: - for name, p in self.htm_proj.named_parameters(): - if _muon_eligible(name, p): - matrix_params.append(p) - if self.engram is not None: - for name, p in self.engram.named_parameters(): - if _muon_eligible(name, p): - matrix_params.append(p) - - # SDR params are intentionally not in any optimizer group — they - # receive no gradient in the current forward, so any update would be - # pure noise (weight_decay × lr on a zero-grad param). - sdr_param_ids = set(id(p) for p in self.sdr_semantic.parameters()) if self.sdr_semantic is not None else set() - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [ - p for p in self.parameters() - if id(p) not in assigned and id(p) not in sdr_param_ids - ] - - total_assigned = len(embedding_params) + len(lm_head_params) + len(matrix_params) + len(scalar_params) - total_params = len(list(self.parameters())) - sdr_excluded = len(list(self.sdr_semantic.parameters())) if self.sdr_semantic is not None else 0 - assert total_assigned + sdr_excluded == total_params, ( - f"Parameter count mismatch: assigned {total_assigned} + sdr_excluded " - f"{sdr_excluded} vs total {total_params}" - ) - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind='adamw', params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind='adamw', params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - - if scalar_params: - param_groups.append( - dict(kind='adamw', params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - # ns_steps: Muon polar-express inner iterations. Default 5 (paper), - # but 3 converges on small matrices (d_model ~ 384) with ~40% lower - # optimizer step cost. Env-tunable for experimentation. - _ns_steps = int(os.environ.get("HYDRA_MUON_NS_STEPS", "3")) - param_groups.append(dict( - kind='muon', params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=_ns_steps, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward(self, idx, targets=None, reduction='mean'): - """idx: (B, T) int64. Returns loss if targets given, else logits. - - Nested bf16 autocast is a no-op when ambient autocast is already on; - when it's off (e.g. integration tests) we establish the dtype contract. - """ - if torch.is_autocast_enabled(): - return self._forward_impl(idx, targets=targets, reduction=reduction) - with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - return self._forward_impl(idx, targets=targets, reduction=reduction) - - def _forward_impl(self, idx, targets=None, reduction='mean'): - B, T = idx.shape - - # Diagnostic: per-subsystem CUDA event timing. Env-gated; zero overhead - # when disabled. Logs one timing line per forward call. Used to isolate - # which subsystem is the tps bottleneck on paid hardware. - _profile = os.environ.get("HYDRA_PROFILE_FORWARD", "0") == "1" - if _profile: - def _ev(): - e = torch.cuda.Event(enable_timing=True) - e.record() - return e - _t0 = _ev() - else: - _t0 = None - + + # Embedding init: GPT-2 / LLaMA convention. std=1.0 was chosen for + # vocab=8192; at larger vocabs, smaller std prevents logit blowup. + # Use std = 1/sqrt(d_model) which scales sensibly with model width. + import math as _math + _d_model = self.wte.weight.shape[1] + wte_std = float(os.environ.get("HYDRA_WTE_STD", str(1.0 / _math.sqrt(_d_model)))) + nn.init.normal_(self.wte.weight, mean=0.0, std=wte_std) + # LM head init: was std=0.001 — PATHOLOGICAL at vocab>=32k because + # logits collapse to zero, loss locks at log(V)~=11, gradient through + # head ∝ 1/V is too small to escape. GPT-2 uses std=0.02; LLaMA uses + # std=1/sqrt(d_model). Pick 0.02 as robust default, env-overridable. + lm_head_std = float(os.environ.get("HYDRA_LM_HEAD_STD", "0.02")) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=lm_head_std) + # F8 (NOT APPLIED): Weight tying would save V*D params but current LR + # groups have embedding_lr=1.0 and unembedding_lr=0.005 × d_model_scale + # — tying forces the shared tensor under a single LR group and either + # the embeddings learn 200x too slow (under unembed LR) or the LM head + # becomes unstable (under embed LR). Short 15-step smoke with tying + + # embed-group update showed initial loss jump 9 -> 20. Deferred until + # LR groups are re-tuned; see docs/OPTIMIZATION_PLAN.md Post-plan. + + for li, block in enumerate(self.blocks): + if hasattr(block, 'in_proj') and hasattr(block.in_proj, 'weight'): + nn.init.uniform_(block.in_proj.weight, -s, s) + if hasattr(block, 'out_proj') and hasattr(block.out_proj, 'weight'): + # GPT-2 residual init: std = 0.02 / sqrt(2 * n_layer). + # NOT zeros — zero init makes the block a permanent pass-through + # (block_out_rms=0, zero gradient flow to SSM internals). + # With non-zero init the block contributes to the residual stream + # from step 1, giving the SSM scan actual gradient signal. + n_layer = self.config.n_layer + out_std = float(os.environ.get( + "HYDRA_OUT_PROJ_STD", + str(0.02 / (2 * n_layer) ** 0.5), + )) + nn.init.normal_(block.out_proj.weight, mean=0.0, std=out_std) + + nn.init.normal_(self.htm_proj.weight, mean=0.0, std=s) + + # SDR proj: tiny init preserves the existing residual dynamics at step 0. + # The signal ramps up gradually as delta_u/delta_v learn via STE gradients. + nn.init.normal_(self.sdr_proj.weight, mean=0.0, std=1e-4) + + # Modules constructed under torch.device("meta") then moved with + # to_empty() have uninitialized storage. Reinitialize SEM-Claw modules + # here so default-on routing is a real architecture, not allocator noise. + if hasattr(self, "cantor") and hasattr(self.cantor, "branch"): + g = torch.Generator(device="cpu") + g.manual_seed(42) + bound = _math.sqrt(3.0 / self.cantor.d_query) + branch = torch.empty( + self.cantor.branch.shape, + device="cpu", + dtype=torch.float32, + ).uniform_(-bound, bound, generator=g) + self.cantor.branch.copy_(branch.to(device=device, dtype=self.cantor.branch.dtype)) + + nn.init.normal_(self.engram.memory, mean=0.0, std=0.01) + nn.init.zeros_(self.engram.gate.weight) + nn.init.constant_(self.engram.gate.bias, 0.0) + + if self.reality_bridge is not None: + nn.init.normal_(self.reality_bridge.to_reality.weight, mean=0.0, std=0.02) + nn.init.normal_(self.reality_bridge.to_tangent2.weight, mean=0.0, std=0.02) + + # Cast to bf16 to match Mamba3 dtype; Muon groups by shape so mixed + # dtypes in the same shape group would break lerp_ dtype checks. + self.wte.to(dtype=torch.bfloat16) + self.htm_proj.to(dtype=torch.bfloat16) + self.sdr_proj.to(dtype=torch.bfloat16) + self.engram.to(dtype=torch.bfloat16) + if self.reality_bridge is not None: + self.reality_bridge.to(dtype=torch.bfloat16) + + def set_bos_token_id(self, bos_id: int) -> None: + """Inform the model of the tokenizer's BOS id so doc-separator + masking (learnability #4) knows which positions to skip. Called from + training setup once the tokenizer is loaded.""" + self._bos_token_id = int(bos_id) + + def invalidate_hyena_caches(self) -> None: + """Invalidate filter-rfft caches on all Hyena blocks. + + MUST be called after each `optimizer.step()` when + `HYDRA_HYENA_FILTER_CACHE=1` is set, otherwise cached rfft values + will be reused with stale filter parameters. + + No-op for blocks that are not HyenaBlock (Mamba3, etc.). + """ + for block in self.blocks: + if hasattr(block, "operator") and hasattr(block.operator, "invalidate_filter_cache"): + block.operator.invalidate_filter_cache() + + def flush_hyena_pending_grads(self) -> None: + """Push pending train-cache filter gradients into filter params. + + Used ONLY when HYDRA_HYENA_TRAIN_CACHE=1. Must be called exactly once + per optimizer step, BEFORE `optimizer.step()` and BEFORE + `invalidate_hyena_caches()`. The lightning_module wires this in + `optimizer_step` around the existing optimizer.step() call. + + No-op if: + * No HyenaBlocks are in the model, OR + * No micro-batch ever ran with grad enabled (e.g. all-eval step). + """ + for block in self.blocks: + if hasattr(block, "operator") and hasattr(block.operator, "flush_pending_filter_grads"): + block.operator.flush_pending_filter_grads() + + def estimate_flops(self) -> int: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + sdr = sum(p.numel() for p in self.sdr_semantic.parameters()) + htm_proj = sum(p.numel() for p in self.htm_proj.parameters()) + engram = sum(p.numel() for p in self.engram.parameters()) + total = sum(p.numel() for p in self.parameters()) + return { + 'wte': wte, 'lm_head': lm_head, 'blocks': blocks, + 'sdr_semantic': sdr, 'htm_proj': htm_proj, + 'engram': engram, 'total': total, + } + + def get_secondary_metrics(self) -> dict: + """Flush any lingering CUDA tensors to host (single sync).""" + flushed = {} + for k, v in self._metrics.items(): + if hasattr(v, 'item'): + try: + flushed[k] = float(v.item()) + except Exception: + flushed[k] = v + else: + flushed[k] = v + return flushed + + def _slow_fast_ortho_named_tensors(self): + targets = set(self._sf_ortho_targets) + if "cantor" in targets and hasattr(self, "cantor") and hasattr(self.cantor, "branch"): + yield "cantor_branch", self.cantor.branch + if "engram" in targets and hasattr(self, "engram") and hasattr(self.engram, "memory"): + yield "engram_memory", self.engram.memory + if "block_out_proj" in targets: + for i, block in enumerate(self.blocks): + out_proj = getattr(block, "out_proj", None) + weight = getattr(out_proj, "weight", None) + if weight is not None and weight.dim() == 2: + yield f"block_{i}_out_proj", weight + if "block_in_proj" in targets: + for i, block in enumerate(self.blocks): + in_proj = getattr(block, "in_proj", None) + weight = getattr(in_proj, "weight", None) + if weight is not None and weight.dim() == 2: + yield f"block_{i}_in_proj", weight + + def _slow_fast_ortho_loss(self) -> torch.Tensor: + vals = [ + paired_slow_fast_orthogonality(w) + for _, w in self._slow_fast_ortho_named_tensors() + if torch.is_tensor(w) and w.dim() == 2 + ] + if not vals: + return self.wte.weight.new_zeros(()) + return torch.stack(vals).mean() + + def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.6, matrix_lr=0.04, + weight_decay=0.2, adam_betas=(0.8, 0.95), scalar_lr=0.5): + """Setup MuonAdamW optimizer with per-component LR groups.""" + model_dim = self.config.d_model + + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + # Muon routing guard: 2D parameters are NOT automatically matrices. + # Exclude: + # (a) params whose name ends in `.freq` — Sin frequency vectors used + # by Hyena's implicit filter MLP. Shape (1, dim) is nominally 2D + # but semantically a per-dim scalar. Muon's polar-express + # orthogonalization would force it toward an orthogonal matrix, + # destroying the learned modulation frequencies. + # (b) 2-D params with min(shape) < MUON_MIN_DIM. Tiny projections + # (e.g. HyenaFilter.implicit_filter.0.weight of shape (64, 3)) + # get collapsed toward near-identity by orthogonalization on the + # narrow axis, damaging expressivity. These belong in AdamW. + # These exclusions route the params into the AdamW scalar/vector group. + MUON_MIN_DIM = 8 + + def _muon_eligible(name: str, p: torch.Tensor) -> bool: + if p.dim() != 2: + return False + if name.endswith(".freq"): + return False + if min(p.shape) < MUON_MIN_DIM: + return False + return True + + # Matrix params -> Muon (2D weight matrices passing the routing guard). + matrix_params = [] + for name, p in self.blocks.named_parameters(): + if _muon_eligible(name, p): + matrix_params.append(p) + # NOTE (W1 audit REG-2): SemanticFoldingSDR.delta_u / delta_v are now + # GRADIENT-ALIVE via the STE path reconnected in _forward_impl: the + # differentiable SDR output feeds into sdr_proj which feeds into the + # engram residual. Gradient flows: LM_loss -> engram -> ... -> sdr_proj + # -> sdr_semantic.forward() -> STE -> delta_u/delta_v. These are 1D + # params (not 2D matrices) so they automatically land in scalar AdamW + # via the exclusion below — no special treatment needed. + # for p in self.sdr_semantic.parameters(): + # if p.dim() == 2: + # matrix_params.append(p) + for name, p in self.htm_proj.named_parameters(): + if _muon_eligible(name, p): + matrix_params.append(p) + for name, p in self.engram.named_parameters(): + if _muon_eligible(name, p): + matrix_params.append(p) + + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + # Extract dt_bias from each Mamba3 block into its own high-LR group. + # dt_bias controls SSM temporal discretization: softplus(dt_bias) gives + # the step size delta_t. A single dt_bias per head is 1D — falls into + # scalar_params by default. Give it a dedicated group with HYDRA_DT_BIAS_LR + # so heads can differentiate their timescales instead of all tracking at + # ln(2) across all layers. + dt_bias_params = [p for name, p in self.blocks.named_parameters() + if name.endswith('dt_bias')] + dt_bias_ids = set(id(p) for p in dt_bias_params) if dt_bias_params else set() + scalar_params = [ + p for p in self.parameters() + if id(p) not in assigned and id(p) not in dt_bias_ids + ] + + total_assigned = len(embedding_params) + len(lm_head_params) + len(matrix_params) + len(scalar_params) + len(dt_bias_params) + total_params = len(list(self.parameters())) + assert total_assigned == total_params, ( + f"Parameter count mismatch: assigned {total_assigned} vs total {total_params}" + ) + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind='adamw', params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind='adamw', params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + + if scalar_params: + param_groups.append( + dict(kind='adamw', params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + + # dt_bias: dedicated group with embed-level LR so each head learns its + # own temporal discretization. Env-overridable for sweeps. + if dt_bias_params: + _dt_bias_lr = float(os.environ.get("HYDRA_DT_BIAS_LR", str(embedding_lr))) + param_groups.append(dict( + kind='adamw', params=dt_bias_params, + lr=_dt_bias_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0, + )) + + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + # ns_steps: Muon polar-express inner iterations. Default 5 (paper), + # but 3 converges on small matrices (d_model ~ 384) with ~40% lower + # optimizer step cost. Env-tunable for experimentation. + _ns_steps = int(os.environ.get("HYDRA_MUON_NS_STEPS", "3")) + param_groups.append(dict( + kind='muon', params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=_ns_steps, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward(self, idx, targets=None, reduction='mean'): + """idx: (B, T) int64. Returns loss if targets given, else logits. + + Nested bf16 autocast is a no-op when ambient autocast is already on; + when it's off (e.g. integration tests) we establish the dtype contract. + """ + if torch.is_autocast_enabled(): + return self._forward_impl(idx, targets=targets, reduction=reduction) + with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + return self._forward_impl(idx, targets=targets, reduction=reduction) + + def _forward_impl(self, idx, targets=None, reduction='mean'): + B, T = idx.shape + + # Diagnostic: per-subsystem CUDA event timing. Env-gated; zero overhead + # when disabled. Logs one timing line per forward call. Used to isolate + # which subsystem is the tps bottleneck on paid hardware. + _profile = os.environ.get("HYDRA_PROFILE_FORWARD", "0") == "1" + if _profile: + def _ev(): + e = torch.cuda.Event(enable_timing=True) + e.record() + return e + _t0 = _ev() + else: + _t0 = None + + # Compact SDR support set used by Reality/Cantor/Engram and by the + # sparse SDR projection below. Do NOT materialize the dense + # (B,T,n_bits) SDR every step: at B16/T1024/n_bits=16384 that dense + # projection dominated runtime. Dense uint8 SDR is built only on HTM + # subsample steps where the HTM subsystem actually consumes it. + sdr_active_indices = self.sdr_semantic.active_indices(idx) + sdr_binary = None + + # HTM subsampling: run HTM on 1 of every N micro-batches within a + # gradient accumulation step, reuse the cached result for the other + # N-1 micro-batches. Cooperative launch monopolizes all SMs (grid.sync + # requires full-grid residency), so HTM and mamba can't overlap via + # streams. Subsampling removes HTM from most micro-batches' critical + # path instead. + # + # Math: N=8, 64 accum steps → 8 HTM calls (10.6ms each) + 56 fast + # calls (4ms each). Total = 84.8 + 224 = 309ms → 106k tps. + # + # HYDRA_HTM_SUBSAMPLE=N (default 8). Set =1 for every-microbatch HTM. + _htm_sub = int(os.environ.get("HYDRA_HTM_SUBSAMPLE", "8")) + if not hasattr(self, '_htm_call_idx'): + self._htm_call_idx = 0 + + _run_htm = (self._htm_call_idx % _htm_sub == 0) + self._htm_call_idx += 1 + + if _run_htm: + sdr_binary = self.sdr_semantic.binary_only(idx) + self._last_sdr = sdr_binary + htm_handle = self.htm.forward_async(sdr_binary) + else: + htm_handle = None + + if _profile: _t_htm_async = _ev() + dense_emb = self.wte(idx) # (B, T, d_model) bf16 - if self._throughput_mode: - self._last_sdr = None - sdr_active_bits = 0.0 - htm_anomaly = dense_emb.new_tensor(0.0) - x = norm(dense_emb) - if _profile: - _t_htm_async = _ev() - _t_wte = _ev() - _t_htm_await = _ev() - _t_htm_proj = _ev() + + if _profile: _t_wte = _ev() + + if _run_htm: + htm_out = self.htm.forward_await(htm_handle) + self._htm_cache = htm_out.detach() # cache for non-HTM micro-batches + elif hasattr(self, '_htm_cache') and self._htm_cache is not None \ + and self._htm_cache.shape[0] == B and self._htm_cache.shape[1] == T: + htm_out = self._htm_cache else: + # Very first call with subsample > 1: run HTM anyway. sdr_binary = self.sdr_semantic.binary_only(idx) self._last_sdr = sdr_binary - _htm_sub = int(os.environ.get("HYDRA_HTM_SUBSAMPLE", "8")) - if not hasattr(self, '_htm_call_idx'): - self._htm_call_idx = 0 - - _run_htm = (self._htm_call_idx % _htm_sub == 0) - self._htm_call_idx += 1 - if _run_htm: - htm_handle = self.htm.forward_async(sdr_binary) - else: - htm_handle = None + htm_handle = self.htm.forward_async(sdr_binary) + htm_out = self.htm.forward_await(htm_handle) + self._htm_cache = htm_out.detach() - if _profile: _t_htm_async = _ev() - if _profile: _t_wte = _ev() + if _profile: _t_htm_await = _ev() + with torch.no_grad(): + sdr_active_bits = float(self.sdr_semantic.target_active) + htm_anomaly = htm_out[..., -1].mean() - if _run_htm: - htm_out = self.htm.forward_await(htm_handle) - self._htm_cache = htm_out.detach() - elif hasattr(self, '_htm_cache') and self._htm_cache is not None and self._htm_cache.shape[0] == B and self._htm_cache.shape[1] == T: - htm_out = self._htm_cache - else: - htm_handle = self.htm.forward_async(sdr_binary) - htm_out = self.htm.forward_await(htm_handle) - self._htm_cache = htm_out.detach() + # Learnability #5: explicit stop-grad on HTM output. htm_rust already + # produces a detached tensor, but making it explicit here hardens the + # contract against future refactors that might route HTM through a + # grad-enabled op. + if self._htm_stop_grad: + htm_out = htm_out.detach() - if _profile: _t_htm_await = _ev() + # Gradient bridge: HTM columns+anomaly -> d_model. + htm_proj_out = self.htm_proj(htm_out.to(dense_emb.dtype)) + x = dense_emb + htm_proj_out + x = norm(x) + + if _profile: _t_htm_proj = _ev() + + # mHC-routed Mamba-3 stack with Engram injection at configured layer. + streams = self.mhc[0].init_streams(x) + _engram_ev = None + + # Per-layer diagnostic panel. The pre-layer merged state h_pre lets us + # measure residual contribution of each layer: delta_N = h_post - h_pre. + # All reads are detached no-grad to avoid autograd graph pollution. + _diag = self._diag_enabled + if _diag: + # Cast to float32 for the diagnostic arithmetic: the layer's + # residual contribution is small (~0.5 × rms-normed block output), + # which underflows in bf16 subtraction (3-digit mantissa) and + # reports delta_ratio=0 at the boundaries. float32 snapshot is + # ~3.8 MB extra memory per diag sample (B=1, T=2048, d=96) — + # negligible vs peak VRAM. with torch.no_grad(): - sdr_active_bits = float(self.sdr_semantic.target_active) - htm_anomaly = htm_out[..., -1].mean() - if self._htm_stop_grad: - htm_out = htm_out.detach() - htm_cols = htm_out[..., :-1].to(dense_emb.dtype) - htm_anom = htm_out[..., -1:].to(dense_emb.dtype) - htm_proj_out = self.htm_proj(htm_cols) + self.htm_anom_proj(htm_anom) - x = norm(dense_emb + htm_proj_out) - if _profile: _t_htm_proj = _ev() - - # mHC-routed Mamba-3 stack with Engram injection at configured layer. - streams = self.mhc[0].init_streams(x) - _engram_ev = None - - # Per-layer diagnostic panel. The pre-layer merged state h_pre lets us - # measure residual contribution of each layer: delta_N = h_post - h_pre. - # All reads are detached no-grad to avoid autograd graph pollution. - _diag = self._diag_enabled - if _diag: - # Cast to float32 for the diagnostic arithmetic: the layer's - # residual contribution is small (~0.5 × rms-normed block output), - # which underflows in bf16 subtraction (3-digit mantissa) and - # reports delta_ratio=0 at the boundaries. float32 snapshot is - # ~3.8 MB extra memory per diag sample (B=1, T=2048, d=96) — - # negligible vs peak VRAM. - with torch.no_grad(): - h_pre = self.mhc[0].merge_streams(streams).detach().float() - _run_svd = (self._diag_step % self._diag_svd_every) == 0 - - for i, (block, mhc_layer) in enumerate(zip(self.blocks, self.mhc)): - def _block_fn(h, _block=block): - return self.drop(_block(norm(h))) - - # Learnability #3: gradient checkpointing. Wrap the block-fn so - # the mhc layer's internal uses of it re-run the block in backward - # (trading compute for activation memory). use_reentrant=False is - # the modern API and works cleanly under autocast. - if self._grad_ckpt and self.training: - import torch.utils.checkpoint as _ckpt - _raw_fn = _block_fn - def _block_fn(h, _raw=_raw_fn): # noqa: E731 - return _ckpt.checkpoint(_raw, h, use_reentrant=False) - - streams = mhc_layer(streams, _block_fn) - - if self.engram is not None and i == self.engram_layer_idx: + h_pre = self.mhc[0].merge_streams(streams).detach().float() + _run_svd = (self._diag_step % self._diag_svd_every) == 0 + + for i, (block, mhc_layer) in enumerate(zip(self.blocks, self.mhc)): + def _block_fn(h, _block=block): + return self.drop(_block(norm(h))) + + # Learnability #3: gradient checkpointing. Wrap the block-fn so + # the mhc layer's internal uses of it re-run the block in backward + # (trading compute for activation memory). use_reentrant=False is + # the modern API and works cleanly under autocast. + if self._grad_ckpt and self.training: + import torch.utils.checkpoint as _ckpt + _raw_fn = _block_fn + def _block_fn(h, _raw=_raw_fn): # noqa: E731 + return _ckpt.checkpoint(_raw, h, use_reentrant=False) + + streams = mhc_layer(streams, _block_fn) + + if i == self.engram_layer_idx: if _profile: _t_pre_engram = _ev() x_mid = mhc_layer.merge_streams(streams) - x_mid, hit_rate = self.engram(x_mid, idx) - streams = mhc_layer.init_streams(x_mid) - self._metrics['engram_hit_rate'] = hit_rate - if _profile: _engram_ev = _ev() - - if _diag: - with torch.no_grad(): - h_post = mhc_layer.merge_streams(streams).detach().float() - in_n = h_pre.pow(2).mean().sqrt() - out_n = h_post.pow(2).mean().sqrt() - d_n = (h_post - h_pre).pow(2).mean().sqrt() - self._metrics[f'layer_{i}_in_norm'] = float(in_n.item()) - self._metrics[f'layer_{i}_out_norm'] = float(out_n.item()) - self._metrics[f'layer_{i}_delta_ratio'] = float((d_n / (in_n + 1e-6)).item()) - self._metrics[f'layer_{i}_feat_std'] = float(h_post.std(dim=-1).mean().item()) - if _run_svd: - # Effective rank via participation ratio of singular values. - # eff_rank = (Σσ)^2 / Σσ² — smooth rank proxy, bounded by d_model. - # Sampled to keep overhead low (SVD is O(min(B*T, D)^2·D)). - flat = h_post.reshape(-1, h_post.shape[-1])[:512].float() - try: - s = torch.linalg.svdvals(flat) - eff_rank = float(((s.sum() ** 2) / (s.pow(2).sum() + 1e-6)).item()) - self._metrics[f'layer_{i}_eff_rank'] = eff_rank - except Exception: - pass - h_pre = h_post - - if _diag: - self._diag_step += 1 - - if _profile: _t_blocks = _ev() - + # Inject differentiable SDR signal into the engram query via + # a lightweight projection (one bf16 matmul per forward step). + # This is the first time LM loss gradients reach the SDR retina + # via delta_u/delta_v — the curated semantic folding patterns + # can now adapt during training instead of staying frozen. + sdr_feat = FusedSDRProject.apply( + sdr_active_indices, + idx, + self.sdr_proj.weight, + self.sdr_semantic.delta_u, + self.sdr_semantic.delta_v, + ) + # Norm the projection to prevent magnitude blowup: the raw STE + # output has 327/16384 1.0 activations per token, and a single + # matmul through sdr_proj (16384→256) with no normalization + # grows weight norm from 1e-4 to ~182 within 2K steps, + # overwhelming the engram residual. + sdr_feat = norm(sdr_feat) + x_mid = x_mid + sdr_feat + x_mid = semantic_gaussian_mollify( + x_mid, + std=self._semantic_smooth_std, + training=self.training, + eval_enabled=self._semantic_smooth_eval, + ) + + # Cantor routing: partition the query space into 2^depth leaves. + # Leaf IDs can constrain Engram column eligibility per query. + leaf_ids = None + if self._cantor_enabled: + leaf_ids, scores = self.cantor( + x_mid, + return_scores=bool(self.cantor.score_grad), + ) + if scores is not None and scores.requires_grad: + self._metrics['cantor_score_mean'] = scores.detach().mean() + # Expose leaf distribution for monitoring. Keep both the + # instantaneous last-forward count and a training-window max; + # final factual probes are tiny and can otherwise overwrite + # the metric with an artificial 1-2 leaf count. + unique = leaf_ids.unique().numel() + self._metrics['cantor_active_leaves'] = unique + self._metrics['cantor_leaf_util'] = unique / self.cantor.n_leaves + if self.training: + self._cantor_active_leaves_train_max = max( + self._cantor_active_leaves_train_max, + int(unique), + ) + self._metrics['cantor_active_leaves_train_max'] = self._cantor_active_leaves_train_max + self._metrics['cantor_leaf_util_train_max'] = ( + self._cantor_active_leaves_train_max / self.cantor.n_leaves + ) + + if self.reality_bridge is not None: + reality = self.reality_bridge(x_mid) + engram_active_indices = reality.l0_indices + self._metrics['reality_poincare_radius'] = reality.poincare.float().norm(dim=-1).mean().detach() + else: + engram_active_indices = self.sdr_semantic.active_indices(idx) + + x_mid, hit_rate = self.engram( + x_mid, + idx, + sdr_active_indices=engram_active_indices, + cantor_leaf_ids=leaf_ids, + cantor_n_leaves=self.cantor.n_leaves if self._cantor_enabled else None, + ) + streams = mhc_layer.init_streams(x_mid) + self._metrics['engram_hit_rate'] = hit_rate + if self.training: + hit = float(hit_rate.detach().item() if hasattr(hit_rate, 'detach') else hit_rate) + self._engram_hit_rate_train_max = max(self._engram_hit_rate_train_max, hit) + self._engram_hit_rate_train_sum += hit + self._engram_hit_rate_train_count += 1 + self._metrics['engram_hit_rate_train_max'] = self._engram_hit_rate_train_max + self._metrics['engram_hit_rate_train_mean'] = ( + self._engram_hit_rate_train_sum / max(1, self._engram_hit_rate_train_count) + ) + self._metrics['engram_hit_rate_train_count'] = self._engram_hit_rate_train_count + if _profile: _engram_ev = _ev() + + if _diag: + with torch.no_grad(): + h_post = mhc_layer.merge_streams(streams).detach().float() + in_n = h_pre.pow(2).mean().sqrt() + out_n = h_post.pow(2).mean().sqrt() + d_n = (h_post - h_pre).pow(2).mean().sqrt() + self._metrics[f'layer_{i}_in_norm'] = float(in_n.item()) + self._metrics[f'layer_{i}_out_norm'] = float(out_n.item()) + self._metrics[f'layer_{i}_delta_ratio'] = float((d_n / (in_n + 1e-6)).item()) + self._metrics[f'layer_{i}_feat_std'] = float(h_post.std(dim=-1).mean().item()) + if _run_svd: + # Effective rank via participation ratio of singular values. + # eff_rank = (Σσ)^2 / Σσ² — smooth rank proxy, bounded by d_model. + # Sampled to keep overhead low (SVD is O(min(B*T, D)^2·D)). + flat = h_post.reshape(-1, h_post.shape[-1])[:512].float() + try: + s = torch.linalg.svdvals(flat) + eff_rank = float(((s.sum() ** 2) / (s.pow(2).sum() + 1e-6)).item()) + self._metrics[f'layer_{i}_eff_rank'] = eff_rank + except Exception: + pass + h_pre = h_post + + if _diag: + self._diag_step += 1 + + if _profile: _t_blocks = _ev() + self._metrics['sdr_active_bits'] = sdr_active_bits self._metrics['htm_anomaly'] = htm_anomaly - - x = self.mhc[-1].merge_streams(streams) - x = norm(x) - - if _profile: _t_merge = _ev() - - softcap = self.softcap - _softcap_clamp = os.environ.get("HYDRA_SOFTCAP_CLAMP", "0") == "1" - if targets is not None: - smoothing = self.config.label_smoothing - V = self.config.vocab_size - - # Learnability #4: doc-separator masking. In packed rows, - # tokenizer.encode(..., prepend=bos_token) places a BOS at every - # document boundary. Without masking, the model is penalized for - # failing to predict "doc B's BOS" from the last tokens of doc A - # — pure noise. We set targets==bos to -1 (ignore_index). Done - # BEFORE MTP/entropy/sampled-softmax branches so all downstream - # losses inherit the mask. - if self._doc_sep_mask and self._bos_token_id >= 0: - targets = torch.where( - targets == self._bos_token_id, - torch.full_like(targets, -1), - targets, - ) - - # Sampled softmax: instead of computing logits for ALL V tokens, - # compute only for the target + K random negatives. Reduces the - # lm_head matmul from (B*T, d) × (d, V) to (B*T, d) × (d, K+1). - # At V=65536 and K=4096: 16× less compute, ~4× tps improvement. - # The log-sum-exp correction adjusts for the sampling bias. - # Set HYDRA_SAMPLED_SOFTMAX=0 to disable (full softmax). - K_neg = int(os.environ.get("HYDRA_SAMPLED_SOFTMAX", "4096")) - use_sampled = K_neg > 0 and K_neg < V and self.training - - if use_sampled: - # Flatten hidden states + targets - h_flat = x.reshape(-1, x.shape[-1]) # (B*T, d) - t_flat = targets.reshape(-1) # (B*T,) - n = h_flat.shape[0] - - # Learnability #4 hardening: sampled-softmax gather crashes on - # negative ids (-1 from doc-sep mask). Replace -1 with 0 for - # gather; the actual loss is masked below. - valid_mask_flat = (t_flat >= 0) - t_flat_safe = torch.where(valid_mask_flat, t_flat, torch.zeros_like(t_flat)) - - # Sample K negatives uniformly from [0, V) - neg_ids = torch.randint(0, V, (K_neg,), device=x.device) - # Gather lm_head weights for target + negatives - all_ids = torch.cat([t_flat_safe, neg_ids]) # (B*T + K,) - sampled_w = self.lm_head.weight[all_ids] # (B*T + K, d) - - # Compute sampled logits: for each position, dot with its - # target weight and all K negative weights. - # Target logit: dot product of h[i] with w[target[i]] - target_w = sampled_w[:n] # (B*T, d) - neg_w = sampled_w[n:] # (K, d) - target_logit = (h_flat * target_w).sum(-1) # (B*T,) - neg_logits = h_flat @ neg_w.t() # (B*T, K) - - if not _softcap_clamp: - target_logit = softcap * torch.tanh(target_logit / softcap) - neg_logits = softcap * torch.tanh(neg_logits / softcap) - - # Sampled softmax loss: -log(exp(target) / (exp(target) + sum(exp(neg)))) - # With log-sum-exp correction for sampling K of V negatives. - # Correction: add log(V/K) to negative logits to account for - # the fact that we're only seeing K of V possible negatives. - log_correction = torch.tensor(V / K_neg, device=x.device).log() - all_logits = torch.cat([ - target_logit.unsqueeze(-1), # (B*T, 1) - neg_logits + log_correction, # (B*T, K) - ], dim=-1).float() # (B*T, K+1) - - # CE with target always at index 0 - ce_targets = torch.zeros(n, dtype=torch.long, device=x.device) - if reduction == 'none': - per_tok = F.cross_entropy(all_logits, ce_targets, reduction='none') - if self._doc_sep_mask and self._bos_token_id >= 0: - per_tok = torch.where(valid_mask_flat, per_tok, torch.zeros_like(per_tok)) - return per_tok - per_tok_ce = F.cross_entropy( - all_logits, ce_targets, reduction='none', - label_smoothing=smoothing, - ) - # Mask doc-separator positions. valid_mask_flat is always - # computed; when doc_sep_mask is off every token is valid so - # this reduces to a plain mean. - valid_f = valid_mask_flat.float() - valid_n = valid_f.sum().clamp(min=1) - out = (per_tok_ce * valid_f).sum() / valid_n - else: - # Full softmax path (eval or HYDRA_SAMPLED_SOFTMAX=0) - chunk_size = int(os.environ.get("HYDRA_CE_CHUNK", "1024")) - if chunk_size <= 0: - MAX_LOGITS_BYTES = 256 * 1024 * 1024 - tokens_per_chunk = max(V, MAX_LOGITS_BYTES // (V * 4)) - chunk_size = max(1, tokens_per_chunk // max(1, B)) - chunk_size = min(chunk_size, T) - - if reduction == 'none': - loss_parts = [] - for start in range(0, T, chunk_size): - end = min(start + chunk_size, T) - chunk_logits = self.lm_head(x[:, start:end, :]).float() - if _softcap_clamp: - chunk_logits = torch.clamp(chunk_logits, -softcap, softcap) - else: - chunk_logits = softcap * torch.tanh(chunk_logits / softcap) - chunk_targets = targets[:, start:end].reshape(-1) - chunk_loss = F.cross_entropy( - chunk_logits.view(-1, chunk_logits.size(-1)), - chunk_targets, ignore_index=-1, reduction='none', - ) - loss_parts.append(chunk_loss) - return torch.cat(loss_parts) - - total_loss = 0.0 - total_tokens = 0 - for start in range(0, T, chunk_size): - end = min(start + chunk_size, T) - chunk_logits = self.lm_head(x[:, start:end, :]).float() - if _softcap_clamp: - chunk_logits = torch.clamp(chunk_logits, -softcap, softcap) - else: - chunk_logits = softcap * torch.tanh(chunk_logits / softcap) - chunk_targets = targets[:, start:end].reshape(-1) - chunk_loss = F.cross_entropy( - chunk_logits.view(-1, chunk_logits.size(-1)), - chunk_targets, ignore_index=-1, reduction='sum', - label_smoothing=smoothing, - ) - total_loss = total_loss + chunk_loss - total_tokens += (chunk_targets != -1).sum() - out = total_loss / total_tokens - - # ----------------------------------------------------------- - # Learnability #1: Multi-Token Prediction. - # For k in {2..K}, add a CE loss at position (t) predicting - # the token at position (t+k), using the SAME lm_head weights - # (weight-tied). Cost: K-1 extra CEs on a subset of positions. - # Only triggered in reduction='mean' path, training only. - # ----------------------------------------------------------- - if reduction == 'mean' and self._mtp_k > 1 and self.training and use_sampled: - # TRUE zero-cost MTP: reuse primary's neg_logits (B*T, K_neg) - # entirely. Only cost per extra head: O(B*T*d) target-weight - # gather + dot product. neg_logits is sliced (view) to match. - mtp_loss_sum = out.new_tensor(0.0) - mtp_terms = 0 - # Reshape primary neg_logits back to (B, T, K_neg) so we can slice positions - neg_logits_bt = neg_logits.view(B, T, K_neg) - for k in range(2, self._mtp_k + 1): - shift = k - 1 - if T <= shift: - continue - n_k = B * (T - shift) - h_k_flat = x[:, :T - shift, :].reshape(n_k, -1) # (n_k, d) - t_k = targets[:, shift:].reshape(-1) # (n_k,) - mask_k = (t_k >= 0) - t_k_safe = torch.where(mask_k, t_k, torch.zeros_like(t_k)) - tgt_w_k = self.lm_head.weight[t_k_safe] # (n_k, d) - tgt_logit_k = (h_k_flat * tgt_w_k).sum(-1) # (n_k,) - if not _softcap_clamp: - tgt_logit_k = softcap * torch.tanh(tgt_logit_k / softcap) - # REUSE primary neg_logits — slice positions [:T-shift] - neg_logits_k = neg_logits_bt[:, :T - shift, :].reshape(n_k, K_neg) - all_logits_k = torch.cat([ - tgt_logit_k.unsqueeze(-1), - neg_logits_k + log_correction, - ], dim=-1).float() - ce_targets_k = torch.zeros(n_k, dtype=torch.long, device=x.device) - per_tok_ce_k = F.cross_entropy( - all_logits_k, ce_targets_k, reduction='none', - label_smoothing=smoothing, - ) - per_tok_ce_k = torch.where(mask_k, per_tok_ce_k, torch.zeros_like(per_tok_ce_k)) - n_valid_k = mask_k.sum().clamp(min=1) - mtp_loss_sum = mtp_loss_sum + per_tok_ce_k.sum() / n_valid_k - mtp_terms += 1 - if mtp_terms > 0: - out = (out + mtp_loss_sum) / float(mtp_terms + 1) - - # ----------------------------------------------------------- - # Learnability #6: output entropy penalty. - # L += -lambda * H(softmax(logits)). Negative entropy penalizes - # peaked distributions; encourages diverse predictions and - # breaks repetition loops. Computed on a small subset of - # positions to keep V-sized logits cost bounded. - # ----------------------------------------------------------- - if reduction == 'mean' and self._entropy_penalty > 0.0 and self.training: - # Sample up to 64 random positions. V-sized logits on 64 - # positions = 64 * V * 4 bytes (~50 MB at V=200k) — fits - # on the 3060 and adds ~2 ms. - h_flat = x.reshape(-1, x.shape[-1]) - n_pos = h_flat.shape[0] - n_sample = min(64, n_pos) - idx_sample = torch.randint(0, n_pos, (n_sample,), device=x.device) - h_sample = h_flat[idx_sample] - logits_s = F.linear(h_sample, self.lm_head.weight).float() - if _softcap_clamp: - logits_s = torch.clamp(logits_s, -softcap, softcap) - else: - logits_s = softcap * torch.tanh(logits_s / softcap) - log_probs = F.log_softmax(logits_s, dim=-1) - probs = log_probs.exp() - entropy = -(probs * log_probs).sum(-1).mean() # scalar, nats - out = out - self._entropy_penalty * entropy - - if _profile: - _t_end = _ev() - torch.cuda.synchronize() - def _ms(a, b): return a.elapsed_time(b) - print( - f"[PROFILE B={B} T={T}] " - f"htm_launch={_ms(_t0, _t_htm_async):.2f} " - f"wte={_ms(_t_htm_async, _t_wte):.2f} " - f"htm_await={_ms(_t_wte, _t_htm_await):.2f} " - f"htm_proj={_ms(_t_htm_await, _t_htm_proj):.2f} " - f"mamba_mhc_engram={_ms(_t_htm_proj, _t_blocks):.2f} " - f"merge={_ms(_t_blocks, _t_merge):.2f} " - f"lm_head_loss={_ms(_t_merge, _t_end):.2f} " - f"total={_ms(_t0, _t_end):.2f} ms", - flush=True, - ) - return out - - logits = self.lm_head(x).float() - if _softcap_clamp: - logits = torch.clamp(logits, -softcap, softcap) - else: - logits = softcap * torch.tanh(logits / softcap) - return logits + + x = self.mhc[-1].merge_streams(streams) + x = norm(x) + + if _profile: _t_merge = _ev() + + softcap = self.softcap + _softcap_clamp = os.environ.get("HYDRA_SOFTCAP_CLAMP", "0") == "1" + if targets is not None: + smoothing = self.config.label_smoothing + V = self.config.vocab_size + + # Learnability #4: doc-separator masking. In packed rows, + # tokenizer.encode(..., prepend=bos_token) places a BOS at every + # document boundary. Without masking, the model is penalized for + # failing to predict "doc B's BOS" from the last tokens of doc A + # — pure noise. We set targets==bos to -1 (ignore_index). Done + # BEFORE MTP/entropy/sampled-softmax branches so all downstream + # losses inherit the mask. + if self._doc_sep_mask and self._bos_token_id >= 0: + targets = torch.where( + targets == self._bos_token_id, + torch.full_like(targets, -1), + targets, + ) + + # Sampled softmax: instead of computing logits for ALL V tokens, + # compute only for the target + K random negatives. Reduces the + # lm_head matmul from (B*T, d) × (d, V) to (B*T, d) × (d, K+1). + # At V=65536 and K=4096: 16× less compute, ~4× tps improvement. + # The log-sum-exp correction adjusts for the sampling bias. + # Set HYDRA_SAMPLED_SOFTMAX=0 to disable (full softmax). + K_neg = int(os.environ.get("HYDRA_SAMPLED_SOFTMAX", "4096")) + use_sampled = K_neg > 0 and K_neg < V and self.training + + if use_sampled: + # Flatten hidden states + targets + h_flat = x.reshape(-1, x.shape[-1]) # (B*T, d) + t_flat = targets.reshape(-1) # (B*T,) + n = h_flat.shape[0] + + # Learnability #4 hardening: sampled-softmax gather crashes on + # negative ids (-1 from doc-sep mask). Replace -1 with 0 for + # gather; the actual loss is masked below. + valid_mask_flat = (t_flat >= 0) + t_flat_safe = torch.where(valid_mask_flat, t_flat, torch.zeros_like(t_flat)) + + # Sample K negatives uniformly from [0, V) + neg_ids = torch.randint(0, V, (K_neg,), device=x.device) + # Gather lm_head weights for target + negatives + all_ids = torch.cat([t_flat_safe, neg_ids]) # (B*T + K,) + sampled_w = self.lm_head.weight[all_ids] # (B*T + K, d) + + # Compute sampled logits: for each position, dot with its + # target weight and all K negative weights. + # Target logit: dot product of h[i] with w[target[i]]. + target_w = sampled_w[:n] # (B*T, d) + neg_w = sampled_w[n:] # (K, d) + log_correction = torch.tensor(V / K_neg, device=x.device).log() + + # B16+ active-stack experiments on the 6GB local GPU can OOM + # if we materialize the full (B*T, K+1) sampled-CE matrix. + # Chunk the sampled loss just like the full-softmax path unless + # MTP needs the full neg_logits view for reuse. + sampled_chunk = int(os.environ.get("HYDRA_SAMPLED_CE_CHUNK", "0")) + if sampled_chunk <= 0: + sampled_chunk = n + if reduction == 'mean' and self._mtp_k <= 1 and sampled_chunk < n: + total_loss = x.new_tensor(0.0) + total_tokens = x.new_tensor(0.0) + ce_targets_chunk = None + for start in range(0, n, sampled_chunk): + end = min(start + sampled_chunk, n) + h_c = h_flat[start:end] + target_w_c = target_w[start:end] + target_logit_c = (h_c * target_w_c).sum(-1) + neg_logits_c = h_c @ neg_w.t() + if not _softcap_clamp: + target_logit_c = softcap * torch.tanh(target_logit_c / softcap) + neg_logits_c = softcap * torch.tanh(neg_logits_c / softcap) + all_logits_c = torch.cat([ + target_logit_c.unsqueeze(-1), + neg_logits_c + log_correction, + ], dim=-1).float() + if ce_targets_chunk is None or ce_targets_chunk.numel() != end - start: + ce_targets_chunk = torch.zeros(end - start, dtype=torch.long, device=x.device) + per_tok_ce_c = F.cross_entropy( + all_logits_c, ce_targets_chunk, reduction='none', + label_smoothing=smoothing, + ) + valid_c = valid_mask_flat[start:end].float() + total_loss = total_loss + (per_tok_ce_c * valid_c).sum() + total_tokens = total_tokens + valid_c.sum() + out = total_loss / total_tokens.clamp(min=1) + neg_logits = None + else: + target_logit = (h_flat * target_w).sum(-1) # (B*T,) + neg_logits = h_flat @ neg_w.t() # (B*T, K) + + if not _softcap_clamp: + target_logit = softcap * torch.tanh(target_logit / softcap) + neg_logits = softcap * torch.tanh(neg_logits / softcap) + + # Sampled softmax loss: -log(exp(target) / (exp(target) + sum(exp(neg)))) + # With log-sum-exp correction for sampling K of V negatives. + # Correction: add log(V/K) to negative logits to account for + # the fact that we're only seeing K of V possible negatives. + all_logits = torch.cat([ + target_logit.unsqueeze(-1), # (B*T, 1) + neg_logits + log_correction, # (B*T, K) + ], dim=-1).float() # (B*T, K+1) + + # CE with target always at index 0 + ce_targets = torch.zeros(n, dtype=torch.long, device=x.device) + if reduction == 'none': + per_tok = F.cross_entropy(all_logits, ce_targets, reduction='none') + if self._doc_sep_mask and self._bos_token_id >= 0: + per_tok = torch.where(valid_mask_flat, per_tok, torch.zeros_like(per_tok)) + return per_tok + per_tok_ce = F.cross_entropy( + all_logits, ce_targets, reduction='none', + label_smoothing=smoothing, + ) + # Mask doc-separator positions. valid_mask_flat is always + # computed; when doc_sep_mask is off every token is valid so + # this reduces to a plain mean. + valid_f = valid_mask_flat.float() + valid_n = valid_f.sum().clamp(min=1) + out = (per_tok_ce * valid_f).sum() / valid_n + else: + # Full softmax path (eval or HYDRA_SAMPLED_SOFTMAX=0) + chunk_size = int(os.environ.get("HYDRA_CE_CHUNK", "1024")) + if chunk_size <= 0: + MAX_LOGITS_BYTES = 256 * 1024 * 1024 + bytes_per_logit = FLOAT32_BYTES + # Bound by token logits memory: each token contributes V + # logits, so the safe token count can be smaller than V. + tokens_per_chunk = max(1, MAX_LOGITS_BYTES // (V * bytes_per_logit)) + chunk_size = max(1, tokens_per_chunk // max(1, B)) + chunk_size = min(chunk_size, T) + + if reduction == 'none': + loss_parts = [] + for start in range(0, T, chunk_size): + end = min(start + chunk_size, T) + chunk_logits = self.lm_head(x[:, start:end, :]).float() + if _softcap_clamp: + chunk_logits = torch.clamp(chunk_logits, -softcap, softcap) + else: + chunk_logits = softcap * torch.tanh(chunk_logits / softcap) + chunk_targets = targets[:, start:end].reshape(-1) + chunk_loss = F.cross_entropy( + chunk_logits.view(-1, chunk_logits.size(-1)), + chunk_targets, ignore_index=-1, reduction='none', + ) + loss_parts.append(chunk_loss) + return torch.cat(loss_parts) + + total_loss = 0.0 + total_tokens = 0 + for start in range(0, T, chunk_size): + end = min(start + chunk_size, T) + chunk_logits = self.lm_head(x[:, start:end, :]).float() + if _softcap_clamp: + chunk_logits = torch.clamp(chunk_logits, -softcap, softcap) + else: + chunk_logits = softcap * torch.tanh(chunk_logits / softcap) + chunk_targets = targets[:, start:end].reshape(-1) + chunk_loss = F.cross_entropy( + chunk_logits.view(-1, chunk_logits.size(-1)), + chunk_targets, ignore_index=-1, reduction='sum', + label_smoothing=smoothing, + ) + total_loss = total_loss + chunk_loss + total_tokens += (chunk_targets != -1).sum() + out = total_loss / total_tokens + + # ----------------------------------------------------------- + # Learnability #1: Multi-Token Prediction. + # For k in {2..K}, add a CE loss at position (t) predicting + # the token at position (t+k), using the SAME lm_head weights + # (weight-tied). Cost: K-1 extra CEs on a subset of positions. + # Only triggered in reduction='mean' path, training only. + # ----------------------------------------------------------- + if reduction == 'mean' and self._mtp_k > 1 and self.training and use_sampled: + # TRUE zero-cost MTP: reuse primary's neg_logits (B*T, K_neg) + # entirely. Only cost per extra head: O(B*T*d) target-weight + # gather + dot product. neg_logits is sliced (view) to match. + mtp_loss_sum = out.new_tensor(0.0) + mtp_terms = 0 + # Reshape primary neg_logits back to (B, T, K_neg) so we can slice positions + neg_logits_bt = neg_logits.view(B, T, K_neg) + for k in range(2, self._mtp_k + 1): + shift = k - 1 + if T <= shift: + continue + n_k = B * (T - shift) + h_k_flat = x[:, :T - shift, :].reshape(n_k, -1) # (n_k, d) + t_k = targets[:, shift:].reshape(-1) # (n_k,) + mask_k = (t_k >= 0) + t_k_safe = torch.where(mask_k, t_k, torch.zeros_like(t_k)) + tgt_w_k = self.lm_head.weight[t_k_safe] # (n_k, d) + tgt_logit_k = (h_k_flat * tgt_w_k).sum(-1) # (n_k,) + if not _softcap_clamp: + tgt_logit_k = softcap * torch.tanh(tgt_logit_k / softcap) + # REUSE primary neg_logits — slice positions [:T-shift] + neg_logits_k = neg_logits_bt[:, :T - shift, :].reshape(n_k, K_neg) + all_logits_k = torch.cat([ + tgt_logit_k.unsqueeze(-1), + neg_logits_k + log_correction, + ], dim=-1).float() + ce_targets_k = torch.zeros(n_k, dtype=torch.long, device=x.device) + per_tok_ce_k = F.cross_entropy( + all_logits_k, ce_targets_k, reduction='none', + label_smoothing=smoothing, + ) + per_tok_ce_k = torch.where(mask_k, per_tok_ce_k, torch.zeros_like(per_tok_ce_k)) + n_valid_k = mask_k.sum().clamp(min=1) + mtp_loss_sum = mtp_loss_sum + per_tok_ce_k.sum() / n_valid_k + mtp_terms += 1 + if mtp_terms > 0: + out = (out + mtp_loss_sum) / float(mtp_terms + 1) + + # ----------------------------------------------------------- + # Learnability #6: output entropy penalty. + # L += -lambda * H(softmax(logits)). Negative entropy penalizes + # peaked distributions; encourages diverse predictions and + # breaks repetition loops. Computed on a small subset of + # positions to keep V-sized logits cost bounded. + # ----------------------------------------------------------- + if reduction == 'mean' and self._entropy_penalty > 0.0 and self.training: + # Sample up to 64 random positions. V-sized logits on 64 + # positions = 64 * V * 4 bytes (~50 MB at V=200k) — fits + # on the 3060 and adds ~2 ms. + h_flat = x.reshape(-1, x.shape[-1]) + n_pos = h_flat.shape[0] + n_sample = min(64, n_pos) + idx_sample = torch.randint(0, n_pos, (n_sample,), device=x.device) + h_sample = h_flat[idx_sample] + logits_s = F.linear(h_sample, self.lm_head.weight).float() + if _softcap_clamp: + logits_s = torch.clamp(logits_s, -softcap, softcap) + else: + logits_s = softcap * torch.tanh(logits_s / softcap) + log_probs = F.log_softmax(logits_s, dim=-1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(-1).mean() # scalar, nats + out = out - self._entropy_penalty * entropy + + if reduction == 'mean' and self.training and ( + self._sf_ortho_lambda > 0.0 or self._sf_ortho_metrics + ): + run_metric = self._sf_ortho_metrics and ( + self._sf_ortho_step % self._sf_ortho_every == 0 + ) + if self._sf_ortho_lambda > 0.0: + sf_ortho = self._slow_fast_ortho_loss() + out = out + self._sf_ortho_lambda * sf_ortho + if run_metric: + self._metrics['slow_fast_ortho_loss'] = sf_ortho.detach() + elif run_metric: + with torch.no_grad(): + self._metrics['slow_fast_ortho_loss'] = self._slow_fast_ortho_loss().detach() + self._sf_ortho_step += 1 + + if _profile: + _t_end = _ev() + torch.cuda.synchronize() + def _ms(a, b): return a.elapsed_time(b) + print( + f"[PROFILE B={B} T={T}] " + f"htm_launch={_ms(_t0, _t_htm_async):.2f} " + f"wte={_ms(_t_htm_async, _t_wte):.2f} " + f"htm_await={_ms(_t_wte, _t_htm_await):.2f} " + f"htm_proj={_ms(_t_htm_await, _t_htm_proj):.2f} " + f"mamba_mhc_engram={_ms(_t_htm_proj, _t_blocks):.2f} " + f"merge={_ms(_t_blocks, _t_merge):.2f} " + f"lm_head_loss={_ms(_t_merge, _t_end):.2f} " + f"total={_ms(_t0, _t_end):.2f} ms", + flush=True, + ) + return out + + logits = self.lm_head(x).float() + if _softcap_clamp: + logits = torch.clamp(logits, -softcap, softcap) + else: + logits = softcap * torch.tanh(logits / softcap) + return logits diff --git a/overlay/hydra/optimizer.py b/overlay/hydra/optimizer.py index 18504cc09d5852b3b270d075e71d99522d05446d..1dec4ee0bad3c61851e2392ce7743756839deaba 100644 --- a/overlay/hydra/optimizer.py +++ b/overlay/hydra/optimizer.py @@ -1,252 +1,252 @@ -"""MuonAdamW optimizer — combined Muon (2D matrices) + AdamW (everything else). - -Extracted verbatim from train.py (W1 modularization). Semantics unchanged. - -F1-F15 state preserved: -- F7 REVERTED: `stacked_params_buf` persistent across steps was REMOVED — each - step calls `torch.stack([p.grad for p in params])` / `torch.stack(params)` - fresh. Persistent copies of param storage would be mutated between forward - passes (via lerp_/sub_ on stacked tensors that share storage with params), - triggering "modified in-place" errors on grad_accum=2 backwards. -- F11/F15: `@torch.compile` on `adamw_step_fused` / `muon_step_fused` intact. -- F15 compile is default-ON (HYDRA_MUON_COMPILE=1), configured with - dynamic=True + mode="default" to avoid the step-17→18 cudagraphs - stream-capture deadlock. See .omc/muon_compile_bug.md for the full - investigation. -""" - -from __future__ import annotations - -import os - -import torch - -# HYDRA_FUSED_ADAMW=1 (default) -> vectorized torch._fused_adamw_ kernel. -_HYDRA_FUSED_ADAMW = os.environ.get("HYDRA_FUSED_ADAMW", "1") == "1" -_HAS_FUSED_ADAMW = hasattr(torch, "_fused_adamw_") - - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - # Per-param AdamW fallback. Fast path is torch._fused_adamw_ (1 CUDA launch - # for the whole group) driven from MuonAdamW._step_adamw below. - grad = grad.to(p.dtype) # handle mixed bf16/fp32 from autocast - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -# --------------------------------------------------------------------------- -# F15 muon_step_fused compile strategy. -# -# HYDRA_MUON_COMPILE env gate: -# "1" (default ON) — wrap with torch.compile(dynamic=True, mode="default"). -# Dynamic=True collapses the per-shape specialization cache so that N -# Muon param-groups with N distinct shapes trigger 1 compile, not N. -# mode="default" keeps the inductor codegen but disables cudagraphs, -# which is what caused the step-17→18 silent deadlock observed under -# the original dynamic=False configuration: cudagraph stream capture -# can deadlock against HTM's CUDA kernels running on the default -# stream, and the failure mode at capture-time is a silent hang -# (100% GPU util, no log output, process state R). -# "0" — fall back to eager Python (slower, ~43k tps vs ~63k compiled). -# Keeps an escape hatch in case a future torch/inductor regression -# reintroduces a deadlock. -# -# Defensive .clone() on stacked_grads before in-place lerp_ eliminates the -# alias-analysis edge case where inductor sees `g is stacked_grads` and -# subsequent `stacked_grads.square()` operating on the post-lerp storage. -# --------------------------------------------------------------------------- -_MUON_COMPILE = os.environ.get("HYDRA_MUON_COMPILE", "1") == "1" - -def _maybe_compile(fn): - if _MUON_COMPILE: - # mode="default" explicitly opts OUT of cudagraphs (which reduce-overhead - # would enable) to avoid stream-capture deadlocks against HTM's CUDA - # kernels. dynamic=True minimizes recompile count across param-group - # shapes. - return torch.compile(fn, fullgraph=False, dynamic=True, mode="default") - return fn - -@_maybe_compile -def muon_step_fused(stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim): - # Cast grads to param dtype AND clone defensively to break any alias - # between the (freshly-stacked) input and the in-place lerp_ below. - # Without this, inductor's alias analysis can emit code that reads from - # post-mutation storage when computing `v_mean = g.square().mean(...)`. - stacked_grads = stacked_grads.to(momentum_buffer.dtype).clone() - # Nesterov momentum - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - # Polar express orthogonalization - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - # NorMuon variance reduction - # Keep beta2 in the state-buffer dtype, not g.dtype, so lerp_ on the - # float32 second_momentum_buffer doesn't hit a dtype mismatch on h200. - beta2 = beta2_t.to(second_momentum_buffer.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - # Cautious weight decay + parameter update - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - # 0-D CPU tensors to avoid torch.compile recompilation when values change - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - params, grads, exp_avgs, exp_avg_sqs, state_steps = [], [], [], [], [] - for p in group['params']: - if p.grad is None: - continue - state = self.state[p] - if not state: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p) - state['exp_avg_sq'] = torch.zeros_like(p) - if 'step_t' not in state: - # _fused_adamw_ wants a per-param float step tensor on-device. - state['step_t'] = torch.tensor( - float(state['step']), dtype=torch.float32, device=p.device - ) - state['step'] += 1 - params.append(p) - grads.append(p.grad.to(p.dtype) if p.grad.dtype != p.dtype else p.grad) - exp_avgs.append(state['exp_avg']) - exp_avg_sqs.append(state['exp_avg_sq']) - state_steps.append(state['step_t']) - - if not params: - return - - if _HYDRA_FUSED_ADAMW and _HAS_FUSED_ADAMW and params[0].is_cuda: - # _fused_adamw_ needs uniform (device, dtype) within a call, so - # group by (device, dtype) — same pattern as PyTorch's own - # AdamW(fused=True) path (_group_tensors_by_device_and_dtype). - buckets = {} - for p, g, ea, es, st in zip(params, grads, exp_avgs, exp_avg_sqs, state_steps): - key = (p.device, p.dtype) - buckets.setdefault(key, ([], [], [], [], [])) - b_p, b_g, b_ea, b_es, b_st = buckets[key] - b_p.append(p); b_g.append(g); b_ea.append(ea); b_es.append(es); b_st.append(st) - - lr_f = float(group['lr']) - b1_f = float(group['betas'][0]) - b2_f = float(group['betas'][1]) - wd_f = float(group['weight_decay']) - eps_f = float(group['eps']) - for (_dev, _dt), (b_p, b_g, b_ea, b_es, b_st) in buckets.items(): - torch._foreach_add_(b_st, 1.0) - torch._fused_adamw_( - b_p, b_g, b_ea, b_es, - [], # max_exp_avg_sqs unused (amsgrad=False) - b_st, - amsgrad=False, - lr=lr_f, beta1=b1_f, beta2=b2_f, - weight_decay=wd_f, eps=eps_f, - maximize=False, - grad_scale=None, found_inf=None, - ) - return - - # Fallback per-param path. - self._adamw_lr_t.fill_(group['lr']) - self._adamw_beta1_t.fill_(group['betas'][0]) - self._adamw_beta2_t.fill_(group['betas'][1]) - self._adamw_eps_t.fill_(group['eps']) - self._adamw_wd_t.fill_(group['weight_decay']) - for p, grad, exp_avg, exp_avg_sq in zip(params, grads, exp_avgs, exp_avg_sqs): - self._adamw_step_t.fill_(self.state[p]['step']) - adamw_step_fused(p, grad, exp_avg, exp_avg_sq, - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t) - - def _step_muon(self, group): - params = [p for p in group['params'] if p.grad is not None] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - if "second_momentum_buffer" not in state: - # Shape must match v_mean = stacked_grads.square().mean(dim=red_dim, keepdim=True) - full_shape = (num_params, *shape) - state_shape = list(full_shape) - state_shape[len(state_shape) + red_dim] = 1 # red_dim is negative - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - # F7 REVERT: fresh stacks each step (no persistent stacked_params_buf). - # This was the autograd-safety fix that unblocks grad_accum>=2. - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused(stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group['kind'] == 'adamw': - self._step_adamw(group) - elif group['kind'] == 'muon': - self._step_muon(group) +"""MuonAdamW optimizer — combined Muon (2D matrices) + AdamW (everything else). + +Extracted verbatim from train.py (W1 modularization). Semantics unchanged. + +F1-F15 state preserved: +- F7 REVERTED: `stacked_params_buf` persistent across steps was REMOVED — each + step calls `torch.stack([p.grad for p in params])` / `torch.stack(params)` + fresh. Persistent copies of param storage would be mutated between forward + passes (via lerp_/sub_ on stacked tensors that share storage with params), + triggering "modified in-place" errors on grad_accum=2 backwards. +- F11/F15: `@torch.compile` on `adamw_step_fused` / `muon_step_fused` intact. +- F15 compile is default-ON (HYDRA_MUON_COMPILE=1), configured with + dynamic=True + mode="default" to avoid the step-17→18 cudagraphs + stream-capture deadlock. See .omc/muon_compile_bug.md for the full + investigation. +""" + +from __future__ import annotations + +import os + +import torch + +# HYDRA_FUSED_ADAMW=1 (default) -> vectorized torch._fused_adamw_ kernel. +_HYDRA_FUSED_ADAMW = os.environ.get("HYDRA_FUSED_ADAMW", "1") == "1" +_HAS_FUSED_ADAMW = hasattr(torch, "_fused_adamw_") + + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + # Per-param AdamW fallback. Fast path is torch._fused_adamw_ (1 CUDA launch + # for the whole group) driven from MuonAdamW._step_adamw below. + grad = grad.to(p.dtype) # handle mixed bf16/fp32 from autocast + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +# --------------------------------------------------------------------------- +# F15 muon_step_fused compile strategy. +# +# HYDRA_MUON_COMPILE env gate: +# "1" (default ON) — wrap with torch.compile(dynamic=True, mode="default"). +# Dynamic=True collapses the per-shape specialization cache so that N +# Muon param-groups with N distinct shapes trigger 1 compile, not N. +# mode="default" keeps the inductor codegen but disables cudagraphs, +# which is what caused the step-17→18 silent deadlock observed under +# the original dynamic=False configuration: cudagraph stream capture +# can deadlock against HTM's CUDA kernels running on the default +# stream, and the failure mode at capture-time is a silent hang +# (100% GPU util, no log output, process state R). +# "0" — fall back to eager Python (slower, ~43k tps vs ~63k compiled). +# Keeps an escape hatch in case a future torch/inductor regression +# reintroduces a deadlock. +# +# Defensive .clone() on stacked_grads before in-place lerp_ eliminates the +# alias-analysis edge case where inductor sees `g is stacked_grads` and +# subsequent `stacked_grads.square()` operating on the post-lerp storage. +# --------------------------------------------------------------------------- +_MUON_COMPILE = os.environ.get("HYDRA_MUON_COMPILE", "1") == "1" + +def _maybe_compile(fn): + if _MUON_COMPILE: + # mode="default" explicitly opts OUT of cudagraphs (which reduce-overhead + # would enable) to avoid stream-capture deadlocks against HTM's CUDA + # kernels. dynamic=True minimizes recompile count across param-group + # shapes. + return torch.compile(fn, fullgraph=False, dynamic=True, mode="default") + return fn + +@_maybe_compile +def muon_step_fused(stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim): + # Cast grads to param dtype AND clone defensively to break any alias + # between the (freshly-stacked) input and the in-place lerp_ below. + # Without this, inductor's alias analysis can emit code that reads from + # post-mutation storage when computing `v_mean = g.square().mean(...)`. + stacked_grads = stacked_grads.to(momentum_buffer.dtype).clone() + # Nesterov momentum + momentum = momentum_t.to(device=momentum_buffer.device, dtype=stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + # Polar express orthogonalization + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + # NorMuon variance reduction + # Keep beta2 in the state-buffer dtype, not g.dtype, so lerp_ on the + # float32 second_momentum_buffer doesn't hit a dtype mismatch on h200. + beta2 = beta2_t.to(device=second_momentum_buffer.device, dtype=second_momentum_buffer.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + # Cautious weight decay + parameter update + lr = lr_t.to(device=stacked_params.device, dtype=g.dtype) + wd = wd_t.to(device=stacked_params.device, dtype=g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + # 0-D CPU tensors to avoid torch.compile recompilation when values change + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + params, grads, exp_avgs, exp_avg_sqs, state_steps = [], [], [], [], [] + for p in group['params']: + if p.grad is None: + continue + state = self.state[p] + if not state: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p) + state['exp_avg_sq'] = torch.zeros_like(p) + if 'step_t' not in state: + # _fused_adamw_ wants a per-param float step tensor on-device. + state['step_t'] = torch.tensor( + float(state['step']), dtype=torch.float32, device=p.device + ) + state['step'] += 1 + params.append(p) + grads.append(p.grad.to(p.dtype) if p.grad.dtype != p.dtype else p.grad) + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + state_steps.append(state['step_t']) + + if not params: + return + + if _HYDRA_FUSED_ADAMW and _HAS_FUSED_ADAMW and params[0].is_cuda: + # _fused_adamw_ needs uniform (device, dtype) within a call, so + # group by (device, dtype) — same pattern as PyTorch's own + # AdamW(fused=True) path (_group_tensors_by_device_and_dtype). + buckets = {} + for p, g, ea, es, st in zip(params, grads, exp_avgs, exp_avg_sqs, state_steps): + key = (p.device, p.dtype) + buckets.setdefault(key, ([], [], [], [], [])) + b_p, b_g, b_ea, b_es, b_st = buckets[key] + b_p.append(p); b_g.append(g); b_ea.append(ea); b_es.append(es); b_st.append(st) + + lr_f = float(group['lr']) + b1_f = float(group['betas'][0]) + b2_f = float(group['betas'][1]) + wd_f = float(group['weight_decay']) + eps_f = float(group['eps']) + for (_dev, _dt), (b_p, b_g, b_ea, b_es, b_st) in buckets.items(): + torch._foreach_add_(b_st, 1.0) + torch._fused_adamw_( + b_p, b_g, b_ea, b_es, + [], # max_exp_avg_sqs unused (amsgrad=False) + b_st, + amsgrad=False, + lr=lr_f, beta1=b1_f, beta2=b2_f, + weight_decay=wd_f, eps=eps_f, + maximize=False, + grad_scale=None, found_inf=None, + ) + return + + # Fallback per-param path. + self._adamw_lr_t.fill_(group['lr']) + self._adamw_beta1_t.fill_(group['betas'][0]) + self._adamw_beta2_t.fill_(group['betas'][1]) + self._adamw_eps_t.fill_(group['eps']) + self._adamw_wd_t.fill_(group['weight_decay']) + for p, grad, exp_avg, exp_avg_sq in zip(params, grads, exp_avgs, exp_avg_sqs): + self._adamw_step_t.fill_(self.state[p]['step']) + adamw_step_fused(p, grad, exp_avg, exp_avg_sq, + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t) + + def _step_muon(self, group): + params = [p for p in group['params'] if p.grad is not None] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + if "second_momentum_buffer" not in state: + # Shape must match v_mean = stacked_grads.square().mean(dim=red_dim, keepdim=True) + full_shape = (num_params, *shape) + state_shape = list(full_shape) + state_shape[len(state_shape) + red_dim] = 1 # red_dim is negative + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + # F7 REVERT: fresh stacks each step (no persistent stacked_params_buf). + # This was the autograd-safety fix that unblocks grad_accum>=2. + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused(stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group['kind'] == 'adamw': + self._step_adamw(group) + elif group['kind'] == 'muon': + self._step_muon(group) diff --git a/overlay/hydra/reality_bridge.py b/overlay/hydra/reality_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..29c21a3b8f5216843381ec24be38f5220ddca57f --- /dev/null +++ b/overlay/hydra/reality_bridge.py @@ -0,0 +1,71 @@ +from __future__ import annotations + +from dataclasses import dataclass + +import torch +import torch.nn as nn + + +@dataclass(frozen=True) +class RealityBridgeOutput: + reality: torch.Tensor + poincare: torch.Tensor + l0_indices: torch.Tensor + l0_values: torch.Tensor + + +class RealityPoincareBridge(nn.Module): + """Default-off SEM-Claw continuous→discrete bridge. + + PyTorch GEMM creates a compact 133-d reality latent, then a differentiable + Poincare-disk projection is kept for metrics/regularizers while a detached + int16 L0/top-k index buffer feeds Engram/Cantor sparse retrieval. This is a + production-shaped version of rs.md's Poincare/Reality Buffer without adding + speculative E7 machinery to the hot path. + """ + + def __init__( + self, + d_model: int, + d_reality: int = 133, + d_poincare: int = 2, + l0_k: int = 64, + ) -> None: + super().__init__() + if d_model <= 0: + raise ValueError(f"d_model must be positive, got {d_model}") + if d_reality <= 0: + raise ValueError(f"d_reality must be positive, got {d_reality}") + if d_poincare != 2: + raise ValueError("Poincare bridge currently expects d_poincare=2") + if l0_k <= 0: + raise ValueError(f"l0_k must be positive, got {l0_k}") + self.d_model = int(d_model) + self.d_reality = int(d_reality) + self.d_poincare = int(d_poincare) + self.l0_k = min(int(l0_k), self.d_reality) + self.to_reality = nn.Linear(d_model, d_reality, bias=False) + self.to_tangent2 = nn.Linear(d_reality, d_poincare, bias=False) + nn.init.normal_(self.to_reality.weight, mean=0.0, std=0.02) + nn.init.normal_(self.to_tangent2.weight, mean=0.0, std=0.02) + + @staticmethod + def poincare_expmap0(tangent2: torch.Tensor, eps: float = 1e-6) -> torch.Tensor: + t = tangent2.float() + r = t.norm(dim=-1, keepdim=True).clamp_min(eps) + y = torch.tanh(r) * (t / r) + return y.to(tangent2.dtype) + + def forward(self, x: torch.Tensor) -> RealityBridgeOutput: + if x.shape[-1] != self.d_model: + raise ValueError(f"expected last dim {self.d_model}, got {x.shape[-1]}") + reality = self.to_reality(x) + tangent2 = self.to_tangent2(reality) + poincare = self.poincare_expmap0(tangent2) + vals, idx = reality.float().abs().topk(self.l0_k, dim=-1) + return RealityBridgeOutput( + reality=reality, + poincare=poincare, + l0_indices=idx.to(torch.int16), + l0_values=vals.to(reality.dtype), + ) diff --git a/overlay/hydra/training.py b/overlay/hydra/training.py index 39a163463e732bd08eca12b30827f6b911575e34..c0a88e85ca7262000089350cb7b5c0eb2487272d 100644 --- a/overlay/hydra/training.py +++ b/overlay/hydra/training.py @@ -1,948 +1,967 @@ -"""HYDRA training entry: setup, train loop, eval, summary. - -Extracted from the monolithic train.py (W1 modularization). Semantics -preserved. Public entrypoint: `main()`. -""" - -from __future__ import annotations - -import gc -import json -import math -import os -import sys -import threading -import time -from dataclasses import asdict -from pathlib import Path - -import torch - -# Line-buffered stdout so `python -u train.py | tee run.log | grep step` is -# live (no \r overwrite, no 4k block-buffered pipe stalls). Safe on Python -# 3.7+ where io.TextIOWrapper.reconfigure exists. -try: - sys.stdout.reconfigure(line_buffering=True) # type: ignore[attr-defined] -except Exception: - pass - -from hydra.config import ( - ADAM_BETAS, CURRICULUM_SHORT_SEQ_LEN, CURRICULUM_SHORT_STEPS, - D_MODEL, D_STATE, DEVICE_BATCH_SIZE, EMA_DECAY, EMBEDDING_LR, - ENGRAM_KEY_DIM, ENGRAM_LAYER_IDX, ENGRAM_N_COLUMNS, EXPAND, - FINAL_LR_FRAC, GPU_BF16_PEAK_FLOPS, HEADDIM, MATRIX_LR, N_HEADS, - N_LAYER, PostSemClawConfig, SCALAR_LR, SEED, TOTAL_BATCH_SIZE, - UNEMBEDDING_LR, USE_EMA, WARMUP_RATIO, WEIGHT_DECAY, -) -from hydra.diffusion_loss import mdlm_masked_forward_process, mdlm_rb_loss -from hydra.eval import run_factual_english, run_factual_probes -from hydra.model import PostSemClawModel - -import prepare as _prepare_mod -from prepare import MAX_SEQ_LEN, TIME_BUDGET as _TIME_BUDGET, Tokenizer, evaluate_bpb as _evaluate_bpb_shards, get_token_bytes, make_dataloader as _make_dataloader_shards - -# Streaming Nemotron path (Super3 recipe). Opt-in via HYDRA_USE_NEMOTRON=1. -if os.environ.get("HYDRA_USE_NEMOTRON", "0") == "1": - import prepare_nemotron as _p_nemo - make_dataloader = _p_nemo.make_dataloader - evaluate_bpb = _p_nemo.evaluate_bpb -else: - make_dataloader = _make_dataloader_shards - evaluate_bpb = _evaluate_bpb_shards - -TIME_BUDGET = int(os.environ.get("HYDRA_TIME_BUDGET", str(_TIME_BUDGET))) -_prepare_mod.TIME_BUDGET = TIME_BUDGET # sync for evaluate_bpb - -CACHE_DIR = Path.home() / ".cache" / "autoresearch" -LATEST_CKPT = CACHE_DIR / "latest.pt" -PRETRAIN_FINAL_CKPT = CACHE_DIR / "pretrain_final.pt" -FAILED_CKPT = CACHE_DIR / "latest_failed.pt" # crash/FAIL path — never overwrites good -BEST_CKPT = CACHE_DIR / "best_bpb.pt" # lowest val_bpb seen -CKPT_INTERVAL = int(os.environ.get("HYDRA_CKPT_INTERVAL", "250")) -CKPT_ROTATIONS = int(os.environ.get("HYDRA_CKPT_ROTATIONS", "3")) # how many .N backups to keep -RESUME_CKPT = os.environ.get("HYDRA_RESUME_CKPT", str(LATEST_CKPT)) - -# MDLM (Masked Diffusion LM) Rao-Blackwellized ELBO loss path. -# HYDRA_USE_MDLM=1 : switch training loss from AR sampled-softmax CE -# to MDLM RB weighted CE (arXiv:2406.07524). -# HYDRA_MDLM_MASK_ID=N : token id used for the MASK sentinel (default: -# last valid id, vocab_size - 1). Ensure this id -# never appears in training targets — typical -# practice is to reserve it. -# HYDRA_MDLM_SCHEDULE=loglinear|linear : noise schedule (default loglinear). -# When enabled, the per-step flow is: -# 1. mdlm_masked_forward_process(y) -> (x_noised, mask_positions, weights) -# 2. logits = model(x_noised) (no targets -> full V logits) -# 3. loss = mdlm_rb_loss(logits, y, mask_positions, weights) -# Sampled-softmax is bypassed in this path because the RB ELBO needs -# full-vocab logits on masked positions. -USE_MDLM = os.environ.get("HYDRA_USE_MDLM", "0") == "1" -MDLM_MASK_ID = int(os.environ.get("HYDRA_MDLM_MASK_ID", "-1")) # -1 => default to vocab_size-1 at runtime -MDLM_SCHEDULE = os.environ.get("HYDRA_MDLM_SCHEDULE", "loglinear") - - -# --------------------------------------------------------------------------- -# Schedules -# --------------------------------------------------------------------------- - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - decay_progress = (progress - WARMUP_RATIO) / (1.0 - WARMUP_RATIO) - return FINAL_LR_FRAC + 0.5 * (1.0 - FINAL_LR_FRAC) * (1 + math.cos(math.pi * decay_progress)) - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -_CKPT_WORKER_THREAD: threading.Thread | None = None - - -def _ckpt_snapshot_state_dicts( - model: PostSemClawModel, - optimizer: torch.optim.Optimizer, -) -> tuple[dict, dict]: - """Detach + CPU-clone every tensor so a bg thread can serialize safely - while the main loop keeps mutating live weights/optimizer state.""" - msd = {k: (v.detach().to("cpu", copy=True) if torch.is_tensor(v) else v) - for k, v in model.state_dict().items()} - # optimizer.state_dict() is a nested dict; walk it. - osd_raw = optimizer.state_dict() - - def _to_cpu(obj): - if torch.is_tensor(obj): - return obj.detach().to("cpu", copy=True) - if isinstance(obj, dict): - return {k: _to_cpu(v) for k, v in obj.items()} - if isinstance(obj, list): - return [_to_cpu(v) for v in obj] - if isinstance(obj, tuple): - return tuple(_to_cpu(v) for v in obj) - return obj - - osd = _to_cpu(osd_raw) - return msd, osd - - -def save_ckpt( - model: PostSemClawModel, - optimizer: torch.optim.Optimizer, - config: PostSemClawConfig, - step: int, - total_training_time: float, - smooth_train_loss: float, - bpt_ema: float, - epoch: int, - path: Path, - *, - val_bpb: float | None = None, - blocking: bool = False, -) -> None: - """Save a training checkpoint. - - Default behavior is async: the GPU→CPU state_dict clone runs on the main - thread (unavoidable; needs to happen before the next optimizer.step that - mutates live weights), then `torch.save` is dispatched to a daemon - worker thread. The next call joins any still-running prior save so only - one disk write is in flight. - - `blocking=True` restores the original synchronous behavior — used for - end-of-training saves where correctness on process exit matters. - """ - global _CKPT_WORKER_THREAD - try: - CACHE_DIR.mkdir(parents=True, exist_ok=True) - msd, osd = _ckpt_snapshot_state_dicts(model, optimizer) - # asdict() recursively converts dataclass fields to a dict and - # renders tuples as lists. hyena_layers therefore round-trips as a - # JSON-safe list; config_from_dict normalizes it back to a tuple. - payload = { - "model_state_dict": msd, - "optimizer_state_dict": osd, - "config": asdict(config), - "step": step, - "epoch": epoch, - "train_seconds": total_training_time, - "smoothed_loss": smooth_train_loss, - "bpt_ema": bpt_ema, - "val_bpb": val_bpb, - } - path_str = str(path) - - def _rotate(p: str) -> None: - """Keep up to CKPT_ROTATIONS previous versions as p.1, p.2, ...""" - if CKPT_ROTATIONS <= 0: - return - try: - # Walk from oldest to newest so we don't clobber newer with older. - for i in range(CKPT_ROTATIONS, 0, -1): - src = f"{p}.{i-1}" if i > 1 else p - dst = f"{p}.{i}" - if os.path.exists(src): - os.replace(src, dst) - except Exception as e: - # Rotation is best-effort; never block a save on it. - print(f"[ckpt] rotate warn {p}: {type(e).__name__}: {e}", flush=True) - - def _write(): - try: - _rotate(path_str) - tmp = path_str + ".tmp" - torch.save(payload, tmp) - os.replace(tmp, path_str) - print(f"[ckpt] saved {path_str} (step={step})", flush=True) - except Exception as e: - print(f"[ckpt] SAVE FAILED {path_str}: {type(e).__name__}: {e}", flush=True) - - if blocking: - _write() - return - - # Join previous writer so at most one torch.save runs at a time. - if _CKPT_WORKER_THREAD is not None and _CKPT_WORKER_THREAD.is_alive(): - _CKPT_WORKER_THREAD.join() - _CKPT_WORKER_THREAD = threading.Thread( - target=_write, daemon=True, name=f"ckpt-save-{step}" - ) - _CKPT_WORKER_THREAD.start() - except Exception as e: - print(f"[ckpt] SNAPSHOT FAILED {path}: {type(e).__name__}: {e}", flush=True) - - -def config_from_dict(cfg_dict: dict) -> PostSemClawConfig: - """Reconstruct a PostSemClawConfig from a checkpoint's asdict() payload. - - Newly-added fields (e.g. `hyena_layers`) are defaulted when absent in - older checkpoints, and list-ified tuples are coerced back to tuples so - the dataclass keeps its declared types. - - This is the ckpt-safe inverse of `asdict(config)` used by save_ckpt and - guarantees that a resume path can rebuild the exact same model topology - (Mamba3 vs HyenaBlock per layer) regardless of env-var state at resume. - """ - # Only keep keys that are actually declared on PostSemClawConfig — extra - # keys in older/newer checkpoints must not crash construction. - field_names = {f.name for f in PostSemClawConfig.__dataclass_fields__.values()} - filtered = {k: v for k, v in cfg_dict.items() if k in field_names} - # asdict renders tuple[int,...] as list[int]; coerce back so the model - # builder sees the declared type. - if "hyena_layers" in filtered and filtered["hyena_layers"] is not None: - filtered["hyena_layers"] = tuple(sorted(int(x) for x in filtered["hyena_layers"])) - return PostSemClawConfig(**filtered) - - -def _try_load_ckpt(path: Path, model, optimizer, device): - """Attempt to load a single ckpt. Returns the tuple on success, None on any failure.""" - if not path.exists(): - return None - ckpt = torch.load(str(path), map_location=device, weights_only=False) - state = ckpt.get("model_state_dict", ckpt) - missing, unexpected = model.load_state_dict(state, strict=False) - if missing: - print(f"[ckpt] {path.name} missing={len(missing)}", flush=True) - if unexpected: - print(f"[ckpt] {path.name} unexpected={len(unexpected)}", flush=True) - optimizer_state = ckpt.get("optimizer_state_dict") - if optimizer_state is not None: - try: - optimizer.load_state_dict(optimizer_state) - except Exception as e: - print(f"[ckpt] optimizer restore failed from {path.name}: {type(e).__name__}: {e}", flush=True) - step = int(ckpt.get("step", 0)) - total_training_time = float(ckpt.get("train_seconds", 0.0)) - smooth_train_loss = float(ckpt.get("smoothed_loss", 0.0)) - bpt_ema = float(ckpt.get("bpt_ema", 0.0)) - epoch = int(ckpt.get("epoch", 0)) - print( - f"[ckpt] resumed {path} step={step} train_seconds={total_training_time:.1f}", - flush=True, - ) - # Warn if resuming a schedule-exhausted ckpt — user is probably warm-starting. - budget = float(os.environ.get("HYDRA_TIME_BUDGET", "0") or 0) - if budget and total_training_time >= 0.99 * budget: - print( - f"[ckpt] WARNING: resumed ckpt used {total_training_time:.0f}s of {budget:.0f}s " - f"budget. LR schedule is essentially exhausted. " - f"Set HYDRA_WARMSTART=1 to reset optimizer + scheduler and keep only weights.", - flush=True, - ) - return step, total_training_time, smooth_train_loss, bpt_ema, epoch - - -def maybe_resume_ckpt( - model: PostSemClawModel, - optimizer: torch.optim.Optimizer, - device: torch.device, -) -> tuple[int, float, float, float, int]: - if not RESUME_CKPT or RESUME_CKPT.lower() == "none": - print("[ckpt] resume disabled; starting fresh", flush=True) - return 0, 0.0, 0.0, 0.0, 0 - - resume_path = Path(os.path.expanduser(RESUME_CKPT)) - # Try the primary path, then rotated backups. This is crucial because a - # partial / killed torch.save on the primary path would leave a corrupt - # file. If that fails we fall back to latest.pt.1, .2, .3 automatically. - candidates: list[Path] = [resume_path] - for i in range(1, CKPT_ROTATIONS + 1): - candidates.append(Path(str(resume_path) + f".{i}")) - - for cand in candidates: - if not cand.exists(): - continue - try: - result = _try_load_ckpt(cand, model, optimizer, device) - if result is not None: - if cand != resume_path: - print(f"[ckpt] fell back to rotation {cand.name}", flush=True) - return result - except Exception as e: - print(f"[ckpt] {cand.name} load failed: {type(e).__name__}: {e}", flush=True) - continue - - print(f"[ckpt] no usable checkpoint in {resume_path} + rotations; starting fresh", flush=True) - return 0, 0.0, 0.0, 0.0, 0 - - -# --------------------------------------------------------------------------- -# Main entry -# --------------------------------------------------------------------------- - -def main() -> None: - t_start = time.time() - torch.manual_seed(SEED) - torch.cuda.manual_seed(SEED) - # Precision / kernel-selection knobs for peak throughput on Ampere. - # - high : matmul uses TF32 (Ampere's 10-bit mantissa accum) for fp32 ops - # - allow_tf32 : explicit for both matmul + cudnn paths - # - cudnn.benchmark : env-gated (HYDRA_CUDNN_BENCHMARK, default OFF). - # TRUE can lock in a locally-better-but-globally-slower algorithm - # after the autotune phase ends, causing tps to degrade 15-20% - # over the first ~100 steps. Observed 2026-04-22 and confirmed by - # differential profiling. Default is now FALSE; set =1 only if you - # see a specific workload where benchmark helps sustained tps. - torch.set_float32_matmul_precision("high") - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - torch.backends.cudnn.benchmark = os.environ.get("HYDRA_CUDNN_BENCHMARK", "0") == "1" - device = torch.device("cuda") - autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) - - # Streaming path skips prepare.py (which normally trains the tokenizer - # and builds the retina), so we must materialize both before model init. +"""HYDRA training entry: setup, train loop, eval, summary. + +Extracted from the monolithic train.py (W1 modularization). Semantics +preserved. Public entrypoint: `main()`. +""" + +from __future__ import annotations + +import gc +import json +import math +import os +import sys +import threading +import time +from dataclasses import asdict +from pathlib import Path + +import torch + +# Line-buffered stdout so `python -u train.py | tee run.log | grep step` is +# live (no \r overwrite, no 4k block-buffered pipe stalls). Safe on Python +# 3.7+ where io.TextIOWrapper.reconfigure exists. +try: + sys.stdout.reconfigure(line_buffering=True) # type: ignore[attr-defined] +except Exception: + pass + +from hydra.config import ( + ADAM_BETAS, CURRICULUM_SHORT_SEQ_LEN, CURRICULUM_SHORT_STEPS, + D_MODEL, D_STATE, DEVICE_BATCH_SIZE, EMA_DECAY, EMBEDDING_LR, + ENGRAM_KEY_DIM, ENGRAM_LAYER_IDX, ENGRAM_N_COLUMNS, EXPAND, + FINAL_LR_FRAC, GPU_BF16_PEAK_FLOPS, HEADDIM, MATRIX_LR, N_HEADS, + N_LAYER, PostSemClawConfig, SCALAR_LR, SEED, TOTAL_BATCH_SIZE, + UNEMBEDDING_LR, USE_EMA, WARMUP_RATIO, WEIGHT_DECAY, +) +from hydra.diffusion_loss import mdlm_masked_forward_process, mdlm_rb_loss +from hydra.eval import run_factual_english, run_factual_probes +from hydra.model import PostSemClawModel + +import prepare as _prepare_mod +from prepare import MAX_SEQ_LEN, TIME_BUDGET as _TIME_BUDGET, Tokenizer, evaluate_bpb as _evaluate_bpb_shards, get_token_bytes, make_dataloader as _make_dataloader_shards + +# Streaming Nemotron path (Super3 recipe). Opt-in via HYDRA_USE_NEMOTRON=1. +if os.environ.get("HYDRA_USE_NEMOTRON", "0") == "1": + import prepare_nemotron as _p_nemo + make_dataloader = _p_nemo.make_dataloader + evaluate_bpb = _p_nemo.evaluate_bpb +else: + make_dataloader = _make_dataloader_shards + evaluate_bpb = _evaluate_bpb_shards + +TIME_BUDGET = int(os.environ.get("HYDRA_TIME_BUDGET", str(_TIME_BUDGET))) +_prepare_mod.TIME_BUDGET = TIME_BUDGET # sync for evaluate_bpb + +CACHE_DIR = Path.home() / ".cache" / "autoresearch" +LATEST_CKPT = CACHE_DIR / "latest.pt" +PRETRAIN_FINAL_CKPT = CACHE_DIR / "pretrain_final.pt" +FAILED_CKPT = CACHE_DIR / "latest_failed.pt" # crash/FAIL path — never overwrites good +BEST_CKPT = CACHE_DIR / "best_bpb.pt" # lowest val_bpb seen +CKPT_INTERVAL = int(os.environ.get("HYDRA_CKPT_INTERVAL", "250")) +CKPT_ROTATIONS = int(os.environ.get("HYDRA_CKPT_ROTATIONS", "3")) # how many .N backups to keep +RESUME_CKPT = os.environ.get("HYDRA_RESUME_CKPT", str(LATEST_CKPT)) + +# MDLM (Masked Diffusion LM) Rao-Blackwellized ELBO loss path. +# HYDRA_USE_MDLM=1 : switch training loss from AR sampled-softmax CE +# to MDLM RB weighted CE (arXiv:2406.07524). +# HYDRA_MDLM_MASK_ID=N : token id used for the MASK sentinel (default: +# last valid id, vocab_size - 1). Ensure this id +# never appears in training targets — typical +# practice is to reserve it. +# HYDRA_MDLM_SCHEDULE=loglinear|linear : noise schedule (default loglinear). +# When enabled, the per-step flow is: +# 1. mdlm_masked_forward_process(y) -> (x_noised, mask_positions, weights) +# 2. logits = model(x_noised) (no targets -> full V logits) +# 3. loss = mdlm_rb_loss(logits, y, mask_positions, weights) +# Sampled-softmax is bypassed in this path because the RB ELBO needs +# full-vocab logits on masked positions. +USE_MDLM = os.environ.get("HYDRA_USE_MDLM", "0") == "1" +MDLM_MASK_ID = int(os.environ.get("HYDRA_MDLM_MASK_ID", "-1")) # -1 => default to vocab_size-1 at runtime +MDLM_SCHEDULE = os.environ.get("HYDRA_MDLM_SCHEDULE", "loglinear") + + +# --------------------------------------------------------------------------- +# Schedules +# --------------------------------------------------------------------------- + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + decay_progress = (progress - WARMUP_RATIO) / (1.0 - WARMUP_RATIO) + return FINAL_LR_FRAC + 0.5 * (1.0 - FINAL_LR_FRAC) * (1 + math.cos(math.pi * decay_progress)) + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +_CKPT_WORKER_THREAD: threading.Thread | None = None + + +def _ckpt_snapshot_state_dicts( + model: PostSemClawModel, + optimizer: torch.optim.Optimizer, +) -> tuple[dict, dict]: + """Detach + CPU-clone every tensor so a bg thread can serialize safely + while the main loop keeps mutating live weights/optimizer state.""" + msd = {k: (v.detach().to("cpu", copy=True) if torch.is_tensor(v) else v) + for k, v in model.state_dict().items()} + # optimizer.state_dict() is a nested dict; walk it. + osd_raw = optimizer.state_dict() + + def _to_cpu(obj): + if torch.is_tensor(obj): + return obj.detach().to("cpu", copy=True) + if isinstance(obj, dict): + return {k: _to_cpu(v) for k, v in obj.items()} + if isinstance(obj, list): + return [_to_cpu(v) for v in obj] + if isinstance(obj, tuple): + return tuple(_to_cpu(v) for v in obj) + return obj + + osd = _to_cpu(osd_raw) + return msd, osd + + +def save_ckpt( + model: PostSemClawModel, + optimizer: torch.optim.Optimizer, + config: PostSemClawConfig, + step: int, + total_training_time: float, + smooth_train_loss: float, + bpt_ema: float, + epoch: int, + path: Path, + *, + val_bpb: float | None = None, + blocking: bool = False, +) -> None: + """Save a training checkpoint. + + Default behavior is async: the GPU→CPU state_dict clone runs on the main + thread (unavoidable; needs to happen before the next optimizer.step that + mutates live weights), then `torch.save` is dispatched to a daemon + worker thread. The next call joins any still-running prior save so only + one disk write is in flight. + + `blocking=True` restores the original synchronous behavior — used for + end-of-training saves where correctness on process exit matters. + """ + global _CKPT_WORKER_THREAD + try: + CACHE_DIR.mkdir(parents=True, exist_ok=True) + msd, osd = _ckpt_snapshot_state_dicts(model, optimizer) + # asdict() recursively converts dataclass fields to a dict and + # renders tuples as lists. hyena_layers therefore round-trips as a + # JSON-safe list; config_from_dict normalizes it back to a tuple. + payload = { + "model_state_dict": msd, + "optimizer_state_dict": osd, + "config": asdict(config), + "step": step, + "epoch": epoch, + "train_seconds": total_training_time, + "smoothed_loss": smooth_train_loss, + "bpt_ema": bpt_ema, + "val_bpb": val_bpb, + } + path_str = str(path) + + def _rotate(p: str) -> None: + """Keep up to CKPT_ROTATIONS previous versions as p.1, p.2, ...""" + if CKPT_ROTATIONS <= 0: + return + try: + # Walk from oldest to newest so we don't clobber newer with older. + for i in range(CKPT_ROTATIONS, 0, -1): + src = f"{p}.{i-1}" if i > 1 else p + dst = f"{p}.{i}" + if os.path.exists(src): + os.replace(src, dst) + except Exception as e: + # Rotation is best-effort; never block a save on it. + print(f"[ckpt] rotate warn {p}: {type(e).__name__}: {e}", flush=True) + + def _write(): + try: + _rotate(path_str) + tmp = path_str + ".tmp" + torch.save(payload, tmp) + os.replace(tmp, path_str) + print(f"[ckpt] saved {path_str} (step={step})", flush=True) + except Exception as e: + print(f"[ckpt] SAVE FAILED {path_str}: {type(e).__name__}: {e}", flush=True) + + if blocking: + _write() + return + + # Join previous writer so at most one torch.save runs at a time. + if _CKPT_WORKER_THREAD is not None and _CKPT_WORKER_THREAD.is_alive(): + _CKPT_WORKER_THREAD.join() + _CKPT_WORKER_THREAD = threading.Thread( + target=_write, daemon=True, name=f"ckpt-save-{step}" + ) + _CKPT_WORKER_THREAD.start() + # Non-default checkpoint paths are usually tests or one-off utilities that + # expect save_ckpt() to be durable when it returns. Keep the hot training + # path async for CACHE_DIR checkpoints, but make explicit custom paths + # deterministic. + if path.parent.resolve() != CACHE_DIR.resolve(): + _CKPT_WORKER_THREAD.join() + except Exception as e: + print(f"[ckpt] SNAPSHOT FAILED {path}: {type(e).__name__}: {e}", flush=True) + + +def config_from_dict(cfg_dict: dict) -> PostSemClawConfig: + """Reconstruct a PostSemClawConfig from a checkpoint's asdict() payload. + + Newly-added fields (e.g. `hyena_layers`) are defaulted when absent in + older checkpoints, and list-ified tuples are coerced back to tuples so + the dataclass keeps its declared types. + + This is the ckpt-safe inverse of `asdict(config)` used by save_ckpt and + guarantees that a resume path can rebuild the exact same model topology + (Mamba3 vs HyenaBlock per layer) regardless of env-var state at resume. + """ + # Only keep keys that are actually declared on PostSemClawConfig — extra + # keys in older/newer checkpoints must not crash construction. + field_names = {f.name for f in PostSemClawConfig.__dataclass_fields__.values()} + filtered = {k: v for k, v in cfg_dict.items() if k in field_names} + # asdict renders tuple[int,...] as list[int]; coerce back so the model + # builder sees the declared type. + if "hyena_layers" in filtered and filtered["hyena_layers"] is not None: + filtered["hyena_layers"] = tuple(sorted(int(x) for x in filtered["hyena_layers"])) + return PostSemClawConfig(**filtered) + + +def _try_load_ckpt(path: Path, model, optimizer, device): + """Attempt to load a single ckpt. Returns the tuple on success, None on any failure.""" + if not path.exists(): + return None + ckpt = torch.load(str(path), map_location=device, weights_only=False) + state = ckpt.get("model_state_dict", ckpt) + missing, unexpected = model.load_state_dict(state, strict=False) + if missing: + print(f"[ckpt] {path.name} missing={len(missing)}", flush=True) + if unexpected: + print(f"[ckpt] {path.name} unexpected={len(unexpected)}", flush=True) + optimizer_state = ckpt.get("optimizer_state_dict") + if optimizer_state is not None: + try: + optimizer.load_state_dict(optimizer_state) + except Exception as e: + print(f"[ckpt] optimizer restore failed from {path.name}: {type(e).__name__}: {e}", flush=True) + step = int(ckpt.get("step", 0)) + total_training_time = float(ckpt.get("train_seconds", 0.0)) + smooth_train_loss = float(ckpt.get("smoothed_loss", 0.0)) + bpt_ema = float(ckpt.get("bpt_ema", 0.0)) + epoch = int(ckpt.get("epoch", 0)) + print( + f"[ckpt] resumed {path} step={step} train_seconds={total_training_time:.1f}", + flush=True, + ) + # Warn if resuming a schedule-exhausted ckpt — user is probably warm-starting. + budget = float(os.environ.get("HYDRA_TIME_BUDGET", "0") or 0) + if budget and total_training_time >= 0.99 * budget: + print( + f"[ckpt] WARNING: resumed ckpt used {total_training_time:.0f}s of {budget:.0f}s " + f"budget. LR schedule is essentially exhausted. " + f"Set HYDRA_WARMSTART=1 to reset optimizer + scheduler and keep only weights.", + flush=True, + ) + return step, total_training_time, smooth_train_loss, bpt_ema, epoch + + +def maybe_resume_ckpt( + model: PostSemClawModel, + optimizer: torch.optim.Optimizer, + device: torch.device, +) -> tuple[int, float, float, float, int]: + if not RESUME_CKPT or RESUME_CKPT.lower() == "none": + print("[ckpt] resume disabled; starting fresh", flush=True) + return 0, 0.0, 0.0, 0.0, 0 + + resume_path = Path(os.path.expanduser(RESUME_CKPT)) + # Try the primary path, then rotated backups. This is crucial because a + # partial / killed torch.save on the primary path would leave a corrupt + # file. If that fails we fall back to latest.pt.1, .2, .3 automatically. + candidates: list[Path] = [resume_path] + for i in range(1, CKPT_ROTATIONS + 1): + candidates.append(Path(str(resume_path) + f".{i}")) + + for cand in candidates: + if not cand.exists(): + continue + try: + result = _try_load_ckpt(cand, model, optimizer, device) + if result is not None: + if cand != resume_path: + print(f"[ckpt] fell back to rotation {cand.name}", flush=True) + return result + except Exception as e: + print(f"[ckpt] {cand.name} load failed: {type(e).__name__}: {e}", flush=True) + continue + + print(f"[ckpt] no usable checkpoint in {resume_path} + rotations; starting fresh", flush=True) + return 0, 0.0, 0.0, 0.0, 0 + + +# --------------------------------------------------------------------------- +# Main entry +# --------------------------------------------------------------------------- + +def main() -> None: + t_start = time.time() + torch.manual_seed(SEED) + torch.cuda.manual_seed(SEED) + # Precision / kernel-selection knobs for peak throughput on Ampere. + # - high : matmul uses TF32 (Ampere's 10-bit mantissa accum) for fp32 ops + # - allow_tf32 : explicit for both matmul + cudnn paths + # - cudnn.benchmark : env-gated (HYDRA_CUDNN_BENCHMARK, default OFF). + # TRUE can lock in a locally-better-but-globally-slower algorithm + # after the autotune phase ends, causing tps to degrade 15-20% + # over the first ~100 steps. Observed 2026-04-22 and confirmed by + # differential profiling. Default is now FALSE; set =1 only if you + # see a specific workload where benchmark helps sustained tps. + torch.set_float32_matmul_precision("high") + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + torch.backends.cudnn.benchmark = os.environ.get("HYDRA_CUDNN_BENCHMARK", "0") == "1" + device = torch.device("cuda") + autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) + + # Streaming path skips prepare.py (which normally trains the tokenizer + # and builds the retina), so we must materialize both before model init. if os.environ.get("HYDRA_USE_NEMOTRON", "0") == "1": _p_nemo.ensure_tokenizer() - if os.environ.get("HYDRA_THROUGHPUT_MODE", "0") != "1": - # Retina: HF Hub cache hit for this (vocab, n_bits, target_active) combo - # returns in seconds; otherwise build_retina streams Nemotron docs to - # compute cooccurrence + train SOM, then uploads back to the cache. - import subsystems.sdr_retina as _sdr_retina - _sdr_retina.build_retina() - tokenizer = Tokenizer.from_directory() - vocab_size = tokenizer.get_vocab_size() - print(f"Vocab size: {vocab_size:,}") - - config = PostSemClawConfig( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, - engram_n_columns=ENGRAM_N_COLUMNS, - engram_key_dim=ENGRAM_KEY_DIM, - engram_layer_idx=ENGRAM_LAYER_IDX, - ) - print(f"Model config: {asdict(config)}") - - with torch.device("meta"): - model = PostSemClawModel(config) - model.to_empty(device=device) - model.init_weights() - - param_counts = model.num_scaling_params() - print("Parameter counts:") - for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") - num_params = param_counts['total'] - num_flops_per_token = model.estimate_flops() - print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - - tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN - assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 - grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - - optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, - ) - - step, total_training_time, smooth_train_loss, bpt_ema, resume_epoch = maybe_resume_ckpt( - model, optimizer, device, - ) - - # Learnability #4: inform the model of the BOS token id so it can mask - # doc-separator positions in packed sequences. Always set (the mask only - # fires when HYDRA_DOC_SEP_MASK=1 is also on). - if hasattr(model, 'set_bos_token_id'): - model.set_bos_token_id(tokenizer.get_bos_token_id()) - - # Learnability #2: EMA shadow copy of weights. AveragedModel clones every - # parameter; we update it after every optimizer step and save it at the - # end alongside the raw checkpoint. Defaults OFF. - ema_model = None - if USE_EMA: - try: - from torch.optim.swa_utils import AveragedModel, get_ema_multi_avg_fn - # decay=EMA_DECAY; avg_fn uses get_ema_multi_avg_fn for numerical - # stability across bf16/fp32 mixed parameter groups. - ema_model = AveragedModel( - model, - multi_avg_fn=get_ema_multi_avg_fn(EMA_DECAY), - ) - print(f"[EMA] enabled with decay={EMA_DECAY}") - except Exception as _e: - print(f"[EMA] disabled — AveragedModel init failed: {_e}") - ema_model = None - - print("torch.compile: Muon step compiled; AdamW uses torch._fused_adamw_ (model blocks use native CUDA kernels)") - - # Learnability #7: curriculum short-then-long. If enabled, build the - # initial dataloader at the short seq_len; we swap to full MAX_SEQ_LEN - # after CURRICULUM_SHORT_STEPS optimizer steps (see loop below). - _curriculum_active = CURRICULUM_SHORT_STEPS > 0 and CURRICULUM_SHORT_SEQ_LEN < MAX_SEQ_LEN - _current_seq_len = CURRICULUM_SHORT_SEQ_LEN if _curriculum_active else MAX_SEQ_LEN - if _curriculum_active: - print( - f"[CURRICULUM] starting at T={_current_seq_len} for " - f"{CURRICULUM_SHORT_STEPS} steps, then switching to T={MAX_SEQ_LEN}" - ) - train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, _current_seq_len, "train") - x, y, epoch = next(train_loader) # prefetch first batch - if resume_epoch > 0: - epoch = max(epoch, resume_epoch) - - print(f"Time budget: {TIME_BUDGET}s") - print(f"Gradient accumulation steps: {grad_accum_steps}") - - # Token→byte LUT for bits-per-byte computation. evaluate_bpb in prepare.py - # uses total_nats / (ln(2) * total_bytes); our live metric needs to match. - # Without this, `bpb = loss/ln(2)` is actually bits-per-TOKEN, which at - # vocab=8192 scales by ~4 and makes live train bpb non-comparable with - # val_bpb (champion 1.279 bpb vs train printing "8.04"). - token_bytes = get_token_bytes(device=device) - - # ----------------------------------------------------------------------- - # Training loop - # ----------------------------------------------------------------------- - - t_start_training = time.time() - - # Async postprocessing — run SOM + Hestia on background threads so - # the GPU doesn't idle during their CPU-bound work. - _ASYNC_POSTPROCESS = os.environ.get("HYDRA_ASYNC_POSTPROCESS", "1") == "1" - _som_thread: threading.Thread | None = None - _hestia_thread: threading.Thread | None = None - _hestia_stream: torch.cuda.Stream | None = ( - torch.cuda.Stream() if _ASYNC_POSTPROCESS else None - ) - - # HYDRA_PROFILE_STEPS=N prints a per-phase cpu/gpu time breakdown for the - # first N steps (and every 100th step thereafter if N<0). Zero overhead - # when disabled. Used to find what's eating CPU budget when GPU should - # be the bottleneck. - _profile_steps = int(os.environ.get("HYDRA_PROFILE_STEPS", "0")) - - while True: - torch.cuda.synchronize() - t0 = time.time() - _prof = _profile_steps and (step < _profile_steps or (_profile_steps < 0 and step % 100 == 0)) - _gpu_ms = 0.0 - _data_ms = 0.0 - for micro_step in range(grad_accum_steps): - if _prof: - torch.cuda.synchronize(); _t_micro = time.time() - if USE_MDLM: - # MDLM path: corrupt y -> x_noised, run model to get full-V logits, - # compute RB weighted CE on masked positions. x (original input) is - # unused in this path — the model only sees the noised version of y. - _mask_id = MDLM_MASK_ID if MDLM_MASK_ID >= 0 else (vocab_size - 1) - x_noised, mask_positions, loss_weights = mdlm_masked_forward_process( - y, mask_token_id=_mask_id, alpha_schedule=MDLM_SCHEDULE, - ) - with autocast_ctx: - logits = model(x_noised) # targets=None -> (B, T, V) logits - loss = mdlm_rb_loss(logits, y, mask_positions, loss_weights) - else: - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - if _prof: - torch.cuda.synchronize() - _gpu_ms += (time.time() - _t_micro) * 1000 - _t_data = time.time() - x, y, epoch = next(train_loader) - if _prof: - _data_ms += (time.time() - _t_data) * 1000 - if _prof: - torch.cuda.synchronize(); _t_fb = time.time() - - # Progress and schedules - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group['kind'] == 'muon': - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) - optimizer.step() - if _prof: - torch.cuda.synchronize(); _t_opt = time.time() - - # Learnability #2: EMA update after every optimizer step. - if ema_model is not None: - try: - ema_model.update_parameters(model) - except Exception as _e: - print(f"[EMA] update failed at step {step}: {_e}", flush=True) - - # Learnability #7: curriculum transition. After - # CURRICULUM_SHORT_STEPS optimizer steps, rebuild the dataloader at - # MAX_SEQ_LEN. Done once, then the flag flips off. - if _curriculum_active and step + 1 >= CURRICULUM_SHORT_STEPS: - print( - f"[CURRICULUM] step={step+1} — switching from T={_current_seq_len} " - f"to T={MAX_SEQ_LEN}", - flush=True, - ) - _current_seq_len = MAX_SEQ_LEN - _curriculum_active = False - train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, _current_seq_len, "train") - # Prefetch the next batch at the new seq_len so the following - # loop iteration consumes fresh data. - x, y, epoch = next(train_loader) - - # Online SOM update — retina is now a plain Python attribute (not a - # registered buffer) so mutations do not invalidate torch.compile guards. - # Runs fully on CPU; safe to overlap with GPU forward pass. - _last_sdr = getattr(model, "_last_sdr", None) - if _last_sdr is not None: - if _ASYNC_POSTPROCESS: - if _som_thread is not None: - _som_thread.join() - # Clone tensors before next step overwrites them - _som_x = x.clone() - _som_sdr = _last_sdr.clone() - _som_thread = threading.Thread( - target=model.sdr_semantic.maybe_som_update, - args=(_som_x, _som_sdr), - daemon=True, - ) - _som_thread.start() - else: - model.sdr_semantic.maybe_som_update(x, _last_sdr) - - # Hestia QAT — anneal temperature every step, snap every N steps. - # apply_to walks all Linear modules (CPU) then does .data.copy_ (GPU). - # Background thread + separate CUDA stream lets this overlap with - # the next forward pass on the default stream. - _hestia_progress = (time.time() - t_start_training) / max(TIME_BUDGET, 1) - _hestia_interval = int(os.environ.get("HYDRA_HESTIA_INTERVAL", "100")) - if step % _hestia_interval == 0: - if _ASYNC_POSTPROCESS: - if _hestia_thread is not None: - _hestia_thread.join() - - def _hestia_bg(mdl: torch.nn.Module, prog: float) -> None: - assert _hestia_stream is not None - with torch.cuda.stream(_hestia_stream): - mdl.hestia.anneal_temperature(prog) - mdl.hestia.apply_to(mdl) - - _hestia_thread = threading.Thread( - target=_hestia_bg, - args=(model, _hestia_progress), - daemon=True, - ) - _hestia_thread.start() - else: - model.hestia.anneal_temperature(_hestia_progress) - model.hestia.apply_to(model) - else: - # anneal_temperature is cheap (~1 us), keep inline - model.hestia.anneal_temperature(_hestia_progress) - - model.zero_grad(set_to_none=True) - - train_loss_f = train_loss.item() - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - # Save to a DIFFERENT file — never clobber a good latest.pt with - # a NaN/diverged state. The good ckpt from the last periodic save - # is the right place to resume from. - save_ckpt( - model, - optimizer, - config, - step, - total_training_time, - smooth_train_loss, - bpt_ema, - epoch, - FAILED_CKPT, - blocking=True, - ) - raise SystemExit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if _prof: - fb = (_t_fb - t0) * 1000 - opt = (_t_opt - _t_fb) * 1000 - rest = (t1 - _t_opt) * 1000 - print( - f"[PROF step={step:05d}] gpu={_gpu_ms:.0f}ms data_fetch={_data_ms:.0f}ms " - f"(sum_fb={fb:.0f}) opt={opt:.0f}ms rest={rest:.0f}ms total={dt*1000:.0f}ms", - flush=True, - ) - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / GPU_BF16_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - # Bytes-per-token for the CURRENT batch. evaluate_bpb in prepare.py - # computes bits-per-BYTE (total_nats / (ln2 * total_bytes)); to match - # that semantics live, we EMA-smooth the per-batch bytes/token and - # divide. Without this, the old `bpb = loss/ln2` was actually - # bits-per-token — ~4× larger than val_bpb at vocab=8192 and - # therefore not comparable to the champion 1.279 bpb metric. - with torch.no_grad(): - y_flat = y.view(-1) - nbytes_batch = token_bytes[y_flat] - mask = nbytes_batch > 0 - mask_count = mask.sum().clamp(min=1).float() - avg_bytes_per_tok = (nbytes_batch.float() * mask.float()).sum() / mask_count - bpt_batch = float(avg_bytes_per_tok.item()) - if step == 0 or bpt_ema <= 0.0: - bpt_ema = bpt_batch - else: - bpt_ema = 0.98 * bpt_ema + 0.02 * bpt_batch - - # Dual metric: bpb (byte-normalized, comparable with val_bpb) AND - # bpt (bits per token, the raw loss in bits). bpt_div exposes the - # current avg bytes-per-token so the conversion is transparent. - bpt = debiased_smooth_loss / math.log(2) - bpb = bpt / max(bpt_ema, 1e-6) - vram_mib = torch.cuda.memory_allocated() / 1024 / 1024 - current_lr = optimizer.param_groups[0]["lr"] - - # Per-step line-buffered log. NOT \r-overwritten so tee/grep see it. - # Keep key=value pairs grep-friendly. - ppl = 2.0 ** bpb # perplexity (byte-level) - print( - f"step={step:05d} loss={debiased_smooth_loss:.4f} bpb={bpb:.4f} ppl={ppl:.3f} " - f"bpt={bpt:.3f} bpt_div={bpt_ema:.2f} " - f"tps={tok_per_sec} dt_ms={dt*1000:.0f} mfu={mfu:.1f} " - f"lr={current_lr:.2e} vram={vram_mib:.0f}MiB " - f"pct={pct_done:.1f} epoch={epoch} remaining={remaining:.0f}s", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - # No periodic gc.collect() — we disabled+froze at step 0 on purpose, - # so a manual collect every 5k steps just re-scans frozen objects - # (burned ~900 ms/event in production) for no live-garbage reason. - - if CKPT_INTERVAL > 0 and step > 0 and step % CKPT_INTERVAL == 0: - save_ckpt( - model, - optimizer, - config, - step, - total_training_time, - smooth_train_loss, - bpt_ema, - epoch, - LATEST_CKPT, - ) - - # Periodic mid-training validation so we can see the model learning - # English in real time (not just at the end). Small val batch so it - # doesn't eat significant training time. - mid_val_interval = int(os.environ.get("HYDRA_MID_VAL_INTERVAL", "500")) - if mid_val_interval > 0 and step > 0 and step % mid_val_interval == 0: - model.eval() - try: - # Defrag GPU memory before eval allocates fresh chunks — - # without this the eval path can OOM on 6GB cards even - # though total usage fits, because the allocator's free - # blocks are fragmented. - torch.cuda.empty_cache() - _orig_mid = _prepare_mod.EVAL_TOKENS - _prepare_mod.EVAL_TOKENS = 262144 # ~260K tokens, fast - with torch.no_grad(): - with autocast_ctx: - mid_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - _prepare_mod.EVAL_TOKENS = _orig_mid - mid_ppl = 2.0 ** mid_bpb - print(f"[MID_VAL] step={step} val_bpb={mid_bpb:.4f} val_ppl={mid_ppl:.3f}", flush=True) - - # Per-layer diagnostic panel. Only printed when HYDRA_LAYER_DIAGNOSTICS=1 - # is set (otherwise the layer_* keys are absent from _metrics). - _diag_metrics = model.get_secondary_metrics() - _layer_keys = sorted([k for k in _diag_metrics.keys() if k.startswith('layer_')]) - if _layer_keys: - # Condense: one row per layer showing the four core signals. - n_layers = len(model.blocks) - print(f"[LAYER_DIAG] step={step}", flush=True) - for li in range(n_layers): - d_ratio = _diag_metrics.get(f'layer_{li}_delta_ratio', float('nan')) - out_n = _diag_metrics.get(f'layer_{li}_out_norm', float('nan')) - g_norm = _diag_metrics.get(f'layer_{li}_grad_norm', float('nan')) - eff_r = _diag_metrics.get(f'layer_{li}_eff_rank', float('nan')) - f_std = _diag_metrics.get(f'layer_{li}_feat_std', float('nan')) - print( - f"[LAYER_DIAG] L{li:02d} delta_ratio={d_ratio:.4f} " - f"out_norm={out_n:.4f} grad_norm={g_norm:.3e} " - f"eff_rank={eff_r:.1f} feat_std={f_std:.4f}", - flush=True, - ) - htm_proj_g = _diag_metrics.get('htm_proj_grad_norm', None) - if htm_proj_g is not None: - print(f"[LAYER_DIAG] htm_proj grad_norm={htm_proj_g:.3e}", flush=True) - except Exception as e: - print(f"[MID_VAL] failed: {e}", flush=True) - model.train() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - - # Drain async postprocessing threads before eval - if _som_thread is not None: - _som_thread.join() - if _hestia_thread is not None: - _hestia_thread.join() - if _hestia_stream is not None: - _hestia_stream.synchronize() - - total_tokens = step * TOTAL_BATCH_SIZE - - # ---------------------------------------------------------------------- - # SAVE ORDER (critical): - # 1. Save PRETRAIN_FINAL_CKPT with val_bpb=None (hedge against eval OOM) - # 2. Save LATEST_CKPT with val_bpb=None (hedge against eval OOM) - # 3. Run eval (may OOM on small GPUs; we survive it) - # 4. Re-save both ckpts with val_bpb filled in - # This way we NEVER lose the final trained weights to an eval crash. - # Previous ordering put eval first, so an eval-time OOM destroyed the - # only record of a 6h training run (2026-04-22 incident). - # ---------------------------------------------------------------------- - - save_ckpt( - model, optimizer, config, step, total_training_time, - smooth_train_loss, bpt_ema, epoch, PRETRAIN_FINAL_CKPT, - val_bpb=None, blocking=True, - ) - save_ckpt( - model, optimizer, config, step, total_training_time, - smooth_train_loss, bpt_ema, epoch, LATEST_CKPT, - val_bpb=None, blocking=True, - ) - - # Now it's safe to eval — ckpts are on disk regardless of what happens here. - # HYDRA_EVAL_BATCH overrides DEVICE_BATCH_SIZE (env-tunable; default halves - # the training batch because eval holds activations for full sequence and - # does not benefit from overlap with backward). HYDRA_EVAL_TOKENS controls - # how many val tokens to sweep (default 2 M, short enough for autoresearch - # 5-min budgets). - val_bpb: float | None = None - _eval_B = int(os.environ.get("HYDRA_EVAL_BATCH", str(max(1, DEVICE_BATCH_SIZE // 2)))) - _eval_tokens = int(os.environ.get("HYDRA_EVAL_TOKENS", str(2 * 524288))) - try: - # Aggressive VRAM reclaim for 6GB cards. Peak training VRAM = 5.1GB - # which leaves < 1GB for the eval forward — the driver can't satisfy - # the allocation. Free EVERY tensor we don't strictly need: - # - optimizer grads (set_to_none releases tensor) - # - optimizer.state (fp32 Muon NS workspace, AdamW moments — ~size-of-params each) - # - model internal caches (HTM subsample cache, SDR stash) - # After this, VRAM should be ~params only (bf16 ≈ 120MB at 60M params). - optimizer.zero_grad(set_to_none=True) - if hasattr(optimizer, 'state') and optimizer.state: - for p, st in list(optimizer.state.items()): - st.clear() - optimizer.state.clear() - for p in model.parameters(): - if p.grad is not None: - p.grad = None - if hasattr(model, '_htm_cache'): - model._htm_cache = None - if hasattr(model, '_last_sdr'): - model._last_sdr = None - import gc as _gc - _gc.collect() - torch.cuda.empty_cache() - torch.cuda.synchronize() - try: - _free_mb = torch.cuda.mem_get_info()[0] / 1024 / 1024 - print(f"[VAL] free_vram_mb={_free_mb:.0f} (cleared optimizer state)", flush=True) - except Exception: - pass - print(f"[VAL] running eval on {_eval_tokens} tokens at B={_eval_B}...", flush=True) - model.eval() - _orig = _prepare_mod.EVAL_TOKENS - _prepare_mod.EVAL_TOKENS = _eval_tokens - with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, _eval_B) - _prepare_mod.EVAL_TOKENS = _orig - val_ppl = 2 ** val_bpb - print(f"[VAL] step={step} val_bpb={val_bpb:.4f} val_ppl={val_ppl:.3f}", flush=True) - except torch.cuda.OutOfMemoryError as e: - print(f"[VAL] SKIPPED (OOM): {e}", flush=True) - torch.cuda.empty_cache() - except Exception as e: - import traceback as _tb - print(f"[VAL] SKIPPED ({type(e).__name__}): {e}", flush=True) - _tb.print_exc() - try: - _free = torch.cuda.mem_get_info()[0] / 1024 / 1024 - print(f"[VAL] post-crash free_vram_mb={_free:.0f}", flush=True) - except Exception: - pass - - # Final ckpts with val_bpb filled in (if eval succeeded). - save_ckpt( - model, optimizer, config, step, total_training_time, - smooth_train_loss, bpt_ema, epoch, LATEST_CKPT, - val_bpb=val_bpb, blocking=True, - ) - save_ckpt( - model, optimizer, config, step, total_training_time, - smooth_train_loss, bpt_ema, epoch, PRETRAIN_FINAL_CKPT, - val_bpb=val_bpb, blocking=True, - ) - - # Learnability #2: persist EMA weights alongside the raw checkpoint. - # latest_ema.pt contains ema_model.module (the Averaged params) so it - # can be loaded by evaluation / inference code that expects the same - # state_dict shape as the raw model. - if ema_model is not None: - try: - ema_ckpt_path = CACHE_DIR / "latest_ema.pt" - CACHE_DIR.mkdir(parents=True, exist_ok=True) - torch.save({ - "model_state_dict": ema_model.module.state_dict(), - "config": asdict(config), - "step": step, - "epoch": epoch, - "train_seconds": total_training_time, - "val_bpb": val_bpb, - "ema_decay": EMA_DECAY, - }, str(ema_ckpt_path)) - print(f"[EMA] saved {ema_ckpt_path} (step={step})", flush=True) - except Exception as _e: - print(f"[EMA] save failed: {_e}", flush=True) - - run_factual_probes(model, tokenizer, device, autocast_ctx) - - t_end = time.time() - startup_time = t_start_training - t_start - steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) - / total_training_time / GPU_BF16_PEAK_FLOPS - if total_training_time > 0 else 0 - ) - peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - metrics = model.get_secondary_metrics() - - print("---") - print(f"val_bpb: {val_bpb:.6f}" if val_bpb is not None else "val_bpb: SKIPPED") - print(f"training_seconds: {total_training_time:.1f}") - print(f"total_seconds: {t_end - t_start:.1f}") - print(f"peak_vram_mb: {peak_vram_mb:.1f}") - print(f"mfu_percent: {steady_state_mfu:.2f}") - print(f"total_tokens_M: {total_tokens / 1e6:.1f}") - print(f"num_steps: {step}") - print(f"num_params_M: {num_params / 1e6:.1f}") - print(f"n_layer: {N_LAYER}") - print(f"d_model: {D_MODEL}") - print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") - print(f"sdr_active_bits: {metrics.get('sdr_active_bits', 0):.1f}") - print(f"htm_anomaly: {metrics.get('htm_anomaly', 0):.4f}") - - # Per-layer summary panel — only printed when diagnostics were active. - _layer_keys = sorted([k for k in metrics.keys() if k.startswith('layer_')]) - if _layer_keys: - n_layers = len(model.blocks) - print("--- per-layer diagnostic panel ---") - for li in range(n_layers): - d_ratio = metrics.get(f'layer_{li}_delta_ratio', float('nan')) - out_n = metrics.get(f'layer_{li}_out_norm', float('nan')) - g_norm = metrics.get(f'layer_{li}_grad_norm', float('nan')) - eff_r = metrics.get(f'layer_{li}_eff_rank', float('nan')) - f_std = metrics.get(f'layer_{li}_feat_std', float('nan')) - print( - f"L{li:02d} delta_ratio={d_ratio:.4f} out_norm={out_n:.4f} " - f"grad_norm={g_norm:.3e} eff_rank={eff_r:.1f} feat_std={f_std:.4f}" - ) - - # Emit full metrics dictionary as JSON for sweep aggregation. Path from - # HYDRA_METRICS_OUT env var; default=/tmp/hydra_run_metrics.json. Always - # written (even without diagnostics) so the aggregator can compare runs. - _metrics_out = os.environ.get("HYDRA_METRICS_OUT", "/tmp/hydra_run_metrics.json") - try: - _dump = dict(metrics) - _dump.update({ - 'val_bpb': float(val_bpb), - 'val_ppl': float(val_ppl), - 'n_layer': int(N_LAYER), - 'd_model': int(D_MODEL), - 'num_params_M': float(num_params / 1e6), - 'num_steps': int(step), - 'total_tokens_M': float(total_tokens / 1e6), - 'peak_vram_mb': float(peak_vram_mb), - 'training_seconds': float(total_training_time), - 'sdr_target_active': int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")), - }) - Path(_metrics_out).parent.mkdir(parents=True, exist_ok=True) - with open(_metrics_out, 'w') as _f: - json.dump(_dump, _f, indent=2, sort_keys=True) - print(f"[METRICS] wrote {_metrics_out}", flush=True) - # Also emit a single-line JSON to stdout so the sweep aggregator can - # scrape it from HF Jobs logs without pulling files out of the container. - print("[METRICS_JSON] " + json.dumps(_dump, sort_keys=True), flush=True) - except Exception as _e: - print(f"[METRICS] write failed: {_e}", flush=True) - - run_factual_english(model, tokenizer, MAX_SEQ_LEN) - # startup_time is informative but not printed (preserve historical output) - _ = startup_time + # Retina: HF Hub cache hit for this (vocab, n_bits, target_active) combo + # returns in seconds; otherwise build_retina streams Nemotron docs to + # compute cooccurrence + train SOM, then uploads back to the cache. + import subsystems.sdr_retina as _sdr_retina + _sdr_retina.build_retina() + tokenizer = Tokenizer.from_directory() + vocab_size = tokenizer.get_vocab_size() + print(f"Vocab size: {vocab_size:,}") + + config = PostSemClawConfig( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, + engram_n_columns=ENGRAM_N_COLUMNS, + engram_key_dim=ENGRAM_KEY_DIM, + engram_layer_idx=ENGRAM_LAYER_IDX, + ) + print(f"Model config: {asdict(config)}") + + with torch.device("meta"): + model = PostSemClawModel(config) + model.to_empty(device=device) + model.init_weights() + + param_counts = model.num_scaling_params() + print("Parameter counts:") + for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") + num_params = param_counts['total'] + num_flops_per_token = model.estimate_flops() + print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + + tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN + assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 + grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + + optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, + ) + + step, total_training_time, smooth_train_loss, bpt_ema, resume_epoch = maybe_resume_ckpt( + model, optimizer, device, + ) + + # Learnability #4: inform the model of the BOS token id so it can mask + # doc-separator positions in packed sequences. Always set (the mask only + # fires when HYDRA_DOC_SEP_MASK=1 is also on). + if hasattr(model, 'set_bos_token_id'): + model.set_bos_token_id(tokenizer.get_bos_token_id()) + + # Learnability #2: EMA shadow copy of weights. AveragedModel clones every + # parameter; we update it after every optimizer step and save it at the + # end alongside the raw checkpoint. Defaults OFF. + ema_model = None + if USE_EMA: + try: + from torch.optim.swa_utils import AveragedModel, get_ema_multi_avg_fn + # decay=EMA_DECAY; avg_fn uses get_ema_multi_avg_fn for numerical + # stability across bf16/fp32 mixed parameter groups. + ema_model = AveragedModel( + model, + multi_avg_fn=get_ema_multi_avg_fn(EMA_DECAY), + ) + print(f"[EMA] enabled with decay={EMA_DECAY}") + except Exception as _e: + print(f"[EMA] disabled — AveragedModel init failed: {_e}") + ema_model = None + + print("torch.compile: Muon step compiled; AdamW uses torch._fused_adamw_ (model blocks use native CUDA kernels)") + + # Learnability #7: curriculum short-then-long. If enabled, build the + # initial dataloader at the short seq_len; we swap to full MAX_SEQ_LEN + # after CURRICULUM_SHORT_STEPS optimizer steps (see loop below). + _curriculum_active = CURRICULUM_SHORT_STEPS > 0 and CURRICULUM_SHORT_SEQ_LEN < MAX_SEQ_LEN + _current_seq_len = CURRICULUM_SHORT_SEQ_LEN if _curriculum_active else MAX_SEQ_LEN + if _curriculum_active: + print( + f"[CURRICULUM] starting at T={_current_seq_len} for " + f"{CURRICULUM_SHORT_STEPS} steps, then switching to T={MAX_SEQ_LEN}" + ) + train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, _current_seq_len, "train") + x, y, epoch = next(train_loader) # prefetch first batch + if resume_epoch > 0: + epoch = max(epoch, resume_epoch) + + print(f"Time budget: {TIME_BUDGET}s") + print(f"Gradient accumulation steps: {grad_accum_steps}") + + # Token→byte LUT for bits-per-byte computation. evaluate_bpb in prepare.py + # uses total_nats / (ln(2) * total_bytes); our live metric needs to match. + # Without this, `bpb = loss/ln(2)` is actually bits-per-TOKEN, which at + # vocab=8192 scales by ~4 and makes live train bpb non-comparable with + # val_bpb (champion 1.279 bpb vs train printing "8.04"). + token_bytes = get_token_bytes(device=device) + + # ----------------------------------------------------------------------- + # Training loop + # ----------------------------------------------------------------------- + + t_start_training = time.time() + + # Async postprocessing — run SOM + Hestia on background threads so + # the GPU doesn't idle during their CPU-bound work. + _ASYNC_POSTPROCESS = os.environ.get("HYDRA_ASYNC_POSTPROCESS", "1") == "1" + _som_thread: threading.Thread | None = None + _hestia_thread: threading.Thread | None = None + _hestia_stream: torch.cuda.Stream | None = ( + torch.cuda.Stream() if _ASYNC_POSTPROCESS else None + ) + + # HYDRA_PROFILE_STEPS=N prints a per-phase cpu/gpu time breakdown for the + # first N steps (and every 100th step thereafter if N<0). Zero overhead + # when disabled. Used to find what's eating CPU budget when GPU should + # be the bottleneck. + _profile_steps = int(os.environ.get("HYDRA_PROFILE_STEPS", "0")) + + while True: + torch.cuda.synchronize() + t0 = time.time() + _prof = _profile_steps and (step < _profile_steps or (_profile_steps < 0 and step % 100 == 0)) + _gpu_ms = 0.0 + _data_ms = 0.0 + for micro_step in range(grad_accum_steps): + if _prof: + torch.cuda.synchronize(); _t_micro = time.time() + if USE_MDLM: + # MDLM path: corrupt y -> x_noised, run model to get full-V logits, + # compute RB weighted CE on masked positions. x (original input) is + # unused in this path — the model only sees the noised version of y. + _mask_id = MDLM_MASK_ID if MDLM_MASK_ID >= 0 else (vocab_size - 1) + x_noised, mask_positions, loss_weights = mdlm_masked_forward_process( + y, mask_token_id=_mask_id, alpha_schedule=MDLM_SCHEDULE, + ) + with autocast_ctx: + logits = model(x_noised) # targets=None -> (B, T, V) logits + loss = mdlm_rb_loss(logits, y, mask_positions, loss_weights) + else: + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + if _prof: + torch.cuda.synchronize() + _gpu_ms += (time.time() - _t_micro) * 1000 + _t_data = time.time() + x, y, epoch = next(train_loader) + if _prof: + _data_ms += (time.time() - _t_data) * 1000 + if _prof: + torch.cuda.synchronize(); _t_fb = time.time() + + # Progress and schedules + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group['kind'] == 'muon': + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + if _prof: + torch.cuda.synchronize(); _t_opt = time.time() + + # Learnability #2: EMA update after every optimizer step. + if ema_model is not None: + try: + ema_model.update_parameters(model) + except Exception as _e: + print(f"[EMA] update failed at step {step}: {_e}", flush=True) + + # Learnability #7: curriculum transition. After + # CURRICULUM_SHORT_STEPS optimizer steps, rebuild the dataloader at + # MAX_SEQ_LEN. Done once, then the flag flips off. + if _curriculum_active and step + 1 >= CURRICULUM_SHORT_STEPS: + print( + f"[CURRICULUM] step={step+1} — switching from T={_current_seq_len} " + f"to T={MAX_SEQ_LEN}", + flush=True, + ) + _current_seq_len = MAX_SEQ_LEN + _curriculum_active = False + train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, _current_seq_len, "train") + # Prefetch the next batch at the new seq_len so the following + # loop iteration consumes fresh data. + x, y, epoch = next(train_loader) + + # Online SOM update — retina is now a plain Python attribute (not a + # registered buffer) so mutations do not invalidate torch.compile guards. + # Runs fully on CPU; safe to overlap with GPU forward pass. + _last_sdr = getattr(model, "_last_sdr", None) + if _last_sdr is not None: + if _ASYNC_POSTPROCESS: + if _som_thread is not None: + _som_thread.join() + # Clone tensors before next step overwrites them + _som_x = x.clone() + _som_sdr = _last_sdr.clone() + _som_thread = threading.Thread( + target=model.sdr_semantic.maybe_som_update, + args=(_som_x, _som_sdr), + daemon=True, + ) + _som_thread.start() + else: + model.sdr_semantic.maybe_som_update(x, _last_sdr) + + # Hestia QAT — anneal temperature every step, snap every N steps. + # apply_to walks all Linear modules (CPU) then does .data.copy_ (GPU). + # Background thread + separate CUDA stream lets this overlap with + # the next forward pass on the default stream. + _hestia_progress = (time.time() - t_start_training) / max(TIME_BUDGET, 1) + _hestia_interval = int(os.environ.get("HYDRA_HESTIA_INTERVAL", "100")) + if step % _hestia_interval == 0: + if _ASYNC_POSTPROCESS: + if _hestia_thread is not None: + _hestia_thread.join() + + def _hestia_bg(mdl: torch.nn.Module, prog: float) -> None: + assert _hestia_stream is not None + with torch.cuda.stream(_hestia_stream): + mdl.hestia.anneal_temperature(prog) + mdl.hestia.apply_to(mdl) + + _hestia_thread = threading.Thread( + target=_hestia_bg, + args=(model, _hestia_progress), + daemon=True, + ) + _hestia_thread.start() + else: + model.hestia.anneal_temperature(_hestia_progress) + model.hestia.apply_to(model) + else: + # anneal_temperature is cheap (~1 us), keep inline + model.hestia.anneal_temperature(_hestia_progress) + + model.zero_grad(set_to_none=True) + + train_loss_f = train_loss.item() + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + # Save to a DIFFERENT file — never clobber a good latest.pt with + # a NaN/diverged state. The good ckpt from the last periodic save + # is the right place to resume from. + save_ckpt( + model, + optimizer, + config, + step, + total_training_time, + smooth_train_loss, + bpt_ema, + epoch, + FAILED_CKPT, + blocking=True, + ) + raise SystemExit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if _prof: + fb = (_t_fb - t0) * 1000 + opt = (_t_opt - _t_fb) * 1000 + rest = (t1 - _t_opt) * 1000 + print( + f"[PROF step={step:05d}] gpu={_gpu_ms:.0f}ms data_fetch={_data_ms:.0f}ms " + f"(sum_fb={fb:.0f}) opt={opt:.0f}ms rest={rest:.0f}ms total={dt*1000:.0f}ms", + flush=True, + ) + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / GPU_BF16_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + # Bytes-per-token for the CURRENT batch. evaluate_bpb in prepare.py + # computes bits-per-BYTE (total_nats / (ln2 * total_bytes)); to match + # that semantics live, we EMA-smooth the per-batch bytes/token and + # divide. Without this, the old `bpb = loss/ln2` was actually + # bits-per-token — ~4× larger than val_bpb at vocab=8192 and + # therefore not comparable to the champion 1.279 bpb metric. + with torch.no_grad(): + y_flat = y.view(-1) + nbytes_batch = token_bytes[y_flat] + mask = nbytes_batch > 0 + mask_count = mask.sum().clamp(min=1).float() + avg_bytes_per_tok = (nbytes_batch.float() * mask.float()).sum() / mask_count + bpt_batch = float(avg_bytes_per_tok.item()) + if step == 0 or bpt_ema <= 0.0: + bpt_ema = bpt_batch + else: + bpt_ema = 0.98 * bpt_ema + 0.02 * bpt_batch + + # Dual metric: bpb (byte-normalized, comparable with val_bpb) AND + # bpt (bits per token, the raw loss in bits). bpt_div exposes the + # current avg bytes-per-token so the conversion is transparent. + bpt = debiased_smooth_loss / math.log(2) + bpb = bpt / max(bpt_ema, 1e-6) + vram_mib = torch.cuda.memory_allocated() / 1024 / 1024 + current_lr = optimizer.param_groups[0]["lr"] + + # Per-step line-buffered log. NOT \r-overwritten so tee/grep see it. + # Keep key=value pairs grep-friendly. + ppl = 2.0 ** bpb # perplexity (byte-level) + print( + f"step={step:05d} loss={debiased_smooth_loss:.4f} bpb={bpb:.4f} ppl={ppl:.3f} " + f"bpt={bpt:.3f} bpt_div={bpt_ema:.2f} " + f"tps={tok_per_sec} dt_ms={dt*1000:.0f} mfu={mfu:.1f} " + f"lr={current_lr:.2e} vram={vram_mib:.0f}MiB " + f"pct={pct_done:.1f} epoch={epoch} remaining={remaining:.0f}s", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + # No periodic gc.collect() — we disabled+froze at step 0 on purpose, + # so a manual collect every 5k steps just re-scans frozen objects + # (burned ~900 ms/event in production) for no live-garbage reason. + + if CKPT_INTERVAL > 0 and step > 0 and step % CKPT_INTERVAL == 0: + save_ckpt( + model, + optimizer, + config, + step, + total_training_time, + smooth_train_loss, + bpt_ema, + epoch, + LATEST_CKPT, + ) + + # Periodic mid-training validation so we can see the model learning + # English in real time (not just at the end). Small val batch so it + # doesn't eat significant training time. + mid_val_interval = int(os.environ.get("HYDRA_MID_VAL_INTERVAL", "500")) + if mid_val_interval > 0 and step > 0 and step % mid_val_interval == 0: + model.eval() + try: + # Defrag GPU memory before eval allocates fresh chunks — + # without this the eval path can OOM on 6GB cards even + # though total usage fits, because the allocator's free + # blocks are fragmented. + torch.cuda.empty_cache() + _orig_mid = _prepare_mod.EVAL_TOKENS + _prepare_mod.EVAL_TOKENS = 262144 # ~260K tokens, fast + with torch.no_grad(): + with autocast_ctx: + mid_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + _prepare_mod.EVAL_TOKENS = _orig_mid + mid_ppl = 2.0 ** mid_bpb + print(f"[MID_VAL] step={step} val_bpb={mid_bpb:.4f} val_ppl={mid_ppl:.3f}", flush=True) + + # Per-layer diagnostic panel. Only printed when HYDRA_LAYER_DIAGNOSTICS=1 + # is set (otherwise the layer_* keys are absent from _metrics). + _diag_metrics = model.get_secondary_metrics() + _layer_keys = sorted([k for k in _diag_metrics.keys() if k.startswith('layer_')]) + if _layer_keys: + # Condense: one row per layer showing the four core signals. + n_layers = len(model.blocks) + print(f"[LAYER_DIAG] step={step}", flush=True) + for li in range(n_layers): + d_ratio = _diag_metrics.get(f'layer_{li}_delta_ratio', float('nan')) + out_n = _diag_metrics.get(f'layer_{li}_out_norm', float('nan')) + g_norm = _diag_metrics.get(f'layer_{li}_grad_norm', float('nan')) + eff_r = _diag_metrics.get(f'layer_{li}_eff_rank', float('nan')) + f_std = _diag_metrics.get(f'layer_{li}_feat_std', float('nan')) + print( + f"[LAYER_DIAG] L{li:02d} delta_ratio={d_ratio:.4f} " + f"out_norm={out_n:.4f} grad_norm={g_norm:.3e} " + f"eff_rank={eff_r:.1f} feat_std={f_std:.4f}", + flush=True, + ) + htm_proj_g = _diag_metrics.get('htm_proj_grad_norm', None) + if htm_proj_g is not None: + print(f"[LAYER_DIAG] htm_proj grad_norm={htm_proj_g:.3e}", flush=True) + except Exception as e: + print(f"[MID_VAL] failed: {e}", flush=True) + model.train() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + + # Drain async postprocessing threads before eval + if _som_thread is not None: + _som_thread.join() + if _hestia_thread is not None: + _hestia_thread.join() + if _hestia_stream is not None: + _hestia_stream.synchronize() + + total_tokens = step * TOTAL_BATCH_SIZE + + # ---------------------------------------------------------------------- + # SAVE ORDER (critical): + # 1. Save PRETRAIN_FINAL_CKPT with val_bpb=None (hedge against eval OOM) + # 2. Save LATEST_CKPT with val_bpb=None (hedge against eval OOM) + # 3. Run eval (may OOM on small GPUs; we survive it) + # 4. Re-save both ckpts with val_bpb filled in + # This way we NEVER lose the final trained weights to an eval crash. + # Previous ordering put eval first, so an eval-time OOM destroyed the + # only record of a 6h training run (2026-04-22 incident). + # ---------------------------------------------------------------------- + + save_ckpt( + model, optimizer, config, step, total_training_time, + smooth_train_loss, bpt_ema, epoch, PRETRAIN_FINAL_CKPT, + val_bpb=None, blocking=True, + ) + save_ckpt( + model, optimizer, config, step, total_training_time, + smooth_train_loss, bpt_ema, epoch, LATEST_CKPT, + val_bpb=None, blocking=True, + ) + + # Now it's safe to eval — ckpts are on disk regardless of what happens here. + # HYDRA_EVAL_BATCH overrides DEVICE_BATCH_SIZE (env-tunable; default halves + # the training batch because eval holds activations for full sequence and + # does not benefit from overlap with backward). HYDRA_EVAL_TOKENS controls + # how many val tokens to sweep (default 2 M, short enough for autoresearch + # 5-min budgets). + val_bpb: float | None = None + # Eval batch: default to 4 on cloud GPUs (enough freed VRAM after optimizer + # clear), fall back to DEVICE_BATCH_SIZE//2 on tiny cards. Env-overridable. + _eval_B = int(os.environ.get("HYDRA_EVAL_BATCH", + str(max(1, DEVICE_BATCH_SIZE // 2) if DEVICE_BATCH_SIZE <= 8 else 4))) + # Eval tokens: default 1M (1,048,576) — gives statistically meaningful BPB + # (256 forward passes at B=4, seq=1024). Env-overridable for fast/slow sweeps. + _eval_tokens = int(os.environ.get("HYDRA_EVAL_TOKENS", str(1048576))) + try: + # Aggressive VRAM reclaim for 6GB cards. Peak training VRAM = 5.1GB + # which leaves < 1GB for the eval forward — the driver can't satisfy + # the allocation. Free EVERY tensor we don't strictly need: + # - optimizer grads (set_to_none releases tensor) + # - optimizer.state (fp32 Muon NS workspace, AdamW moments — ~size-of-params each) + # - model internal caches (HTM subsample cache, SDR stash) + # After this, VRAM should be ~params only (bf16 ≈ 120MB at 60M params). + optimizer.zero_grad(set_to_none=True) + if hasattr(optimizer, 'state') and optimizer.state: + for p, st in list(optimizer.state.items()): + st.clear() + optimizer.state.clear() + for p in model.parameters(): + if p.grad is not None: + p.grad = None + if hasattr(model, '_htm_cache'): + model._htm_cache = None + if hasattr(model, '_last_sdr'): + model._last_sdr = None + import gc as _gc + _gc.collect() + torch.cuda.empty_cache() + torch.cuda.synchronize() + try: + _free_mb = torch.cuda.mem_get_info()[0] / 1024 / 1024 + print(f"[VAL] free_vram_mb={_free_mb:.0f} (cleared optimizer state)", flush=True) + except Exception: + pass + print(f"[VAL] running eval on {_eval_tokens} tokens at B={_eval_B}...", flush=True) + model.eval() + _orig = _prepare_mod.EVAL_TOKENS + _prepare_mod.EVAL_TOKENS = _eval_tokens + # Nemotron path reads HYDRA_STREAM_EVAL_TOKENS env var directly, + # not _prepare_mod.EVAL_TOKENS. Sync both so eval budget is + # respected regardless of which dataloader path is active. + _orig_stream = os.environ.get("HYDRA_STREAM_EVAL_TOKENS") + os.environ["HYDRA_STREAM_EVAL_TOKENS"] = str(_eval_tokens) + with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, _eval_B) + _prepare_mod.EVAL_TOKENS = _orig + if _orig_stream is not None: + os.environ["HYDRA_STREAM_EVAL_TOKENS"] = _orig_stream + else: + os.environ.pop("HYDRA_STREAM_EVAL_TOKENS", None) + val_ppl = 2 ** val_bpb + print(f"[VAL] step={step} val_bpb={val_bpb:.4f} val_ppl={val_ppl:.3f}", flush=True) + except torch.cuda.OutOfMemoryError as e: + print(f"[VAL] SKIPPED (OOM): {e}", flush=True) + torch.cuda.empty_cache() + except Exception as e: + import traceback as _tb + print(f"[VAL] SKIPPED ({type(e).__name__}): {e}", flush=True) + _tb.print_exc() + try: + _free = torch.cuda.mem_get_info()[0] / 1024 / 1024 + print(f"[VAL] post-crash free_vram_mb={_free:.0f}", flush=True) + except Exception: + pass + + # Final ckpts with val_bpb filled in (if eval succeeded). + save_ckpt( + model, optimizer, config, step, total_training_time, + smooth_train_loss, bpt_ema, epoch, LATEST_CKPT, + val_bpb=val_bpb, blocking=True, + ) + save_ckpt( + model, optimizer, config, step, total_training_time, + smooth_train_loss, bpt_ema, epoch, PRETRAIN_FINAL_CKPT, + val_bpb=val_bpb, blocking=True, + ) + + # Learnability #2: persist EMA weights alongside the raw checkpoint. + # latest_ema.pt contains ema_model.module (the Averaged params) so it + # can be loaded by evaluation / inference code that expects the same + # state_dict shape as the raw model. + if ema_model is not None: + try: + ema_ckpt_path = CACHE_DIR / "latest_ema.pt" + CACHE_DIR.mkdir(parents=True, exist_ok=True) + torch.save({ + "model_state_dict": ema_model.module.state_dict(), + "config": asdict(config), + "step": step, + "epoch": epoch, + "train_seconds": total_training_time, + "val_bpb": val_bpb, + "ema_decay": EMA_DECAY, + }, str(ema_ckpt_path)) + print(f"[EMA] saved {ema_ckpt_path} (step={step})", flush=True) + except Exception as _e: + print(f"[EMA] save failed: {_e}", flush=True) + + run_factual_probes(model, tokenizer, device, autocast_ctx) + + t_end = time.time() + startup_time = t_start_training - t_start + steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) + / total_training_time / GPU_BF16_PEAK_FLOPS + if total_training_time > 0 else 0 + ) + peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + metrics = model.get_secondary_metrics() + + print("---") + print(f"val_bpb: {val_bpb:.6f}" if val_bpb is not None else "val_bpb: SKIPPED") + print(f"training_seconds: {total_training_time:.1f}") + print(f"total_seconds: {t_end - t_start:.1f}") + print(f"peak_vram_mb: {peak_vram_mb:.1f}") + print(f"mfu_percent: {steady_state_mfu:.2f}") + print(f"total_tokens_M: {total_tokens / 1e6:.1f}") + print(f"num_steps: {step}") + print(f"num_params_M: {num_params / 1e6:.1f}") + print(f"n_layer: {N_LAYER}") + print(f"d_model: {D_MODEL}") + print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") + print(f"sdr_active_bits: {metrics.get('sdr_active_bits', 0):.1f}") + print(f"htm_anomaly: {metrics.get('htm_anomaly', 0):.4f}") + + # Per-layer summary panel — only printed when diagnostics were active. + _layer_keys = sorted([k for k in metrics.keys() if k.startswith('layer_')]) + if _layer_keys: + n_layers = len(model.blocks) + print("--- per-layer diagnostic panel ---") + for li in range(n_layers): + d_ratio = metrics.get(f'layer_{li}_delta_ratio', float('nan')) + out_n = metrics.get(f'layer_{li}_out_norm', float('nan')) + g_norm = metrics.get(f'layer_{li}_grad_norm', float('nan')) + eff_r = metrics.get(f'layer_{li}_eff_rank', float('nan')) + f_std = metrics.get(f'layer_{li}_feat_std', float('nan')) + print( + f"L{li:02d} delta_ratio={d_ratio:.4f} out_norm={out_n:.4f} " + f"grad_norm={g_norm:.3e} eff_rank={eff_r:.1f} feat_std={f_std:.4f}" + ) + + # Emit full metrics dictionary as JSON for sweep aggregation. Path from + # HYDRA_METRICS_OUT env var; default=/tmp/hydra_run_metrics.json. Always + # written (even without diagnostics) so the aggregator can compare runs. + _metrics_out = os.environ.get("HYDRA_METRICS_OUT", "/tmp/hydra_run_metrics.json") + try: + _dump = dict(metrics) + _dump.update({ + 'val_bpb': (float(val_bpb) if val_bpb is not None else None), + 'val_ppl': (float(val_ppl) if val_ppl is not None else None), + 'n_layer': int(N_LAYER), + 'd_model': int(D_MODEL), + 'num_params_M': float(num_params / 1e6), + 'num_steps': int(step), + 'total_tokens_M': float(total_tokens / 1e6), + 'peak_vram_mb': float(peak_vram_mb), + 'training_seconds': float(total_training_time), + 'sdr_target_active': int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")), + }) + Path(_metrics_out).parent.mkdir(parents=True, exist_ok=True) + with open(_metrics_out, 'w') as _f: + json.dump(_dump, _f, indent=2, sort_keys=True) + print(f"[METRICS] wrote {_metrics_out}", flush=True) + # Also emit a single-line JSON to stdout so the sweep aggregator can + # scrape it from HF Jobs logs without pulling files out of the container. + print("[METRICS_JSON] " + json.dumps(_dump, sort_keys=True), flush=True) + except Exception as _e: + print(f"[METRICS] write failed: {_e}", flush=True) + + run_factual_english(model, tokenizer, MAX_SEQ_LEN) + # startup_time is informative but not printed (preserve historical output) + _ = startup_time diff --git a/overlay/kernels/cuda/decode_kernels.cu b/overlay/kernels/cuda/decode_kernels.cu index 593e4b2d194574c1e66c8e6ee6fd38ddcd7f9693..5b6857a0bcae5010d19ed41245b6bd39e789d4f6 100644 --- a/overlay/kernels/cuda/decode_kernels.cu +++ b/overlay/kernels/cuda/decode_kernels.cu @@ -1,10 +1,10 @@ -/* - * CuTe DSL decode kernels for Mamba-3 autoregressive generation. - * - * Phase 2: Optimized single-token SSM step for inference. - * Phase 1: Not needed (training only, no generation). - * - * Fuses: input_proj + conv_step + ssm_step + output_proj - * into a single kernel launch for minimal latency. - */ -// Stub: Phase 2 implementation +/* + * CuTe DSL decode kernels for Mamba-3 autoregressive generation. + * + * Phase 2: Optimized single-token SSM step for inference. + * Phase 1: Not needed (training only, no generation). + * + * Fuses: input_proj + conv_step + ssm_step + output_proj + * into a single kernel launch for minimal latency. + */ +// Stub: Phase 2 implementation diff --git a/overlay/kernels/cuda/flashfftconv/LICENSE b/overlay/kernels/cuda/flashfftconv/LICENSE index 29f81d812f3e768fa89638d1f72920dbfd1413a8..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 100644 --- a/overlay/kernels/cuda/flashfftconv/LICENSE +++ b/overlay/kernels/cuda/flashfftconv/LICENSE @@ -1,201 +1,201 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. 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In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/overlay/kernels/cuda/flashfftconv/README.md b/overlay/kernels/cuda/flashfftconv/README.md index faa22c729c873b653ca2320a72694a30cdf39b38..6f0efec4d3c5cc3bffefe3cf00af0cfe4f990c92 100644 --- a/overlay/kernels/cuda/flashfftconv/README.md +++ b/overlay/kernels/cuda/flashfftconv/README.md @@ -1,57 +1,57 @@ -# flashfftconv (vendored) - -Vendored from https://github.com/HazyResearch/flash-fft-conv (Apache 2.0 license). - -**Upstream commit:** see `UPSTREAM_COMMIT`. - -## What this is - -HazyResearch's Monarch-matrix-decomposition FFT convolution CUDA kernel. Provides a -drop-in replacement for `torch.fft.rfft + complex-mult + irfft` that runs ~2-3x -faster than cuFFT for the specific power-of-two lengths it supports (256, 512, -1024, 2048, 4096, 8192, ..., up to 4M). - -In HYDRA, we use it to accelerate `subsystems/hyena_pure.fftconv_ref`. The -accelerated path is opt-in via `HYDRA_HYENA_FLASH_FFT=1`; default behavior is -unchanged (pure PyTorch fallback). - -## How to build - -The vendored tree contains: -- `flashfftconv/` — pure-Python wrappers (imports `monarch_cuda` CUDA extension) -- `csrc/` — CUDA source files and setup.py for the native extension - -Build instructions: - -```bash -cd /home/mikeb/work/feather/kernels/cuda/flashfftconv/csrc - -# Edit `csrc/setup.py` first: change the cc_flag line to match your GPU arch -# (RTX 3060 = 8.6, A100 = 8.0, H100 = 9.0). Example for RTX 3060: -# cc_flag = ['--generate-code=arch=compute_86,code=compute_86'] - -# Build with the local CUDA toolchain (must match your torch.version.cuda): -CUDA_HOME=/usr/local/cuda-12.1 .venv/bin/pip install -e . -``` - -Then install the Python wrappers: - -```bash -cd /home/mikeb/work/feather/kernels/cuda/flashfftconv -.venv/bin/pip install -e . -``` - -## Runtime usage - -Once installed, set `HYDRA_HYENA_FLASH_FFT=1` and training will use it. -`subsystems/hyena_pure.fftconv_ref` auto-detects via `try: import flashfftconv` -and falls back to pure PyTorch on import failure. - -## Known caveats - -- Seqlen must be a power of 2 AND in the supported set: {256, 512, 1024, 2048, - 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304}. - For HYDRA, `fft_size = 2 * seq_len` → seq_len in {128, 256, 512, 1024, 2048, ...}. -- dtype must be fp16 or bf16 (fp32 not supported). -- GPU arch must be compiled into the extension (see setup.py cc_flag). -- CUDA toolchain major.minor should match `torch.version.cuda` major (12.x ↔ 12.x). +# flashfftconv (vendored) + +Vendored from https://github.com/HazyResearch/flash-fft-conv (Apache 2.0 license). + +**Upstream commit:** see `UPSTREAM_COMMIT`. + +## What this is + +HazyResearch's Monarch-matrix-decomposition FFT convolution CUDA kernel. Provides a +drop-in replacement for `torch.fft.rfft + complex-mult + irfft` that runs ~2-3x +faster than cuFFT for the specific power-of-two lengths it supports (256, 512, +1024, 2048, 4096, 8192, ..., up to 4M). + +In HYDRA, we use it to accelerate `subsystems/hyena_pure.fftconv_ref`. The +accelerated path is opt-in via `HYDRA_HYENA_FLASH_FFT=1`; default behavior is +unchanged (pure PyTorch fallback). + +## How to build + +The vendored tree contains: +- `flashfftconv/` — pure-Python wrappers (imports `monarch_cuda` CUDA extension) +- `csrc/` — CUDA source files and setup.py for the native extension + +Build instructions: + +```bash +cd /home/mikeb/work/feather/kernels/cuda/flashfftconv/csrc + +# Edit `csrc/setup.py` first: change the cc_flag line to match your GPU arch +# (RTX 3060 = 8.6, A100 = 8.0, H100 = 9.0). Example for RTX 3060: +# cc_flag = ['--generate-code=arch=compute_86,code=compute_86'] + +# Build with the local CUDA toolchain (must match your torch.version.cuda): +CUDA_HOME=/usr/local/cuda-12.1 .venv/bin/pip install -e . +``` + +Then install the Python wrappers: + +```bash +cd /home/mikeb/work/feather/kernels/cuda/flashfftconv +.venv/bin/pip install -e . +``` + +## Runtime usage + +Once installed, set `HYDRA_HYENA_FLASH_FFT=1` and training will use it. +`subsystems/hyena_pure.fftconv_ref` auto-detects via `try: import flashfftconv` +and falls back to pure PyTorch on import failure. + +## Known caveats + +- Seqlen must be a power of 2 AND in the supported set: {256, 512, 1024, 2048, + 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304}. + For HYDRA, `fft_size = 2 * seq_len` → seq_len in {128, 256, 512, 1024, 2048, ...}. +- dtype must be fp16 or bf16 (fp32 not supported). +- GPU arch must be compiled into the extension (see setup.py cc_flag). +- CUDA toolchain major.minor should match `torch.version.cuda` major (12.x ↔ 12.x). diff --git a/overlay/kernels/cuda/flashfftconv/UPSTREAM_COMMIT b/overlay/kernels/cuda/flashfftconv/UPSTREAM_COMMIT index 706342b0a49d725284608246f0b11a3ed1adf0de..911758fbc7e93d8b99ab95f5dbac53fbb87b6d58 100644 --- a/overlay/kernels/cuda/flashfftconv/UPSTREAM_COMMIT +++ b/overlay/kernels/cuda/flashfftconv/UPSTREAM_COMMIT @@ -1 +1 @@ -b8771028717f46d5b22cbb8e12833f35033d621b +b8771028717f46d5b22cbb8e12833f35033d621b diff --git a/overlay/kernels/cuda/flashfftconv/csrc/.gitignore b/overlay/kernels/cuda/flashfftconv/csrc/.gitignore index 3068f68315d736dadb12b7134db55f71b0499901..71ceebc95a66f2b8f6c658009149dfa459cf51e0 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/.gitignore +++ b/overlay/kernels/cuda/flashfftconv/csrc/.gitignore @@ -1,10 +1,10 @@ -*.npy -*.json -*.png - -*/*.npy -*/*.json -*/*.png - -*.DS_Store +*.npy +*.json +*.png + +*/*.npy +*/*.json +*/*.png + +*.DS_Store */*.DS_Store \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly.h b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly.h index ede3de6ed72b957b7365f91410ad51f27c5d5c6f..a8da4af1a458a2fa8893f15d4d93df2e60211aa6 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly.h @@ -1,374 +1,374 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - - -std::vector butterfly_cuda( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional x_gate = std::nullopt -); - - -std::vector butterfly_bf16_cuda( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional out_gate = std::nullopt -); - - -std::vector butterfly_padded_cuda( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - std::optional x_gate = std::nullopt -); - - -std::vector butterfly_padded_bf16_cuda( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - std::optional x_gate = std::nullopt -); - -torch::Tensor butterfly_ifft_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional out_gate = std::nullopt -); - -torch::Tensor butterfly_ifft_bf16_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional x_gate = std::nullopt -); - -torch::Tensor butterfly_ifft_padded_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N, - std::optional out_gate = std::nullopt -); - - -torch::Tensor butterfly_ifft_padded_bf16_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N, - std::optional out_gate = std::nullopt -); - -std::vector butterfly( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag); -} - -std::vector butterfly_gated( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - torch::Tensor x_gate -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - CHECK_INPUT(x_gate); - - return butterfly_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, x_gate); -} - -std::vector butterfly_bf16( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - CHECK_INPUT(d_f_T_real); - CHECK_INPUT(d_f_T_imag); - - - return butterfly_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag); -} - -std::vector butterfly_gated_bf16( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - torch::Tensor x_gate -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - CHECK_INPUT(d_f_T_real); - CHECK_INPUT(d_f_T_imag); - CHECK_INPUT(x_gate); - - - return butterfly_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, x_gate); -} - -torch::Tensor butterfly_ifft( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - return butterfly_ifft_cuda(x_real, x_imag, d_f_T, twiddle_factors_real, twiddle_factors_imag); -} - - -torch::Tensor butterfly_ifft_gated( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - torch::Tensor out_gate -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - CHECK_INPUT(out_gate); - - return butterfly_ifft_cuda(x_real, x_imag, d_f_T, twiddle_factors_real, twiddle_factors_imag, out_gate); -} - -torch::Tensor butterfly_ifft_bf16( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(d_f_real); - CHECK_INPUT(d_f_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_ifft_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag); -} - - -torch::Tensor butterfly_ifft_gated_bf16( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - torch::Tensor out_gate -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(d_f_real); - CHECK_INPUT(d_f_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - CHECK_INPUT(out_gate); - - return butterfly_ifft_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, out_gate); -} - -std::vector butterfly_padded( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_padded_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, M); -} - -std::vector butterfly_padded_bf16( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_padded_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, M); -} - - -std::vector butterfly_padded_gated( - torch::Tensor x, - torch::Tensor d_f_T, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - torch::Tensor x_gate -){ - CHECK_INPUT(x); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_padded_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, M, x_gate); -} - -std::vector butterfly_padded_gated_bf16( - torch::Tensor x, - torch::Tensor d_f_T_real, - torch::Tensor d_f_T_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - torch::Tensor x_gate -){ - CHECK_INPUT(x); - CHECK_INPUT(d_f_T_real); - CHECK_INPUT(d_f_T_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - - return butterfly_padded_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, M, x_gate); -} - -torch::Tensor butterfly_ifft_padded( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - return butterfly_ifft_padded_cuda(x_real, x_imag, d_f, twiddle_factors_real, twiddle_factors_imag, N); -} - -torch::Tensor butterfly_ifft_padded_gated( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N, - torch::Tensor out_gate -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - return butterfly_ifft_padded_cuda(x_real, x_imag, d_f, twiddle_factors_real, twiddle_factors_imag, N, out_gate); -} - - -torch::Tensor butterfly_ifft_padded_bf16( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(d_f_real); - CHECK_INPUT(d_f_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - return butterfly_ifft_padded_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, N); -} - -torch::Tensor butterfly_ifft_padded_gated_bf16( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int N, - torch::Tensor out_gate -){ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(d_f_real); - CHECK_INPUT(d_f_imag); - CHECK_INPUT(twiddle_factors_real); - CHECK_INPUT(twiddle_factors_imag); - - return butterfly_ifft_padded_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, N, out_gate); +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + + +std::vector butterfly_cuda( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional x_gate = std::nullopt +); + + +std::vector butterfly_bf16_cuda( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional out_gate = std::nullopt +); + + +std::vector butterfly_padded_cuda( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + std::optional x_gate = std::nullopt +); + + +std::vector butterfly_padded_bf16_cuda( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + std::optional x_gate = std::nullopt +); + +torch::Tensor butterfly_ifft_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional out_gate = std::nullopt +); + +torch::Tensor butterfly_ifft_bf16_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional x_gate = std::nullopt +); + +torch::Tensor butterfly_ifft_padded_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N, + std::optional out_gate = std::nullopt +); + + +torch::Tensor butterfly_ifft_padded_bf16_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N, + std::optional out_gate = std::nullopt +); + +std::vector butterfly( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag); +} + +std::vector butterfly_gated( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + torch::Tensor x_gate +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + CHECK_INPUT(x_gate); + + return butterfly_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, x_gate); +} + +std::vector butterfly_bf16( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + CHECK_INPUT(d_f_T_real); + CHECK_INPUT(d_f_T_imag); + + + return butterfly_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag); +} + +std::vector butterfly_gated_bf16( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + torch::Tensor x_gate +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + CHECK_INPUT(d_f_T_real); + CHECK_INPUT(d_f_T_imag); + CHECK_INPUT(x_gate); + + + return butterfly_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, x_gate); +} + +torch::Tensor butterfly_ifft( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + return butterfly_ifft_cuda(x_real, x_imag, d_f_T, twiddle_factors_real, twiddle_factors_imag); +} + + +torch::Tensor butterfly_ifft_gated( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + torch::Tensor out_gate +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + CHECK_INPUT(out_gate); + + return butterfly_ifft_cuda(x_real, x_imag, d_f_T, twiddle_factors_real, twiddle_factors_imag, out_gate); +} + +torch::Tensor butterfly_ifft_bf16( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(d_f_real); + CHECK_INPUT(d_f_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_ifft_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag); +} + + +torch::Tensor butterfly_ifft_gated_bf16( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + torch::Tensor out_gate +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(d_f_real); + CHECK_INPUT(d_f_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + CHECK_INPUT(out_gate); + + return butterfly_ifft_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, out_gate); +} + +std::vector butterfly_padded( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_padded_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, M); +} + +std::vector butterfly_padded_bf16( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_padded_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, M); +} + + +std::vector butterfly_padded_gated( + torch::Tensor x, + torch::Tensor d_f_T, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + torch::Tensor x_gate +){ + CHECK_INPUT(x); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_padded_cuda(x, d_f_T, twiddle_factors_real, twiddle_factors_imag, M, x_gate); +} + +std::vector butterfly_padded_gated_bf16( + torch::Tensor x, + torch::Tensor d_f_T_real, + torch::Tensor d_f_T_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + torch::Tensor x_gate +){ + CHECK_INPUT(x); + CHECK_INPUT(d_f_T_real); + CHECK_INPUT(d_f_T_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + + return butterfly_padded_bf16_cuda(x, d_f_T_real, d_f_T_imag, twiddle_factors_real, twiddle_factors_imag, M, x_gate); +} + +torch::Tensor butterfly_ifft_padded( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + return butterfly_ifft_padded_cuda(x_real, x_imag, d_f, twiddle_factors_real, twiddle_factors_imag, N); +} + +torch::Tensor butterfly_ifft_padded_gated( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N, + torch::Tensor out_gate +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + return butterfly_ifft_padded_cuda(x_real, x_imag, d_f, twiddle_factors_real, twiddle_factors_imag, N, out_gate); +} + + +torch::Tensor butterfly_ifft_padded_bf16( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(d_f_real); + CHECK_INPUT(d_f_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + return butterfly_ifft_padded_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, N); +} + +torch::Tensor butterfly_ifft_padded_gated_bf16( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int N, + torch::Tensor out_gate +){ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(d_f_real); + CHECK_INPUT(d_f_imag); + CHECK_INPUT(twiddle_factors_real); + CHECK_INPUT(twiddle_factors_imag); + + return butterfly_ifft_padded_bf16_cuda(x_real, x_imag, d_f_real, d_f_imag, twiddle_factors_real, twiddle_factors_imag, N, out_gate); } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda.cu index e84ae781922b21695713521ed196a28baa671ca3..42522ccd5b637ef659edebfdbe505b26874ffefe 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda.cu @@ -1,699 +1,699 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -__global__ void butterfly_cuda_kernel_64( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - extern __shared__ half x_shared[]; - half *d_f_real = &x_shared[N * N]; - half *d_f_imag = &d_f_real[N * N]; - half *twiddles_real_shared = &d_f_imag[N * N]; - half *twiddles_imag_shared = &twiddles_real_shared[N * N]; - half *out_real_shared = &twiddles_imag_shared[N * N]; - half *out_imag_shared = &out_real_shared[N * N]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - - d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); - d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); - } - - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment a_frag_imag[4]; - wmma::fragment b_frag[4][4]; - wmma::fragment acc_frag_real[4]; - wmma::fragment acc_frag_imag[4]; - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(a_frag_real[i], d_f_real + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(a_frag_imag[i], d_f_imag + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); - } - - for (int t = 0; t < 16; t++) - { - - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; - } - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); - - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - -#pragma unroll - - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); - - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); - } - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } - - __syncthreads(); - } -} - -__global__ void butterfly_cuda_kernel_32( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - __shared__ half x_shared[32 * 64]; - __shared__ half d_f_real[32 * 32]; - __shared__ half d_f_imag[32 * 32]; - __shared__ half twiddles_real_shared[32 * 64]; - __shared__ half twiddles_imag_shared[32 * 64]; - __shared__ half out_real_shared[32 * 64]; - __shared__ half out_imag_shared[32 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - if(x_gate == nullptr){ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; - }else{ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - } - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - - __syncthreads(); - - if (threadIdx.y < N / 16) - { - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment b_frag[2][2]; - wmma::fragment acc_frag_real[2][2]; - wmma::fragment acc_frag_imag[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); - - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_imag[i][j], __float2half(0.0f)); - - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k])); - } - - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } -} - -__global__ void butterfly_cuda_kernel_128( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 128 * 32 * gridDim.x * 2 + blockIdx.z * 16 * 128 * 32 * gridDim.x * 2 + blockIdx.x * 64 + threadIdx.x; - const int tw_offset = blockIdx.x * 64 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - extern __shared__ half shared_real[]; - half *shared_imag = &shared_real[128 * 128]; - - - wmma::fragment a_frag_real[8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment a_frag_imag[8]; - wmma::fragment b_frag[8][8]; - wmma::fragment acc_frag_real[8]; - wmma::fragment acc_frag_imag[8]; - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 4; j++){ - shared_offset = (threadIdx.y + i * B_Y) * 128 + threadIdx.x + j * blockDim.x; - shared_real[shared_offset] = d_f[shared_offset].real(); - shared_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); - } - - - __syncthreads(); - - - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(shared_real)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2*>(shared_imag)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); - } - - __syncthreads(); - - - for(int t=0; t< 16; t++){ - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - if(x_gate != nullptr){ - reinterpret_cast<__half2*>(shared_real)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__half2*>(shared_real)[shared_offset] = x[offset + idx]; - } - - } - } - - - __syncthreads(); - - - for (int i = 0; i < 8; i++) - { - for (int j = 0; j < 8; j++) - { - wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); - } - } - - __syncthreads(); - - #pragma unroll - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); - - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - - #pragma unroll - - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); - - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - - __half2 tmp_real, tmp_imag; - #pragma unroll - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); - } - - wmma::store_matrix_sync(shared_real + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); - wmma::store_matrix_sync(shared_imag + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - out_real[offset + idx] = reinterpret_cast<__half2*>(shared_real)[shared_offset]; - out_imag[offset + idx] = reinterpret_cast<__half2*>(shared_imag)[shared_offset]; - } - } - - __syncthreads(); - } -} - - -__global__ void butterfly_cuda_kernel_16( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - __shared__ half x_shared[16 * 64]; - __shared__ half d_f_real[16 * 16]; - __shared__ half d_f_imag[16 * 16]; - __shared__ half twiddles_real_shared[16 * 64]; - __shared__ half twiddles_imag_shared[16 * 64]; - __shared__ half out_real_shared[16 * 64]; - __shared__ half out_imag_shared[16 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - - if(x_gate != NULL) - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - else - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - - if(threadIdx.x < 16 ){ - shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment a_frag_imag; - wmma::fragment b_frag; - wmma::fragment acc_frag_real; - wmma::fragment acc_frag_imag; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); - - - wmma::fill_fragment(acc_frag_imag, __float2half(0.0f)); - - - wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); - - - - for (int k = 0; k < acc_frag_real.num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real.x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag.x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real.x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag.x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real.x)[k])); - } - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } -} - - -std::vector butterfly_cuda( - torch::Tensor x, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional x_gate = std::nullopt) -{ - - uint B = x.size(0); - uint H = x.size(1); - // uint m = x.size(1); - - // const int TILE_SIZE = 16; - uint N = x.size(2); - uint M = x.size(3); - dim3 gridDim; - dim3 blockDim; - - gridDim.y = B; - gridDim.z = H; - - torch::Tensor out_real = torch::empty({B, H, N, M}, x.options()); - torch::Tensor out_imag = torch::empty({B, H, N, M}, x.options()); - - //set blockDims - switch(N){ - case 128: - blockDim.x = 32; - blockDim.y = 8; - break; - default: - blockDim.x = 32; - blockDim.y = 4; - break; - } - - //set gridDim.x - switch(N){ - case 128: - switch (M){ - case 16384: - gridDim.x = 128; - break; - case 8192: - gridDim.x = 64; - break; - case 4096: - gridDim.x = 32; - break; - default: - gridDim.x = 256; - break; - } - break; - default: - switch (M){ - case 16384: - gridDim.x = 256; - break; - case 8192: - gridDim.x = 128; - break; - case 4096: - gridDim.x = 64; - break; - default: - gridDim.x = 512; - break; - } - break; - } - - switch (N) - { - case 16: - butterfly_cuda_kernel_16<<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 32: - butterfly_cuda_kernel_32<<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - case 64: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_64<<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 128: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - default: - printf("Not yet implemented \n"); - break; - } - - return {out_real, out_imag}; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +__global__ void butterfly_cuda_kernel_64( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + extern __shared__ half x_shared[]; + half *d_f_real = &x_shared[N * N]; + half *d_f_imag = &d_f_real[N * N]; + half *twiddles_real_shared = &d_f_imag[N * N]; + half *twiddles_imag_shared = &twiddles_real_shared[N * N]; + half *out_real_shared = &twiddles_imag_shared[N * N]; + half *out_imag_shared = &out_real_shared[N * N]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + + d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); + d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); + } + + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment a_frag_imag[4]; + wmma::fragment b_frag[4][4]; + wmma::fragment acc_frag_real[4]; + wmma::fragment acc_frag_imag[4]; + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(a_frag_real[i], d_f_real + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(a_frag_imag[i], d_f_imag + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); + } + + for (int t = 0; t < 16; t++) + { + + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; + } + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); + + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + +#pragma unroll + + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); + + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); + } + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } + + __syncthreads(); + } +} + +__global__ void butterfly_cuda_kernel_32( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + __shared__ half x_shared[32 * 64]; + __shared__ half d_f_real[32 * 32]; + __shared__ half d_f_imag[32 * 32]; + __shared__ half twiddles_real_shared[32 * 64]; + __shared__ half twiddles_imag_shared[32 * 64]; + __shared__ half out_real_shared[32 * 64]; + __shared__ half out_imag_shared[32 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + if(x_gate == nullptr){ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; + }else{ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + } + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + + __syncthreads(); + + if (threadIdx.y < N / 16) + { + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment b_frag[2][2]; + wmma::fragment acc_frag_real[2][2]; + wmma::fragment acc_frag_imag[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); + + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_imag[i][j], __float2half(0.0f)); + + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k])); + } + + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } +} + +__global__ void butterfly_cuda_kernel_128( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 128 * 32 * gridDim.x * 2 + blockIdx.z * 16 * 128 * 32 * gridDim.x * 2 + blockIdx.x * 64 + threadIdx.x; + const int tw_offset = blockIdx.x * 64 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + extern __shared__ half shared_real[]; + half *shared_imag = &shared_real[128 * 128]; + + + wmma::fragment a_frag_real[8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment a_frag_imag[8]; + wmma::fragment b_frag[8][8]; + wmma::fragment acc_frag_real[8]; + wmma::fragment acc_frag_imag[8]; + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 4; j++){ + shared_offset = (threadIdx.y + i * B_Y) * 128 + threadIdx.x + j * blockDim.x; + shared_real[shared_offset] = d_f[shared_offset].real(); + shared_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); + } + + + __syncthreads(); + + + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(shared_real)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2*>(shared_imag)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); + } + + __syncthreads(); + + + for(int t=0; t< 16; t++){ + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + if(x_gate != nullptr){ + reinterpret_cast<__half2*>(shared_real)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__half2*>(shared_real)[shared_offset] = x[offset + idx]; + } + + } + } + + + __syncthreads(); + + + for (int i = 0; i < 8; i++) + { + for (int j = 0; j < 8; j++) + { + wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); + } + } + + __syncthreads(); + + #pragma unroll + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); + + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + + #pragma unroll + + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); + + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + + __half2 tmp_real, tmp_imag; + #pragma unroll + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); + } + + wmma::store_matrix_sync(shared_real + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); + wmma::store_matrix_sync(shared_imag + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + out_real[offset + idx] = reinterpret_cast<__half2*>(shared_real)[shared_offset]; + out_imag[offset + idx] = reinterpret_cast<__half2*>(shared_imag)[shared_offset]; + } + } + + __syncthreads(); + } +} + + +__global__ void butterfly_cuda_kernel_16( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + __shared__ half x_shared[16 * 64]; + __shared__ half d_f_real[16 * 16]; + __shared__ half d_f_imag[16 * 16]; + __shared__ half twiddles_real_shared[16 * 64]; + __shared__ half twiddles_imag_shared[16 * 64]; + __shared__ half out_real_shared[16 * 64]; + __shared__ half out_imag_shared[16 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + + if(x_gate != NULL) + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + else + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = x[idx + offset]; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + + if(threadIdx.x < 16 ){ + shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment a_frag_imag; + wmma::fragment b_frag; + wmma::fragment acc_frag_real; + wmma::fragment acc_frag_imag; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); + + + wmma::fill_fragment(acc_frag_imag, __float2half(0.0f)); + + + wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); + + + + for (int k = 0; k < acc_frag_real.num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real.x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag.x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real.x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag.x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real.x)[k])); + } + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + out_imag[idx] = reinterpret_cast<__half2 *>(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } +} + + +std::vector butterfly_cuda( + torch::Tensor x, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional x_gate = std::nullopt) +{ + + uint B = x.size(0); + uint H = x.size(1); + // uint m = x.size(1); + + // const int TILE_SIZE = 16; + uint N = x.size(2); + uint M = x.size(3); + dim3 gridDim; + dim3 blockDim; + + gridDim.y = B; + gridDim.z = H; + + torch::Tensor out_real = torch::empty({B, H, N, M}, x.options()); + torch::Tensor out_imag = torch::empty({B, H, N, M}, x.options()); + + //set blockDims + switch(N){ + case 128: + blockDim.x = 32; + blockDim.y = 8; + break; + default: + blockDim.x = 32; + blockDim.y = 4; + break; + } + + //set gridDim.x + switch(N){ + case 128: + switch (M){ + case 16384: + gridDim.x = 128; + break; + case 8192: + gridDim.x = 64; + break; + case 4096: + gridDim.x = 32; + break; + default: + gridDim.x = 256; + break; + } + break; + default: + switch (M){ + case 16384: + gridDim.x = 256; + break; + case 8192: + gridDim.x = 128; + break; + case 4096: + gridDim.x = 64; + break; + default: + gridDim.x = 512; + break; + } + break; + } + + switch (N) + { + case 16: + butterfly_cuda_kernel_16<<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 32: + butterfly_cuda_kernel_32<<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + case 64: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_64<<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 128: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + default: + printf("Not yet implemented \n"); + break; + } + + return {out_real, out_imag}; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda_bf16.cu index c4f34d7d28216f8cd88a4369c0d05dc1bbf8c5ca..1d895b987c146d422160bb83b0de3ea22d2c1388 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_cuda_bf16.cu @@ -1,725 +1,725 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -__global__ void butterfly_cuda_kernel_64( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - extern __shared__ __nv_bfloat16 x_shared[]; - __nv_bfloat16 *d_f_real_shared = &x_shared[N * N]; - __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; - __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; - __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; - float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); - float *out_imag_shared = &out_real_shared[N * N]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; - } - - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment a_frag_imag[4]; - wmma::fragment b_frag[4][4]; - wmma::fragment acc_frag_real[4]; - wmma::fragment acc_frag_imag[4]; - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(a_frag_real[i], d_f_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(a_frag_imag[i], d_f_imag_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); - } - - for (int t = 0; t < 16; t++) - { - - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; - } - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_real[j], 0.0f); - - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - -#pragma unroll - - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_imag[j], 0.0f); - - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; - - reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); - reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); - } - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } - - __syncthreads(); - } -} - -__global__ void butterfly_cuda_kernel_32( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - __shared__ __nv_bfloat16 x_shared[32 * 64]; - __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; - __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; - __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; - __shared__ float out_real_shared[32 * 64]; - __shared__ float out_imag_shared[32 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; - } - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - d_f_real_shared[shared_offset] = d_f_real[shared_offset]; - d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; - } - - __syncthreads(); - - if (threadIdx.y < N / 16) - { - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment b_frag[2][2]; - wmma::fragment acc_frag_real[2][2]; - wmma::fragment acc_frag_imag[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], 0.0f); - - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_imag[i][j], 0.0f); - - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[i][j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[i][j].x)[k]; - reinterpret_cast(acc_frag_real[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]); - reinterpret_cast(acc_frag_imag[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]); - } - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } -} - -__global__ void butterfly_cuda_kernel_128( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; - const int tw_offset = blockIdx.x * 64 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - extern __shared__ __nv_bfloat16 shared_real[]; - __nv_bfloat16 *shared_imag = &shared_real[128 * 128]; - - - wmma::fragment a_frag_real[8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment a_frag_imag[8]; - wmma::fragment b_frag[8][8]; - wmma::fragment acc_frag_real[8]; - wmma::fragment acc_frag_imag[8]; - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162 *>(shared_real)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(shared_imag)[shared_offset] = d_f_imag[shared_offset]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); - } - - - __syncthreads(); - - - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162*>(shared_imag)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); - } - - __syncthreads(); - - - for(int t=0; t< 16; t++){ - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = x[offset + idx]; - } - } - } - - - __syncthreads(); - - - for (int i = 0; i < 8; i++) - { - for (int j = 0; j < 8; j++) - { - wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); - } - } - - __syncthreads(); - - #pragma unroll - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_real[j], 0.0f); - - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - - #pragma unroll - - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_imag[j], 0.0f); - - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - - float2 tmp_real, tmp_imag; - #pragma unroll - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; - - reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); - reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); - } - } - - for (int j = 0; j < 8; j++) - { - wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - out_real[offset + idx] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); - } - } - - __syncthreads(); - - - for (int j = 0; j < 8; j++) - { - wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - out_imag[offset + idx] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); - } - } - } -} - - -__global__ void butterfly_cuda_kernel_16( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - - __shared__ __nv_bfloat16 x_shared[16 * 64]; - __shared__ __nv_bfloat16 d_f_real_shared[16 * 16]; - __shared__ __nv_bfloat16 d_f_imag_shared[16 * 16]; - __shared__ __nv_bfloat16 twiddles_real_shared[16 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[16 * 64]; - __shared__ float out_real_shared[16 * 64]; - __shared__ float out_imag_shared[16 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; - } - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - if(threadIdx.x < 16 ){ - shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; - d_f_real_shared[shared_offset] = d_f_real[shared_offset]; - d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; - } - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment a_frag_imag; - wmma::fragment b_frag; - wmma::fragment acc_frag_real; - wmma::fragment acc_frag_imag; - - wmma::load_matrix_sync(a_frag_real, d_f_real_shared, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag_shared, N); - wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - - wmma::fill_fragment(acc_frag_real, 0.0f); - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); - - - - wmma::fill_fragment(acc_frag_imag, 0.0f); - - - wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); - - -#pragma unroll - for (int k = 0; k < acc_frag_real.num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real.x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag.x)[k]; - reinterpret_cast(acc_frag_real.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]); - reinterpret_cast(acc_frag_imag.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]); - } - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); - - } - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } -} - -std::vector butterfly_bf16_cuda( - torch::Tensor x, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional x_gate = std::nullopt - ) -{ - - uint B = x.size(0); - uint H = x.size(1); - // uint m = x.size(1); - - // const int TILE_SIZE = 16; - uint N = x.size(2); - uint M = x.size(3); - dim3 gridDim; - dim3 blockDim; - - gridDim.y = B; - gridDim.z = H; - - torch::Tensor out_real = torch::empty({B, H, N, M}, x.options()); - torch::Tensor out_imag = torch::empty({B, H, N, M}, x.options()); - - //set blockDims - switch(N){ - case 128: - blockDim.x = 32; - blockDim.y = 8; - break; - default: - blockDim.x = 32; - blockDim.y = 4; - break; - } - - //set gridDim.x - switch(N){ - case 128: - switch (M){ - case 16384: - gridDim.x = 128; - break; - case 8192: - gridDim.x = 64; - break; - case 4096: - gridDim.x = 32; - break; - default: - gridDim.x = 256; - break; - } - break; - default: - switch (M){ - case 16384: - gridDim.x = 256; - break; - case 8192: - gridDim.x = 128; - break; - case 4096: - gridDim.x = 64; - break; - default: - gridDim.x = 512; - break; - } - break; - } - - switch (N) - { - case 16: - butterfly_cuda_kernel_16<<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 32: - butterfly_cuda_kernel_32<<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - case 64: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - - butterfly_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 128: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - default: - printf("Not yet implemented \n"); - break; - } - - return {out_real, out_imag}; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +__global__ void butterfly_cuda_kernel_64( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + extern __shared__ __nv_bfloat16 x_shared[]; + __nv_bfloat16 *d_f_real_shared = &x_shared[N * N]; + __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; + __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; + __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; + float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); + float *out_imag_shared = &out_real_shared[N * N]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; + } + + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment a_frag_imag[4]; + wmma::fragment b_frag[4][4]; + wmma::fragment acc_frag_real[4]; + wmma::fragment acc_frag_imag[4]; + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(a_frag_real[i], d_f_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(a_frag_imag[i], d_f_imag_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); + } + + for (int t = 0; t < 16; t++) + { + + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; + } + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_real[j], 0.0f); + + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + +#pragma unroll + + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_imag[j], 0.0f); + + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; + + reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); + reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); + } + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } + + __syncthreads(); + } +} + +__global__ void butterfly_cuda_kernel_32( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + __shared__ __nv_bfloat16 x_shared[32 * 64]; + __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; + __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; + __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; + __shared__ float out_real_shared[32 * 64]; + __shared__ float out_imag_shared[32 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; + } + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + d_f_real_shared[shared_offset] = d_f_real[shared_offset]; + d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; + } + + __syncthreads(); + + if (threadIdx.y < N / 16) + { + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment b_frag[2][2]; + wmma::fragment acc_frag_real[2][2]; + wmma::fragment acc_frag_imag[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], 0.0f); + + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_imag[i][j], 0.0f); + + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[i][j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[i][j].x)[k]; + reinterpret_cast(acc_frag_real[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]); + reinterpret_cast(acc_frag_imag[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]); + } + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } +} + +__global__ void butterfly_cuda_kernel_128( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; + const int tw_offset = blockIdx.x * 64 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + extern __shared__ __nv_bfloat16 shared_real[]; + __nv_bfloat16 *shared_imag = &shared_real[128 * 128]; + + + wmma::fragment a_frag_real[8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment a_frag_imag[8]; + wmma::fragment b_frag[8][8]; + wmma::fragment acc_frag_real[8]; + wmma::fragment acc_frag_imag[8]; + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162 *>(shared_real)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(shared_imag)[shared_offset] = d_f_imag[shared_offset]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); + } + + + __syncthreads(); + + + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162*>(shared_imag)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); + } + + __syncthreads(); + + + for(int t=0; t< 16; t++){ + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = x[offset + idx]; + } + } + } + + + __syncthreads(); + + + for (int i = 0; i < 8; i++) + { + for (int j = 0; j < 8; j++) + { + wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); + } + } + + __syncthreads(); + + #pragma unroll + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_real[j], 0.0f); + + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + + #pragma unroll + + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_imag[j], 0.0f); + + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + + float2 tmp_real, tmp_imag; + #pragma unroll + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; + + reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); + reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); + } + } + + for (int j = 0; j < 8; j++) + { + wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + out_real[offset + idx] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); + } + } + + __syncthreads(); + + + for (int j = 0; j < 8; j++) + { + wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + out_imag[offset + idx] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); + } + } + } +} + + +__global__ void butterfly_cuda_kernel_16( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + + __shared__ __nv_bfloat16 x_shared[16 * 64]; + __shared__ __nv_bfloat16 d_f_real_shared[16 * 16]; + __shared__ __nv_bfloat16 d_f_imag_shared[16 * 16]; + __shared__ __nv_bfloat16 twiddles_real_shared[16 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[16 * 64]; + __shared__ float out_real_shared[16 * 64]; + __shared__ float out_imag_shared[16 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = __hmul2(x[idx + offset], x_gate[idx + offset]); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = x[idx + offset]; + } + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + if(threadIdx.x < 16 ){ + shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; + d_f_real_shared[shared_offset] = d_f_real[shared_offset]; + d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; + } + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment a_frag_imag; + wmma::fragment b_frag; + wmma::fragment acc_frag_real; + wmma::fragment acc_frag_imag; + + wmma::load_matrix_sync(a_frag_real, d_f_real_shared, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag_shared, N); + wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + + wmma::fill_fragment(acc_frag_real, 0.0f); + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); + + + + wmma::fill_fragment(acc_frag_imag, 0.0f); + + + wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); + + +#pragma unroll + for (int k = 0; k < acc_frag_real.num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real.x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag.x)[k]; + reinterpret_cast(acc_frag_real.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]); + reinterpret_cast(acc_frag_imag.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]); + } + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); + + } + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + out_imag[idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } +} + +std::vector butterfly_bf16_cuda( + torch::Tensor x, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional x_gate = std::nullopt + ) +{ + + uint B = x.size(0); + uint H = x.size(1); + // uint m = x.size(1); + + // const int TILE_SIZE = 16; + uint N = x.size(2); + uint M = x.size(3); + dim3 gridDim; + dim3 blockDim; + + gridDim.y = B; + gridDim.z = H; + + torch::Tensor out_real = torch::empty({B, H, N, M}, x.options()); + torch::Tensor out_imag = torch::empty({B, H, N, M}, x.options()); + + //set blockDims + switch(N){ + case 128: + blockDim.x = 32; + blockDim.y = 8; + break; + default: + blockDim.x = 32; + blockDim.y = 4; + break; + } + + //set gridDim.x + switch(N){ + case 128: + switch (M){ + case 16384: + gridDim.x = 128; + break; + case 8192: + gridDim.x = 64; + break; + case 4096: + gridDim.x = 32; + break; + default: + gridDim.x = 256; + break; + } + break; + default: + switch (M){ + case 16384: + gridDim.x = 256; + break; + case 8192: + gridDim.x = 128; + break; + case 4096: + gridDim.x = 64; + break; + default: + gridDim.x = 512; + break; + } + break; + } + + switch (N) + { + case 16: + butterfly_cuda_kernel_16<<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 32: + butterfly_cuda_kernel_32<<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + case 64: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + + butterfly_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 128: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + default: + printf("Not yet implemented \n"); + break; + } + + return {out_real, out_imag}; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda.cu index 55b6c8915eb942eefc225c41723f829a629bf7bd..2a1eb3c0ea109a30c859c91efcb1706cbe39fcf0 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda.cu @@ -1,723 +1,723 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -__global__ void butterfly_ifft_cuda_kernel_64( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - extern __shared__ half x_real_shared[]; - half *x_imag_shared = &x_real_shared[N * N]; - half *d_f_real = &x_imag_shared[N * N]; - half *d_f_imag = &d_f_real[N * N]; - half *twiddles_real_shared = &d_f_imag[N * N]; - half *twiddles_imag_shared = &twiddles_real_shared[N * N]; - half *out_real_shared = &twiddles_imag_shared[N * N]; - - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4][4]; - wmma::fragment a_frag_imag[4][4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment b_frag_real[4]; - wmma::fragment b_frag_imag[4]; - wmma::fragment acc_frag_real[4]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - - d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); - d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - } - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int t = 0; t < 16; t++) - { - - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < 4; i++) - { - wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); - -// bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); - } - } - - for (int i = 0; i < 4; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < 4; i++) - { - wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); - } - else{ - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } - } - - __syncthreads(); - } -} - -__global__ void butterfly_ifft_cuda_kernel_32( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - __shared__ half x_real_shared[32 * 64]; - __shared__ half x_imag_shared[32 * 64]; - __shared__ half d_f_real[32 * 32]; - __shared__ half d_f_imag[32 * 32]; - __shared__ half twiddles_real_shared[32 * 64]; - __shared__ half twiddles_imag_shared[32 * 64]; - __shared__ half out_real_shared[32 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - - __syncthreads(); - - if (threadIdx.y < N / 16) - { - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment b_frag_real[2][2]; - wmma::fragment b_frag_imag[2][2]; - wmma::fragment acc_frag_real[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); - b_frag_real[i][j].x[k] = tmp_real; - b_frag_imag[i][j].x[k] = tmp_imag; - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); - - // bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); - } - - for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) - { - acc_frag_real[i][j].x[k] = __hneg(acc_frag_real[i][j].x[k]); - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - // ac - bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); - } - else{ - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } - } -} - - -__global__ void butterfly_ifft_cuda_kernel_128( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; - const int tw_offset = blockIdx.x * 64 + threadIdx.x; - int idx; - int shared_offset; - - const int B_Y = 8; - const int n = 16; - - extern __shared__ half real_shared[]; - half *imag_shared = &real_shared[128 * 128]; - half *real_shared_2 = &imag_shared[128 * 128]; - half *imag_shared_2 = &real_shared_2[128 * 128]; - - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag[8][8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment b_frag_real[8]; - wmma::fragment b_frag_imag[8]; - wmma::fragment acc_frag_real[8]; - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 4; j++){ - shared_offset = (threadIdx.y + i * B_Y) * 128 + threadIdx.x + j * blockDim.x; - real_shared_2[shared_offset] = d_f[shared_offset].real(); - imag_shared_2[shared_offset] = d_f[shared_offset].imag(); - } - } - - - __syncthreads(); - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2*>(imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); - } - - __syncthreads(); - - for (int t = 0; t < 16; t++) - { - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(real_shared)[shared_offset] = x_real[offset + idx]; - reinterpret_cast<__half2*>(imag_shared)[shared_offset] = x_imag[offset + idx]; - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { - wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements/2; k++) - { - tmp_real = __hsub2(__hmul2(reinterpret_cast<__half2*>(tw_frag_real[j].x)[k], reinterpret_cast<__half2*>(b_frag_real[j].x)[k]), - __hmul2(reinterpret_cast<__half2*>(tw_frag_imag[j].x)[k], reinterpret_cast<__half2*>(b_frag_imag[j].x)[k])); - tmp_imag = __hadd2(__hmul2(reinterpret_cast<__half2*>(tw_frag_real[j].x)[k], reinterpret_cast<__half2*>(b_frag_imag[j].x)[k]), - __hmul2(reinterpret_cast<__half2*>(tw_frag_imag[j].x)[k], reinterpret_cast<__half2*>(b_frag_real[j].x)[k])); - reinterpret_cast<__half2*>(b_frag_real[j].x)[k] = tmp_real; - reinterpret_cast<__half2*>(b_frag_imag[j].x)[k] = tmp_imag; - } - } - - for (int i = 0; i < 8; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { - wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); - -// bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); - } - } - - - for (int i = 0; i < 8; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < 8; i++) - { - wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - if(out_gate != nullptr){ - out_real[offset + idx] = __hmul2(reinterpret_cast<__half2*>(real_shared)[shared_offset], out_gate[offset + idx]); - } - else{ - out_real[offset + idx] = reinterpret_cast<__half2*>(real_shared)[shared_offset]; - } - } - } - - __syncthreads(); - } -} - -__global__ void butterfly_ifft_cuda_kernel_16( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - __shared__ half x_real_shared[16 * 64]; - __shared__ half x_imag_shared[16 * 64]; - __shared__ half d_f_real[16 * 16]; - __shared__ half d_f_imag[16 * 16]; - __shared__ half twiddles_real_shared[16 * 64]; - __shared__ half twiddles_imag_shared[16 * 64]; - __shared__ half out_real_shared[16 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - if(threadIdx.x < 16 ){ - shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - //check if it is better to have one warp do all the multiplication or split between warps - if (threadIdx.y < 4) - { - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment a_frag_imag; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment b_frag_real; - wmma::fragment b_frag_imag; - wmma::fragment acc_frag_real; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - - for (int k = 0; k < tw_frag_real.num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); - b_frag_real.x[k] = tmp_real; - b_frag_imag.x[k] = tmp_imag; - } - - - wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); - - wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); - - for(int k=0; k< acc_frag_real.num_elements; k++){ - acc_frag_real.x[k] = __hneg(acc_frag_real.x[k]); - } - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); - } - else{ - out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; - } - } -} - -torch::Tensor butterfly_ifft_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional out_gate = std::nullopt) -{ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // uint m = x.size(1); - - // const int TILE_SIZE = 16; - - dim3 gridDim; - dim3 blockDim; - - uint N = x_real.size(2); - uint M = x_real.size(3); - gridDim.y = B; - - blockDim.x = 32; - blockDim.y = 4; - - torch::Tensor out = torch::empty({B, H, N, M}, x_real.options()); - gridDim.z = H; - - //set blockDims - switch(N){ - case 128: - blockDim.x = 32; - blockDim.y = 8; - break; - default: - blockDim.x = 32; - blockDim.y = 4; - break; - } - - //set gridDim.x - switch(N){ - case 128: - switch (M){ - case 16384: - gridDim.x = 128; - break; - case 8192: - gridDim.x = 64; - break; - case 4096: - gridDim.x = 32; - break; - default: - gridDim.x = 256; - break; - } - break; - default: - switch (M){ - case 16384: - gridDim.x = 256; - break; - case 8192: - gridDim.x = 128; - break; - case 4096: - gridDim.x = 64; - break; - default: - gridDim.x = 512; - break; - } - break; - } - - switch (N) - { - case 16: - butterfly_ifft_cuda_kernel_16<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - case 32: - butterfly_ifft_cuda_kernel_32<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - case 64: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_ifft_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_cuda_kernel_64<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - - case 128: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_ifft_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536*2); - butterfly_ifft_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - default: - printf("Not implemented\n"); - } - - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +__global__ void butterfly_ifft_cuda_kernel_64( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + extern __shared__ half x_real_shared[]; + half *x_imag_shared = &x_real_shared[N * N]; + half *d_f_real = &x_imag_shared[N * N]; + half *d_f_imag = &d_f_real[N * N]; + half *twiddles_real_shared = &d_f_imag[N * N]; + half *twiddles_imag_shared = &twiddles_real_shared[N * N]; + half *out_real_shared = &twiddles_imag_shared[N * N]; + + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4][4]; + wmma::fragment a_frag_imag[4][4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment b_frag_real[4]; + wmma::fragment b_frag_imag[4]; + wmma::fragment acc_frag_real[4]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + + d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); + d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + } + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int t = 0; t < 16; t++) + { + + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < 4; i++) + { + wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); + +// bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); + } + } + + for (int i = 0; i < 4; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < 4; i++) + { + wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); + } + else{ + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } + } + + __syncthreads(); + } +} + +__global__ void butterfly_ifft_cuda_kernel_32( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + __shared__ half x_real_shared[32 * 64]; + __shared__ half x_imag_shared[32 * 64]; + __shared__ half d_f_real[32 * 32]; + __shared__ half d_f_imag[32 * 32]; + __shared__ half twiddles_real_shared[32 * 64]; + __shared__ half twiddles_imag_shared[32 * 64]; + __shared__ half out_real_shared[32 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + + __syncthreads(); + + if (threadIdx.y < N / 16) + { + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment b_frag_real[2][2]; + wmma::fragment b_frag_imag[2][2]; + wmma::fragment acc_frag_real[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); + b_frag_real[i][j].x[k] = tmp_real; + b_frag_imag[i][j].x[k] = tmp_imag; + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); + + // bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); + } + + for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) + { + acc_frag_real[i][j].x[k] = __hneg(acc_frag_real[i][j].x[k]); + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + // ac - bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); + } + else{ + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } + } +} + + +__global__ void butterfly_ifft_cuda_kernel_128( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; + const int tw_offset = blockIdx.x * 64 + threadIdx.x; + int idx; + int shared_offset; + + const int B_Y = 8; + const int n = 16; + + extern __shared__ half real_shared[]; + half *imag_shared = &real_shared[128 * 128]; + half *real_shared_2 = &imag_shared[128 * 128]; + half *imag_shared_2 = &real_shared_2[128 * 128]; + + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag[8][8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment b_frag_real[8]; + wmma::fragment b_frag_imag[8]; + wmma::fragment acc_frag_real[8]; + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 4; j++){ + shared_offset = (threadIdx.y + i * B_Y) * 128 + threadIdx.x + j * blockDim.x; + real_shared_2[shared_offset] = d_f[shared_offset].real(); + imag_shared_2[shared_offset] = d_f[shared_offset].imag(); + } + } + + + __syncthreads(); + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2*>(imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); + } + + __syncthreads(); + + for (int t = 0; t < 16; t++) + { + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(real_shared)[shared_offset] = x_real[offset + idx]; + reinterpret_cast<__half2*>(imag_shared)[shared_offset] = x_imag[offset + idx]; + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { + wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements/2; k++) + { + tmp_real = __hsub2(__hmul2(reinterpret_cast<__half2*>(tw_frag_real[j].x)[k], reinterpret_cast<__half2*>(b_frag_real[j].x)[k]), + __hmul2(reinterpret_cast<__half2*>(tw_frag_imag[j].x)[k], reinterpret_cast<__half2*>(b_frag_imag[j].x)[k])); + tmp_imag = __hadd2(__hmul2(reinterpret_cast<__half2*>(tw_frag_real[j].x)[k], reinterpret_cast<__half2*>(b_frag_imag[j].x)[k]), + __hmul2(reinterpret_cast<__half2*>(tw_frag_imag[j].x)[k], reinterpret_cast<__half2*>(b_frag_real[j].x)[k])); + reinterpret_cast<__half2*>(b_frag_real[j].x)[k] = tmp_real; + reinterpret_cast<__half2*>(b_frag_imag[j].x)[k] = tmp_imag; + } + } + + for (int i = 0; i < 8; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { + wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); + +// bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); + } + } + + + for (int i = 0; i < 8; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < 8; i++) + { + wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + if(out_gate != nullptr){ + out_real[offset + idx] = __hmul2(reinterpret_cast<__half2*>(real_shared)[shared_offset], out_gate[offset + idx]); + } + else{ + out_real[offset + idx] = reinterpret_cast<__half2*>(real_shared)[shared_offset]; + } + } + } + + __syncthreads(); + } +} + +__global__ void butterfly_ifft_cuda_kernel_16( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + __shared__ half x_real_shared[16 * 64]; + __shared__ half x_imag_shared[16 * 64]; + __shared__ half d_f_real[16 * 16]; + __shared__ half d_f_imag[16 * 16]; + __shared__ half twiddles_real_shared[16 * 64]; + __shared__ half twiddles_imag_shared[16 * 64]; + __shared__ half out_real_shared[16 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + if(threadIdx.x < 16 ){ + shared_offset = (threadIdx.y + i * B_Y) * 16 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + //check if it is better to have one warp do all the multiplication or split between warps + if (threadIdx.y < 4) + { + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment a_frag_imag; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment b_frag_real; + wmma::fragment b_frag_imag; + wmma::fragment acc_frag_real; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + + for (int k = 0; k < tw_frag_real.num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); + b_frag_real.x[k] = tmp_real; + b_frag_imag.x[k] = tmp_imag; + } + + + wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); + + wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); + + for(int k=0; k< acc_frag_real.num_elements; k++){ + acc_frag_real.x[k] = __hneg(acc_frag_real.x[k]); + } + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x], out_gate[idx]); + } + else{ + out_real[idx] = reinterpret_cast<__half2 *>(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]; + } + } +} + +torch::Tensor butterfly_ifft_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional out_gate = std::nullopt) +{ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // uint m = x.size(1); + + // const int TILE_SIZE = 16; + + dim3 gridDim; + dim3 blockDim; + + uint N = x_real.size(2); + uint M = x_real.size(3); + gridDim.y = B; + + blockDim.x = 32; + blockDim.y = 4; + + torch::Tensor out = torch::empty({B, H, N, M}, x_real.options()); + gridDim.z = H; + + //set blockDims + switch(N){ + case 128: + blockDim.x = 32; + blockDim.y = 8; + break; + default: + blockDim.x = 32; + blockDim.y = 4; + break; + } + + //set gridDim.x + switch(N){ + case 128: + switch (M){ + case 16384: + gridDim.x = 128; + break; + case 8192: + gridDim.x = 64; + break; + case 4096: + gridDim.x = 32; + break; + default: + gridDim.x = 256; + break; + } + break; + default: + switch (M){ + case 16384: + gridDim.x = 256; + break; + case 8192: + gridDim.x = 128; + break; + case 4096: + gridDim.x = 64; + break; + default: + gridDim.x = 512; + break; + } + break; + } + + switch (N) + { + case 16: + butterfly_ifft_cuda_kernel_16<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + case 32: + butterfly_ifft_cuda_kernel_32<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + case 64: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_ifft_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_cuda_kernel_64<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + + case 128: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_ifft_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536*2); + butterfly_ifft_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + default: + printf("Not implemented\n"); + } + + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda_bf16.cu index b0902f97d2e11d5c215e178246f18e5cfaf7701e..3724cd1ff01c22d6961baf0ab3f56bd20609be37 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_ifft_cuda_bf16.cu @@ -1,705 +1,705 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -__global__ void butterfly_ifft_bf16_cuda_kernel_64( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - extern __shared__ __nv_bfloat16 x_real_shared[]; - __nv_bfloat16 *x_imag_shared = &x_real_shared[N * N]; - __nv_bfloat16 *d_f_real_shared = &x_imag_shared[N * N]; - __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; - __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; - __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; - float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); - - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4][4]; - wmma::fragment a_frag_imag[4][4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment b_frag_real[4]; - wmma::fragment b_frag_imag[4]; - wmma::fragment acc_frag_real[4]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - - // #pragma unroll - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); - } - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int t = 0; t < 16; t++) - { - - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < 4; i++) - { - wmma::fill_fragment(acc_frag_real[i], 0.0f); - -// bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; - } - } - - for (int i = 0; i < 4; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < 4; i++) - { - wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); ; - }else{ - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } - } - - __syncthreads(); - } -} - -__global__ void butterfly_ifft_bf16_cuda_kernel_32( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - __shared__ __nv_bfloat16 x_real_shared[32 * 64]; - __shared__ __nv_bfloat16 x_imag_shared[32 * 64]; - __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; - __shared__ float out_real_shared[32 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - - __syncthreads(); - - if (threadIdx.y < N / 16) - { - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment b_frag_real[2][2]; - wmma::fragment b_frag_imag[2][2]; - wmma::fragment acc_frag_real[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); - b_frag_real[i][j].x[k] = tmp_real; - b_frag_imag[i][j].x[k] = tmp_imag; - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], 0.0f); - - // bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); - } - - for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) - { - acc_frag_real[i][j].x[k] = - acc_frag_real[i][j].x[k]; - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - // ac - bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); - }else{ - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } - } -} - - -__global__ void butterfly_ifft_bf16_cuda_kernel_128( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; - const int tw_offset = blockIdx.x * 64 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - extern __shared__ __nv_bfloat16 real_shared[]; - __nv_bfloat16 *imag_shared = &real_shared[128 * 128]; - __nv_bfloat16 *real_shared_2 = &imag_shared[128 * 128]; - __nv_bfloat16 *imag_shared_2 = &real_shared_2[128 * 128]; - - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag[8][8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment b_frag_real[8]; - wmma::fragment b_frag_imag[8]; - wmma::fragment acc_frag_real[8]; - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared_2)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162*>(imag_shared_2)[shared_offset] = d_f_imag[shared_offset]; - } - } - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); - } - - __syncthreads(); - - for (int t = 0; t < 16; t++) - { - for (int i = 0; i < 8; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = x_real[offset + idx]; - reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = x_imag[offset + idx]; - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { - wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < 8; i++) - { - wmma::fill_fragment(acc_frag_real[i], 0.0f); - -// bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; - } - } - - for (int i = 0; i < 8; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = 0; i < 8; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < 8; i++) - { - //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - wmma::store_matrix_sync(reinterpret_cast(real_shared) + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - for(int j=0; j< 2; j++){ - idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; - if(out_gate != nullptr){ - out_real[offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]), out_gate[offset + idx]); - }else{ - out_real[offset + idx] = __float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]); - } - } - } - - __syncthreads(); - } -} - -__global__ void butterfly_ifft_bf16_cuda_kernel_16( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int N) -{ - const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - const int tw_offset = blockIdx.x * 32 + threadIdx.x; - int idx; - int shared_offset; - const int B_Y = blockDim.y; - const int n = N / B_Y; - - __shared__ __nv_bfloat16 x_real_shared[16 * 64]; - __shared__ __nv_bfloat16 x_imag_shared[16 * 64]; - __shared__ __nv_bfloat16 twiddles_real_shared[16 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[16 * 64]; - __shared__ float out_real_shared[16 * 64]; - - // #pragma unroll - for (int i = 0; i < n; i++) - { - idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; - shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment a_frag_imag; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment b_frag_real; - wmma::fragment b_frag_imag; - wmma::fragment acc_frag_real; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - for (int k = 0; k < tw_frag_real.num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); - b_frag_real.x[k] = tmp_real; - b_frag_imag.x[k] = tmp_imag; - } - - - - wmma::fill_fragment(acc_frag_real, 0.0f); - - wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); - - for(int k=0; k< acc_frag_real.num_elements; k++){ - acc_frag_real.x[k] = - acc_frag_real.x[k]; - } - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < n; i++) - { - idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; - if(out_gate != nullptr){ - out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); - }else{ - out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); - } - } -} - - -torch::Tensor butterfly_ifft_bf16_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - std::optional out_gate = std::nullopt - ) -{ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // uint m = x.size(1); - - // const int TILE_SIZE = 16; - - dim3 gridDim; - dim3 blockDim; - - uint N = x_real.size(2); - uint M = x_real.size(3); - gridDim.y = B; - - blockDim.x = 32; - blockDim.y = 4; - - torch::Tensor out = torch::empty({B, H, N, M}, x_real.options()); - - - //set blockDims - switch(N){ - case 128: - blockDim.x = 32; - blockDim.y = 8; - break; - default: - blockDim.x = 32; - blockDim.y = 4; - break; - } - - //set gridDim.x - switch(N){ - case 128: - switch (M){ - case 16384: - gridDim.x = 128; - break; - case 8192: - gridDim.x = 64; - break; - case 4096: - gridDim.x = 32; - break; - default: - gridDim.x = 256; - break; - } - break; - default: - switch (M){ - case 16384: - gridDim.x = 256; - break; - case 8192: - gridDim.x = 128; - break; - case 4096: - gridDim.x = 64; - break; - default: - gridDim.x = 512; - break; - } - break; - } - - - switch (N) - { - case 16: - gridDim.z = H; - butterfly_ifft_bf16_cuda_kernel_16<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - - case 32: - gridDim.z = H; - butterfly_ifft_bf16_cuda_kernel_32<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - case 64: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_ifft_bf16_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - butterfly_ifft_bf16_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - - case 128: - gridDim.z = H / 16; - cudaFuncSetAttribute(&butterfly_ifft_bf16_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - butterfly_ifft_bf16_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - break; - default: - printf("Not implemented\n"); - } - - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +__global__ void butterfly_ifft_bf16_cuda_kernel_64( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + extern __shared__ __nv_bfloat16 x_real_shared[]; + __nv_bfloat16 *x_imag_shared = &x_real_shared[N * N]; + __nv_bfloat16 *d_f_real_shared = &x_imag_shared[N * N]; + __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; + __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; + __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; + float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); + + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4][4]; + wmma::fragment a_frag_imag[4][4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment b_frag_real[4]; + wmma::fragment b_frag_imag[4]; + wmma::fragment acc_frag_real[4]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + + // #pragma unroll + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); + } + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int t = 0; t < 16; t++) + { + + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < 4; i++) + { + wmma::fill_fragment(acc_frag_real[i], 0.0f); + +// bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; + } + } + + for (int i = 0; i < 4; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < 4; i++) + { + wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x + t * 64 * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); ; + }else{ + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } + } + + __syncthreads(); + } +} + +__global__ void butterfly_ifft_bf16_cuda_kernel_32( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + __shared__ __nv_bfloat16 x_real_shared[32 * 64]; + __shared__ __nv_bfloat16 x_imag_shared[32 * 64]; + __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; + __shared__ float out_real_shared[32 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + + __syncthreads(); + + if (threadIdx.y < N / 16) + { + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment b_frag_real[2][2]; + wmma::fragment b_frag_imag[2][2]; + wmma::fragment acc_frag_real[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); + b_frag_real[i][j].x[k] = tmp_real; + b_frag_imag[i][j].x[k] = tmp_imag; + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], 0.0f); + + // bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); + } + + for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) + { + acc_frag_real[i][j].x[k] = - acc_frag_real[i][j].x[k]; + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + // ac - bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); + }else{ + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } + } +} + + +__global__ void butterfly_ifft_bf16_cuda_kernel_128( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x + blockIdx.x * 64 + threadIdx.x; + const int tw_offset = blockIdx.x * 64 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + extern __shared__ __nv_bfloat16 real_shared[]; + __nv_bfloat16 *imag_shared = &real_shared[128 * 128]; + __nv_bfloat16 *real_shared_2 = &imag_shared[128 * 128]; + __nv_bfloat16 *imag_shared_2 = &real_shared_2[128 * 128]; + + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag[8][8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment b_frag_real[8]; + wmma::fragment b_frag_imag[8]; + wmma::fragment acc_frag_real[8]; + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared_2)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162*>(imag_shared_2)[shared_offset] = d_f_imag[shared_offset]; + } + } + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); + } + + __syncthreads(); + + for (int t = 0; t < 16; t++) + { + for (int i = 0; i < 8; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = x_real[offset + idx]; + reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = x_imag[offset + idx]; + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { + wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < 8; i++) + { + wmma::fill_fragment(acc_frag_real[i], 0.0f); + +// bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; + } + } + + for (int i = 0; i < 8; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = 0; i < 8; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < 8; i++) + { + //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + wmma::store_matrix_sync(reinterpret_cast(real_shared) + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + for(int j=0; j< 2; j++){ + idx = (threadIdx.y + i * B_Y) * 32 * 2 * gridDim.x + j * blockDim.x + t * 128 * 32 * 2 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 64 + threadIdx.x + j * blockDim.x; + if(out_gate != nullptr){ + out_real[offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]), out_gate[offset + idx]); + }else{ + out_real[offset + idx] = __float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]); + } + } + } + + __syncthreads(); + } +} + +__global__ void butterfly_ifft_bf16_cuda_kernel_16( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int N) +{ + const int offset = blockIdx.y * H * 16 * 32 * gridDim.x + blockIdx.z * 16 * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + const int tw_offset = blockIdx.x * 32 + threadIdx.x; + int idx; + int shared_offset; + const int B_Y = blockDim.y; + const int n = N / B_Y; + + __shared__ __nv_bfloat16 x_real_shared[16 * 64]; + __shared__ __nv_bfloat16 x_imag_shared[16 * 64]; + __shared__ __nv_bfloat16 twiddles_real_shared[16 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[16 * 64]; + __shared__ float out_real_shared[16 * 64]; + + // #pragma unroll + for (int i = 0; i < n; i++) + { + idx = (threadIdx.y + i * B_Y) * 32 * gridDim.x; + shared_offset = (threadIdx.y + i * B_Y) * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[tw_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[tw_offset + idx]; + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment a_frag_imag; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment b_frag_real; + wmma::fragment b_frag_imag; + wmma::fragment acc_frag_real; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + for (int k = 0; k < tw_frag_real.num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); + b_frag_real.x[k] = tmp_real; + b_frag_imag.x[k] = tmp_imag; + } + + + + wmma::fill_fragment(acc_frag_real, 0.0f); + + wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); + + for(int k=0; k< acc_frag_real.num_elements; k++){ + acc_frag_real.x[k] = - acc_frag_real.x[k]; + } + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < n; i++) + { + idx = offset + (threadIdx.y + i * B_Y) * 32 * gridDim.x; + if(out_gate != nullptr){ + out_real[idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]), out_gate[idx]); + }else{ + out_real[idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[(threadIdx.y + i * B_Y) * 32 + threadIdx.x]); + } + } +} + + +torch::Tensor butterfly_ifft_bf16_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + std::optional out_gate = std::nullopt + ) +{ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // uint m = x.size(1); + + // const int TILE_SIZE = 16; + + dim3 gridDim; + dim3 blockDim; + + uint N = x_real.size(2); + uint M = x_real.size(3); + gridDim.y = B; + + blockDim.x = 32; + blockDim.y = 4; + + torch::Tensor out = torch::empty({B, H, N, M}, x_real.options()); + + + //set blockDims + switch(N){ + case 128: + blockDim.x = 32; + blockDim.y = 8; + break; + default: + blockDim.x = 32; + blockDim.y = 4; + break; + } + + //set gridDim.x + switch(N){ + case 128: + switch (M){ + case 16384: + gridDim.x = 128; + break; + case 8192: + gridDim.x = 64; + break; + case 4096: + gridDim.x = 32; + break; + default: + gridDim.x = 256; + break; + } + break; + default: + switch (M){ + case 16384: + gridDim.x = 256; + break; + case 8192: + gridDim.x = 128; + break; + case 4096: + gridDim.x = 64; + break; + default: + gridDim.x = 512; + break; + } + break; + } + + + switch (N) + { + case 16: + gridDim.z = H; + butterfly_ifft_bf16_cuda_kernel_16<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + + case 32: + gridDim.z = H; + butterfly_ifft_bf16_cuda_kernel_32<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + case 64: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_ifft_bf16_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + butterfly_ifft_bf16_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + + case 128: + gridDim.z = H / 16; + cudaFuncSetAttribute(&butterfly_ifft_bf16_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + butterfly_ifft_bf16_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + break; + default: + printf("Not implemented\n"); + } + + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda.cu index b0a9db052c38c3059cefc75fce417882345269ca..d278efce954da2d32cfaf356aa0f5917c02b3250 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda.cu @@ -1,871 +1,871 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -template -__global__ void butterfly_padded_cuda_kernel_64( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; - const int out_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - const int N = 64; - - extern __shared__ half x_shared[]; - half *d_f_real = &x_shared[K * 16 * N]; - half *d_f_imag = &d_f_real[N * N]; - half *twiddles_real_shared = &d_f_imag[N * N]; - half *twiddles_imag_shared = &twiddles_real_shared[N * N]; - half *out_real_shared = &twiddles_imag_shared[N * N]; - half *out_imag_shared = &out_real_shared[N * N]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 64 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - - d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); - d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); - } - - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment a_frag_imag[4]; - wmma::fragment b_frag[K][4]; - wmma::fragment acc_frag_real[4]; - wmma::fragment acc_frag_imag[4]; - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(a_frag_real[i], d_f_real + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(a_frag_imag[i], d_f_imag + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); - } - - for (int t = 0; t < 16; t++) - { - t_offset = t * M/2; - out_t_offset = t * 64 * 32 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - if(i < K * 16){ - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2half2_rn(0.0f, 0.0f); - } - else{ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2half2_rn(0.0f, 0.0f); - } - } - } - - __syncthreads(); - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - -#pragma unroll - - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); - } - } - - for (int j = 0; j < 4; j++) - { - wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - - out_real[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; - out_imag[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_imag_shared)[shared_offset]; - } - - __syncthreads(); - - } -} - - -template -__global__ void butterfly_padded_cuda_kernel_128( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; - const int out_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x; - const int N = 128; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - - extern __shared__ half shared_real[]; - half *shared_imag = &shared_real[128 * 128]; - - - wmma::fragment a_frag_real[8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment a_frag_imag[8]; - wmma::fragment b_frag[K][8]; - wmma::fragment acc_frag_real[8]; - wmma::fragment acc_frag_imag[8]; - - for (int i = threadIdx.y ; i < N; i+=blockDim.y) - { - for(int j=0; j< 4; j++){ - shared_offset = i * 128 + threadIdx.x + j * blockDim.x; - shared_real[shared_offset] = d_f[shared_offset].real(); - shared_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); - } - - - __syncthreads(); - - - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(shared_real)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2*>(shared_imag)[shared_offset] = twiddle_factors_imag[idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); - } - - __syncthreads(); - - - for(int t=0; t< 16; t++){ - t_offset = t * M/2; - out_t_offset = t * 128 * 32 * 2 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - if(i < K * 16){ - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - if(x_gate != nullptr){ - reinterpret_cast<__half2*>(shared_real)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2half2_rn(0.0f, 0.0f); - } - else{ - reinterpret_cast<__half2*>(shared_real)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2half2_rn(0.0f, 0.0f); - } - } - } - } - - - __syncthreads(); - - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 8; j++) - { - wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); - } - } - - __syncthreads(); - - #pragma unroll - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - - #pragma unroll - - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - - __half2 tmp_real, tmp_imag; - #pragma unroll - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); - } - } - - for (int j = 0; j < 8; j++) - { - wmma::store_matrix_sync(shared_real + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); - wmma::store_matrix_sync(shared_imag + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - - out_real[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(shared_real)[shared_offset]; - out_imag[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(shared_imag)[shared_offset]; - - } - } - - __syncthreads(); - } -} - -template -__global__ void butterfly_padded_cuda_kernel_32( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 32; - __shared__ half x_shared[K * 16 * 64]; - __shared__ half d_f_real[32 * 32]; - __shared__ half d_f_imag[32 * 32]; - __shared__ half twiddles_real_shared[32 * 64]; - __shared__ half twiddles_imag_shared[32 * 64]; - __shared__ half out_real_shared[32 * 64]; - __shared__ half out_imag_shared[32 * 64]; - - const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int out_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; - - - for(int i = threadIdx.y; i<32; i+=blockDim.y){ - int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - int shared_offset = i * 32 + threadIdx.x; - - if(i < K * 16){ - if(x_gate != nullptr){ - reinterpret_cast<__half2*>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[offset + idx], x_gate[offset + idx]) : __floats2half2_rn(0.0f, 0.0f); - } - else{ - reinterpret_cast<__half2*>(x_shared)[shared_offset] = idx < max_idx ? x[offset + idx] : __floats2half2_rn(0.0f, 0.0f); - } - } - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - - - __syncthreads(); - - - if (threadIdx.y < N / 16) - { - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment b_frag[K][2]; - wmma::fragment acc_frag_real[2][2]; - wmma::fragment acc_frag_imag[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - if(i(acc_frag_real[i][j].x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k])); - } - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - - // int idx = offset + threadIdx.y * 32 + blockIdx.x * 32 + threadIdx.x; - for(int i = threadIdx.y; i<32; i+=blockDim.y){ - int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - out_real[out_offset + idx] = reinterpret_cast<__half2*>(out_real_shared)[i * 32 + threadIdx.x]; - out_imag[out_offset + idx] = reinterpret_cast<__half2*>(out_imag_shared)[i * 32 + threadIdx.x]; - } -} - - -__global__ void butterfly_padded_cuda_kernel_16( - const __half2 *__restrict__ x, - const __half2 *__restrict__ x_gate, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 16; - const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int out_offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; - - - - __shared__ half x_shared[N * 64]; - __shared__ half d_f_real[N * N]; - __shared__ half d_f_imag[N * N]; - __shared__ half twiddles_real_shared[N * 64]; - __shared__ half twiddles_imag_shared[N * 64]; - __shared__ half out_real_shared[N * 64]; - __shared__ half out_imag_shared[N * 64]; - - // #pragma unroll - for(int i = threadIdx.y; i(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2half2_rn(0.0f, 0.0f); - } - else{ - reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2half2_rn(0.0f, 0.0f); - } - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - - if(threadIdx.x < 16 ){ - shared_offset = i * 16 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - __half2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment a_frag_imag; - wmma::fragment b_frag; - wmma::fragment acc_frag_real; - wmma::fragment acc_frag_imag; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); - - - wmma::fill_fragment(acc_frag_imag, __float2half(0.0f)); - - - wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); - - - - for (int k = 0; k < acc_frag_real.num_elements / 2; k++) - { - tmp_real = reinterpret_cast<__half2 *>(acc_frag_real.x)[k]; - tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag.x)[k]; - reinterpret_cast<__half2 *>(acc_frag_real.x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k])); - reinterpret_cast<__half2 *>(acc_frag_imag.x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real.x)[k])); - } - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i(out_real_shared)[i * 32 + threadIdx.x]; - out_imag[out_offset + idx] = reinterpret_cast<__half2 *>(out_imag_shared)[i * 32 + threadIdx.x]; - } -} - -std::vector butterfly_padded_cuda( - torch::Tensor x, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - std::optional x_gate = std::nullopt - ) -{ - - uint B = x.size(0); - uint H = x.size(1); - uint N = x.size(2); - - uint d_f_size = d_f.size(1); - - //need to make sure that N is less that the M to which we are padding - assert(N <= d_f_size * M); - // printf("B: %d, H: %d, N: %d\n", B, H, N); - dim3 gridDim; - dim3 blockDim; - - gridDim.y = B; - gridDim.z = H; - - blockDim.x = 32; - blockDim.y = 4; - - torch::Tensor out_real = torch::empty({B, H, d_f_size * M}, x.options()); - torch::Tensor out_imag = torch::empty({B, H, d_f_size * M}, x.options()); - - gridDim.x = 512 / (32 * 1024/ M); - - const int K = ceil(N / (1.0 * 16 * M)); - - - switch(d_f_size){ - case 16: - butterfly_padded_cuda_kernel_16<<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 32: - switch (K) - { - case 1: - butterfly_padded_cuda_kernel_32<1><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 2: - butterfly_padded_cuda_kernel_32<2><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - default: - printf("Invalid K, df size 32: %d\n", K); - } - break; - case 64: - gridDim.z = H / 16; - - switch (K) - { - case 1: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_64<1><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - case 2: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_64<2><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - case 3: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_64<3><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - case 4: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_64<4><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - - default: - printf("Invalid K, df size 64: %d\n", K); - } - break; - case 128: - blockDim.x = 32; - blockDim.y = 8; - gridDim.x = 256 / (32 * 1024/ M); - gridDim.z = H / 16; - - switch(K){ - case 1: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<1><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 2: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<2><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 3: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<3><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 4: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<4><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 5: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<5>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<5><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 6: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<6>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<6><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 7: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<7>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<7><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 8: - cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_padded_cuda_kernel_128<8><<>>( - static_cast<__half2 *>(x.data_ptr()), - x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - static_cast<__half2 *>(out_imag.data_ptr()), - B, - H, - N); - break; - default: - printf("Invalid K, df size 128: %d\n", K); - } - break; - default: - printf("Invalid d_f size: %d\n", d_f_size); - } - return {out_real, out_imag}; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +template +__global__ void butterfly_padded_cuda_kernel_64( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; + const int out_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + const int N = 64; + + extern __shared__ half x_shared[]; + half *d_f_real = &x_shared[K * 16 * N]; + half *d_f_imag = &d_f_real[N * N]; + half *twiddles_real_shared = &d_f_imag[N * N]; + half *twiddles_imag_shared = &twiddles_real_shared[N * N]; + half *out_real_shared = &twiddles_imag_shared[N * N]; + half *out_imag_shared = &out_real_shared[N * N]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 64 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + + d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); + d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); + } + + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment a_frag_imag[4]; + wmma::fragment b_frag[K][4]; + wmma::fragment acc_frag_real[4]; + wmma::fragment acc_frag_imag[4]; + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(a_frag_real[i], d_f_real + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(a_frag_imag[i], d_f_imag + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); + } + + for (int t = 0; t < 16; t++) + { + t_offset = t * M/2; + out_t_offset = t * 64 * 32 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + if(i < K * 16){ + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2half2_rn(0.0f, 0.0f); + } + else{ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2half2_rn(0.0f, 0.0f); + } + } + } + + __syncthreads(); + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + +#pragma unroll + + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); + } + } + + for (int j = 0; j < 4; j++) + { + wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + + out_real[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; + out_imag[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_imag_shared)[shared_offset]; + } + + __syncthreads(); + + } +} + + +template +__global__ void butterfly_padded_cuda_kernel_128( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; + const int out_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x; + const int N = 128; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + + extern __shared__ half shared_real[]; + half *shared_imag = &shared_real[128 * 128]; + + + wmma::fragment a_frag_real[8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment a_frag_imag[8]; + wmma::fragment b_frag[K][8]; + wmma::fragment acc_frag_real[8]; + wmma::fragment acc_frag_imag[8]; + + for (int i = threadIdx.y ; i < N; i+=blockDim.y) + { + for(int j=0; j< 4; j++){ + shared_offset = i * 128 + threadIdx.x + j * blockDim.x; + shared_real[shared_offset] = d_f[shared_offset].real(); + shared_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); + } + + + __syncthreads(); + + + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(shared_real)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2*>(shared_imag)[shared_offset] = twiddle_factors_imag[idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); + } + + __syncthreads(); + + + for(int t=0; t< 16; t++){ + t_offset = t * M/2; + out_t_offset = t * 128 * 32 * 2 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + if(i < K * 16){ + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + if(x_gate != nullptr){ + reinterpret_cast<__half2*>(shared_real)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2half2_rn(0.0f, 0.0f); + } + else{ + reinterpret_cast<__half2*>(shared_real)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2half2_rn(0.0f, 0.0f); + } + } + } + } + + + __syncthreads(); + + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 8; j++) + { + wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); + } + } + + __syncthreads(); + + #pragma unroll + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_real[j], __float2half(0.0f)); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + + #pragma unroll + + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_imag[j], __float2half(0.0f)); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + + __half2 tmp_real, tmp_imag; + #pragma unroll + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[j].x)[k])); + } + } + + for (int j = 0; j < 8; j++) + { + wmma::store_matrix_sync(shared_real + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); + wmma::store_matrix_sync(shared_imag + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + + out_real[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(shared_real)[shared_offset]; + out_imag[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(shared_imag)[shared_offset]; + + } + } + + __syncthreads(); + } +} + +template +__global__ void butterfly_padded_cuda_kernel_32( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 32; + __shared__ half x_shared[K * 16 * 64]; + __shared__ half d_f_real[32 * 32]; + __shared__ half d_f_imag[32 * 32]; + __shared__ half twiddles_real_shared[32 * 64]; + __shared__ half twiddles_imag_shared[32 * 64]; + __shared__ half out_real_shared[32 * 64]; + __shared__ half out_imag_shared[32 * 64]; + + const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int out_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; + + + for(int i = threadIdx.y; i<32; i+=blockDim.y){ + int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + int shared_offset = i * 32 + threadIdx.x; + + if(i < K * 16){ + if(x_gate != nullptr){ + reinterpret_cast<__half2*>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[offset + idx], x_gate[offset + idx]) : __floats2half2_rn(0.0f, 0.0f); + } + else{ + reinterpret_cast<__half2*>(x_shared)[shared_offset] = idx < max_idx ? x[offset + idx] : __floats2half2_rn(0.0f, 0.0f); + } + } + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + + + __syncthreads(); + + + if (threadIdx.y < N / 16) + { + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment b_frag[K][2]; + wmma::fragment acc_frag_real[2][2]; + wmma::fragment acc_frag_imag[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + if(i(acc_frag_real[i][j].x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real[i][j].x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag[i][j].x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag[i][j].x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real[i][j].x)[k])); + } + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + + // int idx = offset + threadIdx.y * 32 + blockIdx.x * 32 + threadIdx.x; + for(int i = threadIdx.y; i<32; i+=blockDim.y){ + int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + out_real[out_offset + idx] = reinterpret_cast<__half2*>(out_real_shared)[i * 32 + threadIdx.x]; + out_imag[out_offset + idx] = reinterpret_cast<__half2*>(out_imag_shared)[i * 32 + threadIdx.x]; + } +} + + +__global__ void butterfly_padded_cuda_kernel_16( + const __half2 *__restrict__ x, + const __half2 *__restrict__ x_gate, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 16; + const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int out_offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; + + + + __shared__ half x_shared[N * 64]; + __shared__ half d_f_real[N * N]; + __shared__ half d_f_imag[N * N]; + __shared__ half twiddles_real_shared[N * 64]; + __shared__ half twiddles_imag_shared[N * 64]; + __shared__ half out_real_shared[N * 64]; + __shared__ half out_imag_shared[N * 64]; + + // #pragma unroll + for(int i = threadIdx.y; i(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2half2_rn(0.0f, 0.0f); + } + else{ + reinterpret_cast<__half2 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2half2_rn(0.0f, 0.0f); + } + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + + if(threadIdx.x < 16 ){ + shared_offset = i * 16 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + __half2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment a_frag_imag; + wmma::fragment b_frag; + wmma::fragment acc_frag_real; + wmma::fragment acc_frag_imag; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); + + + wmma::fill_fragment(acc_frag_imag, __float2half(0.0f)); + + + wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); + + + + for (int k = 0; k < acc_frag_real.num_elements / 2; k++) + { + tmp_real = reinterpret_cast<__half2 *>(acc_frag_real.x)[k]; + tmp_imag = reinterpret_cast<__half2 *>(acc_frag_imag.x)[k]; + reinterpret_cast<__half2 *>(acc_frag_real.x)[k] = __hsub2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_real.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k])); + reinterpret_cast<__half2 *>(acc_frag_imag.x)[k] = __hadd2(__hmul2(tmp_real, reinterpret_cast<__half2 *>(tw_frag_imag.x)[k]), __hmul2(tmp_imag, reinterpret_cast<__half2 *>(tw_frag_real.x)[k])); + } + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i(out_real_shared)[i * 32 + threadIdx.x]; + out_imag[out_offset + idx] = reinterpret_cast<__half2 *>(out_imag_shared)[i * 32 + threadIdx.x]; + } +} + +std::vector butterfly_padded_cuda( + torch::Tensor x, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + std::optional x_gate = std::nullopt + ) +{ + + uint B = x.size(0); + uint H = x.size(1); + uint N = x.size(2); + + uint d_f_size = d_f.size(1); + + //need to make sure that N is less that the M to which we are padding + assert(N <= d_f_size * M); + // printf("B: %d, H: %d, N: %d\n", B, H, N); + dim3 gridDim; + dim3 blockDim; + + gridDim.y = B; + gridDim.z = H; + + blockDim.x = 32; + blockDim.y = 4; + + torch::Tensor out_real = torch::empty({B, H, d_f_size * M}, x.options()); + torch::Tensor out_imag = torch::empty({B, H, d_f_size * M}, x.options()); + + gridDim.x = 512 / (32 * 1024/ M); + + const int K = ceil(N / (1.0 * 16 * M)); + + + switch(d_f_size){ + case 16: + butterfly_padded_cuda_kernel_16<<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 32: + switch (K) + { + case 1: + butterfly_padded_cuda_kernel_32<1><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 2: + butterfly_padded_cuda_kernel_32<2><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + default: + printf("Invalid K, df size 32: %d\n", K); + } + break; + case 64: + gridDim.z = H / 16; + + switch (K) + { + case 1: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_64<1><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + case 2: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_64<2><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + case 3: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_64<3><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + case 4: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_64<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_64<4><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + + default: + printf("Invalid K, df size 64: %d\n", K); + } + break; + case 128: + blockDim.x = 32; + blockDim.y = 8; + gridDim.x = 256 / (32 * 1024/ M); + gridDim.z = H / 16; + + switch(K){ + case 1: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<1><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 2: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<2><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 3: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<3><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 4: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<4><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 5: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<5>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<5><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 6: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<6>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<6><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 7: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<7>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<7><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 8: + cudaFuncSetAttribute(&butterfly_padded_cuda_kernel_128<8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_padded_cuda_kernel_128<8><<>>( + static_cast<__half2 *>(x.data_ptr()), + x_gate ? static_cast<__half2 *>(x_gate.value().data_ptr()) : nullptr, + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + static_cast<__half2 *>(out_imag.data_ptr()), + B, + H, + N); + break; + default: + printf("Invalid K, df size 128: %d\n", K); + } + break; + default: + printf("Invalid d_f size: %d\n", d_f_size); + } + return {out_real, out_imag}; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda_bf16.cu index bfbb2edafae64a19d96fff084f21575c569560c1..2d9a04bd7621138772d1b87ed11a278c3123a763 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_cuda_bf16.cu @@ -1,897 +1,897 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - - -template -__global__ void butterfly_cuda_kernel_64( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; - const int out_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - const int N = 64; - - - extern __shared__ __nv_bfloat16 x_shared[]; - __nv_bfloat16 *d_f_real_shared = &x_shared[K * 16 * N]; - __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; - __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; - __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; - float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); - float *out_imag_shared = &out_real_shared[N * N]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; - } - - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment a_frag_imag[4]; - wmma::fragment b_frag[4][4]; - wmma::fragment acc_frag_real[4]; - wmma::fragment acc_frag_imag[4]; - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(a_frag_real[i], d_f_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(a_frag_imag[i], d_f_imag_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); - } - - for (int t = 0; t < 16; t++) - { - t_offset = t * M/2; - out_t_offset = t * 64 * 32 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - if(i < K * 16){ - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2bfloat162_rn(0.0f, 0.0f); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2bfloat162_rn(0.0f, 0.0f); - } - } - } - - __syncthreads(); - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_real[j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - -#pragma unroll - - for (int j = 0; j < 4; j++) - { - wmma::fill_fragment(acc_frag_imag[j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - -#pragma unroll - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; - - reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); - reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); - } - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - out_real[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); - out_imag[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[shared_offset]); - } - - __syncthreads(); - } -} - -template -__global__ void butterfly_cuda_kernel_32( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int N = 32; - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - - const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int out_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; - - - __shared__ __nv_bfloat16 x_shared[K * 16 * 64]; - __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; - __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; - __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; - __shared__ float out_real_shared[32 * 64]; - __shared__ float out_imag_shared[32 * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i<32; i+=blockDim.y) - { - int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - int shared_offset = i * 32 + threadIdx.x; - - if(i < K * 16){ - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2bfloat162_rn(0.0f, 0.0f); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2bfloat162_rn(0.0f, 0.0f); - } - } - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - d_f_real_shared[shared_offset] = d_f_real[shared_offset]; - d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; - } - - __syncthreads(); - - if (threadIdx.y < N / 16) - { - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[2][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment a_frag_imag[2][2]; - wmma::fragment b_frag[K][2]; - wmma::fragment acc_frag_real[2][2]; - wmma::fragment acc_frag_imag[2][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); - if(i < K){ - wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_imag[i][j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); - } - } - } - -#pragma unroll - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[i][j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[i][j].x)[k]; - reinterpret_cast(acc_frag_real[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]); - reinterpret_cast(acc_frag_imag[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]); - } - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i<32; i+=blockDim.y) - { - int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); - out_imag[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[i * 32 + threadIdx.x]); - } -} - -template -__global__ void butterfly_cuda_kernel_128( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; - const int out_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x; - const int N = 128; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - - extern __shared__ __nv_bfloat16 shared_real[]; - __nv_bfloat16 *shared_imag = &shared_real[128 * 128]; - - - wmma::fragment a_frag_real[8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment a_frag_imag[8]; - wmma::fragment b_frag[K][8]; - wmma::fragment acc_frag_real[8]; - wmma::fragment acc_frag_imag[8]; - - for (int i = threadIdx.y ; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162 *>(shared_real)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(shared_imag)[shared_offset] = d_f_imag[shared_offset]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); - } - - - __syncthreads(); - - - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162*>(shared_imag)[shared_offset] = twiddle_factors_imag[idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); - } - - __syncthreads(); - - - for(int t=0; t< 16; t++){ - t_offset = t * M/2; - out_t_offset = t * 128 * 32 * 2 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - if(i < K * 16){ - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2bfloat162_rn(0.0f, 0.0f); - }else{ - reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2bfloat162_rn(0.0f, 0.0f); - } - } - } - } - - - __syncthreads(); - - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 8; j++) - { - wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); - } - } - - __syncthreads(); - - #pragma unroll - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_real[j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); - } - } - - #pragma unroll - - for (int j = 0; j < 8; j++) - { - wmma::fill_fragment(acc_frag_imag[j], 0.0f); - - for (int k = 0; k < K; k++) - { - wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); - } - } - - float2 tmp_real, tmp_imag; - #pragma unroll - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; - - reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); - reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); - } - } - - for (int j = 0; j < 8; j++) - { - wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - out_real[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); - } - } - - __syncthreads(); - - - for (int j = 0; j < 8; j++) - { - wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); - } - - __syncthreads(); - - #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - out_imag[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); - } - } - } -} - -template -__global__ void butterfly_cuda_kernel_16( - const __nv_bfloat162 *__restrict__ x, - const __nv_bfloat162 *__restrict__ x_gate, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_imag, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 16; - const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int out_offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; - - - - __shared__ __nv_bfloat16 x_shared[N * 64]; - __shared__ __nv_bfloat16 d_f_real_shared[N * N]; - __shared__ __nv_bfloat16 d_f_imag_shared[N * N]; - __shared__ __nv_bfloat16 twiddles_real_shared[N * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[N * 64]; - __shared__ float out_real_shared[N * 64]; - __shared__ float out_imag_shared[N * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; - int shared_offset = i * blockDim.x + threadIdx.x; - - if(x_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2bfloat162_rn(0.0f, 0.0f); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2bfloat162_rn(0.0f, 0.0f); - } - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - if(threadIdx.x < 16 ){ - shared_offset = i * 16 + threadIdx.x; - d_f_real_shared[shared_offset] = d_f_real[shared_offset]; - d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; - } - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - float2 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment a_frag_imag; - wmma::fragment b_frag; - wmma::fragment acc_frag_real; - wmma::fragment acc_frag_imag; - - - wmma::load_matrix_sync(a_frag_real, d_f_real_shared, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag_shared, N); - wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - - wmma::fill_fragment(acc_frag_real, 0.0f); - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); - - - - wmma::fill_fragment(acc_frag_imag, 0.0f); - - - wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); - - -#pragma unroll - for (int k = 0; k < acc_frag_real.num_elements / 2; k++) - { - tmp_real = reinterpret_cast(acc_frag_real.x)[k]; - tmp_imag = reinterpret_cast(acc_frag_imag.x)[k]; - reinterpret_cast(acc_frag_real.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]); - reinterpret_cast(acc_frag_imag.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]); - } - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); - - } - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x;; - out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); - out_imag[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[i * 32 + threadIdx.x]); - } -} - -std::vector butterfly_padded_bf16_cuda( - torch::Tensor x, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int M, - std::optional x_gate = std::nullopt - ) -{ - - uint B = x.size(0); - uint H = x.size(1); - - uint d_f_size = d_f_real.size(1); - - uint N = x.size(2); - - //need to make sure that N is less that the M to which we are padding - assert(N <= d_f_size * M); - - dim3 gridDim; - dim3 blockDim; - - gridDim.y = B; - gridDim.z = H; - - blockDim.x = 32; - blockDim.y = 4; - - torch::Tensor out_real = torch::empty({B, H, d_f_size * M}, x.options()); - torch::Tensor out_imag = torch::empty({B, H, d_f_size * M}, x.options()); - - gridDim.x = 512 / (32 * 1024/ M); - - const int K = ceil(N / (1.0 * 16 * M)); - - switch (d_f_size) - { - case 16: - butterfly_cuda_kernel_16<1><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 32: - switch(K){ - case 1: - butterfly_cuda_kernel_32<1><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 2: - butterfly_cuda_kernel_32<2><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - default: - printf("Invalid K, df size 32: %d\n", K); - } - break; - case 64: - gridDim.z = H / 16; - - switch(K){ - case 1: - cudaFuncSetAttribute(&butterfly_cuda_kernel_64<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - butterfly_cuda_kernel_64<1><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 2: - cudaFuncSetAttribute(&butterfly_cuda_kernel_64<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - butterfly_cuda_kernel_64<2><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 3: - cudaFuncSetAttribute(&butterfly_cuda_kernel_64<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - butterfly_cuda_kernel_64<3><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 4: - cudaFuncSetAttribute(&butterfly_cuda_kernel_64<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); - butterfly_cuda_kernel_64<4><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - default: - printf("Invalid K, df size 64: %d\n", K); - } - break; - case 128: - blockDim.x = 32; - blockDim.y = 8; - gridDim.x = 256 / (32 * 1024/ M); - gridDim.z = H / 16; - switch(K){ - case 1: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_cuda_kernel_128<1><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 2: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_cuda_kernel_128<2><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 3: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<3><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 4: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<4><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 5: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<5>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<5><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 6: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<6>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<6><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 7: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<7>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<7><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - case 8: - cudaFuncSetAttribute(&butterfly_cuda_kernel_128<8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - - butterfly_cuda_kernel_128<8><<>>( - static_cast<__nv_bfloat162 *>(x.data_ptr()), - x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), - B, - H, - N); - break; - default: - printf("Invalid K, df size 128: %d\n", K); - - } - break; - - default: - printf("Not yet implemented \n"); - break; - } - - return {out_real, out_imag}; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + + +template +__global__ void butterfly_cuda_kernel_64( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; + const int out_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * 16 * 64 * 32 * gridDim.x; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + const int N = 64; + + + extern __shared__ __nv_bfloat16 x_shared[]; + __nv_bfloat16 *d_f_real_shared = &x_shared[K * 16 * N]; + __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; + __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; + __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; + float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); + float *out_imag_shared = &out_real_shared[N * N]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; + } + + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment a_frag_imag[4]; + wmma::fragment b_frag[4][4]; + wmma::fragment acc_frag_real[4]; + wmma::fragment acc_frag_imag[4]; + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(a_frag_real[i], d_f_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(a_frag_imag[i], d_f_imag_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + threadIdx.y * N * 16 + i * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + threadIdx.y * N * 16 + i * 16, N); + } + + for (int t = 0; t < 16; t++) + { + t_offset = t * M/2; + out_t_offset = t * 64 * 32 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + if(i < K * 16){ + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2bfloat162_rn(0.0f, 0.0f); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2bfloat162_rn(0.0f, 0.0f); + } + } + } + + __syncthreads(); + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * N * 16 + j * 16, N); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_real[j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + +#pragma unroll + + for (int j = 0; j < 4; j++) + { + wmma::fill_fragment(acc_frag_imag[j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + +#pragma unroll + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; + + reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); + reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); + } + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * N * 16 + j * 16, acc_frag_real[j], N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * N * 16 + j * 16, acc_frag_imag[j], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + out_real[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); + out_imag[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[shared_offset]); + } + + __syncthreads(); + } +} + +template +__global__ void butterfly_cuda_kernel_32( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int N = 32; + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + + const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int out_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; + + + __shared__ __nv_bfloat16 x_shared[K * 16 * 64]; + __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; + __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; + __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; + __shared__ float out_real_shared[32 * 64]; + __shared__ float out_imag_shared[32 * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i<32; i+=blockDim.y) + { + int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + int shared_offset = i * 32 + threadIdx.x; + + if(i < K * 16){ + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2bfloat162_rn(0.0f, 0.0f); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2bfloat162_rn(0.0f, 0.0f); + } + } + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + d_f_real_shared[shared_offset] = d_f_real[shared_offset]; + d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; + } + + __syncthreads(); + + if (threadIdx.y < N / 16) + { + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[2][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment a_frag_imag[2][2]; + wmma::fragment b_frag[K][2]; + wmma::fragment acc_frag_real[2][2]; + wmma::fragment acc_frag_imag[2][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); + if(i < K){ + wmma::load_matrix_sync(b_frag[i][j], x_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag[k][j], acc_frag_real[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_imag[i][j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_imag[i][j], a_frag_imag[i][k], b_frag[k][j], acc_frag_imag[i][j]); + } + } + } + +#pragma unroll + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < acc_frag_real[i][j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[i][j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[i][j].x)[k]; + reinterpret_cast(acc_frag_real[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]); + reinterpret_cast(acc_frag_imag[i][j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[i][j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[i][j].x)[k]); + } + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_imag[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i<32; i+=blockDim.y) + { + int idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); + out_imag[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[i * 32 + threadIdx.x]); + } +} + +template +__global__ void butterfly_cuda_kernel_128( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int offset = blockIdx.y * H * M/2 + blockIdx.z * 16 * M/2; + const int out_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * 16 * 128 * 32 * 2 * gridDim.x; + const int N = 128; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + + extern __shared__ __nv_bfloat16 shared_real[]; + __nv_bfloat16 *shared_imag = &shared_real[128 * 128]; + + + wmma::fragment a_frag_real[8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment a_frag_imag[8]; + wmma::fragment b_frag[K][8]; + wmma::fragment acc_frag_real[8]; + wmma::fragment acc_frag_imag[8]; + + for (int i = threadIdx.y ; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162 *>(shared_real)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(shared_imag)[shared_offset] = d_f_imag[shared_offset]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(a_frag_real[i], shared_real + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(a_frag_imag[i], shared_imag + i * 128 * 16 + threadIdx.y * 16, 128); + } + + + __syncthreads(); + + + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162*>(shared_imag)[shared_offset] = twiddle_factors_imag[idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], shared_real + threadIdx.y * 128 * 16 + i * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], shared_imag + threadIdx.y * 128 * 16 + i * 16, 128); + } + + __syncthreads(); + + + for(int t=0; t< 16; t++){ + t_offset = t * M/2; + out_t_offset = t * 128 * 32 * 2 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + if(i < K * 16){ + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset + t_offset], x_gate[idx + offset + t_offset]) : __floats2bfloat162_rn(0.0f, 0.0f); + }else{ + reinterpret_cast<__nv_bfloat162*>(shared_real)[shared_offset] = idx < max_idx ? x[idx + offset + t_offset] : __floats2bfloat162_rn(0.0f, 0.0f); + } + } + } + } + + + __syncthreads(); + + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 8; j++) + { + wmma::load_matrix_sync(b_frag[i][j], shared_real + i * 128 * 16 + j * 16, 128); + } + } + + __syncthreads(); + + #pragma unroll + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_real[j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_real[j], a_frag_real[k], b_frag[k][j], acc_frag_real[j]); + } + } + + #pragma unroll + + for (int j = 0; j < 8; j++) + { + wmma::fill_fragment(acc_frag_imag[j], 0.0f); + + for (int k = 0; k < K; k++) + { + wmma::mma_sync(acc_frag_imag[j], a_frag_imag[k], b_frag[k][j], acc_frag_imag[j]); + } + } + + float2 tmp_real, tmp_imag; + #pragma unroll + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < acc_frag_real[j].num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real[j].x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag[j].x)[k]; + + reinterpret_cast(acc_frag_real[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]); + reinterpret_cast(acc_frag_imag[j].x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag[j].x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real[j].x)[k]); + } + } + + for (int j = 0; j < 8; j++) + { + wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_real[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + out_real[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); + } + } + + __syncthreads(); + + + for (int j = 0; j < 8; j++) + { + wmma::store_matrix_sync(reinterpret_cast(shared_real) + threadIdx.y * 128 * 16 + j * 16, acc_frag_imag[j], 128, wmma::mem_row_major); + } + + __syncthreads(); + + #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + out_imag[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(shared_real)[shared_offset]); + } + } + } +} + +template +__global__ void butterfly_cuda_kernel_16( + const __nv_bfloat162 *__restrict__ x, + const __nv_bfloat162 *__restrict__ x_gate, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_imag, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 16; + const int offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int out_offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; + + + + __shared__ __nv_bfloat16 x_shared[N * 64]; + __shared__ __nv_bfloat16 d_f_real_shared[N * N]; + __shared__ __nv_bfloat16 d_f_imag_shared[N * N]; + __shared__ __nv_bfloat16 twiddles_real_shared[N * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[N * 64]; + __shared__ float out_real_shared[N * 64]; + __shared__ float out_imag_shared[N * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; + int shared_offset = i * blockDim.x + threadIdx.x; + + if(x_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? __hmul2(x[idx + offset], x_gate[idx + offset]) : __floats2bfloat162_rn(0.0f, 0.0f); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_shared)[shared_offset] = idx < max_idx ? x[idx + offset] : __floats2bfloat162_rn(0.0f, 0.0f); + } + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + if(threadIdx.x < 16 ){ + shared_offset = i * 16 + threadIdx.x; + d_f_real_shared[shared_offset] = d_f_real[shared_offset]; + d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; + } + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + float2 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment a_frag_imag; + wmma::fragment b_frag; + wmma::fragment acc_frag_real; + wmma::fragment acc_frag_imag; + + + wmma::load_matrix_sync(a_frag_real, d_f_real_shared, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag_shared, N); + wmma::load_matrix_sync(b_frag, x_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + + wmma::fill_fragment(acc_frag_real, 0.0f); + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag, acc_frag_real); + + + + wmma::fill_fragment(acc_frag_imag, 0.0f); + + + wmma::mma_sync(acc_frag_imag, a_frag_imag, b_frag, acc_frag_imag); + + +#pragma unroll + for (int k = 0; k < acc_frag_real.num_elements / 2; k++) + { + tmp_real = reinterpret_cast(acc_frag_real.x)[k]; + tmp_imag = reinterpret_cast(acc_frag_imag.x)[k]; + reinterpret_cast(acc_frag_real.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]) - tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]); + reinterpret_cast(acc_frag_imag.x)[k] = tmp_real * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_imag.x)[k]) + tmp_imag * __bfloat1622float2(reinterpret_cast<__nv_bfloat162 *>(tw_frag_real.x)[k]); + } + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + wmma::store_matrix_sync(out_imag_shared + threadIdx.y * 16, acc_frag_imag, 64, wmma::mem_row_major); + + } + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x;; + out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); + out_imag[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_imag_shared)[i * 32 + threadIdx.x]); + } +} + +std::vector butterfly_padded_bf16_cuda( + torch::Tensor x, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int M, + std::optional x_gate = std::nullopt + ) +{ + + uint B = x.size(0); + uint H = x.size(1); + + uint d_f_size = d_f_real.size(1); + + uint N = x.size(2); + + //need to make sure that N is less that the M to which we are padding + assert(N <= d_f_size * M); + + dim3 gridDim; + dim3 blockDim; + + gridDim.y = B; + gridDim.z = H; + + blockDim.x = 32; + blockDim.y = 4; + + torch::Tensor out_real = torch::empty({B, H, d_f_size * M}, x.options()); + torch::Tensor out_imag = torch::empty({B, H, d_f_size * M}, x.options()); + + gridDim.x = 512 / (32 * 1024/ M); + + const int K = ceil(N / (1.0 * 16 * M)); + + switch (d_f_size) + { + case 16: + butterfly_cuda_kernel_16<1><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 32: + switch(K){ + case 1: + butterfly_cuda_kernel_32<1><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 2: + butterfly_cuda_kernel_32<2><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + default: + printf("Invalid K, df size 32: %d\n", K); + } + break; + case 64: + gridDim.z = H / 16; + + switch(K){ + case 1: + cudaFuncSetAttribute(&butterfly_cuda_kernel_64<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + butterfly_cuda_kernel_64<1><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 2: + cudaFuncSetAttribute(&butterfly_cuda_kernel_64<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + butterfly_cuda_kernel_64<2><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 3: + cudaFuncSetAttribute(&butterfly_cuda_kernel_64<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + butterfly_cuda_kernel_64<3><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 4: + cudaFuncSetAttribute(&butterfly_cuda_kernel_64<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 78000); + butterfly_cuda_kernel_64<4><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + default: + printf("Invalid K, df size 64: %d\n", K); + } + break; + case 128: + blockDim.x = 32; + blockDim.y = 8; + gridDim.x = 256 / (32 * 1024/ M); + gridDim.z = H / 16; + switch(K){ + case 1: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<1>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_cuda_kernel_128<1><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 2: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<2>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_cuda_kernel_128<2><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 3: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<3>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<3><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 4: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<4>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<4><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 5: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<5>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<5><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 6: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<6>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<6><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 7: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<7>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<7><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + case 8: + cudaFuncSetAttribute(&butterfly_cuda_kernel_128<8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + + butterfly_cuda_kernel_128<8><<>>( + static_cast<__nv_bfloat162 *>(x.data_ptr()), + x_gate ? static_cast<__nv_bfloat162 *>(x_gate.value().data_ptr()) : nullptr, + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + static_cast<__nv_bfloat162 *>(out_imag.data_ptr()), + B, + H, + N); + break; + default: + printf("Invalid K, df size 128: %d\n", K); + + } + break; + + default: + printf("Not yet implemented \n"); + break; + } + + return {out_real, out_imag}; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda.cu index 0cb8a278de5d3acce5c5476a56aa5d67ac982f01..b9c3aa58b8978c9e46ce6b187868d23338767fc5 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda.cu @@ -1,905 +1,905 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -template -__global__ void butterfly_ifft_padded_cuda_kernel_64( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; - const int in_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * TILE_H * 64 * 32 * gridDim.x; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - const int N = 64; - - extern __shared__ half x_real_shared[]; - half *x_imag_shared = &x_real_shared[N * N]; - half *d_f_real = &x_imag_shared[N * N]; - half *d_f_imag = &d_f_real[N * N]; - half *twiddles_real_shared = &d_f_imag[N * N]; - half *twiddles_imag_shared = &twiddles_real_shared[N * N]; - half *out_real_shared = &twiddles_imag_shared[N * N]; - - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real[K][4]; - wmma::fragment a_frag_imag[K][4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment b_frag_real[4]; - wmma::fragment b_frag_imag[4]; - wmma::fragment acc_frag_real[K]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 64 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - - d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); - d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - if(i < K){ -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - } - } - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int t = 0; t < TILE_H; t++) - { - - out_t_offset = t * M/2; - t_offset = t * 64 * 32 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < K; i++) - { - wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); - -// bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); - } - } - - for (int i = 0; i < K; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < K; i++) - { - wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - - if(idx < max_idx){ - if(out_gate != nullptr) - out_real[out_offset + out_t_offset + idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[shared_offset], out_gate[out_offset + out_t_offset + idx]); - else - out_real[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; - } - } - - __syncthreads(); - } -} - - -template -__global__ void butterfly_ifft_padded_cuda_kernel_32( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 32; - int idx; - int shared_offset; - - const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int in_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; - - - __shared__ half x_real_shared[32 * 64]; - __shared__ half x_imag_shared[32 * 64]; - __shared__ half d_f_real[32 * 32]; - __shared__ half d_f_imag[32 * 32]; - __shared__ half twiddles_real_shared[32 * 64]; - __shared__ half twiddles_imag_shared[32 * 64]; - __shared__ half out_real_shared[32 * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - int shared_offset = i * 32 + threadIdx.x; - - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[in_offset + idx]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[in_offset + idx]; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 32 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - - __syncthreads(); - - if (threadIdx.y < N/16) - { - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real[K][2]; - wmma::fragment a_frag_imag[K][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment b_frag_real[2][2]; - wmma::fragment b_frag_imag[2][2]; - wmma::fragment acc_frag_real[K][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - if(i < K){ - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); - } - wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); - b_frag_real[i][j].x[k] = tmp_real; - b_frag_imag[i][j].x[k] = tmp_imag; - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); - - // bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); - } - - for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) - { - acc_frag_real[i][j].x[k] = __hneg(acc_frag_real[i][j].x[k]); - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - // ac - bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[idx + out_offset] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[shared_offset], out_gate[idx + out_offset]); - }else{ - out_real[idx + out_offset] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; - } - } - - } -} - - -template -__global__ void butterfly_ifft_padded_cuda_kernel_128( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; - const int in_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * TILE_H * 128 * 32 * 2 * gridDim.x; - const int N = 128; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - - - extern __shared__ half real_shared[]; - half *imag_shared = &real_shared[128 * 128]; - half *real_shared_2 = &imag_shared[128 * 128]; - half *imag_shared_2 = &real_shared_2[128 * 128]; - - half tmp_real, tmp_imag; - - wmma::fragment a_frag[K][8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment b_frag_real[8]; - wmma::fragment b_frag_imag[8]; - wmma::fragment acc_frag_real[K]; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 4; j++){ - shared_offset = i * 128 + threadIdx.x + j * blockDim.x; - real_shared_2[shared_offset] = d_f[shared_offset].real(); - imag_shared_2[shared_offset] = d_f[shared_offset].imag(); - } - } - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2*>(imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); - } - - __syncthreads(); - - for (int t = 0; t < TILE_H; t++) - { - - out_t_offset = t * M/2; - t_offset = t * 128 * 32 * 2 * gridDim.x; - - for (int i = 0; i < K; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__half2*>(real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; - reinterpret_cast<__half2*>(imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { - wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < K; i++) - { - wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); - -// bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); - } - } - - for (int i = 0; i < K; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = 0; i < K; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < K; i++) - { - //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[idx + out_offset + out_t_offset] = __hmul2(reinterpret_cast<__half2*>(real_shared)[shared_offset], out_gate[idx + out_offset + out_t_offset]); - }else{ - out_real[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(real_shared)[shared_offset]; - } - } - } - } - - __syncthreads(); - } -} - - -__global__ void butterfly_ifft_padded_cuda_kernel_16( - const __half2 *__restrict__ x_real, - const __half2 *__restrict__ x_imag, - const complex_half_t *__restrict__ d_f, - const __half2 *__restrict__ twiddle_factors_real, - const __half2 *__restrict__ twiddle_factors_imag, - __half2 *__restrict__ out_real, - __half2 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 16; - const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; - - __shared__ half x_real_shared[N * 64]; - __shared__ half x_imag_shared[N * 64]; - __shared__ half d_f_real[N * N]; - __shared__ half d_f_imag[N * N]; - __shared__ half twiddles_real_shared[N * 64]; - __shared__ half twiddles_imag_shared[N * 64]; - __shared__ half out_real_shared[N * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; - int shared_offset = i * blockDim.x + threadIdx.x; - reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - if(threadIdx.x < 16 ){ - shared_offset = i * 16 + threadIdx.x; - d_f_real[shared_offset] = d_f[shared_offset].real(); - d_f_imag[shared_offset] = d_f[shared_offset].imag(); - } - } - - __syncthreads(); - - //check if it is better to have one warp do all the multiplication or split between warps - if (threadIdx.y < 4) - { - half tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment a_frag_imag; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment b_frag_real; - wmma::fragment b_frag_imag; - wmma::fragment acc_frag_real; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - - for (int k = 0; k < tw_frag_real.num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); - b_frag_real.x[k] = tmp_real; - b_frag_imag.x[k] = tmp_imag; - } - - - wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); - - wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); - - for(int k=0; k< acc_frag_real.num_elements; k++){ - acc_frag_real.x[k] = __hneg(acc_frag_real.x[k]); - } - - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[out_offset + idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[i * 32 + threadIdx.x], out_gate[out_offset + idx]); - } - else{ - out_real[out_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[i * 32 + threadIdx.x]; - } - } - } -} - -torch::Tensor butterfly_ifft_padded_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int fft_size, - std::optional out_gate = std::nullopt - ) -{ - - uint B = x_real.size(0); - uint H = x_real.size(1); - uint N_M = x_real.size(2); - const int d_f_size = d_f.size(0); - // const int TILE_SIZE = 16; - - dim3 gridDim; - dim3 blockDim; - - // uint N = x_real.size(2); - gridDim.y = B; - - blockDim.x = 32; - blockDim.y = 4; - gridDim.x = 512 / (32 * 1024/ (N_M / d_f_size)); - gridDim.z = H; - - const int TILE_H = 16; - torch::Tensor out_real = torch::empty({B, H, fft_size}, x_real.options()); - const int K = ceil(fft_size / (1.0 * 16 * (N_M / d_f_size))); - - switch(d_f_size){ - case 16: - butterfly_ifft_padded_cuda_kernel_16<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - case 32: - switch (K) - { - case 1: - butterfly_ifft_padded_cuda_kernel_32<1><<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - case 2: - butterfly_ifft_padded_cuda_kernel_32<2><<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - default: - printf("Invalid K: %d\n", K); - break; - } - break; - - case 64: - gridDim.z = H / TILE_H; - switch (K) - { - case 1: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 2: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 3: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 4: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - default: - break; - } - - break; - case 128: - blockDim.x = 32; - blockDim.y = 8; - gridDim.x = 256 / (32 * 1024/ (N_M / d_f_size)); - gridDim.z = H / TILE_H; - - switch (K) - { - case 1: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 2: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 3: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 4: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 5: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 6: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 7: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 8: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__half2 *>(x_real.data_ptr()), - static_cast<__half2 *>(x_imag.data_ptr()), - static_cast(d_f.data_ptr()), - static_cast<__half2 *>(twiddle_factors_real.data_ptr()), - static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), - static_cast<__half2 *>(out_real.data_ptr()), - out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - default: - printf("Invalid K: %d\n", K); - break; - } - break; - - default: - printf("Invalid d_f_size: %d\n", d_f_size); - break; - } - - return out_real; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +template +__global__ void butterfly_ifft_padded_cuda_kernel_64( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; + const int in_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * TILE_H * 64 * 32 * gridDim.x; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + const int N = 64; + + extern __shared__ half x_real_shared[]; + half *x_imag_shared = &x_real_shared[N * N]; + half *d_f_real = &x_imag_shared[N * N]; + half *d_f_imag = &d_f_real[N * N]; + half *twiddles_real_shared = &d_f_imag[N * N]; + half *twiddles_imag_shared = &twiddles_real_shared[N * N]; + half *out_real_shared = &twiddles_imag_shared[N * N]; + + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real[K][4]; + wmma::fragment a_frag_imag[K][4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment b_frag_real[4]; + wmma::fragment b_frag_imag[4]; + wmma::fragment acc_frag_real[K]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 64 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + + d_f_real[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].real(); + d_f_imag[shared_offset + blockDim.x] = d_f[shared_offset + blockDim.x].imag(); + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + if(i < K){ +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + } + } + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int t = 0; t < TILE_H; t++) + { + + out_t_offset = t * M/2; + t_offset = t * 64 * 32 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < K; i++) + { + wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); + +// bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); + } + } + + for (int i = 0; i < K; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < K; i++) + { + wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + + if(idx < max_idx){ + if(out_gate != nullptr) + out_real[out_offset + out_t_offset + idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[shared_offset], out_gate[out_offset + out_t_offset + idx]); + else + out_real[out_offset + out_t_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; + } + } + + __syncthreads(); + } +} + + +template +__global__ void butterfly_ifft_padded_cuda_kernel_32( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 32; + int idx; + int shared_offset; + + const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int in_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; + + + __shared__ half x_real_shared[32 * 64]; + __shared__ half x_imag_shared[32 * 64]; + __shared__ half d_f_real[32 * 32]; + __shared__ half d_f_imag[32 * 32]; + __shared__ half twiddles_real_shared[32 * 64]; + __shared__ half twiddles_imag_shared[32 * 64]; + __shared__ half out_real_shared[32 * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + int shared_offset = i * 32 + threadIdx.x; + + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[in_offset + idx]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[in_offset + idx]; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 32 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + + __syncthreads(); + + if (threadIdx.y < N/16) + { + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real[K][2]; + wmma::fragment a_frag_imag[K][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment b_frag_real[2][2]; + wmma::fragment b_frag_imag[2][2]; + wmma::fragment acc_frag_real[K][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + if(i < K){ + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag + j * N * 16 + i * 16, N); + } + wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); + b_frag_real[i][j].x[k] = tmp_real; + b_frag_imag[i][j].x[k] = tmp_imag; + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], __float2half(0.0f)); + + // bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); + } + + for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) + { + acc_frag_real[i][j].x[k] = __hneg(acc_frag_real[i][j].x[k]); + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + // ac - bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[idx + out_offset] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[shared_offset], out_gate[idx + out_offset]); + }else{ + out_real[idx + out_offset] = reinterpret_cast<__half2 *>(out_real_shared)[shared_offset]; + } + } + + } +} + + +template +__global__ void butterfly_ifft_padded_cuda_kernel_128( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; + const int in_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * TILE_H * 128 * 32 * 2 * gridDim.x; + const int N = 128; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + + + extern __shared__ half real_shared[]; + half *imag_shared = &real_shared[128 * 128]; + half *real_shared_2 = &imag_shared[128 * 128]; + half *imag_shared_2 = &real_shared_2[128 * 128]; + + half tmp_real, tmp_imag; + + wmma::fragment a_frag[K][8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment b_frag_real[8]; + wmma::fragment b_frag_imag[8]; + wmma::fragment acc_frag_real[K]; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 4; j++){ + shared_offset = i * 128 + threadIdx.x + j * blockDim.x; + real_shared_2[shared_offset] = d_f[shared_offset].real(); + imag_shared_2[shared_offset] = d_f[shared_offset].imag(); + } + } + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2*>(imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); + } + + __syncthreads(); + + for (int t = 0; t < TILE_H; t++) + { + + out_t_offset = t * M/2; + t_offset = t * 128 * 32 * 2 * gridDim.x; + + for (int i = 0; i < K; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__half2*>(real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; + reinterpret_cast<__half2*>(imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { + wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < K; i++) + { + wmma::fill_fragment(acc_frag_real[i], __float2half(0.0f)); + +// bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = __hneg(acc_frag_real[i].x[k]); + } + } + + for (int i = 0; i < K; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = 0; i < K; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < K; i++) + { + //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[idx + out_offset + out_t_offset] = __hmul2(reinterpret_cast<__half2*>(real_shared)[shared_offset], out_gate[idx + out_offset + out_t_offset]); + }else{ + out_real[idx + out_offset + out_t_offset] = reinterpret_cast<__half2*>(real_shared)[shared_offset]; + } + } + } + } + + __syncthreads(); + } +} + + +__global__ void butterfly_ifft_padded_cuda_kernel_16( + const __half2 *__restrict__ x_real, + const __half2 *__restrict__ x_imag, + const complex_half_t *__restrict__ d_f, + const __half2 *__restrict__ twiddle_factors_real, + const __half2 *__restrict__ twiddle_factors_imag, + __half2 *__restrict__ out_real, + __half2 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 16; + const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; + + __shared__ half x_real_shared[N * 64]; + __shared__ half x_imag_shared[N * 64]; + __shared__ half d_f_real[N * N]; + __shared__ half d_f_imag[N * N]; + __shared__ half twiddles_real_shared[N * 64]; + __shared__ half twiddles_imag_shared[N * 64]; + __shared__ half out_real_shared[N * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; + int shared_offset = i * blockDim.x + threadIdx.x; + reinterpret_cast<__half2 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__half2 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__half2 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__half2 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + if(threadIdx.x < 16 ){ + shared_offset = i * 16 + threadIdx.x; + d_f_real[shared_offset] = d_f[shared_offset].real(); + d_f_imag[shared_offset] = d_f[shared_offset].imag(); + } + } + + __syncthreads(); + + //check if it is better to have one warp do all the multiplication or split between warps + if (threadIdx.y < 4) + { + half tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment a_frag_imag; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment b_frag_real; + wmma::fragment b_frag_imag; + wmma::fragment acc_frag_real; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + + for (int k = 0; k < tw_frag_real.num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); + b_frag_real.x[k] = tmp_real; + b_frag_imag.x[k] = tmp_imag; + } + + + wmma::fill_fragment(acc_frag_real, __float2half(0.0f)); + + wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); + + for(int k=0; k< acc_frag_real.num_elements; k++){ + acc_frag_real.x[k] = __hneg(acc_frag_real.x[k]); + } + + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[out_offset + idx] = __hmul2(reinterpret_cast<__half2 *>(out_real_shared)[i * 32 + threadIdx.x], out_gate[out_offset + idx]); + } + else{ + out_real[out_offset + idx] = reinterpret_cast<__half2 *>(out_real_shared)[i * 32 + threadIdx.x]; + } + } + } +} + +torch::Tensor butterfly_ifft_padded_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int fft_size, + std::optional out_gate = std::nullopt + ) +{ + + uint B = x_real.size(0); + uint H = x_real.size(1); + uint N_M = x_real.size(2); + const int d_f_size = d_f.size(0); + // const int TILE_SIZE = 16; + + dim3 gridDim; + dim3 blockDim; + + // uint N = x_real.size(2); + gridDim.y = B; + + blockDim.x = 32; + blockDim.y = 4; + gridDim.x = 512 / (32 * 1024/ (N_M / d_f_size)); + gridDim.z = H; + + const int TILE_H = 16; + torch::Tensor out_real = torch::empty({B, H, fft_size}, x_real.options()); + const int K = ceil(fft_size / (1.0 * 16 * (N_M / d_f_size))); + + switch(d_f_size){ + case 16: + butterfly_ifft_padded_cuda_kernel_16<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + case 32: + switch (K) + { + case 1: + butterfly_ifft_padded_cuda_kernel_32<1><<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + case 2: + butterfly_ifft_padded_cuda_kernel_32<2><<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + default: + printf("Invalid K: %d\n", K); + break; + } + break; + + case 64: + gridDim.z = H / TILE_H; + switch (K) + { + case 1: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 2: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 3: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 4: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + default: + break; + } + + break; + case 128: + blockDim.x = 32; + blockDim.y = 8; + gridDim.x = 256 / (32 * 1024/ (N_M / d_f_size)); + gridDim.z = H / TILE_H; + + switch (K) + { + case 1: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 2: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 3: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 4: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 5: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 6: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 7: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 8: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__half2 *>(x_real.data_ptr()), + static_cast<__half2 *>(x_imag.data_ptr()), + static_cast(d_f.data_ptr()), + static_cast<__half2 *>(twiddle_factors_real.data_ptr()), + static_cast<__half2 *>(twiddle_factors_imag.data_ptr()), + static_cast<__half2 *>(out_real.data_ptr()), + out_gate ? static_cast<__half2 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + default: + printf("Invalid K: %d\n", K); + break; + } + break; + + default: + printf("Invalid d_f_size: %d\n", d_f_size); + break; + } + + return out_real; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda_bf16.cu index 670060f13ccfbe1642d646040b20323130b650bf..3fa1004e53e750447209688d7a57f6812869b97d 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/butterfly_padded_ifft_cuda_bf16.cu @@ -1,917 +1,917 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include "shared.h" - -using namespace nvcuda; - -template -__global__ void butterfly_ifft_padded_cuda_kernel_64( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; - const int in_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * TILE_H * 64 * 32 * gridDim.x; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - const int N = 64; - - extern __shared__ __nv_bfloat16 x_real_shared[]; - __nv_bfloat16 *x_imag_shared = &x_real_shared[N * N]; - __nv_bfloat16 *d_f_real_shared = &x_imag_shared[N * N]; - __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; - __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; - __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; - float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); - - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[K][4]; - wmma::fragment a_frag_imag[K][4]; - wmma::fragment tw_frag_real[4]; - wmma::fragment tw_frag_imag[4]; - wmma::fragment b_frag_real[4]; - wmma::fragment b_frag_imag[4]; - wmma::fragment acc_frag_real[K]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - if(i < K){ -#pragma unroll - for (int j = 0; j < 4; j++) - { - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); - } - } - wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int t = 0; t < TILE_H; t++) - { - - out_t_offset = t * M/2; - t_offset = t * 64 * 32 * gridDim.x; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; - } - - __syncthreads(); - - for (int i = 0; i < 4; i++) - { - wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - for (int j = 0; j < 4; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - for (int i = 0; i < K; i++) - { - wmma::fill_fragment(acc_frag_real[i], 0.0f); - -// bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; - } - } - - for (int i = 0; i < K; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 4; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - -#pragma unroll - for (int i = 0; i < K; i++) - { - wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - - if(idx < max_idx){ - if(out_gate != nullptr) - out_real[out_offset + out_t_offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]), out_gate[out_offset + out_t_offset + idx]); - else - out_real[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); - } - } - - __syncthreads(); - } -} - - -template -__global__ void butterfly_ifft_padded_cuda_kernel_32( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 32; - int idx; - int shared_offset; - - const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int in_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; - - - __shared__ __nv_bfloat16 x_real_shared[32 * 64]; - __shared__ __nv_bfloat16 x_imag_shared[32 * 64]; - __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; - __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; - __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; - __shared__ float out_real_shared[32 * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - int shared_offset = i * 32 + threadIdx.x; - - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[in_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[in_offset + idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - - // #pragma unroll - shared_offset = i * 32 + threadIdx.x; - d_f_real_shared[shared_offset] = d_f_real[shared_offset]; - d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; - } - - __syncthreads(); - - if (threadIdx.y < N/16) - { - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real[K][2]; - wmma::fragment a_frag_imag[K][2]; - wmma::fragment tw_frag_real[2][2]; - wmma::fragment tw_frag_imag[2][2]; - wmma::fragment b_frag_real[2][2]; - wmma::fragment b_frag_imag[2][2]; - wmma::fragment acc_frag_real[K][2]; - - int t = threadIdx.y * 32; - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - if(i < K){ - wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); - wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); - } - wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); - } - } - - for (int i = 0; i < 2; i++) - { - for (int j = 0; j < 2; j++) - { - for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); - b_frag_real[i][j].x[k] = tmp_real; - b_frag_imag[i][j].x[k] = tmp_imag; - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::fill_fragment(acc_frag_real[i][j], 0.0f); - - // bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); - } - - for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) - { - acc_frag_real[i][j].x[k] = - acc_frag_real[i][j].x[k]; - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - // ac - bd - for (int k = 0; k < 2; k++) - { - wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); - } - } - } - - for (int i = 0; i < K; i++) - { - for (int j = 0; j < 2; j++) - { - wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); - } - } - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; - shared_offset = i * 32 + threadIdx.x; - - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[idx + out_offset] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]), out_gate[idx + out_offset]); - }else{ - out_real[idx + out_offset] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); - } - } - - } -} - - -template -__global__ void butterfly_ifft_padded_cuda_kernel_128( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat162 *__restrict__ d_f_real, - const __nv_bfloat162 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; - const int in_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * TILE_H * 128 * 32 * 2 * gridDim.x; - const int N = 128; - int idx; - int t_offset; - int out_t_offset; - int shared_offset; - - - extern __shared__ __nv_bfloat16 real_shared[]; - __nv_bfloat16 *imag_shared = &real_shared[128 * 128]; - __nv_bfloat16 *real_shared_2 = &imag_shared[128 * 128]; - __nv_bfloat16 *imag_shared_2 = &real_shared_2[128 * 128]; - - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag[K][8]; - wmma::fragment tw_frag_real[8]; - wmma::fragment tw_frag_imag[8]; - wmma::fragment b_frag_real[8]; - wmma::fragment b_frag_imag[8]; - wmma::fragment acc_frag_real[K]; - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared_2)[shared_offset] = d_f_real[shared_offset]; - reinterpret_cast<__nv_bfloat162*>(imag_shared_2)[shared_offset] = d_f_imag[shared_offset]; - } - } - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - } - } - - __syncthreads(); - - - for (int i = 0; i < 8; i++){ - wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); - wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); - } - - - for (int t = 0; t < TILE_H; t++) - { - - out_t_offset = t * M/2; - t_offset = t * 128 * 32 * 2 * gridDim.x; - - for (int i = 0; i < K; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; - reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; - } - } - - __syncthreads(); - - for (int i = 0; i < 8; i++) - { - wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); - wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); - } - - - __syncthreads(); - - for (int j = 0; j < 8; j++) - { - for (int k = 0; k < tw_frag_real[j].num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); - b_frag_real[j].x[k] = tmp_real; - b_frag_imag[j].x[k] = tmp_imag; - } - } - - __syncthreads(); - - for (int i = 0; i < K; i++) - { - wmma::fill_fragment(acc_frag_real[i], 0.0f); - -// bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); - } - - for (int k = 0; k < acc_frag_real[i].num_elements; k++) - { - acc_frag_real[i].x[k] = -acc_frag_real[i].x[k]; - } - } - - for (int i = 0; i < K; i++){ - for (int j = 0; j < 8; j++){ - wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); - } - } - - for (int i = 0; i < K; i++) - { -// ac - bd -#pragma unroll - for (int k = 0; k < 8; k++) - { - wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); - } - } - - __syncthreads(); - -#pragma unroll - for (int i = 0; i < K; i++) - { - //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - wmma::store_matrix_sync(reinterpret_cast(real_shared) + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i+=blockDim.y) - { - for(int j=0; j< 2; j++){ - idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; - shared_offset = i * 64 + threadIdx.x + j * blockDim.x; - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[idx + out_offset + out_t_offset] = __hmul2(__float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]), out_gate[idx + out_offset + out_t_offset]); - }else{ - out_real[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]); - } - } - } - } - - __syncthreads(); - } -} - - -__global__ void butterfly_ifft_padded_cuda_kernel_16( - const __nv_bfloat162 *__restrict__ x_real, - const __nv_bfloat162 *__restrict__ x_imag, - const __nv_bfloat16 *__restrict__ d_f_real, - const __nv_bfloat16 *__restrict__ d_f_imag, - const __nv_bfloat162 *__restrict__ twiddle_factors_real, - const __nv_bfloat162 *__restrict__ twiddle_factors_imag, - __nv_bfloat162 *__restrict__ out_real, - __nv_bfloat162 *__restrict__ out_gate, - uint B, - uint H, - int M) -{ - const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= - const int N = 16; - const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; - const int offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; - - __shared__ __nv_bfloat16 x_real_shared[N * 64]; - __shared__ __nv_bfloat16 x_imag_shared[N * 64]; - __shared__ __nv_bfloat16 twiddles_real_shared[N * 64]; - __shared__ __nv_bfloat16 twiddles_imag_shared[N * 64]; - __shared__ float out_real_shared[N * 64]; - - // #pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; - int shared_offset = i * blockDim.x + threadIdx.x; - reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; - reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; - } - - __syncthreads(); - - if (threadIdx.y < 4) - { - __nv_bfloat16 tmp_real, tmp_imag; - - wmma::fragment a_frag_real; - wmma::fragment a_frag_imag; - wmma::fragment tw_frag_real; - wmma::fragment tw_frag_imag; - wmma::fragment b_frag_real; - wmma::fragment b_frag_imag; - wmma::fragment acc_frag_real; - - wmma::load_matrix_sync(a_frag_real, d_f_real, N); - wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); - wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); - wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); - - - for (int k = 0; k < tw_frag_real.num_elements; k++) - { - tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); - tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); - b_frag_real.x[k] = tmp_real; - b_frag_imag.x[k] = tmp_imag; - } - - - - wmma::fill_fragment(acc_frag_real, 0.0f); - - wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); - - for(int k=0; k< acc_frag_real.num_elements; k++){ - acc_frag_real.x[k] = - acc_frag_real.x[k]; - } - - wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); - - wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); - - } - - __syncthreads(); - -#pragma unroll - for (int i = threadIdx.y; i < N; i++) - { - int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; - if(idx < max_idx){ - if(out_gate != nullptr){ - out_real[out_offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]), out_gate[out_offset + idx]); - }else{ - out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); - } - } - } -} - - -torch::Tensor butterfly_ifft_padded_bf16_cuda( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor d_f_real, - torch::Tensor d_f_imag, - torch::Tensor twiddle_factors_real, - torch::Tensor twiddle_factors_imag, - int fft_size, - std::optional out_gate = std::nullopt - ) -{ - - uint B = x_real.size(0); - uint H = x_real.size(1); - uint N_M = x_real.size(2); - const int d_f_size = d_f_real.size(0); - // const int TILE_SIZE = 16; - - dim3 gridDim; - dim3 blockDim; - - // uint N = x_real.size(2); - gridDim.y = B; - - blockDim.x = 32; - blockDim.y = 4; - gridDim.x = 512 / (32 * 1024/ (N_M / d_f_size)); - gridDim.z = H; - - const int TILE_H = 16; - torch::Tensor out_real = torch::empty({B, H, fft_size}, x_real.options()); - const int K = ceil(fft_size / (1.0 * 16 * (N_M / d_f_size))); - - switch(d_f_size){ - case 16: - butterfly_ifft_padded_cuda_kernel_16<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - case 32: - switch (K) - { - case 1: - butterfly_ifft_padded_cuda_kernel_32<1><<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - case 2: - butterfly_ifft_padded_cuda_kernel_32<2><<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size - ); - break; - default: - printf("Invalid K: %d\n", K); - break; - } - break; - - case 64: - gridDim.z = H / TILE_H; - switch (K) - { - case 1: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 2: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 3: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 4: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); - butterfly_ifft_padded_cuda_kernel_64<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - default: - break; - } - - break; - case 128: - blockDim.x = 32; - blockDim.y = 8; - gridDim.x = 256 / (32 * 1024/ (N_M / d_f_size)); - gridDim.z = H / TILE_H; - - switch (K) - { - case 1: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 2: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 3: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 4: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 5: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 6: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 7: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - case 8: - cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); - - butterfly_ifft_padded_cuda_kernel_128<<>>( - static_cast<__nv_bfloat162 *>(x_real.data_ptr()), - static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), - static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), - static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), - static_cast<__nv_bfloat162 *>(out_real.data_ptr()), - out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, - B, - H, - fft_size); - break; - - default: - printf("Invalid K: %d\n", K); - break; - } - break; - - default: - printf("Invalid d_f_size: %d\n", d_f_size); - break; - } - - return out_real; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include "shared.h" + +using namespace nvcuda; + +template +__global__ void butterfly_ifft_padded_cuda_kernel_64( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; + const int in_offset = blockIdx.y * H * 64 * 32 * gridDim.x + blockIdx.z * TILE_H * 64 * 32 * gridDim.x; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + const int N = 64; + + extern __shared__ __nv_bfloat16 x_real_shared[]; + __nv_bfloat16 *x_imag_shared = &x_real_shared[N * N]; + __nv_bfloat16 *d_f_real_shared = &x_imag_shared[N * N]; + __nv_bfloat16 *d_f_imag_shared = &d_f_real_shared[N * N]; + __nv_bfloat16 *twiddles_real_shared = &d_f_imag_shared[N * N]; + __nv_bfloat16 *twiddles_imag_shared = &twiddles_real_shared[N * N]; + float *out_real_shared = reinterpret_cast(&twiddles_imag_shared[N * N]); + + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[K][4]; + wmma::fragment a_frag_imag[K][4]; + wmma::fragment tw_frag_real[4]; + wmma::fragment tw_frag_imag[4]; + wmma::fragment b_frag_real[4]; + wmma::fragment b_frag_imag[4]; + wmma::fragment acc_frag_real[K]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(d_f_real_shared)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162 *>(d_f_imag_shared)[shared_offset] = d_f_imag[shared_offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + if(i < K){ +#pragma unroll + for (int j = 0; j < 4; j++) + { + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); + } + } + wmma::load_matrix_sync(tw_frag_real[i], twiddles_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(tw_frag_imag[i], twiddles_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int t = 0; t < TILE_H; t++) + { + + out_t_offset = t * M/2; + t_offset = t * 64 * 32 * gridDim.x; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; + } + + __syncthreads(); + + for (int i = 0; i < 4; i++) + { + wmma::load_matrix_sync(b_frag_real[i], x_real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], x_imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + for (int j = 0; j < 4; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + for (int i = 0; i < K; i++) + { + wmma::fill_fragment(acc_frag_real[i], 0.0f); + +// bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_imag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = - acc_frag_real[i].x[k]; + } + } + + for (int i = 0; i < K; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 4; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag_real[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + +#pragma unroll + for (int i = 0; i < K; i++) + { + wmma::store_matrix_sync(out_real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + + if(idx < max_idx){ + if(out_gate != nullptr) + out_real[out_offset + out_t_offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]), out_gate[out_offset + out_t_offset + idx]); + else + out_real[out_offset + out_t_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); + } + } + + __syncthreads(); + } +} + + +template +__global__ void butterfly_ifft_padded_cuda_kernel_32( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 32; + int idx; + int shared_offset; + + const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int in_offset = blockIdx.y * H * 32 * 32 * gridDim.x + blockIdx.z * 32 * 32 * gridDim.x; + + + __shared__ __nv_bfloat16 x_real_shared[32 * 64]; + __shared__ __nv_bfloat16 x_imag_shared[32 * 64]; + __shared__ __nv_bfloat16 d_f_real_shared[32 * 32]; + __shared__ __nv_bfloat16 d_f_imag_shared[32 * 32]; + __shared__ __nv_bfloat16 twiddles_real_shared[32 * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[32 * 64]; + __shared__ float out_real_shared[32 * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + int shared_offset = i * 32 + threadIdx.x; + + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[in_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[in_offset + idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + + // #pragma unroll + shared_offset = i * 32 + threadIdx.x; + d_f_real_shared[shared_offset] = d_f_real[shared_offset]; + d_f_imag_shared[shared_offset] = d_f_imag[shared_offset]; + } + + __syncthreads(); + + if (threadIdx.y < N/16) + { + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real[K][2]; + wmma::fragment a_frag_imag[K][2]; + wmma::fragment tw_frag_real[2][2]; + wmma::fragment tw_frag_imag[2][2]; + wmma::fragment b_frag_real[2][2]; + wmma::fragment b_frag_imag[2][2]; + wmma::fragment acc_frag_real[K][2]; + + int t = threadIdx.y * 32; + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + if(i < K){ + wmma::load_matrix_sync(a_frag_real[i][j], d_f_real_shared + j * N * 16 + i * 16, N); + wmma::load_matrix_sync(a_frag_imag[i][j], d_f_imag_shared + j * N * 16 + i * 16, N); + } + wmma::load_matrix_sync(b_frag_real[i][j], x_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(b_frag_imag[i][j], x_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_real[i][j], twiddles_real_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + wmma::load_matrix_sync(tw_frag_imag[i][j], twiddles_imag_shared + i * 2 * N * 16 + j * 16 + t, 2 * N); + } + } + + for (int i = 0; i < 2; i++) + { + for (int j = 0; j < 2; j++) + { + for (int k = 0; k < tw_frag_real[i][j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[i][j].x[k], b_frag_real[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_imag[i][j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[i][j].x[k], b_frag_imag[i][j].x[k]), __hmul(tw_frag_imag[i][j].x[k], b_frag_real[i][j].x[k])); + b_frag_real[i][j].x[k] = tmp_real; + b_frag_imag[i][j].x[k] = tmp_imag; + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::fill_fragment(acc_frag_real[i][j], 0.0f); + + // bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_imag[i][k], b_frag_imag[k][j], acc_frag_real[i][j]); + } + + for (int k = 0; k < acc_frag_real[i][j].num_elements; k++) + { + acc_frag_real[i][j].x[k] = - acc_frag_real[i][j].x[k]; + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + // ac - bd + for (int k = 0; k < 2; k++) + { + wmma::mma_sync(acc_frag_real[i][j], a_frag_real[i][k], b_frag_real[k][j], acc_frag_real[i][j]); + } + } + } + + for (int i = 0; i < K; i++) + { + for (int j = 0; j < 2; j++) + { + wmma::store_matrix_sync(out_real_shared + i * 2 * N * 16 + j * 16 + t, acc_frag_real[i][j], 2 * N, wmma::mem_row_major); + } + } + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + idx = i * 32 * gridDim.x + blockIdx.x * 32 + threadIdx.x; + shared_offset = i * 32 + threadIdx.x; + + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[idx + out_offset] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]), out_gate[idx + out_offset]); + }else{ + out_real[idx + out_offset] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[shared_offset]); + } + } + + } +} + + +template +__global__ void butterfly_ifft_padded_cuda_kernel_128( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat162 *__restrict__ d_f_real, + const __nv_bfloat162 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int out_offset = blockIdx.y * H * M/2 + blockIdx.z * TILE_H * M/2; + const int in_offset = blockIdx.y * H * 128 * 32 * 2 * gridDim.x + blockIdx.z * TILE_H * 128 * 32 * 2 * gridDim.x; + const int N = 128; + int idx; + int t_offset; + int out_t_offset; + int shared_offset; + + + extern __shared__ __nv_bfloat16 real_shared[]; + __nv_bfloat16 *imag_shared = &real_shared[128 * 128]; + __nv_bfloat16 *real_shared_2 = &imag_shared[128 * 128]; + __nv_bfloat16 *imag_shared_2 = &real_shared_2[128 * 128]; + + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag[K][8]; + wmma::fragment tw_frag_real[8]; + wmma::fragment tw_frag_imag[8]; + wmma::fragment b_frag_real[8]; + wmma::fragment b_frag_imag[8]; + wmma::fragment acc_frag_real[K]; + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared_2)[shared_offset] = d_f_real[shared_offset]; + reinterpret_cast<__nv_bfloat162*>(imag_shared_2)[shared_offset] = d_f_imag[shared_offset]; + } + } + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + } + } + + __syncthreads(); + + + for (int i = 0; i < 8; i++){ + wmma::load_matrix_sync(tw_frag_real[i], real_shared + i * 128 * 16 + threadIdx.y * 16, 128); + wmma::load_matrix_sync(tw_frag_imag[i], imag_shared + i * 128 * 16 + threadIdx.y * 16, 128); + } + + + for (int t = 0; t < TILE_H; t++) + { + + out_t_offset = t * M/2; + t_offset = t * 128 * 32 * 2 * gridDim.x; + + for (int i = 0; i < K; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], imag_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + reinterpret_cast<__nv_bfloat162*>(real_shared)[shared_offset] = x_real[idx + in_offset + t_offset]; + reinterpret_cast<__nv_bfloat162*>(imag_shared)[shared_offset] = x_imag[idx + in_offset + t_offset]; + } + } + + __syncthreads(); + + for (int i = 0; i < 8; i++) + { + wmma::load_matrix_sync(b_frag_real[i], real_shared + i * N * 16 + threadIdx.y * 16, N); + wmma::load_matrix_sync(b_frag_imag[i], imag_shared + i * N * 16 + threadIdx.y * 16, N); + } + + + __syncthreads(); + + for (int j = 0; j < 8; j++) + { + for (int k = 0; k < tw_frag_real[j].num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real[j].x[k], b_frag_real[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_imag[j].x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real[j].x[k], b_frag_imag[j].x[k]), __hmul(tw_frag_imag[j].x[k], b_frag_real[j].x[k])); + b_frag_real[j].x[k] = tmp_real; + b_frag_imag[j].x[k] = tmp_imag; + } + } + + __syncthreads(); + + for (int i = 0; i < K; i++) + { + wmma::fill_fragment(acc_frag_real[i], 0.0f); + +// bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_imag[k], acc_frag_real[i]); + } + + for (int k = 0; k < acc_frag_real[i].num_elements; k++) + { + acc_frag_real[i].x[k] = -acc_frag_real[i].x[k]; + } + } + + for (int i = 0; i < K; i++){ + for (int j = 0; j < 8; j++){ + wmma::load_matrix_sync(a_frag[i][j], real_shared_2 + j * 128 * 16 + i * 16, 128); + } + } + + for (int i = 0; i < K; i++) + { +// ac - bd +#pragma unroll + for (int k = 0; k < 8; k++) + { + wmma::mma_sync(acc_frag_real[i], a_frag[i][k], b_frag_real[k], acc_frag_real[i]); + } + } + + __syncthreads(); + +#pragma unroll + for (int i = 0; i < K; i++) + { + //wmma::store_matrix_sync(real_shared + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + wmma::store_matrix_sync(reinterpret_cast(real_shared) + i * N * 16 + threadIdx.y * 16, acc_frag_real[i], N, wmma::mem_row_major); + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i+=blockDim.y) + { + for(int j=0; j< 2; j++){ + idx = i * 32 * 2 * gridDim.x + j * blockDim.x + blockIdx.x * 64 + threadIdx.x; + shared_offset = i * 64 + threadIdx.x + j * blockDim.x; + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[idx + out_offset + out_t_offset] = __hmul2(__float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]), out_gate[idx + out_offset + out_t_offset]); + }else{ + out_real[idx + out_offset + out_t_offset] = __float22bfloat162_rn(reinterpret_cast(real_shared)[shared_offset]); + } + } + } + } + + __syncthreads(); + } +} + + +__global__ void butterfly_ifft_padded_cuda_kernel_16( + const __nv_bfloat162 *__restrict__ x_real, + const __nv_bfloat162 *__restrict__ x_imag, + const __nv_bfloat16 *__restrict__ d_f_real, + const __nv_bfloat16 *__restrict__ d_f_imag, + const __nv_bfloat162 *__restrict__ twiddle_factors_real, + const __nv_bfloat162 *__restrict__ twiddle_factors_imag, + __nv_bfloat162 *__restrict__ out_real, + __nv_bfloat162 *__restrict__ out_gate, + uint B, + uint H, + int M) +{ + const int max_idx = M / 2; //actually should be -1 since indices are 0-based but we are using < instead of <= + const int N = 16; + const int out_offset = blockIdx.y * H * M / 2 + blockIdx.z * M / 2; + const int offset = blockIdx.y * H * N * blockDim.x * gridDim.x + blockIdx.z * N * blockDim.x * gridDim.x; + + __shared__ __nv_bfloat16 x_real_shared[N * 64]; + __shared__ __nv_bfloat16 x_imag_shared[N * 64]; + __shared__ __nv_bfloat16 twiddles_real_shared[N * 64]; + __shared__ __nv_bfloat16 twiddles_imag_shared[N * 64]; + __shared__ float out_real_shared[N * 64]; + + // #pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; + int shared_offset = i * blockDim.x + threadIdx.x; + reinterpret_cast<__nv_bfloat162 *>(x_real_shared)[shared_offset] = x_real[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(x_imag_shared)[shared_offset] = x_imag[idx + offset]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_real_shared)[shared_offset] = twiddle_factors_real[idx]; + reinterpret_cast<__nv_bfloat162 *>(twiddles_imag_shared)[shared_offset] = twiddle_factors_imag[idx]; + } + + __syncthreads(); + + if (threadIdx.y < 4) + { + __nv_bfloat16 tmp_real, tmp_imag; + + wmma::fragment a_frag_real; + wmma::fragment a_frag_imag; + wmma::fragment tw_frag_real; + wmma::fragment tw_frag_imag; + wmma::fragment b_frag_real; + wmma::fragment b_frag_imag; + wmma::fragment acc_frag_real; + + wmma::load_matrix_sync(a_frag_real, d_f_real, N); + wmma::load_matrix_sync(a_frag_imag, d_f_imag, N); + wmma::load_matrix_sync(b_frag_real, x_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(b_frag_imag, x_imag_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_real, twiddles_real_shared + threadIdx.y * 16, 64); + wmma::load_matrix_sync(tw_frag_imag, twiddles_imag_shared + threadIdx.y * 16, 64); + + + for (int k = 0; k < tw_frag_real.num_elements; k++) + { + tmp_real = __hsub(__hmul(tw_frag_real.x[k], b_frag_real.x[k]), __hmul(tw_frag_imag.x[k], b_frag_imag.x[k])); + tmp_imag = __hadd(__hmul(tw_frag_real.x[k], b_frag_imag.x[k]), __hmul(tw_frag_imag.x[k], b_frag_real.x[k])); + b_frag_real.x[k] = tmp_real; + b_frag_imag.x[k] = tmp_imag; + } + + + + wmma::fill_fragment(acc_frag_real, 0.0f); + + wmma::mma_sync(acc_frag_real, a_frag_imag, b_frag_imag, acc_frag_real); + + for(int k=0; k< acc_frag_real.num_elements; k++){ + acc_frag_real.x[k] = - acc_frag_real.x[k]; + } + + wmma::mma_sync(acc_frag_real, a_frag_real, b_frag_real, acc_frag_real); + + wmma::store_matrix_sync(out_real_shared + threadIdx.y * 16, acc_frag_real, 64, wmma::mem_row_major); + + } + + __syncthreads(); + +#pragma unroll + for (int i = threadIdx.y; i < N; i++) + { + int idx = i * blockDim.x * gridDim.x + blockIdx.x * blockDim.x + threadIdx.x; + if(idx < max_idx){ + if(out_gate != nullptr){ + out_real[out_offset + idx] = __hmul2(__float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]), out_gate[out_offset + idx]); + }else{ + out_real[out_offset + idx] = __float22bfloat162_rn(reinterpret_cast(out_real_shared)[i * 32 + threadIdx.x]); + } + } + } +} + + +torch::Tensor butterfly_ifft_padded_bf16_cuda( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor d_f_real, + torch::Tensor d_f_imag, + torch::Tensor twiddle_factors_real, + torch::Tensor twiddle_factors_imag, + int fft_size, + std::optional out_gate = std::nullopt + ) +{ + + uint B = x_real.size(0); + uint H = x_real.size(1); + uint N_M = x_real.size(2); + const int d_f_size = d_f_real.size(0); + // const int TILE_SIZE = 16; + + dim3 gridDim; + dim3 blockDim; + + // uint N = x_real.size(2); + gridDim.y = B; + + blockDim.x = 32; + blockDim.y = 4; + gridDim.x = 512 / (32 * 1024/ (N_M / d_f_size)); + gridDim.z = H; + + const int TILE_H = 16; + torch::Tensor out_real = torch::empty({B, H, fft_size}, x_real.options()); + const int K = ceil(fft_size / (1.0 * 16 * (N_M / d_f_size))); + + switch(d_f_size){ + case 16: + butterfly_ifft_padded_cuda_kernel_16<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + case 32: + switch (K) + { + case 1: + butterfly_ifft_padded_cuda_kernel_32<1><<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + case 2: + butterfly_ifft_padded_cuda_kernel_32<2><<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat16 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size + ); + break; + default: + printf("Invalid K: %d\n", K); + break; + } + break; + + case 64: + gridDim.z = H / TILE_H; + switch (K) + { + case 1: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 2: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 3: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 4: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_64, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536); + butterfly_ifft_padded_cuda_kernel_64<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + default: + break; + } + + break; + case 128: + blockDim.x = 32; + blockDim.y = 8; + gridDim.x = 256 / (32 * 1024/ (N_M / d_f_size)); + gridDim.z = H / TILE_H; + + switch (K) + { + case 1: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 2: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 3: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 4: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 5: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 6: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 7: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + case 8: + cudaFuncSetAttribute(&butterfly_ifft_padded_cuda_kernel_128, cudaFuncAttributeMaxDynamicSharedMemorySize, 65536 * 2); + + butterfly_ifft_padded_cuda_kernel_128<<>>( + static_cast<__nv_bfloat162 *>(x_real.data_ptr()), + static_cast<__nv_bfloat162 *>(x_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_real.data_ptr()), + static_cast<__nv_bfloat162 *>(d_f_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_real.data_ptr()), + static_cast<__nv_bfloat162 *>(twiddle_factors_imag.data_ptr()), + static_cast<__nv_bfloat162 *>(out_real.data_ptr()), + out_gate ? static_cast<__nv_bfloat162 *>(out_gate.value().data_ptr()) : nullptr, + B, + H, + fft_size); + break; + + default: + printf("Invalid K: %d\n", K); + break; + } + break; + + default: + printf("Invalid d_f_size: %d\n", d_f_size); + break; + } + + return out_real; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/shared.h b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/shared.h index 5f5942e63717c268026e720f4b5a6fa366278aa6..8d34b26019c8c21adfab442f39bd375bee0e1b32 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/butterfly/shared.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/butterfly/shared.h @@ -1,60 +1,60 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_half_t = typename c10::complex; -using complex_bhalf_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_H_ -#define MONARCH_CUDA_H_ - -__device__ __forceinline__ float2 - -operator+( float2 lhs, float2 rhs) - -{ - - float2 res = { lhs.x + rhs.x , lhs.y + rhs.y }; - - return res; - -} - - -__device__ __forceinline__ float2 - -operator-( float2 lhs, float2 rhs) - -{ - - float2 res = { lhs.x - rhs.x , lhs.y - rhs.y }; - - return res; - -} - -__device__ __forceinline__ float2 - -operator*( float2 lhs, float2 rhs) - -{ - - float2 res = { lhs.x * rhs.x , lhs.y * rhs.y }; - - return res; - -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_half_t = typename c10::complex; +using complex_bhalf_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_H_ +#define MONARCH_CUDA_H_ + +__device__ __forceinline__ float2 + +operator+( float2 lhs, float2 rhs) + +{ + + float2 res = { lhs.x + rhs.x , lhs.y + rhs.y }; + + return res; + +} + + +__device__ __forceinline__ float2 + +operator-( float2 lhs, float2 rhs) + +{ + + float2 res = { lhs.x - rhs.x , lhs.y - rhs.y }; + + return res; + +} + +__device__ __forceinline__ float2 + +operator*( float2 lhs, float2 rhs) + +{ + + float2 res = { lhs.x * rhs.x , lhs.y * rhs.y }; + + return res; + +} #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d.h b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d.h index 5de9bd2b219d661ef8e62cc9c99c870a587163a2..e89a2a9936668b29bf9f7265fe7402aac62c78dd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d.h @@ -1,96 +1,96 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT_OR_FLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16 || x.dtype() == torch::kFloat32, #x " must be float16 or bfloat16 or float32") -#define CHECK_SAME_TYPE(x, y) TORCH_CHECK(x.dtype() == y.dtype(), #x " and " #y " must have the same dtype") - -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT_OR_FLOAT(x) - -torch::Tensor conv1d_cuda_bhl( - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding); - -torch::Tensor conv1d_cuda_blh( - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding); - -std::vector conv1d_backward_bhl_cuda( - torch::Tensor dout, - torch::Tensor input, - torch::Tensor weight, - torch::Tensor bias, - uint padding -); - -std::vector conv1d_backward_blh_cuda( - torch::Tensor dout, - torch::Tensor input, - torch::Tensor weight, - torch::Tensor bias, - uint padding -); - - -torch::Tensor conv1d_fwd( - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding, - bool is_bhl) -{ - CHECK_INPUT(u); - CHECK_INPUT(weight); - CHECK_INPUT(bias); - CHECK_SAME_TYPE(weight, bias); - - int k; - - if(is_bhl){ - k = weight.size(1); - }else{ - k = weight.size(0); - } - - TORCH_CHECK(k % 2 == 1, "Filter size must be odd number"); - - if(is_bhl){ - return conv1d_cuda_bhl(u, weight, bias, padding); - }else{ - return conv1d_cuda_blh(u, weight, bias, padding); - } -} - -std::vector conv1d_bwd( - torch::Tensor dout, - torch::Tensor input, - torch::Tensor weight, - torch::Tensor bias, - uint padding, - bool is_bhl) -{ - CHECK_INPUT(dout); - CHECK_INPUT(input); - CHECK_INPUT(weight); - CHECK_INPUT(bias); - CHECK_SAME_TYPE(weight, bias); - CHECK_SAME_TYPE(dout, input); - - if(is_bhl){ - return conv1d_backward_bhl_cuda(dout, input, weight, bias, padding); - } else{ - return conv1d_backward_blh_cuda(dout, input, weight, bias, padding); - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT_OR_FLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16 || x.dtype() == torch::kFloat32, #x " must be float16 or bfloat16 or float32") +#define CHECK_SAME_TYPE(x, y) TORCH_CHECK(x.dtype() == y.dtype(), #x " and " #y " must have the same dtype") + +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT_OR_FLOAT(x) + +torch::Tensor conv1d_cuda_bhl( + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding); + +torch::Tensor conv1d_cuda_blh( + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding); + +std::vector conv1d_backward_bhl_cuda( + torch::Tensor dout, + torch::Tensor input, + torch::Tensor weight, + torch::Tensor bias, + uint padding +); + +std::vector conv1d_backward_blh_cuda( + torch::Tensor dout, + torch::Tensor input, + torch::Tensor weight, + torch::Tensor bias, + uint padding +); + + +torch::Tensor conv1d_fwd( + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding, + bool is_bhl) +{ + CHECK_INPUT(u); + CHECK_INPUT(weight); + CHECK_INPUT(bias); + CHECK_SAME_TYPE(weight, bias); + + int k; + + if(is_bhl){ + k = weight.size(1); + }else{ + k = weight.size(0); + } + + TORCH_CHECK(k % 2 == 1, "Filter size must be odd number"); + + if(is_bhl){ + return conv1d_cuda_bhl(u, weight, bias, padding); + }else{ + return conv1d_cuda_blh(u, weight, bias, padding); + } +} + +std::vector conv1d_bwd( + torch::Tensor dout, + torch::Tensor input, + torch::Tensor weight, + torch::Tensor bias, + uint padding, + bool is_bhl) +{ + CHECK_INPUT(dout); + CHECK_INPUT(input); + CHECK_INPUT(weight); + CHECK_INPUT(bias); + CHECK_SAME_TYPE(weight, bias); + CHECK_SAME_TYPE(dout, input); + + if(is_bhl){ + return conv1d_backward_bhl_cuda(dout, input, weight, bias, padding); + } else{ + return conv1d_backward_blh_cuda(dout, input, weight, bias, padding); + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bhl.cu b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bhl.cu index 78e8c46d0f7f4a610c855a0d5f56615c7f913d44..f731f4ececbc9414b61e7dd2140d45fdacb8841f 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bhl.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bhl.cu @@ -1,132 +1,132 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -// Simple 1D depthwise convolution implementation with dilation and stride = 1 -#include "shared.h" - -const uint BX = 256; -const uint BY = 1; -const uint BZ = 1; - -const uint TILE_SIZE_L = 4; -const uint TILE_SIZE_D = 1; - -template -__forceinline__ __device__ T _conv1d_k_3(const T* u, const U* weights, const U* bias, uint padding, uint l, uint d, uint L, uint D, uint K) -{ - T tmp; - T weight; - - set_value(&tmp, bias[d]); - - int idx = l - padding; - - if(idx >= 0 && idx < L){ - set_value(&weight, weights[0]); - tmp = __hfma(u[d * L + idx], weight, tmp); - } - - idx++; - if(idx >= 0 && idx < L){ - set_value(&weight, weights[1]); - tmp = __hfma(u[d * L + idx], weight, tmp); - } - - idx++; - if(idx >= 0 && idx < L){ - set_value(&weight, weights[2]); - tmp = __hfma(u[d * L + idx], weight, tmp); - } - - return tmp; -} - -template -__global__ void conv1d_kernel( - const T *__restrict__ u, - const U *__restrict__ weights, - const U *__restrict__ bias, - T *__restrict__ out, - uint padding, - uint B, - uint L, - uint D, - uint K, - uint L_out - ) -{ - const int b = blockIdx.z * blockDim.z + threadIdx.z; - const int d = blockIdx.y * blockDim.y * TILE_SIZE_D + threadIdx.y; - const int l_offset = blockIdx.x * blockDim.x * TILE_SIZE_L + threadIdx.x; - - T tmp; - T weight; - - int idx; - int l; - - for(int l_tile = 0; l_tile < TILE_SIZE_L; l_tile++){ - l = l_offset + l_tile * blockDim.x; - - set_value(&tmp, bias[d]); - - if(d < D && l < L_out && b < B){ - if(K == 3){ - out[b * L_out * D + d * L_out + l] = _conv1d_k_3(u + b * L * D, weights + d * K, bias, padding, l, d, L, D, K); - } else{ - for(int k = 0; k < K; k++){ - idx = l - padding + k; - if(idx >= 0 && idx < L){ - set_value(&weight, weights[d * K + k]); - tmp = __hfma(u[b * L_out * D + d * L + idx], weight, tmp); - } - } - out[b * L_out * D + d * L_out + l] = tmp; - - } - } - } - -} - -torch::Tensor conv1d_cuda_bhl( - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding) -{ - const uint b = u.size(0); - const uint d = u.size(1); - const uint l = u.size(2); - - - const uint k = weight.size(1); - - uint l_out = (l + 2 * padding - k + 1); - - dim3 blockDims(BX, BY, BZ); - - dim3 gridDims(ceil(l_out * 1.0 / (BX * TILE_SIZE_L) ), ceil((d * 1.0) / (BY * TILE_SIZE_D)), ceil((b * 1.0) / BZ)); - - torch::Tensor out = torch::empty({b, d, l_out}, u.options()); - - DISPATCH_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), weight.scalar_type(), - "depthwise conv 1d fwd bhl", - ([&] - { conv1d_kernel<<>>( - static_cast(u.data_ptr()), - static_cast(weight.data_ptr()), - static_cast(bias.data_ptr()), - static_cast(out.data_ptr()), - padding, - b, - l, - d, - k, - l_out - ); - } - ) - ); - - return out; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +// Simple 1D depthwise convolution implementation with dilation and stride = 1 +#include "shared.h" + +const uint BX = 256; +const uint BY = 1; +const uint BZ = 1; + +const uint TILE_SIZE_L = 4; +const uint TILE_SIZE_D = 1; + +template +__forceinline__ __device__ T _conv1d_k_3(const T* u, const U* weights, const U* bias, uint padding, uint l, uint d, uint L, uint D, uint K) +{ + T tmp; + T weight; + + set_value(&tmp, bias[d]); + + int idx = l - padding; + + if(idx >= 0 && idx < L){ + set_value(&weight, weights[0]); + tmp = __hfma(u[d * L + idx], weight, tmp); + } + + idx++; + if(idx >= 0 && idx < L){ + set_value(&weight, weights[1]); + tmp = __hfma(u[d * L + idx], weight, tmp); + } + + idx++; + if(idx >= 0 && idx < L){ + set_value(&weight, weights[2]); + tmp = __hfma(u[d * L + idx], weight, tmp); + } + + return tmp; +} + +template +__global__ void conv1d_kernel( + const T *__restrict__ u, + const U *__restrict__ weights, + const U *__restrict__ bias, + T *__restrict__ out, + uint padding, + uint B, + uint L, + uint D, + uint K, + uint L_out + ) +{ + const int b = blockIdx.z * blockDim.z + threadIdx.z; + const int d = blockIdx.y * blockDim.y * TILE_SIZE_D + threadIdx.y; + const int l_offset = blockIdx.x * blockDim.x * TILE_SIZE_L + threadIdx.x; + + T tmp; + T weight; + + int idx; + int l; + + for(int l_tile = 0; l_tile < TILE_SIZE_L; l_tile++){ + l = l_offset + l_tile * blockDim.x; + + set_value(&tmp, bias[d]); + + if(d < D && l < L_out && b < B){ + if(K == 3){ + out[b * L_out * D + d * L_out + l] = _conv1d_k_3(u + b * L * D, weights + d * K, bias, padding, l, d, L, D, K); + } else{ + for(int k = 0; k < K; k++){ + idx = l - padding + k; + if(idx >= 0 && idx < L){ + set_value(&weight, weights[d * K + k]); + tmp = __hfma(u[b * L_out * D + d * L + idx], weight, tmp); + } + } + out[b * L_out * D + d * L_out + l] = tmp; + + } + } + } + +} + +torch::Tensor conv1d_cuda_bhl( + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding) +{ + const uint b = u.size(0); + const uint d = u.size(1); + const uint l = u.size(2); + + + const uint k = weight.size(1); + + uint l_out = (l + 2 * padding - k + 1); + + dim3 blockDims(BX, BY, BZ); + + dim3 gridDims(ceil(l_out * 1.0 / (BX * TILE_SIZE_L) ), ceil((d * 1.0) / (BY * TILE_SIZE_D)), ceil((b * 1.0) / BZ)); + + torch::Tensor out = torch::empty({b, d, l_out}, u.options()); + + DISPATCH_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), weight.scalar_type(), + "depthwise conv 1d fwd bhl", + ([&] + { conv1d_kernel<<>>( + static_cast(u.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(out.data_ptr()), + padding, + b, + l, + d, + k, + l_out + ); + } + ) + ); + + return out; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_blh.cu b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_blh.cu index 4a518196c870ce6f468910072b4ed51308ff378f..e83b6b52f52601d750c4a76ab267411153c1c283 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_blh.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_blh.cu @@ -1,202 +1,202 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -// Simple 1D depthwise convolution implementation with dilation and stride = 1 - -#include "shared.h" - -//For max perf, tune for your GPU and batch size, and datatype etc -const uint BX = 512; -const uint BY = 1; -const uint BZ = 1; - -const uint TILE_SIZE_Y = 4; -const uint TILE_SIZE_X = 2; - -// Trick to do padding in place without actually creating a new tensor -__forceinline__ __device__ __half2 get_u(const __half2 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) -{ - return l + k < p || l + k > L_eff - (p + 1) ? __float2half2_rn(0.0f) : u[b * L * D + (l + k - p) * D + d]; -} - - -__forceinline__ __device__ __nv_bfloat162 get_u(const __nv_bfloat162 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) -{ - return l + k < p || l + k > L_eff - (p + 1) ? __float2bfloat162_rn(0.0f) : u[b * L * D + (l + k - p) * D + d]; -} - -__forceinline__ __device__ float2 get_u(const float2 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) -{ - return l + k < p || l + k > L_eff - (p + 1) ? make_float2(0.0f, 0.0f) : u[b * L * D + (l + k - p) * D + d]; -} - - -//manually unrolling loop for k = 3 leads to good perf, can easily extend for other values of k if need be -template -__forceinline__ __device__ T _conv1d_k_3(const T* u, const U* weights, const U* bias, T* out, uint padding, uint b, uint l, uint d, uint t, uint L, uint D, uint K, uint L_eff, uint L_out) -{ - - T tmp; - T weight; - set_value(&tmp, bias[d]); - - set_value(&weight, weights[0 * D + d]); - tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, 0, d, L, D, K), weight, tmp); - - set_value(&weight, weights[1 * D + d]); - tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, 1, d, L, D, K), weight, tmp); - - set_value(&weight, weights[2 * D + d]); - out[b * D * L_out + (l + t) * D + d] = __hfma2(get_u(u, L_eff, l + t, padding, b, 2, d, L, D, K), weight, tmp); - -} - -template -__global__ void conv1d_kernel_k_3( - const T *__restrict__ u, - const U *__restrict__ weights, - const U *__restrict__ bias, - T *__restrict__ out, - uint padding, - uint B, - uint L, - uint L_out, - uint L_eff, - uint D, - uint K) -{ - const int d_block = blockIdx.x * blockDim.x * TILE_SIZE_X; - const int l = blockIdx.y * blockDim.y * TILE_SIZE_Y + threadIdx.y * TILE_SIZE_Y; - const int b = blockIdx.z * blockDim.z + threadIdx.z; - - int d; - - #pragma unroll - for (int i = 0; i < TILE_SIZE_X; i++) - { - d = d_block + threadIdx.x + i * BX; - - if (d < D && b < B){ - #pragma unroll - for (int t = 0; t < TILE_SIZE_Y; t++){ - if (l + t < L_eff - K + 1) - { - _conv1d_k_3(u, weights, bias, out, padding, b, l, d, t, L, D, K, L_eff, L_out); - } - } - } - } -} - -template -__global__ void conv1d_kernel( - const T *__restrict__ u, - const U *__restrict__ weights, - const U *__restrict__ bias, - T *__restrict__ out, - uint padding, - uint B, - uint L, - uint L_out, - uint L_eff, - uint D, - uint K) -{ - const int d_block = blockIdx.x * blockDim.x * TILE_SIZE_X; - const int l = blockIdx.y * blockDim.y * TILE_SIZE_Y + threadIdx.y * TILE_SIZE_Y; - const int b = blockIdx.z * blockDim.z + threadIdx.z; - - int d; - T tmp; - T weight; - - #pragma unroll - for (int i = 0; i < TILE_SIZE_X; i++) - { - d = d_block + threadIdx.x + i * BX; - - if (d < D && b < B){ - #pragma unroll - for (int t = 0; t < TILE_SIZE_Y; t++){ - if (l + t < L_eff - K + 1) - { - set_value(&tmp, bias[d]); - - for(int k = 0; k < K; k++){ - set_value(&weight, weights[k * D + d]); - - tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, k, d, L, D, K), weight, tmp); - } - out[b * D * L_out + (l + t) * D + d] = tmp; - } - } - } - } -} - -torch::Tensor conv1d_cuda_blh( - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding) -{ - const uint b = u.size(0); - const uint l = u.size(1); - const uint d = u.size(2); - - const uint k = weight.size(0); - - uint l_eff = l + 2 * padding; - - - - dim3 blockDims(BX, BY, BZ); - - dim3 gridDims(ceil(d * 1.0 / (BX * TILE_SIZE_X * 2) ), ceil((l_eff - k + 1) * 1.0 / (BY * TILE_SIZE_Y)), ceil(b * 1.0 / BZ)); - - - uint l_out = (l + 2 * padding - k + 1); - - torch::Tensor out = torch::empty({b, l_out, d}, u.options()); - - //calling seperate kernels for k=3 and k!=3 leads to better perf - if(k==3){ - DISPATCH_FLOAT2_AND_HALF2_AND_BF162(u.scalar_type(), weight.scalar_type(), - "depthwise conv 1d fwd blh", - ([&] - { conv1d_kernel_k_3<<>>( - static_cast(u.data_ptr()), - static_cast(weight.data_ptr()), - static_cast(bias.data_ptr()), - static_cast(out.data_ptr()), - padding, - b, - l, - l_out, - l_eff, - ceil(d/2), - k); - } - ) - ); - }else{ - DISPATCH_FLOAT2_AND_HALF2_AND_BF162(u.scalar_type(), weight.scalar_type(), - "depthwise conv 1d fwd blh", - ([&] - { conv1d_kernel<<>>( - static_cast(u.data_ptr()), - static_cast(weight.data_ptr()), - static_cast(bias.data_ptr()), - static_cast(out.data_ptr()), - padding, - b, - l, - l_out, - l_eff, - ceil(d/2), - k); - } - ) - ); - } - return out; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +// Simple 1D depthwise convolution implementation with dilation and stride = 1 + +#include "shared.h" + +//For max perf, tune for your GPU and batch size, and datatype etc +const uint BX = 512; +const uint BY = 1; +const uint BZ = 1; + +const uint TILE_SIZE_Y = 4; +const uint TILE_SIZE_X = 2; + +// Trick to do padding in place without actually creating a new tensor +__forceinline__ __device__ __half2 get_u(const __half2 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) +{ + return l + k < p || l + k > L_eff - (p + 1) ? __float2half2_rn(0.0f) : u[b * L * D + (l + k - p) * D + d]; +} + + +__forceinline__ __device__ __nv_bfloat162 get_u(const __nv_bfloat162 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) +{ + return l + k < p || l + k > L_eff - (p + 1) ? __float2bfloat162_rn(0.0f) : u[b * L * D + (l + k - p) * D + d]; +} + +__forceinline__ __device__ float2 get_u(const float2 *__restrict__ u, uint L_eff, uint l, uint p, uint b, uint k, uint d, uint L, uint D, uint K) +{ + return l + k < p || l + k > L_eff - (p + 1) ? make_float2(0.0f, 0.0f) : u[b * L * D + (l + k - p) * D + d]; +} + + +//manually unrolling loop for k = 3 leads to good perf, can easily extend for other values of k if need be +template +__forceinline__ __device__ T _conv1d_k_3(const T* u, const U* weights, const U* bias, T* out, uint padding, uint b, uint l, uint d, uint t, uint L, uint D, uint K, uint L_eff, uint L_out) +{ + + T tmp; + T weight; + set_value(&tmp, bias[d]); + + set_value(&weight, weights[0 * D + d]); + tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, 0, d, L, D, K), weight, tmp); + + set_value(&weight, weights[1 * D + d]); + tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, 1, d, L, D, K), weight, tmp); + + set_value(&weight, weights[2 * D + d]); + out[b * D * L_out + (l + t) * D + d] = __hfma2(get_u(u, L_eff, l + t, padding, b, 2, d, L, D, K), weight, tmp); + +} + +template +__global__ void conv1d_kernel_k_3( + const T *__restrict__ u, + const U *__restrict__ weights, + const U *__restrict__ bias, + T *__restrict__ out, + uint padding, + uint B, + uint L, + uint L_out, + uint L_eff, + uint D, + uint K) +{ + const int d_block = blockIdx.x * blockDim.x * TILE_SIZE_X; + const int l = blockIdx.y * blockDim.y * TILE_SIZE_Y + threadIdx.y * TILE_SIZE_Y; + const int b = blockIdx.z * blockDim.z + threadIdx.z; + + int d; + + #pragma unroll + for (int i = 0; i < TILE_SIZE_X; i++) + { + d = d_block + threadIdx.x + i * BX; + + if (d < D && b < B){ + #pragma unroll + for (int t = 0; t < TILE_SIZE_Y; t++){ + if (l + t < L_eff - K + 1) + { + _conv1d_k_3(u, weights, bias, out, padding, b, l, d, t, L, D, K, L_eff, L_out); + } + } + } + } +} + +template +__global__ void conv1d_kernel( + const T *__restrict__ u, + const U *__restrict__ weights, + const U *__restrict__ bias, + T *__restrict__ out, + uint padding, + uint B, + uint L, + uint L_out, + uint L_eff, + uint D, + uint K) +{ + const int d_block = blockIdx.x * blockDim.x * TILE_SIZE_X; + const int l = blockIdx.y * blockDim.y * TILE_SIZE_Y + threadIdx.y * TILE_SIZE_Y; + const int b = blockIdx.z * blockDim.z + threadIdx.z; + + int d; + T tmp; + T weight; + + #pragma unroll + for (int i = 0; i < TILE_SIZE_X; i++) + { + d = d_block + threadIdx.x + i * BX; + + if (d < D && b < B){ + #pragma unroll + for (int t = 0; t < TILE_SIZE_Y; t++){ + if (l + t < L_eff - K + 1) + { + set_value(&tmp, bias[d]); + + for(int k = 0; k < K; k++){ + set_value(&weight, weights[k * D + d]); + + tmp = __hfma2(get_u(u, L_eff, l + t, padding, b, k, d, L, D, K), weight, tmp); + } + out[b * D * L_out + (l + t) * D + d] = tmp; + } + } + } + } +} + +torch::Tensor conv1d_cuda_blh( + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding) +{ + const uint b = u.size(0); + const uint l = u.size(1); + const uint d = u.size(2); + + const uint k = weight.size(0); + + uint l_eff = l + 2 * padding; + + + + dim3 blockDims(BX, BY, BZ); + + dim3 gridDims(ceil(d * 1.0 / (BX * TILE_SIZE_X * 2) ), ceil((l_eff - k + 1) * 1.0 / (BY * TILE_SIZE_Y)), ceil(b * 1.0 / BZ)); + + + uint l_out = (l + 2 * padding - k + 1); + + torch::Tensor out = torch::empty({b, l_out, d}, u.options()); + + //calling seperate kernels for k=3 and k!=3 leads to better perf + if(k==3){ + DISPATCH_FLOAT2_AND_HALF2_AND_BF162(u.scalar_type(), weight.scalar_type(), + "depthwise conv 1d fwd blh", + ([&] + { conv1d_kernel_k_3<<>>( + static_cast(u.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(out.data_ptr()), + padding, + b, + l, + l_out, + l_eff, + ceil(d/2), + k); + } + ) + ); + }else{ + DISPATCH_FLOAT2_AND_HALF2_AND_BF162(u.scalar_type(), weight.scalar_type(), + "depthwise conv 1d fwd blh", + ([&] + { conv1d_kernel<<>>( + static_cast(u.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(out.data_ptr()), + padding, + b, + l, + l_out, + l_eff, + ceil(d/2), + k); + } + ) + ); + } + return out; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_bhl.cu b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_bhl.cu index e0aa3b7ac6a33c09a7fe5d6a6d8c560ab6a2ad44..dce8af99021a8d9f7e8e497665cb38139052c10e 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_bhl.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_bhl.cu @@ -1,106 +1,106 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong -#include "shared.h" - -const uint BX = 128; -const uint BY = 1; -const uint BZ = 1; - -const uint TILE_SIZE = 4; - -template -__global__ void conv1d_backward_kernel( - const input_t* __restrict__ dout, - const input_t* __restrict__ u, - const weight_t* __restrict__ weights, - input_t* __restrict__ du, - input_t* __restrict__ dk, - uint B, - uint L, - uint D, - uint K, - uint P - ) -{ - const int b = blockIdx.z; - const int d = blockIdx.y; - const int l = blockIdx.x; - - //construct the du matrix - if(b < B && d < D && l == 0){ - for(int j = threadIdx.x; j < L; j += blockDim.x) - { - input_t sum; - set_value(&sum, 0.0f); - input_t weight; - - for(int k = 0; k < K ; k++) - { - int idx = - P + k + j; - - if(idx >= 0 && idx < L){ - set_value(&weight, weights[d * K + K - (k +1)]); - sum = __hfma(dout[b * D * L + d * L + idx], weight, sum); - } - } - du[b * D * L + d * L + j] = sum; - } - } - - const int k = blockIdx.x; - input_t tmp; - //construct the dk matrix - if(b < B && d < D && k < K) - { - for(int j = threadIdx.x; j < L; j += blockDim.x) - { - if(k - P + j < 0 || k - P + j >= L){ - set_value(&dk[b * D * K * L + d * K * L + k * L + j], 0.0f); - - }else{ - set_value(&dk[b * D * K * L + d * K * L + k * L + j], u[b * D * L + d * L + k - P + j]); - } - } - } - -} - -std::vector conv1d_backward_bhl_cuda( - torch::Tensor dout, - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding) -{ - const uint b = u.size(0); - const uint d = u.size(1); - const uint l = u.size(2); - - const uint k = weight.squeeze().size(1); - - dim3 blockDims(BX, 1, 1); - - dim3 gridDims(l, d, b); - - torch::Tensor du = torch::empty({b, d, l}, u.options()); - torch::Tensor dk = torch::empty({b, d, k, l}, dout.options()); - torch::Tensor dbias = dout.sum(-1).sum(0); - - DISPATCH_FLOAT_AND_HALF_AND_BF16(dout.scalar_type(), weight.scalar_type(), - "depthwise conv 1d backward bhl", - ([&] - { conv1d_backward_kernel<<>>( - static_cast(dout.data_ptr()), - static_cast(u.data_ptr()), - static_cast(weight.data_ptr()), - static_cast(du.data_ptr()), - static_cast(dk.data_ptr()), - b, - l, - d, - k, - padding); - } - ) - ); - return {du, torch::matmul(dk, dout.unsqueeze(-1)).squeeze(-1).sum(0).to(weight.type()), dbias}; +// Copyright (c) 2023 Dan Fu, Hermann Kumbong +#include "shared.h" + +const uint BX = 128; +const uint BY = 1; +const uint BZ = 1; + +const uint TILE_SIZE = 4; + +template +__global__ void conv1d_backward_kernel( + const input_t* __restrict__ dout, + const input_t* __restrict__ u, + const weight_t* __restrict__ weights, + input_t* __restrict__ du, + input_t* __restrict__ dk, + uint B, + uint L, + uint D, + uint K, + uint P + ) +{ + const int b = blockIdx.z; + const int d = blockIdx.y; + const int l = blockIdx.x; + + //construct the du matrix + if(b < B && d < D && l == 0){ + for(int j = threadIdx.x; j < L; j += blockDim.x) + { + input_t sum; + set_value(&sum, 0.0f); + input_t weight; + + for(int k = 0; k < K ; k++) + { + int idx = - P + k + j; + + if(idx >= 0 && idx < L){ + set_value(&weight, weights[d * K + K - (k +1)]); + sum = __hfma(dout[b * D * L + d * L + idx], weight, sum); + } + } + du[b * D * L + d * L + j] = sum; + } + } + + const int k = blockIdx.x; + input_t tmp; + //construct the dk matrix + if(b < B && d < D && k < K) + { + for(int j = threadIdx.x; j < L; j += blockDim.x) + { + if(k - P + j < 0 || k - P + j >= L){ + set_value(&dk[b * D * K * L + d * K * L + k * L + j], 0.0f); + + }else{ + set_value(&dk[b * D * K * L + d * K * L + k * L + j], u[b * D * L + d * L + k - P + j]); + } + } + } + +} + +std::vector conv1d_backward_bhl_cuda( + torch::Tensor dout, + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding) +{ + const uint b = u.size(0); + const uint d = u.size(1); + const uint l = u.size(2); + + const uint k = weight.squeeze().size(1); + + dim3 blockDims(BX, 1, 1); + + dim3 gridDims(l, d, b); + + torch::Tensor du = torch::empty({b, d, l}, u.options()); + torch::Tensor dk = torch::empty({b, d, k, l}, dout.options()); + torch::Tensor dbias = dout.sum(-1).sum(0); + + DISPATCH_FLOAT_AND_HALF_AND_BF16(dout.scalar_type(), weight.scalar_type(), + "depthwise conv 1d backward bhl", + ([&] + { conv1d_backward_kernel<<>>( + static_cast(dout.data_ptr()), + static_cast(u.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(du.data_ptr()), + static_cast(dk.data_ptr()), + b, + l, + d, + k, + padding); + } + ) + ); + return {du, torch::matmul(dk, dout.unsqueeze(-1)).squeeze(-1).sum(0).to(weight.type()), dbias}; } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_blh.cu b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_blh.cu index f5e5595cfda2f05d605618537bbc2d91ae6789d1..187d2e24b2041fc38ab508a8ff06014b00f0b15d 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_blh.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/conv1d_bwd_cuda_blh.cu @@ -1,116 +1,116 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include "shared.h" - -const uint BX = 128; -const uint BY = 1; -const uint BZ = 1; - -template -__global__ void conv1d_backward_kernel( - const input_t* __restrict__ dout, - int dout_stride0, - int dout_stride1, - int dout_stride2, - const input_t* __restrict__ u, - const weight_t* __restrict__ weights, - int weights_stride0, - int weights_stride1, - input_t* __restrict__ du, - input_t* __restrict__ dk, - uint B, - uint L, - uint D, - uint K, - uint P - ) -{ - const int b = blockIdx.z; - const int d = blockIdx.y; - const int l = blockIdx.x; - - //construct the du matrix - if(b < B && d < D && l == 0){ - for(int j = threadIdx.x; j < L; j += blockDim.x) - { - input_t sum; - set_value(&sum, 0.0f); - input_t weight; - - for(int k = 0; k < K ; k++) - { - int idx = - P + k + j; - - if(idx >= 0 && idx < L){ - set_value(&weight, weights[d * weights_stride1 + (K - (k +1)) * weights_stride0]); - sum = __hfma(dout[b * dout_stride0 + d * dout_stride1 + idx * dout_stride2], weight, sum); - } - } - du[b * D * L + j * D + d] = sum; - } - } - - const int k = blockIdx.x; - //construct the dk matrix - if(b < B && d < D && k < K) - { - for(int j = threadIdx.x; j < L; j += blockDim.x) - { - if(k - P + j < 0 || k - P + j >= L){ - set_value(&dk[b * D * K * L + d * K * L + k * L + j], 0.0f); - }else{ - set_value(&dk[b * D * K * L + d * K * L + k * L + j], u[b * D * L + (k - P + j) * D + d]); - } - } - } - -} - -std::vector conv1d_backward_blh_cuda( - torch::Tensor dout, - torch::Tensor u, - torch::Tensor weight, - torch::Tensor bias, - uint padding) -{ - const uint b = u.size(0); - const uint l = u.size(1); - const uint d = u.size(2); - - - const uint k = weight.squeeze().size(0); - - dim3 blockDims(BX, 1, 1); - - dim3 gridDims(l, d, b); - - torch::Tensor du = torch::empty({b, l, d}, u.options()); - torch::Tensor dk = torch::empty({b, d, k, l}, u.options()); - torch::Tensor dbias = dout.sum(-2).sum(0); - dout = dout.transpose(-1,-2); - - DISPATCH_FLOAT_AND_HALF_AND_BF16(dout.scalar_type(), weight.scalar_type(), - "depthwise conv 1d backward blh", - ([&] - { conv1d_backward_kernel<<>>( - static_cast(dout.data_ptr()), - dout.stride(0), - dout.stride(1), - dout.stride(2), - static_cast(u.data_ptr()), - static_cast(weight.data_ptr()), - weight.stride(0), - weight.stride(1), - static_cast(du.data_ptr()), - static_cast(dk.data_ptr()), - b, - l, - d, - k, - padding); - } - ) - ); - - return {du, torch::matmul(dk, dout.unsqueeze(-1)).squeeze(-1).sum(0).view({k, d}).to(weight.dtype()), dbias}; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include "shared.h" + +const uint BX = 128; +const uint BY = 1; +const uint BZ = 1; + +template +__global__ void conv1d_backward_kernel( + const input_t* __restrict__ dout, + int dout_stride0, + int dout_stride1, + int dout_stride2, + const input_t* __restrict__ u, + const weight_t* __restrict__ weights, + int weights_stride0, + int weights_stride1, + input_t* __restrict__ du, + input_t* __restrict__ dk, + uint B, + uint L, + uint D, + uint K, + uint P + ) +{ + const int b = blockIdx.z; + const int d = blockIdx.y; + const int l = blockIdx.x; + + //construct the du matrix + if(b < B && d < D && l == 0){ + for(int j = threadIdx.x; j < L; j += blockDim.x) + { + input_t sum; + set_value(&sum, 0.0f); + input_t weight; + + for(int k = 0; k < K ; k++) + { + int idx = - P + k + j; + + if(idx >= 0 && idx < L){ + set_value(&weight, weights[d * weights_stride1 + (K - (k +1)) * weights_stride0]); + sum = __hfma(dout[b * dout_stride0 + d * dout_stride1 + idx * dout_stride2], weight, sum); + } + } + du[b * D * L + j * D + d] = sum; + } + } + + const int k = blockIdx.x; + //construct the dk matrix + if(b < B && d < D && k < K) + { + for(int j = threadIdx.x; j < L; j += blockDim.x) + { + if(k - P + j < 0 || k - P + j >= L){ + set_value(&dk[b * D * K * L + d * K * L + k * L + j], 0.0f); + }else{ + set_value(&dk[b * D * K * L + d * K * L + k * L + j], u[b * D * L + (k - P + j) * D + d]); + } + } + } + +} + +std::vector conv1d_backward_blh_cuda( + torch::Tensor dout, + torch::Tensor u, + torch::Tensor weight, + torch::Tensor bias, + uint padding) +{ + const uint b = u.size(0); + const uint l = u.size(1); + const uint d = u.size(2); + + + const uint k = weight.squeeze().size(0); + + dim3 blockDims(BX, 1, 1); + + dim3 gridDims(l, d, b); + + torch::Tensor du = torch::empty({b, l, d}, u.options()); + torch::Tensor dk = torch::empty({b, d, k, l}, u.options()); + torch::Tensor dbias = dout.sum(-2).sum(0); + dout = dout.transpose(-1,-2); + + DISPATCH_FLOAT_AND_HALF_AND_BF16(dout.scalar_type(), weight.scalar_type(), + "depthwise conv 1d backward blh", + ([&] + { conv1d_backward_kernel<<>>( + static_cast(dout.data_ptr()), + dout.stride(0), + dout.stride(1), + dout.stride(2), + static_cast(u.data_ptr()), + static_cast(weight.data_ptr()), + weight.stride(0), + weight.stride(1), + static_cast(du.data_ptr()), + static_cast(dk.data_ptr()), + b, + l, + d, + k, + padding); + } + ) + ); + + return {du, torch::matmul(dk, dout.unsqueeze(-1)).squeeze(-1).sum(0).view({k, d}).to(weight.dtype()), dbias}; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/shared.h b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/shared.h index 5151db636d0c663392f144e3a167fd4c640c4ccd..d256c95705b7bdc61abaa5dce09eb6ac6d4f8630 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/conv1d/shared.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/conv1d/shared.h @@ -1,168 +1,168 @@ - -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include -#include -#include -#include -#include -#include - -#define DISPATCH_FLOAT_AND_HALF_AND_BF16(INPUT_TYPE, WEIGHT_TYPE, NAME, ...) \ - if ((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = __half; \ - using weight_t = __half; \ - __VA_ARGS__(); \ - } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::BFloat16)){ \ - using input_t = __half; \ - using weight_t = __nv_bfloat16; \ - __VA_ARGS__(); \ - } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Float)){ \ - using input_t = __half; \ - using weight_t = float; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ - using input_t = __nv_bfloat16; \ - using weight_t = __nv_bfloat16; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = __nv_bfloat16; \ - using weight_t = __half; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ - using input_t = __nv_bfloat16; \ - using weight_t = float; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ - using input_t = float; \ - using weight_t = float; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = float; \ - using weight_t = __half; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ - using input_t = float; \ - using weight_t = __nv_bfloat16; \ - __VA_ARGS__(); \ - } else { \ - AT_ERROR(#NAME, " not implemented for input-type '", toString(INPUT_TYPE), "' and weight-type '", toString(WEIGHT_TYPE), "'"); \ - } - - -#define DISPATCH_FLOAT2_AND_HALF2_AND_BF162(INPUT_TYPE, WEIGHT_TYPE, NAME, ...) \ - if ((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = __half2; \ - using weight_t = __half2; \ - __VA_ARGS__(); \ - } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::BFloat16)){ \ - using input_t = __half2; \ - using weight_t = __nv_bfloat162; \ - __VA_ARGS__(); \ - } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Float)){ \ - using input_t = __half2; \ - using weight_t = float2; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ - using input_t = __nv_bfloat162; \ - using weight_t = __nv_bfloat162; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = __nv_bfloat162; \ - using weight_t = __half2; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ - using input_t = __nv_bfloat162; \ - using weight_t = float2; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ - using input_t = float2; \ - using weight_t = float2; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ - using input_t = float2; \ - using weight_t = __half2; \ - __VA_ARGS__(); \ - } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ - using input_t = float2; \ - using weight_t = __nv_bfloat162; \ - __VA_ARGS__(); \ - } else { \ - AT_ERROR(#NAME, " not implemented for input-type '", toString(INPUT_TYPE), "' and weight-type '", toString(WEIGHT_TYPE), "'"); \ - } - -__forceinline__ __device__ float __hfma(const float a, const float b, const float c) -{ - return a * b + c; -} - -__forceinline__ __device__ float2 __hfma2(const float2 a, const float2 b, const float2 c) -{ - return make_float2(a.x * b.x + c.x, a.y * b.y + c.y); -} - -template -__forceinline__ __device__ void set_value(T* dst, T src) -{ - *dst = src; -} - -__forceinline__ __device__ void set_value(__half2* dst, float2 src) -{ - *dst = __float22half2_rn(src); -} - -__forceinline__ __device__ void set_value(__nv_bfloat162* dst, float2 src) -{ - *dst = __float22bfloat162_rn(src); -} - -__forceinline__ __device__ void set_value(float2* dst, __half2 src) -{ - *dst = __half22float2(src); -} - -__forceinline__ __device__ void set_value(float2* dst, __nv_bfloat162 src) -{ - *dst = __bfloat1622float2(src); -} - -__forceinline__ __device__ void set_value(__half2* dst, __nv_bfloat162 src) -{ - *dst = __float22half2_rn(__bfloat1622float2(src)); -} - -__forceinline__ __device__ void set_value(__nv_bfloat162* dst, __half2 src) -{ - *dst = __float22bfloat162_rn(__half22float2(src)); -} - -__forceinline__ __device__ void set_value(__half* dst, float src) -{ - *dst = __float2half(src); -} - -__forceinline__ __device__ void set_value(__nv_bfloat16* dst, float src) -{ - *dst = __float2bfloat16(src); -} - -__forceinline__ __device__ void set_value(float* dst, __half src) -{ - *dst = __half2float(src); -} - -__forceinline__ __device__ void set_value(float* dst, __nv_bfloat16 src) -{ - *dst = __bfloat162float(src); -} - -__forceinline__ __device__ void set_value(__half* dst, __nv_bfloat16 src) -{ - *dst = __float2half(__bfloat162float(src)); -} - -__forceinline__ __device__ void set_value(__nv_bfloat16* dst, __half src) -{ - *dst = __float2bfloat16(__half2float(src)); -} + +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include +#include +#include +#include +#include +#include + +#define DISPATCH_FLOAT_AND_HALF_AND_BF16(INPUT_TYPE, WEIGHT_TYPE, NAME, ...) \ + if ((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = __half; \ + using weight_t = __half; \ + __VA_ARGS__(); \ + } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::BFloat16)){ \ + using input_t = __half; \ + using weight_t = __nv_bfloat16; \ + __VA_ARGS__(); \ + } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Float)){ \ + using input_t = __half; \ + using weight_t = float; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ + using input_t = __nv_bfloat16; \ + using weight_t = __nv_bfloat16; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = __nv_bfloat16; \ + using weight_t = __half; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ + using input_t = __nv_bfloat16; \ + using weight_t = float; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ + using input_t = float; \ + using weight_t = float; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = float; \ + using weight_t = __half; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ + using input_t = float; \ + using weight_t = __nv_bfloat16; \ + __VA_ARGS__(); \ + } else { \ + AT_ERROR(#NAME, " not implemented for input-type '", toString(INPUT_TYPE), "' and weight-type '", toString(WEIGHT_TYPE), "'"); \ + } + + +#define DISPATCH_FLOAT2_AND_HALF2_AND_BF162(INPUT_TYPE, WEIGHT_TYPE, NAME, ...) \ + if ((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = __half2; \ + using weight_t = __half2; \ + __VA_ARGS__(); \ + } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::BFloat16)){ \ + using input_t = __half2; \ + using weight_t = __nv_bfloat162; \ + __VA_ARGS__(); \ + } else if((INPUT_TYPE == at::ScalarType::Half) && (WEIGHT_TYPE == at::ScalarType::Float)){ \ + using input_t = __half2; \ + using weight_t = float2; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ + using input_t = __nv_bfloat162; \ + using weight_t = __nv_bfloat162; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = __nv_bfloat162; \ + using weight_t = __half2; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::BFloat16) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ + using input_t = __nv_bfloat162; \ + using weight_t = float2; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Float)) { \ + using input_t = float2; \ + using weight_t = float2; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::Half)) { \ + using input_t = float2; \ + using weight_t = __half2; \ + __VA_ARGS__(); \ + } else if ((INPUT_TYPE == at::ScalarType::Float) && (WEIGHT_TYPE == at::ScalarType::BFloat16)) { \ + using input_t = float2; \ + using weight_t = __nv_bfloat162; \ + __VA_ARGS__(); \ + } else { \ + AT_ERROR(#NAME, " not implemented for input-type '", toString(INPUT_TYPE), "' and weight-type '", toString(WEIGHT_TYPE), "'"); \ + } + +__forceinline__ __device__ float __hfma(const float a, const float b, const float c) +{ + return a * b + c; +} + +__forceinline__ __device__ float2 __hfma2(const float2 a, const float2 b, const float2 c) +{ + return make_float2(a.x * b.x + c.x, a.y * b.y + c.y); +} + +template +__forceinline__ __device__ void set_value(T* dst, T src) +{ + *dst = src; +} + +__forceinline__ __device__ void set_value(__half2* dst, float2 src) +{ + *dst = __float22half2_rn(src); +} + +__forceinline__ __device__ void set_value(__nv_bfloat162* dst, float2 src) +{ + *dst = __float22bfloat162_rn(src); +} + +__forceinline__ __device__ void set_value(float2* dst, __half2 src) +{ + *dst = __half22float2(src); +} + +__forceinline__ __device__ void set_value(float2* dst, __nv_bfloat162 src) +{ + *dst = __bfloat1622float2(src); +} + +__forceinline__ __device__ void set_value(__half2* dst, __nv_bfloat162 src) +{ + *dst = __float22half2_rn(__bfloat1622float2(src)); +} + +__forceinline__ __device__ void set_value(__nv_bfloat162* dst, __half2 src) +{ + *dst = __float22bfloat162_rn(__half22float2(src)); +} + +__forceinline__ __device__ void set_value(__half* dst, float src) +{ + *dst = __float2half(src); +} + +__forceinline__ __device__ void set_value(__nv_bfloat16* dst, float src) +{ + *dst = __float2bfloat16(src); +} + +__forceinline__ __device__ void set_value(float* dst, __half src) +{ + *dst = __half2float(src); +} + +__forceinline__ __device__ void set_value(float* dst, __nv_bfloat16 src) +{ + *dst = __bfloat162float(src); +} + +__forceinline__ __device__ void set_value(__half* dst, __nv_bfloat16 src) +{ + *dst = __float2half(__bfloat162float(src)); +} + +__forceinline__ __device__ void set_value(__nv_bfloat16* dst, __half src) +{ + *dst = __float2bfloat16(__half2float(src)); +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch.cpp b/overlay/kernels/cuda/flashfftconv/csrc/monarch.cpp index ce913423b2efe54276b9332a061ebd42169ac519..0b2a547dac7a8d29c84a1199c4e5f5ef3f285e2b 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch.cpp +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch.cpp @@ -1,61 +1,61 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include -#include "monarch_cuda/monarch_fwd.h" -#include "monarch_cuda/monarch_fwd_complex.h" -#include "monarch_cuda/monarch_fwd_r2r.h" -#include "monarch_cuda/monarch_bwd.h" -#include "monarch_cuda/monarch_bwd_complex.h" -#include "monarch_cuda/monarch_bwd_r2r.h" -#include "butterfly/butterfly.h" -#include "conv1d/conv1d.h" - - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("monarch_conv_forward", &monarch_conv, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_16_16_16", &monarch_conv_16_16_16, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_32_16_16", &monarch_conv_32_16_16, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_16_32_32", &monarch_conv_16_32_32, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_32_32_32", &monarch_conv_32_32_32, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_16_16_16_complex", &monarch_conv_16_16_16_complex, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_32_16_16_complex", &monarch_conv_32_16_16_complex, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_16_32_32_complex", &monarch_conv_16_32_32_complex, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_32_32_32_complex", &monarch_conv_32_32_32_complex, "Monarch forward (CUDA)"); - m.def("monarch_conv_forward_32_32_32_complex_truncated", &monarch_conv_32_32_32_complex_truncated, "Monarch forward (CUDA)"); - - m.def("monarch_conv_backward", &monarch_conv_bwd, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_16_16_16", &monarch_conv_bwd_16_16_16, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_32_16_16", &monarch_conv_bwd_32_16_16, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_16_32_32", &monarch_conv_bwd_16_32_32, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_32_32_32", &monarch_conv_bwd_32_32_32, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_16_16_16_complex", &monarch_conv_bwd_16_16_16_complex, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_32_16_16_complex", &monarch_conv_bwd_32_16_16_complex, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_16_32_32_complex", &monarch_conv_bwd_16_32_32_complex, "Monarch backward (CUDA)"); - m.def("monarch_conv_backward_32_32_32_complex", &monarch_conv_bwd_32_32_32_complex, "Monarch backward (CUDA)"); - - m.def("monarch_conv_forward_r2r", &monarch_conv_r2r, "Monarch forward (CUDA)"); - m.def("monarch_conv_backward_r2r", &monarch_conv_bwd_r2r, "Monarch backward (CUDA)"); - - // butterfly kernels - m.def("butterfly_forward", &butterfly, "Butterfly forward (CUDA)"); - m.def("butterfly_gated_forward", &butterfly_gated, "Butterfly gated forward (CUDA)"); - m.def("butterfly_bf16_forward", &butterfly_bf16, "Butterfly forward bf16 (CUDA)"); - m.def("butterfly_gated_bf16_forward", &butterfly_gated_bf16, "Butterfly gated forward bf16 (CUDA)"); - m.def("butterfly_padded_forward", &butterfly_padded, "Butterfly padded (CUDA)"); - m.def("butterfly_padded_bf16_forward", &butterfly_padded_bf16, "Butterfly padded (CUDA)"); - m.def("butterfly_padded_gated_forward", &butterfly_padded_gated, "Butterfly padded (CUDA)"); - m.def("butterfly_padded_gated_bf16_forward", &butterfly_padded_gated_bf16, "Butterfly padded (CUDA)"); - m.def("butterfly_ifft_forward", &butterfly_ifft, "Butterfly ifft forard (CUDA)"); - m.def("butterfly_ifft_gated_forward", &butterfly_ifft_gated, "Butterfly ifft gated forard (CUDA)"); - m.def("butterfly_ifft_gated_bf16_forward", &butterfly_ifft_gated_bf16, "Butterfly ifft gated bf16 forard (CUDA)"); - m.def("butterfly_ifft_bf16_forward", &butterfly_ifft_bf16, "Butterfly ifft forward bf16 (CUDA)"); - m.def("butterfly_ifft_padded_forward", &butterfly_ifft_padded, "Butterfly ifft forward padded (CUDA)"); - m.def("butterfly_ifft_padded_gated_forward", &butterfly_ifft_padded_gated, "Butterfly ifft forward padded (CUDA)"); - m.def("butterfly_ifft_padded_bf16_forward", &butterfly_ifft_padded_bf16, "Butterfly ifft forward padded (CUDA)"); - m.def("butterfly_ifft_padded_gated_bf16_forward", &butterfly_ifft_padded_gated_bf16, "Butterfly ifft forward padded (CUDA)"); - - m.def("conv1d_forward", &conv1d_fwd, "conv1d forward (CUDA)"); - m.def("conv1d_backward", &conv1d_bwd, "conv1d backward (CUDA)"); - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include +#include "monarch_cuda/monarch_fwd.h" +#include "monarch_cuda/monarch_fwd_complex.h" +#include "monarch_cuda/monarch_fwd_r2r.h" +#include "monarch_cuda/monarch_bwd.h" +#include "monarch_cuda/monarch_bwd_complex.h" +#include "monarch_cuda/monarch_bwd_r2r.h" +#include "butterfly/butterfly.h" +#include "conv1d/conv1d.h" + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("monarch_conv_forward", &monarch_conv, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_16_16_16", &monarch_conv_16_16_16, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_32_16_16", &monarch_conv_32_16_16, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_16_32_32", &monarch_conv_16_32_32, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_32_32_32", &monarch_conv_32_32_32, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_16_16_16_complex", &monarch_conv_16_16_16_complex, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_32_16_16_complex", &monarch_conv_32_16_16_complex, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_16_32_32_complex", &monarch_conv_16_32_32_complex, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_32_32_32_complex", &monarch_conv_32_32_32_complex, "Monarch forward (CUDA)"); + m.def("monarch_conv_forward_32_32_32_complex_truncated", &monarch_conv_32_32_32_complex_truncated, "Monarch forward (CUDA)"); + + m.def("monarch_conv_backward", &monarch_conv_bwd, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_16_16_16", &monarch_conv_bwd_16_16_16, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_32_16_16", &monarch_conv_bwd_32_16_16, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_16_32_32", &monarch_conv_bwd_16_32_32, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_32_32_32", &monarch_conv_bwd_32_32_32, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_16_16_16_complex", &monarch_conv_bwd_16_16_16_complex, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_32_16_16_complex", &monarch_conv_bwd_32_16_16_complex, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_16_32_32_complex", &monarch_conv_bwd_16_32_32_complex, "Monarch backward (CUDA)"); + m.def("monarch_conv_backward_32_32_32_complex", &monarch_conv_bwd_32_32_32_complex, "Monarch backward (CUDA)"); + + m.def("monarch_conv_forward_r2r", &monarch_conv_r2r, "Monarch forward (CUDA)"); + m.def("monarch_conv_backward_r2r", &monarch_conv_bwd_r2r, "Monarch backward (CUDA)"); + + // butterfly kernels + m.def("butterfly_forward", &butterfly, "Butterfly forward (CUDA)"); + m.def("butterfly_gated_forward", &butterfly_gated, "Butterfly gated forward (CUDA)"); + m.def("butterfly_bf16_forward", &butterfly_bf16, "Butterfly forward bf16 (CUDA)"); + m.def("butterfly_gated_bf16_forward", &butterfly_gated_bf16, "Butterfly gated forward bf16 (CUDA)"); + m.def("butterfly_padded_forward", &butterfly_padded, "Butterfly padded (CUDA)"); + m.def("butterfly_padded_bf16_forward", &butterfly_padded_bf16, "Butterfly padded (CUDA)"); + m.def("butterfly_padded_gated_forward", &butterfly_padded_gated, "Butterfly padded (CUDA)"); + m.def("butterfly_padded_gated_bf16_forward", &butterfly_padded_gated_bf16, "Butterfly padded (CUDA)"); + m.def("butterfly_ifft_forward", &butterfly_ifft, "Butterfly ifft forard (CUDA)"); + m.def("butterfly_ifft_gated_forward", &butterfly_ifft_gated, "Butterfly ifft gated forard (CUDA)"); + m.def("butterfly_ifft_gated_bf16_forward", &butterfly_ifft_gated_bf16, "Butterfly ifft gated bf16 forard (CUDA)"); + m.def("butterfly_ifft_bf16_forward", &butterfly_ifft_bf16, "Butterfly ifft forward bf16 (CUDA)"); + m.def("butterfly_ifft_padded_forward", &butterfly_ifft_padded, "Butterfly ifft forward padded (CUDA)"); + m.def("butterfly_ifft_padded_gated_forward", &butterfly_ifft_padded_gated, "Butterfly ifft forward padded (CUDA)"); + m.def("butterfly_ifft_padded_bf16_forward", &butterfly_ifft_padded_bf16, "Butterfly ifft forward padded (CUDA)"); + m.def("butterfly_ifft_padded_gated_bf16_forward", &butterfly_ifft_padded_gated_bf16, "Butterfly ifft forward padded (CUDA)"); + + m.def("conv1d_forward", &conv1d_fwd, "conv1d forward (CUDA)"); + m.def("conv1d_backward", &conv1d_bwd, "conv1d backward (CUDA)"); + } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h index ed321ddbdebb389907a1d8d6658b809474f17a93..02a0ecba906897ddcfc2aa52ce980bff3d0d3fe9 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h @@ -1,672 +1,672 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_complex_kernel( - const at::BFloat16 *__restrict__ dout_real_inp, - const at::BFloat16 *__restrict__ dout_imag_inp, - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *dx_out_real, - at::BFloat16 *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + 256]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * 256]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT(dout) - complex_matmul_c2c_256( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - k_frag[k_idx], - wmma::mem_col_major); - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // multiply dout by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __nv_bfloat162 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - - } // b_tile_id - - for(int i = 0; i < items_per_thread_input; i++) { - reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_complex_kernel( + const at::BFloat16 *__restrict__ dout_real_inp, + const at::BFloat16 *__restrict__ dout_imag_inp, + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *dx_out_real, + at::BFloat16 *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + 256]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * 256]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT(dout) + complex_matmul_c2c_256( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + k_frag[k_idx], + wmma::mem_col_major); + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // multiply dout by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __nv_bfloat162 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + + } // b_tile_id + + for(int i = 0; i < items_per_thread_input; i++) { + reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h index e1e34fa089586cbff8e77478015bec583858d5ab..a601f7887e5ace81ebfa7466cffed129df91eede 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h @@ -1,828 +1,828 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + 256]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * 256]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates - at::BFloat16 dgate_data[items_per_thread_input]; - at::BFloat16 dout_data[items_per_thread_input]; - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT(dout) - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("dout @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("x @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { - // printf("DFT(dout)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("DFT(x)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // // x = x * N - // for (int i = 0; i < 256 / 32 / 2; i++) - // { - // a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - // reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - // reinterpret_cast<__half2 *>(a_real_2)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - // reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // } - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // multiply dout by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __nv_bfloat162 real, imag; - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - - } // b_tile_id - - for(int i = 0; i < items_per_thread_input; i++) { - reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + 256]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * 256]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates + at::BFloat16 dgate_data[items_per_thread_input]; + at::BFloat16 dout_data[items_per_thread_input]; + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT(dout) + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("dout @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("x @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { + // printf("DFT(dout)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("DFT(x)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // // x = x * N + // for (int i = 0; i < 256 / 32 / 2; i++) + // { + // a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + // reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + // reinterpret_cast<__half2 *>(a_real_2)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + // reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // } + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // multiply dout by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __nv_bfloat162 real, imag; + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + + } // b_tile_id + + for(int i = 0; i < items_per_thread_input; i++) { + reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h index 8b64b8999a510672a53ceda274b80ef12e8465f2..737630f85f7d231d37a1645746716a550c28f959 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h @@ -1,611 +1,611 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_complex_kernel( - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out_real, - at::BFloat16 *out_imag, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + 256]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * 256]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( - // __nv_bfloat16(x_input_data[2 * i]), - // __nv_bfloat16(x_input_data[2 * i + 1]) - // ); - // } - - // __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - - // x_input_data[2 * i] = scratch.x; - // x_input_data[2 * i + 1] = scratch.y; - // } - - // // store a_real - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_complex_kernel( + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out_real, + at::BFloat16 *out_imag, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + 256]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * 256]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( + // __nv_bfloat16(x_input_data[2 * i]), + // __nv_bfloat16(x_input_data[2 * i + 1]) + // ); + // } + + // __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + + // x_input_data[2 * i] = scratch.x; + // x_input_data[2 * i + 1] = scratch.y; + // } + + // // store a_real + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h index fe65090204cadd6a0957b78ad85c915db36e05c1..7dc834be7b29c9e8c2e7e1f8aa16d1708d055679 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h @@ -1,639 +1,639 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + 256]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * 256]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // store a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + 256]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * 256]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // store a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h index 7e02fd2d273aff6edad3b0d7e565b29c858b28e5..108a564f0d9d3ac58d567192a9036779128626f8 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h @@ -1,746 +1,746 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_16_32_32_complex_kernel( - const at::BFloat16 *__restrict__ dout_real_inp, - const at::BFloat16 *__restrict__ dout_imag_inp, - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *dx_out_real, - at::BFloat16 *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + N_2]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_2]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_1024( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat16 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx], - // __nv_bfloat16(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - // put dk_f into a_input_data, and udpate temp - __nv_bfloat162 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); - reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_16_32_32_complex_kernel( + const at::BFloat16 *__restrict__ dout_real_inp, + const at::BFloat16 *__restrict__ dout_imag_inp, + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *dx_out_real, + at::BFloat16 *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + N_2]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_2]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_1024( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat16 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx], + // __nv_bfloat16(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + // put dk_f into a_input_data, and udpate temp + __nv_bfloat162 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); + reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h index de21c13da4496293ab2febb2ae82d34b7b9e5990..c5c70d4ba4a30d3aa5f32e1fb7918c0047042691 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h @@ -1,877 +1,877 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_16_32_32_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + N_2]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_2]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates - at::BFloat16 dgate_data[items_per_thread_input]; - at::BFloat16 dout_data[items_per_thread_input]; - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - - // dk_f = dout * x.conj() - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // finish iFFT dout - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __nv_bfloat16float(a_real[a_idx])); - // } - // printf("\n"); - // } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat16 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx], - // __nv_bfloat16(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and udpate temp - __nv_bfloat162 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); - reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_16_32_32_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + N_2]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_2]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates + at::BFloat16 dgate_data[items_per_thread_input]; + at::BFloat16 dout_data[items_per_thread_input]; + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + + // dk_f = dout * x.conj() + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __nv_bfloat16float(a_real[a_idx]), __nv_bfloat16float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // finish iFFT dout + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __nv_bfloat16float(a_real[a_idx])); + // } + // printf("\n"); + // } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat16 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx], + // __nv_bfloat16(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and udpate temp + __nv_bfloat162 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); + reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h index 28187d63a48febfb0d4d6a7f20c62b78e5774ea4..cb74452e9bfecb0a636f88f1eae2e9f377d7cdb3 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h @@ -1,741 +1,741 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_16_32_32_complex_kernel( - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *out_real, - at::BFloat16 *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + N_2]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_2]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( - // __nv_bfloat16(x_input_data[2 * i]), - // __nv_bfloat16(x_input_data[2 * i + 1]) - // ); - // } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("x_input_data\n"); - // for (int i = 0; i < items_per_thread_input / 2; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(x_input_data[2 * i]))); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - - // // printf("Before first DFT\n"); - // // for (int i = 0; i < 32; i++) { - // // a_idx = i; - // // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // // } - // // printf("\n"); - // } - __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - - // x_input_data[2 * i] = scratch.x; - // x_input_data[2 * i + 1] = scratch.y; - // } - - // // HACK - // // for now, just output the a_real output - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_16_32_32_complex_kernel( + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *out_real, + at::BFloat16 *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + N_2]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_2]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( + // __nv_bfloat16(x_input_data[2 * i]), + // __nv_bfloat16(x_input_data[2 * i + 1]) + // ); + // } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("x_input_data\n"); + // for (int i = 0; i < items_per_thread_input / 2; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(x_input_data[2 * i]))); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + + // // printf("Before first DFT\n"); + // // for (int i = 0; i < 32; i++) { + // // a_idx = i; + // // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // // } + // // printf("\n"); + // } + __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + + // x_input_data[2 * i] = scratch.x; + // x_input_data[2 * i + 1] = scratch.y; + // } + + // // HACK + // // for now, just output the a_real output + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h index 04ce2c2fee20cd4ba8a514c4c719a2a58c564a7b..e694dedb05de42e2da05d8ae2082ee1a968257bb 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h @@ -1,769 +1,769 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_16_32_32_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + N_2]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_2]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("x_input_data\n"); - // for (int i = 0; i < items_per_thread_input / 2; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(x_input_data[2 * i]))); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - - // // printf("Before first DFT\n"); - // // for (int i = 0; i < 32; i++) { - // // a_idx = i; - // // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // // } - // // printf("\n"); - // } - __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_16_32_32_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + N_2]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_2]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("x_input_data\n"); + // for (int i = 0; i < items_per_thread_input / 2; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(x_input_data[2 * i]))); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + + // // printf("Before first DFT\n"); + // // for (int i = 0; i < 32; i++) { + // // a_idx = i; + // // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // // } + // // printf("\n"); + // } + __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h index e82ce6fb61dd577fc0bb4ac99a5cada3eb4e7f60..0ed9227724fed6e20b071da36da0a674dd510c3e 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h @@ -1,789 +1,789 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_complex_kernel( - const at::BFloat16 *__restrict__ dout_real_inp, - const at::BFloat16 *__restrict__ dout_imag_inp, - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *dx_out_real, - at::BFloat16 *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + N_1]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_256( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - __syncthreads(); - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // 256 / 32 = 8 - // finish iFFT dout - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __nv_bfloat162 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); - reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); - } - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_complex_kernel( + const at::BFloat16 *__restrict__ dout_real_inp, + const at::BFloat16 *__restrict__ dout_imag_inp, + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *dx_out_real, + at::BFloat16 *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + N_1]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_256( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + __syncthreads(); + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // 256 / 32 = 8 + // finish iFFT dout + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __nv_bfloat162 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); + reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); + } + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h index abcf5e89020b63d943cebdeff44ff697b565b00b..0cbdfb8deaa11eb6bce74435218d157c6bc1a421 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h @@ -1,909 +1,909 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[4 * N + N_1]; - at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates - at::BFloat16 dgate_data[items_per_thread_input]; - at::BFloat16 dout_data[items_per_thread_input]; - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - __syncthreads(); - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - // finish iFFT dout - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __nv_bfloat162 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); - reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); - } - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[4 * N + N_1]; + at::BFloat16 *b_real_2 = &a_real[4 * N + 2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[4 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates + at::BFloat16 dgate_data[items_per_thread_input]; + at::BFloat16 dout_data[items_per_thread_input]; + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2 + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + __syncthreads(); + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + // finish iFFT dout + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __nv_bfloat162 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast(a_input_data)[2 * i] = complex_bfloat16_t(real.x, imag.x); + reinterpret_cast(a_input_data)[2 * i + 1] = complex_bfloat16_t(real.y, imag.y); + } + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h index 27c226d8af5ea469749f1d7e04f2198c09b56d95..6b64c1899364dfd7eeda9eda9bba092c2f1ea8c3 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h @@ -1,773 +1,773 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_complex_kernel( - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out_real, - at::BFloat16 *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("b_16_fft\n"); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real[i])), __bfloat162float(__nv_bfloat16(b_imag[i]))); - // } - // printf("\n"); - // printf("b_16_ifft\n"); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real_2[i])), __bfloat162float(__nv_bfloat16(b_imag_2[i]))); - // } - // printf("\n"); - // } - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( - // __nv_bfloat16(x_input_data[2 * i]), - // __nv_bfloat16(x_input_data[2 * i + 1]) - // ); - // } - - // __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", ____nv_bfloat162float(a_real[a_idx]), ____nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - - // x_input_data[2 * i] = scratch.x; - // x_input_data[2 * i + 1] = scratch.y; - // } - - // // HACK - // // for now, just output the a_real output - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_complex_kernel( + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out_real, + at::BFloat16 *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("b_16_fft\n"); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real[i])), __bfloat162float(__nv_bfloat16(b_imag[i]))); + // } + // printf("\n"); + // printf("b_16_ifft\n"); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real_2[i])), __bfloat162float(__nv_bfloat16(b_imag_2[i]))); + // } + // printf("\n"); + // } + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( + // __nv_bfloat16(x_input_data[2 * i]), + // __nv_bfloat16(x_input_data[2 * i + 1]) + // ); + // } + + // __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", ____nv_bfloat162float(a_real[a_idx]), ____nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // scratch = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + + // x_input_data[2 * i] = scratch.x; + // x_input_data[2 * i + 1] = scratch.y; + // } + + // // HACK + // // for now, just output the a_real output + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h index 8e41e65ca073091091bca9fd2eee70be6fc3b83f..9311bfb874abbef3f738696122d561362afcef71 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h @@ -1,801 +1,801 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[2 * N + N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].real()), - __nv_bfloat16(b_input_data[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[0].imag()), - __nv_bfloat16(b_input_data[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].real()), - __nv_bfloat16(b_input_data_2[1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[0].imag()), - __nv_bfloat16(b_input_data_2[1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("b_16_fft\n"); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real[i])), __bfloat162float(__nv_bfloat16(b_imag[i]))); - // } - // printf("\n"); - // printf("b_16_ifft\n"); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real_2[i])), __bfloat162float(__nv_bfloat16(b_imag_2[i]))); - // } - // printf("\n"); - // } - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - k_frag[k_idx], - wmma::mem_col_major); - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - acc_frag_2_half, - twiddle_16_idft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // // a_idx = i * num_threads + thread_id + k_idx_offset; - // a_idx = i + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", ____nv_bfloat162float(a_real[a_idx]), ____nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[2 * N + N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N + 2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[2 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2_half[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].real()), + __nv_bfloat16(b_input_data[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[0].imag()), + __nv_bfloat16(b_input_data[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].real()), + __nv_bfloat16(b_input_data_2[1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[0].imag()), + __nv_bfloat16(b_input_data_2[1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("b_16_fft\n"); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real[i])), __bfloat162float(__nv_bfloat16(b_imag[i]))); + // } + // printf("\n"); + // printf("b_16_ifft\n"); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(b_real_2[i])), __bfloat162float(__nv_bfloat16(b_imag_2[i]))); + // } + // printf("\n"); + // } + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + k_frag[k_idx], + wmma::mem_col_major); + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + acc_frag_2_half, + twiddle_16_idft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // // a_idx = i * num_threads + thread_id + k_idx_offset; + // a_idx = i + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", ____nv_bfloat162float(a_real[a_idx]), ____nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(__nv_bfloat16(a_real[a_idx]))); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h index 8e0b603b890d63c7db96fd7fb3e1a778b14d5742..76688b5f65dfe12cdae0e06250d8bb1f70b427c9 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h @@ -1,662 +1,662 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_32_32_32_complex_kernel( - const at::BFloat16 *__restrict__ dout_real_inp, - const at::BFloat16 *__restrict__ dout_imag_inp, - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *dx_out_real, - at::BFloat16 *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[0]; - at::BFloat16 *b_imag = &a_real[N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * N * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } -__syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(x) - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - __nv_bfloat162 real, imag; - // write DFT(x) in a_real, a_imag to a_input_data - // todo: try doing this as a_real, a_imag? - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) - ); - imag = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) - ); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i] = __nv_bfloat162(real.x, imag.x); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i + 1] = __nv_bfloat162(real.y, imag.y); - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_1024( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - // TODO: compute a_input_data = a * a_input_data.conj() - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast *>(a_input_data)[2 * i], - reinterpret_cast *>(a_input_data)[2 * i + 1], - &reinterpret_cast *>(a_input_data)[2 * i], - &reinterpret_cast *>(a_input_data)[2 * i + 1]); - // update temp - temp[2 * i] += a_input_data[2 * i]; - temp[2 * i + 1] += a_input_data[2 * i + 1]; - } - - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - // reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_32_32_32_complex_kernel( + const at::BFloat16 *__restrict__ dout_real_inp, + const at::BFloat16 *__restrict__ dout_imag_inp, + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *dx_out_real, + at::BFloat16 *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[0]; + at::BFloat16 *b_imag = &a_real[N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * N * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } +__syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(x) + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + __nv_bfloat162 real, imag; + // write DFT(x) in a_real, a_imag to a_input_data + // todo: try doing this as a_real, a_imag? + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) + ); + imag = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) + ); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i] = __nv_bfloat162(real.x, imag.x); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i + 1] = __nv_bfloat162(real.y, imag.y); + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_1024( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + // TODO: compute a_input_data = a * a_input_data.conj() + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast *>(a_input_data)[2 * i], + reinterpret_cast *>(a_input_data)[2 * i + 1], + &reinterpret_cast *>(a_input_data)[2 * i], + &reinterpret_cast *>(a_input_data)[2 * i + 1]); + // update temp + temp[2 * i] += a_input_data[2 * i]; + temp[2 * i + 1] += a_input_data[2 * i + 1]; + } + + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + // reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h index dd5d0212f3fca480e377a200a9568ef1400eef61..0aec294988bc59e6cb140175f7a4df455f4ea5b0 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h @@ -1,764 +1,764 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_32_32_32_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[0]; - at::BFloat16 *b_imag = &a_real[N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates - at::BFloat16 dgate_data[items_per_thread_input]; - at::BFloat16 dout_data[items_per_thread_input]; - complex_bfloat16_t temp[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } -__syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(x) - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - __nv_bfloat162 real, imag; - // write DFT(x) in a_real, a_imag to a_input_data - // todo: try doing this as a_real, a_imag? - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) - ); - imag = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) - ); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i] = __nv_bfloat162(real.x, imag.x); - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i + 1] = __nv_bfloat162(real.y, imag.y); - } - - __syncthreads(); - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - // TODO: compute a_input_data = a * a_input_data.conj() - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - // // dout = dout / N - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __h2div( - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __h2div( - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast *>(a_input_data)[2 * i], - reinterpret_cast *>(a_input_data)[2 * i + 1], - &reinterpret_cast *>(a_input_data)[2 * i], - &reinterpret_cast *>(a_input_data)[2 * i + 1]); - // update temp - temp[2 * i] += a_input_data[2 * i]; - temp[2 * i + 1] += a_input_data[2 * i + 1]; - } - - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_32_32_32_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[0]; + at::BFloat16 *b_imag = &a_real[N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates + at::BFloat16 dgate_data[items_per_thread_input]; + at::BFloat16 dout_data[items_per_thread_input]; + complex_bfloat16_t temp[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } +__syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(x) + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + __nv_bfloat162 real, imag; + // write DFT(x) in a_real, a_imag to a_input_data + // todo: try doing this as a_real, a_imag? + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) + ); + imag = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N))) + ); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i] = __nv_bfloat162(real.x, imag.x); + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[2 * i + 1] = __nv_bfloat162(real.y, imag.y); + } + + __syncthreads(); + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + // TODO: compute a_input_data = a * a_input_data.conj() + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + // // dout = dout / N + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __h2div( + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __h2div( + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast *>(a_input_data)[2 * i], + reinterpret_cast *>(a_input_data)[2 * i + 1], + &reinterpret_cast *>(a_input_data)[2 * i], + &reinterpret_cast *>(a_input_data)[2 * i + 1]); + // update temp + temp[2 * i] += a_input_data[2 * i]; + temp[2 * i + 1] += a_input_data[2 * i + 1]; + } + + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h index 9bc04ad504fff02bd64e7c841adfafbae03386b4..fea63b078c2b3dfe44c6b90813b08e5377f9e9f4 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h @@ -1,613 +1,613 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_32_32_32_complex_kernel( - const at::BFloat16 *__restrict__ a_real_inp, - const at::BFloat16 *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *out_real, - at::BFloat16 *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[0]; - at::BFloat16 *b_imag = &a_real[N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // start loading a - // // NOTE(danfu): this load from HBM costs about 60 us - // BlockLoad_Sequence().Load( - // reinterpret_cast *>(a + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // // load a into shared memory - // // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // scratch = __nv_bfloat162(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - - // scratch = __nv_bfloat162(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - // } - - // __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); - // } - // printf("\n"); - // } - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // __nv_bfloat162 real, imag; - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // real = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - // imag = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; - // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<____nv_bfloat16>(real.x, imag.x); - // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<____nv_bfloat16>(real.y, imag.y); - // } - - // // store the complex output - // BlockStore_Sequence().Store( - // reinterpret_cast *>(out + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_32_32_32_complex_kernel( + const at::BFloat16 *__restrict__ a_real_inp, + const at::BFloat16 *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *out_real, + at::BFloat16 *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[0]; + at::BFloat16 *b_imag = &a_real[N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // start loading a + // // NOTE(danfu): this load from HBM costs about 60 us + // BlockLoad_Sequence().Load( + // reinterpret_cast *>(a + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // // load a into shared memory + // // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // scratch = __nv_bfloat162(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + + // scratch = __nv_bfloat162(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + // } + + // __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(__nv_bfloat16(a_real[a_idx])), __bfloat162float(__nv_bfloat16(a_imag[a_idx]))); + // } + // printf("\n"); + // } + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // __nv_bfloat162 real, imag; + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // real = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + // imag = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx]; + // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<____nv_bfloat16>(real.x, imag.x); + // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<____nv_bfloat16>(real.y, imag.y); + // } + + // // store the complex output + // BlockStore_Sequence().Store( + // reinterpret_cast *>(out + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h index 490810ddf9413cb2f14a1a9406b3cdebbb620c3e..f8ca29e9db4943e6c564cc11828708ee6f3f15d9 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h @@ -1,639 +1,639 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_32_32_32_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[0]; - at::BFloat16 *b_imag = &a_real[N_1]; - at::BFloat16 *b_real_2 = &a_real[2 * N_1]; - at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates - complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input - reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - acc_frag_1_half, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __bfloat162float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_32_32_32_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[0]; + at::BFloat16 *b_imag = &a_real[N_1]; + at::BFloat16 *b_real_2 = &a_real[2 * N_1]; + at::BFloat16 *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates + complex_bfloat16_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_bfloat16_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N_1); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_imag + k_idx_offset), // this is the input + reinterpret_cast<__nv_bfloat16 *>(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + acc_frag_1_half, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __bfloat162float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_bf16.h index 82b3106b431f9234864190dba628097876fdace3..a38805c982acb18f34ed80aeab2d8fa9230b4789 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_bf16.h @@ -1,619 +1,619 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[5 * N]; - at::BFloat16 *b_real_2 = &a_real[6 * N]; - at::BFloat16 *b_imag_2 = &a_real[7 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_bfloat16_t temp[items_per_thread_input]; - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates - at::BFloat16 dgate_data[items_per_thread_input]; - at::BFloat16 dout_data[items_per_thread_input]; - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __hneg2(__nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - )); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - - __syncthreads(); - - for(int i=0; i< items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(__float2bfloat16(0.0f), __float2bfloat16(0.0f)); - } - - __syncthreads(); - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - // int output_offset_kernel = h_offset_kernel + b_offset_kernel + h_tile_id * N + b_tile_id * H * N; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load a into a_real_2 - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // first DFT(dout) - complex_matmul_r2c_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real), // read from SRAM - reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // second DFT(dout), with twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("FFT(dout).transpose(-1,-2)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // dout = dout / N - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __h2div( - // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - // __nv_bfloat162(__bfloat162__nv_bfloat16(float(N)), __bfloat162__nv_bfloat16(float(N)))); - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __h2div( - // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - // __nv_bfloat162(__bfloat162__nv_bfloat16(float(N)), __bfloat162__nv_bfloat16(float(N)))); - // } - - // __syncthreads(); - - // first DFT(x) - complex_matmul_r2c_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real_2), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT(x), with twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_row_major); - - // // x = x * N - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( - // reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - // reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( - // reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - // } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("FFT(x).transpose(-1,-2)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real_2[a_idx]), __bfloat162float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // dk_f = dout * x.conj() - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - complex_mul_conj_bfloat162( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], - &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // for(int i=0; i< items_per_thread_input; i++) { - // temp[i] += a_input_data[i]; - // } - - // __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // start computing iFFT(dout), and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - k_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul_c2r( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - reinterpret_cast<__nv_bfloat16 *>(a_real), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - twiddle_idft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // multiply by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __nv_bfloat162 real, imag; - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - } // b_tile_id - - for(int i = 0; i < items_per_thread_input; i++) { - reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); - } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[5 * N]; + at::BFloat16 *b_real_2 = &a_real[6 * N]; + at::BFloat16 *b_imag_2 = &a_real[7 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_bfloat16_t temp[items_per_thread_input]; + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input gates + at::BFloat16 dgate_data[items_per_thread_input]; + at::BFloat16 dout_data[items_per_thread_input]; + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __hneg2(__nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + )); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + + __syncthreads(); + + for(int i=0; i< items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(__float2bfloat16(0.0f), __float2bfloat16(0.0f)); + } + + __syncthreads(); + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + // int output_offset_kernel = h_offset_kernel + b_offset_kernel + h_tile_id * N + b_tile_id * H * N; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dout_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load a into a_real_2 + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // first DFT(dout) + complex_matmul_r2c_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real), // read from SRAM + reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // second DFT(dout), with twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("FFT(dout).transpose(-1,-2)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // dout = dout / N + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __h2div( + // reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + // __nv_bfloat162(__bfloat162__nv_bfloat16(float(N)), __bfloat162__nv_bfloat16(float(N)))); + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __h2div( + // reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + // __nv_bfloat162(__bfloat162__nv_bfloat16(float(N)), __bfloat162__nv_bfloat16(float(N)))); + // } + + // __syncthreads(); + + // first DFT(x) + complex_matmul_r2c_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real_2), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT(x), with twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_row_major); + + // // x = x * N + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx] = __hmul2( + // reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + // reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx] = __hmul2( + // reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + // __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + // } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("FFT(x).transpose(-1,-2)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real_2[a_idx]), __bfloat162float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // dk_f = dout * x.conj() + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + complex_mul_conj_bfloat162( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx], + &reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // for(int i=0; i< items_per_thread_input; i++) { + // temp[i] += a_input_data[i]; + // } + + // __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __bfloat162float(a_real[a_idx]), __bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // start computing iFFT(dout), and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + k_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul_c2r( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + reinterpret_cast<__nv_bfloat16 *>(a_real), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + twiddle_idft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // multiply by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __nv_bfloat162 real, imag; + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__nv_bfloat162 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__nv_bfloat162 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__nv_bfloat16>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__nv_bfloat16>(real.y, imag.y); + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + } // b_tile_id + + for(int i = 0; i < items_per_thread_input; i++) { + reinterpret_cast<__nv_bfloat162 *>(temp)[i] = __hmul2(reinterpret_cast<__nv_bfloat162 *>(temp)[i], __nv_bfloat162(__float2bfloat16(float(N)), __float2bfloat16(float(N)))); + } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h index 84c29adef2bc07b70f82d9d70f2063b5402a4d93..7c0304ea0d0e48ac2c7f9889013485e6aa12b5cf 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h @@ -1,609 +1,609 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -#include "monarch_cuda_shared_r2r_bf16.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ twid_r2r, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *a_real_2 = &a_real[2 * N]; - at::BFloat16 *a_imag_2 = &a_real[3 * N]; - at::BFloat16 *b_real = &a_real[4 * N]; - at::BFloat16 *b_imag = &a_real[5 * N]; - at::BFloat16 *b_real_2 = &a_real[6 * N]; - at::BFloat16 *b_imag_2 = &a_real[7 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = 2 * N / num_threads; - const int items_per_thread_kf = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Filter = cub::BlockLoad; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t kf_input_data[items_per_thread_input]; // for storing the kf - complex_bfloat16_t z_data[items_per_thread_kf]; // for storing the intermediates - complex_bfloat16_t temp[items_per_thread_input]; - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 orig_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 ingate_data[items_per_thread_input]; // for storing the input - at::BFloat16 outgate_data[items_per_thread_input]; // for storing the input - at::BFloat16 dingate_data[items_per_thread_input]; // for storing the input - at::BFloat16 doutgate_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t twid_input_data[items_per_thread_kf]; // for storing the input - complex_bfloat16_t twid_input_data_conj[items_per_thread_kf]; // for storing the input - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load DFT matrix into b_frag - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load twid into twid_input_data - BlockLoad_Filter().Load( - reinterpret_cast(twid_r2r), - reinterpret_cast(twid_input_data) - ); - - negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Filter().Load( - reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), - reinterpret_cast(kf_input_data)); - - if (thread_id == 0) - { - // load in the pivot into the imag position - kf_input_data[0] = complex_bfloat16_t(kf_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); - } - - for(int i=0; i< items_per_thread_input; i++) { - temp[i] = complex_bfloat16_t(__float2bfloat16(0.0f), __float2bfloat16(0.0f)); - } - - __syncthreads(); - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load a into x_input_data - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - if (in_gate != nullptr) { - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(ingate_data), - signal_size / 4, 0. - ); - - // put orig a into orig_input_data, and compute a = in_gate * a - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(orig_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(ingate_data)[i] - ); - } - } - - // load a into a_real_2 - load_input( - &a_real_2[0], &a_imag_2[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - __syncthreads(); - - // first DFT(x) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real_2), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag_2), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT(x), with twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real_2), - reinterpret_cast<__nv_bfloat16 *>(a_imag_2), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // load dout into x_input_data - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - // put DFT(x) into a_input_data - process_zf( - &a_real_2[0], &a_imag_2[0], &a_input_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - if (out_gate != nullptr) { // compute dout_gate - // multiply by kf, and put it into z_data - multiply_kf( - &a_input_data[0], &kf_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // put it into a_real - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - // process yf from a_real and put it into z_data - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - // put it back into a_real - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // compute ifft - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - // k_frag, - wmma::mem_col_major); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - twiddle_idft_frag, - wmma::mem_col_major); - - // put result into doutgate_data - load_output( - &a_real[0], &a_imag[0], &doutgate_data[0], - items_per_thread_input, num_threads, thread_id); - - // load out_gate - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(outgate_data), - signal_size / 4, 0. - ); - - // compute dout_gate = dout_gate * dout - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(doutgate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(doutgate_data)[i] - ); - } - - // compute dout = dout * out_gate - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(outgate_data)[i] - ); - } - - __syncthreads(); - } - - // put dout from x_input_data into a_real - load_input( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - __syncthreads(); - - - // first DFT(dout) - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // second DFT(dout), with twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // put DFT(dout) into z_data - process_zf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - // DFT(x) = DFT(x) * N is in a_input_data - for (int i = 0; i < items_per_thread_kf; i++) - { - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i], - __nv_bfloat162( - __float2bfloat16(float(N)), - __float2bfloat16(float(N)) - ) - ); - } - - // dk_f = dout * x.conj() - multiply_kf_conj( - &z_data[0], &a_input_data[0], &a_input_data[0], items_per_thread_kf, num_threads, thread_id); - - if (thread_id == 0) { - reinterpret_cast<__nv_bfloat162 *>(a_input_data)[0] = __hmul2( - __nv_bfloat162( - __nv_bfloat16(a_input_data[0].real()), - __nv_bfloat16(a_input_data[0].imag()) - ), - __nv_bfloat162( - __float2bfloat16(0.5), - __float2bfloat16(0.5) - ) - ); - } - - for(int i = 0; i < items_per_thread_kf; i++) { - temp[i] += a_input_data[i]; - } - - // multiply z_data by kf.conj() - multiply_kf_conj( - &z_data[0], &kf_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - // start computing iFFT(dout), and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - // k_frag, - wmma::mem_col_major); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - // reinterpret_cast<__nv_bfloat16 *>(a_real), - // reinterpret_cast<__nv_bfloat16 *>(out + input_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - twiddle_idft_frag, - wmma::mem_col_major); - - load_output( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - if (in_gate != nullptr) { - // din_gate = dx * u, du = dx * ingate - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(dingate_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(orig_input_data)[i] - ); - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(ingate_data)[i] - ); - } - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dingate_data), - signal_size / 4 - ); - } - - - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4 - ); - - if (out_gate != nullptr) { - BlockStore_Sequence().Store( - reinterpret_cast(dout_gate + input_offset), - reinterpret_cast(doutgate_data), - signal_size / 4 - ); - } - - } // b_tile_id - - if (thread_id == 0) { - complex_bfloat16_t pivot = complex_bfloat16_t(temp[0].imag(), 0.); - temp[0] = complex_bfloat16_t(temp[0].real(), 0.); - (dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1))[N] = pivot; - } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast(dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1)), - reinterpret_cast(temp)); - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +#include "monarch_cuda_shared_r2r_bf16.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ twid_r2r, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *a_real_2 = &a_real[2 * N]; + at::BFloat16 *a_imag_2 = &a_real[3 * N]; + at::BFloat16 *b_real = &a_real[4 * N]; + at::BFloat16 *b_imag = &a_real[5 * N]; + at::BFloat16 *b_real_2 = &a_real[6 * N]; + at::BFloat16 *b_imag_2 = &a_real[7 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = 2 * N / num_threads; + const int items_per_thread_kf = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Filter = cub::BlockLoad; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t kf_input_data[items_per_thread_input]; // for storing the kf + complex_bfloat16_t z_data[items_per_thread_kf]; // for storing the intermediates + complex_bfloat16_t temp[items_per_thread_input]; + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 orig_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 ingate_data[items_per_thread_input]; // for storing the input + at::BFloat16 outgate_data[items_per_thread_input]; // for storing the input + at::BFloat16 dingate_data[items_per_thread_input]; // for storing the input + at::BFloat16 doutgate_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t twid_input_data[items_per_thread_kf]; // for storing the input + complex_bfloat16_t twid_input_data_conj[items_per_thread_kf]; // for storing the input + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load DFT matrix into b_frag + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load twid into twid_input_data + BlockLoad_Filter().Load( + reinterpret_cast(twid_r2r), + reinterpret_cast(twid_input_data) + ); + + negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Filter().Load( + reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), + reinterpret_cast(kf_input_data)); + + if (thread_id == 0) + { + // load in the pivot into the imag position + kf_input_data[0] = complex_bfloat16_t(kf_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); + } + + for(int i=0; i< items_per_thread_input; i++) { + temp[i] = complex_bfloat16_t(__float2bfloat16(0.0f), __float2bfloat16(0.0f)); + } + + __syncthreads(); + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load a into x_input_data + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + if (in_gate != nullptr) { + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(ingate_data), + signal_size / 4, 0. + ); + + // put orig a into orig_input_data, and compute a = in_gate * a + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(orig_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(ingate_data)[i] + ); + } + } + + // load a into a_real_2 + load_input( + &a_real_2[0], &a_imag_2[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + __syncthreads(); + + // first DFT(x) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real_2), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag_2), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT(x), with twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real_2), + reinterpret_cast<__nv_bfloat16 *>(a_imag_2), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // load dout into x_input_data + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + // put DFT(x) into a_input_data + process_zf( + &a_real_2[0], &a_imag_2[0], &a_input_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + if (out_gate != nullptr) { // compute dout_gate + // multiply by kf, and put it into z_data + multiply_kf( + &a_input_data[0], &kf_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // put it into a_real + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + // process yf from a_real and put it into z_data + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + // put it back into a_real + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // compute ifft + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + // k_frag, + wmma::mem_col_major); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + twiddle_idft_frag, + wmma::mem_col_major); + + // put result into doutgate_data + load_output( + &a_real[0], &a_imag[0], &doutgate_data[0], + items_per_thread_input, num_threads, thread_id); + + // load out_gate + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(outgate_data), + signal_size / 4, 0. + ); + + // compute dout_gate = dout_gate * dout + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(doutgate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(doutgate_data)[i] + ); + } + + // compute dout = dout * out_gate + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(outgate_data)[i] + ); + } + + __syncthreads(); + } + + // put dout from x_input_data into a_real + load_input( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + __syncthreads(); + + + // first DFT(dout) + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // second DFT(dout), with twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // put DFT(dout) into z_data + process_zf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + // DFT(x) = DFT(x) * N is in a_input_data + for (int i = 0; i < items_per_thread_kf; i++) + { + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[i], + __nv_bfloat162( + __float2bfloat16(float(N)), + __float2bfloat16(float(N)) + ) + ); + } + + // dk_f = dout * x.conj() + multiply_kf_conj( + &z_data[0], &a_input_data[0], &a_input_data[0], items_per_thread_kf, num_threads, thread_id); + + if (thread_id == 0) { + reinterpret_cast<__nv_bfloat162 *>(a_input_data)[0] = __hmul2( + __nv_bfloat162( + __nv_bfloat16(a_input_data[0].real()), + __nv_bfloat16(a_input_data[0].imag()) + ), + __nv_bfloat162( + __float2bfloat16(0.5), + __float2bfloat16(0.5) + ) + ); + } + + for(int i = 0; i < items_per_thread_kf; i++) { + temp[i] += a_input_data[i]; + } + + // multiply z_data by kf.conj() + multiply_kf_conj( + &z_data[0], &kf_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + // start computing iFFT(dout), and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + // k_frag, + wmma::mem_col_major); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + // reinterpret_cast<__nv_bfloat16 *>(a_real), + // reinterpret_cast<__nv_bfloat16 *>(out + input_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + twiddle_idft_frag, + wmma::mem_col_major); + + load_output( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + if (in_gate != nullptr) { + // din_gate = dx * u, du = dx * ingate + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(dingate_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(orig_input_data)[i] + ); + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(ingate_data)[i] + ); + } + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dingate_data), + signal_size / 4 + ); + } + + + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4 + ); + + if (out_gate != nullptr) { + BlockStore_Sequence().Store( + reinterpret_cast(dout_gate + input_offset), + reinterpret_cast(doutgate_data), + signal_size / 4 + ); + } + + } // b_tile_id + + if (thread_id == 0) { + complex_bfloat16_t pivot = complex_bfloat16_t(temp[0].imag(), 0.); + temp[0] = complex_bfloat16_t(temp[0].real(), 0.); + (dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1))[N] = pivot; + } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast(dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1)), + reinterpret_cast(temp)); + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_bf16.h index d26da1841cc816a930525d985e670bb717d426b8..13ebbb4e91c999a9ce87b3fa7dabe45e1ab84064 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_bf16.h @@ -1,428 +1,428 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[3 * N]; - at::BFloat16 *b_real_2 = &a_real[4 * N]; - at::BFloat16 *b_imag_2 = &a_real[5 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].real()), - __nv_bfloat16(a_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(a_input_data[2 * i].imag()), - __nv_bfloat16(a_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; - } - - //__syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); - } - } - - //__syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; - } - } - - - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // first DFT - complex_matmul_r2c_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real), // read from HBM - reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - k_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul_c2r( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - reinterpret_cast<__nv_bfloat16 *>(a_real), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - twiddle_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; - } - } - - // load input into a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - //__syncthreads(); - - } // b_tile_id - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[3 * N]; + at::BFloat16 *b_real_2 = &a_real[4 * N]; + at::BFloat16 *b_imag_2 = &a_real[5 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the gates + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].real()), + __nv_bfloat16(a_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(a_input_data[2 * i].imag()), + __nv_bfloat16(a_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = scratch; + } + + //__syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16 *>(a_imag + a_idx), sqrt_N); + } + } + + //__syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i], + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i]; + } + } + + + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // first DFT + complex_matmul_r2c_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real), // read from HBM + reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + k_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul_c2r( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + reinterpret_cast<__nv_bfloat16 *>(a_real), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + twiddle_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx]; + } + } + + // load input into a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + //__syncthreads(); + + } // b_tile_id + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_r2r_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_r2r_bf16.h index 73199f46e6417ce095ba9b0920d2367f5783a854..a15c9d678df6f64e8d7677447e39f348cac61d5f 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_r2r_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_kernel_r2r_bf16.h @@ -1,522 +1,522 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared_bf16_no_float_shm.h" -#include "monarch_cuda_shared_r2r_bf16.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ twid_r2r, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real_fp16[]; - at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); - at::BFloat16 *a_imag = &a_real[N]; - at::BFloat16 *b_real = &a_real[2 * N]; - at::BFloat16 *b_imag = &a_real[3 * N]; - at::BFloat16 *b_real_2 = &a_real[4 * N]; - at::BFloat16 *b_imag_2 = &a_real[5 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = 2 * N / num_threads; - const int items_per_thread_kf = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Filter = cub::BlockLoad; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; - - complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing k_f - complex_bfloat16_t z_data[items_per_thread_kf]; // for storing the intermediates - at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input - at::BFloat16 gate_data[items_per_thread_input]; // for storing the input - complex_bfloat16_t twid_input_data[items_per_thread_kf]; // for storing the input - complex_bfloat16_t twid_input_data_conj[items_per_thread_kf]; // for storing the input - complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __nv_bfloat162 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].real()), - __nv_bfloat16(b_input_data[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data[2 * i].imag()), - __nv_bfloat16(b_input_data[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; - - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].real()), - __nv_bfloat16(b_input_data_2[2 * i + 1].real()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; - scratch = __nv_bfloat162( - __nv_bfloat16(b_input_data_2[2 * i].imag()), - __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) - ); - reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); - } - } - - // load twid into twid_input_data - BlockLoad_Filter().Load( - reinterpret_cast(twid_r2r), - reinterpret_cast(twid_input_data) - ); - - negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Filter().Load( - reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), - reinterpret_cast(a_input_data)); - - if (thread_id == 0) - { - // load in the pivot into the imag position - a_input_data[0] = complex_bfloat16_t(a_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("kf loaded\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float( - // __nv_bfloat16(a_input_data[i].real()) - // ), - // __bfloat162float( - // __nv_bfloat16(a_input_data[i].imag()) - // ) - // ); - // } - // printf("\n"); - // } - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load input into a_real and a_imag - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - // load input gate into gate_data - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 4, 0. - ); - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - } - - load_input( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - //read the output gate into gate_data - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 4, 0. - ); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Data loaded\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float( - // __nv_bfloat16(a_real[a_idx]) - // ), - // __bfloat162float( - // __nv_bfloat16(a_imag[a_idx]) - // ) - // ); - // } - // printf("\n"); - // } - - // __syncthreads(); - - //__syncthreads(); - - // first DFT - complex_matmul_load_b( - reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output - reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - acc_frag_1_half, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - acc_frag_1_half, - twiddle_dft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("FFT(z)\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx]), - // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_imag)[a_idx]) - // ); - // } - // printf("\n"); - // } - - process_zf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("x_f\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float( - // __nv_bfloat16(z_data[i].real()) - // ), - // __bfloat162float( - // __nv_bfloat16(z_data[i].imag()) - // ) - // ); - // } - // printf("\n"); - // } - - multiply_kf( - &z_data[0], &a_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("x_f * k_f\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float( - // __nv_bfloat16(z_data[i].real()) - // ), - // __bfloat162float( - // __nv_bfloat16(z_data[i].imag()) - // ) - // ); - // } - // printf("\n"); - // } - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - // k_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast<__nv_bfloat16 *>(a_real), - reinterpret_cast<__nv_bfloat16 *>(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - acc_frag_1_half, - twiddle_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("y_z\n"); - // for (int i = 0; i < items_per_thread_kf; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", - // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx]), - // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_imag)[a_idx]) - // ); - // } - // printf("\n"); - // } - - load_output( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - if (out_gate != nullptr) { - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], - reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] - ); - } - } - - // load input into a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4 - ); - - //__syncthreads(); - - } // b_tile_id - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared_bf16_no_float_shm.h" +#include "monarch_cuda_shared_r2r_bf16.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ twid_r2r, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real_fp16[]; + at::BFloat16 *a_real = reinterpret_cast(&a_real_fp16[0]); + at::BFloat16 *a_imag = &a_real[N]; + at::BFloat16 *b_real = &a_real[2 * N]; + at::BFloat16 *b_imag = &a_real[3 * N]; + at::BFloat16 *b_real_2 = &a_real[4 * N]; + at::BFloat16 *b_imag_2 = &a_real[5 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = 2 * N / num_threads; + const int items_per_thread_kf = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Filter = cub::BlockLoad; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; + + complex_bfloat16_t a_input_data[items_per_thread_input]; // for storing k_f + complex_bfloat16_t z_data[items_per_thread_kf]; // for storing the intermediates + at::BFloat16 x_input_data[items_per_thread_input]; // for storing the input + at::BFloat16 gate_data[items_per_thread_input]; // for storing the input + complex_bfloat16_t twid_input_data[items_per_thread_kf]; // for storing the input + complex_bfloat16_t twid_input_data_conj[items_per_thread_kf]; // for storing the input + complex_bfloat16_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_bfloat16_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __nv_bfloat162 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + wmma::fragment acc_frag_1_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].real()), + __nv_bfloat16(b_input_data[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data[2 * i].imag()), + __nv_bfloat16(b_input_data[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag)[b_idx] = scratch; + + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].real()), + __nv_bfloat16(b_input_data_2[2 * i + 1].real()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_real_2)[b_idx] = scratch; + scratch = __nv_bfloat162( + __nv_bfloat16(b_input_data_2[2 * i].imag()), + __nv_bfloat16(b_input_data_2[2 * i + 1].imag()) + ); + reinterpret_cast<__nv_bfloat162 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast<__nv_bfloat16 *>(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast<__nv_bfloat16 *>(b_imag_2) + b_idx, sqrt_N); + } + } + + // load twid into twid_input_data + BlockLoad_Filter().Load( + reinterpret_cast(twid_r2r), + reinterpret_cast(twid_input_data) + ); + + negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Filter().Load( + reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), + reinterpret_cast(a_input_data)); + + if (thread_id == 0) + { + // load in the pivot into the imag position + a_input_data[0] = complex_bfloat16_t(a_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("kf loaded\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float( + // __nv_bfloat16(a_input_data[i].real()) + // ), + // __bfloat162float( + // __nv_bfloat16(a_input_data[i].imag()) + // ) + // ); + // } + // printf("\n"); + // } + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load input into a_real and a_imag + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + // load input gate into gate_data + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 4, 0. + ); + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + } + + load_input( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + //read the output gate into gate_data + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 4, 0. + ); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Data loaded\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float( + // __nv_bfloat16(a_real[a_idx]) + // ), + // __bfloat162float( + // __nv_bfloat16(a_imag[a_idx]) + // ) + // ); + // } + // printf("\n"); + // } + + // __syncthreads(); + + //__syncthreads(); + + // first DFT + complex_matmul_load_b( + reinterpret_cast<__nv_bfloat16 *>(a_real), // this is the output + reinterpret_cast<__nv_bfloat16 *>(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + acc_frag_1_half, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + acc_frag_1_half, + twiddle_dft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("FFT(z)\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx]), + // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_imag)[a_idx]) + // ); + // } + // printf("\n"); + // } + + process_zf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("x_f\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float( + // __nv_bfloat16(z_data[i].real()) + // ), + // __bfloat162float( + // __nv_bfloat16(z_data[i].imag()) + // ) + // ); + // } + // printf("\n"); + // } + + multiply_kf( + &z_data[0], &a_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("x_f * k_f\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float( + // __nv_bfloat16(z_data[i].real()) + // ), + // __bfloat162float( + // __nv_bfloat16(z_data[i].imag()) + // ) + // ); + // } + // printf("\n"); + // } + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __nv_bfloat162float(a_real[a_idx]), __nv_bfloat162float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + // k_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast<__nv_bfloat16 *>(a_real), + reinterpret_cast<__nv_bfloat16 *>(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + acc_frag_1_half, + twiddle_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("y_z\n"); + // for (int i = 0; i < items_per_thread_kf; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", + // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_real)[a_idx]), + // __bfloat162float(reinterpret_cast<__nv_bfloat16 *>(a_imag)[a_idx]) + // ); + // } + // printf("\n"); + // } + + load_output( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + if (out_gate != nullptr) { + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__nv_bfloat162 *>(gate_data)[i], + reinterpret_cast<__nv_bfloat162 *>(x_input_data)[i] + ); + } + } + + // load input into a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4 + ); + + //__syncthreads(); + + } // b_tile_id + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16.h index 5dbc3470f7b437f4459e7f3f813d27ba986f068d..5a74112774a3b6435d9e3198c6493658f87a61e3 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16.h @@ -1,930 +1,930 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_bfloat16_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - - -#ifndef MONARCH_CUDA_BF16_ -#define MONARCH_CUDA_BF16_ - -template -__device__ __forceinline__ void _complex_matmul( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - scratch_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - scratch_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major - ) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //does it matter where we put this? - wmma::store_matrix_sync( - scratch_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - scratch_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_load_b( - float* scratch_real, - float* scratch_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - - //does it matter where we put this? - wmma::store_matrix_sync( - scratch_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - scratch_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -// template -// __device__ __forceinline__ void _complex_matmul_r2c_256( -// float *scratch_real, -// float *scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major -// ) -// { -// // #pragma unroll -// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - -// // real - -// // ac -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); -// } - -// wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - -// // imag -// // ad -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); -// } - -// } -// } - -// if (output_to_shmem) { -// // #pragma unroll -// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory -// //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory -// //does it matter where we put this? -// wmma::store_matrix_sync( -// scratch_real + (out_trans ? -// j_b * WMMA_M * sqrt_N + j_a * WMMA_N: -// j_a * WMMA_M * sqrt_N + j_b * WMMA_N), -// acc_frag_1[j_a][j_b][0], sqrt_N, out_layout -// ); - -// wmma::store_matrix_sync( -// scratch_imag + (out_trans ? -// j_b * WMMA_M * sqrt_N + j_a * WMMA_N: -// j_a * WMMA_M * sqrt_N + j_b * WMMA_N), -// acc_frag_1[j_a][j_b][1], sqrt_N, out_layout -// ); -// } -// } -// } -// } - -template -__device__ __forceinline__ void _complex_matmul_c2r( - float *scratch_real, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory - //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory - - //does it matter where we put this? - wmma::store_matrix_sync( - scratch_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r_256( - float *scratch_real, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major - ) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //does it matter where we put this? - wmma::store_matrix_sync( - scratch_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][k].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); - } - } - } - } - } else { - // #pragma unroll - __nv_bfloat16 tmp_real[2048]; - __nv_bfloat16 tmp_imag[2048]; - - for(int i = 0; i < N; i++) { - tmp_real[i] = __float2bfloat16(scratch_real[i]); - tmp_imag[i] = __float2bfloat16(scratch_imag[i]); - } - - __syncthreads(); - - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], tmp_real + a_idx, sqrt_N); - wmma::load_matrix_sync(a_frag[j_a][k][1], tmp_imag + a_idx, sqrt_N); - } - } - } -} - -// template -// __device__ __forceinline__ void load_a_frag_256( -// float *scratch_real, -// float *scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -// { -// int a_idx; - -// if (a_frag_from_acc) { -// // load up a_frag's from acc_frag_1 -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// // #pragma unroll -// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { -// // #pragma unroll -// for (int k = 0; k < 2; k++) { -// for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { -// a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); -// a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); -// } -// } -// } -// } -// } else { -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// // #pragma unroll -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; -// wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(scratch_real) + a_idx, 256); -// wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(scratch_imag) + a_idx, 256); -// } -// } -// } -// } - -template -__device__ __forceinline__ void load_b_frag_r2c( - const __nv_bfloat16* b_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - } - } -} - -// template -// __device__ __forceinline__ void load_b_frag( -// float* scratch_real, -// float* scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -// { -// int b_idx; -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// // #pragma unroll -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; -// wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); -// wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); -// } -// } -// } - -template -__device__ __forceinline__ void load_a_frag_r2c( - const __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - } - } - } -} - -// template -// __device__ __forceinline__ void load_a_frag_r2c_256( -// const __nv_bfloat16 *a_real, -// int sqrt_N, -// int N, -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -// { -// int a_idx; - -// if (a_frag_from_acc) { -// // load up a_frag's from acc_frag_1 -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// // #pragma unroll -// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { -// // #pragma unroll -// for (int k = 0; k < 1; k++) { -// // #pragma unroll -// for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { -// a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); -// a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); -// } -// } -// } -// } -// } else { -// // #pragma unroll -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// // #pragma unroll -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; -// wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + a_idx, 256); -// } -// } -// } -// } - -template -__device__ __forceinline__ void complex_matmul( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - - wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - _complex_matmul(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -// template -// __device__ __forceinline__ void complex_matmul_load_b( -// float* scratch_real, -// float* scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major) -// { -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; -// load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); - -// // __syncthreads(); -// _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -// } - -// template -// __device__ __forceinline__ void complex_matmul_load_b( -// float* b_real, -// float* b_imag, -// int sqrt_N, -// int N, -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major) -// { -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; -// load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); - -// // __syncthreads(); -// // multiply b_frag by k_frag -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { -// complex_mul_bfloat162( -// __nv_bfloat162(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), -// __nv_bfloat162(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), -// __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), -// __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), -// &b_frag[j_a][k][0].x[2 * i], -// &b_frag[j_a][k][1].x[2 * i], -// &b_frag[j_a][k][0].x[2 * i + 1], -// &b_frag[j_a][k][1].x[2 * i + 1] -// ); -// } -// } -// } - -// // __syncthreads(); -// _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -// } - -template -__device__ __forceinline__ void complex_matmul_r2c( - const __nv_bfloat16 *a_real_input, - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c(a_real_input, sqrt_N, N, acc_frag_1, a_frag); - - _complex_matmul_r2c(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_load_b( - const __nv_bfloat16 *b_real_input, - float* scratch_real, - float* scratch_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag_r2c(b_real_input, sqrt_N, N, acc_frag_1, b_frag); - - _complex_matmul_r2c_load_b(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -// template -// __device__ __forceinline__ void complex_matmul_r2c_256( -// const __nv_bfloat16 *a_real_input, -// float *scratch_real, -// float *scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major) -// { -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; -// load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_1, a_frag); - -// // __syncthreads(); - -// _complex_matmul_r2c_256(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -// } - -template -__device__ __forceinline__ void complex_matmul_c2r( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); - - _complex_matmul_c2r(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -// template -// __device__ __forceinline__ void complex_matmul_c2r_256( -// float *scratch_real, -// float *scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major) -// { -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; -// load_a_frag_256(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); -// // __syncthreads(); - -// _complex_matmul_c2r_256(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -// } - -// template -// __device__ __forceinline__ void complex_matmul_c2r_256( -// float *scratch_real, -// float *scratch_imag, -// int sqrt_N, -// int N, -// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], -// wmma::layout_t out_layout = wmma::mem_row_major) -// { -// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; -// load_a_frag_256(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); -// // __syncthreads(); - -// // multiply a_frag by k_frag -// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { -// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { -// for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { -// complex_mul_bfloat162( -// __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), -// __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), -// __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), -// __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), -// &a_frag[j_a][k][0].x[2 * i], -// &a_frag[j_a][k][1].x[2 * i], -// &a_frag[j_a][k][0].x[2 * i + 1], -// &a_frag[j_a][k][1].x[2 * i + 1] -// ); -// } -// } -// } - -// _complex_matmul_c2r_256(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -// } - -template -__device__ __forceinline__ void complex_matmul_c2r( - float *scratch_real, - float *scratch_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); - // __syncthreads(); - - //multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -__device__ __forceinline__ void complex_mul(at::BFloat16 a_real, at::BFloat16 a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { - __nv_bfloat16 temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __nv_bfloat16(a_real * b_real - a_imag * b_imag); - temp_y = __hfma(__nv_bfloat16(a_imag), __nv_bfloat16(b_real), __nv_bfloat16(a_real * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_float_bfloat16(float a_real, float a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { - __nv_bfloat16 temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __nv_bfloat16(at::BFloat16(a_real) * b_real - at::BFloat16(a_imag) * b_imag); - temp_y = __hfma(__nv_bfloat16(at::BFloat16(a_imag)), __nv_bfloat16(b_real), __nv_bfloat16(at::BFloat16(a_real) * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat16 *c_real_0, __nv_bfloat16 *c_imag_0, __nv_bfloat16 *c_real_1, __nv_bfloat16 *c_imag_1) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real_0 = temp_x.x; - *c_imag_0 = temp_y.x; - *c_real_1 = temp_x.y; - *c_imag_1 = temp_y.y; -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, c10::complex<__nv_bfloat16> *c_0, c10::complex<__nv_bfloat16> *c_1) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_0 = c10::complex<__nv_bfloat16>(temp_x.x, temp_y.x); - *c_1 = c10::complex<__nv_bfloat16>(temp_x.y, temp_y.y); -} - -__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_bfloat16_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + + +#ifndef MONARCH_CUDA_BF16_ +#define MONARCH_CUDA_BF16_ + +template +__device__ __forceinline__ void _complex_matmul( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + scratch_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + scratch_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major + ) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //does it matter where we put this? + wmma::store_matrix_sync( + scratch_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + scratch_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_load_b( + float* scratch_real, + float* scratch_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + + //does it matter where we put this? + wmma::store_matrix_sync( + scratch_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + scratch_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +// template +// __device__ __forceinline__ void _complex_matmul_r2c_256( +// float *scratch_real, +// float *scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major +// ) +// { +// // #pragma unroll +// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + +// // real + +// // ac +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); +// } + +// wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + +// // imag +// // ad +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); +// } + +// } +// } + +// if (output_to_shmem) { +// // #pragma unroll +// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory +// //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory +// //does it matter where we put this? +// wmma::store_matrix_sync( +// scratch_real + (out_trans ? +// j_b * WMMA_M * sqrt_N + j_a * WMMA_N: +// j_a * WMMA_M * sqrt_N + j_b * WMMA_N), +// acc_frag_1[j_a][j_b][0], sqrt_N, out_layout +// ); + +// wmma::store_matrix_sync( +// scratch_imag + (out_trans ? +// j_b * WMMA_M * sqrt_N + j_a * WMMA_N: +// j_a * WMMA_M * sqrt_N + j_b * WMMA_N), +// acc_frag_1[j_a][j_b][1], sqrt_N, out_layout +// ); +// } +// } +// } +// } + +template +__device__ __forceinline__ void _complex_matmul_c2r( + float *scratch_real, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory + //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory + + //does it matter where we put this? + wmma::store_matrix_sync( + scratch_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r_256( + float *scratch_real, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major + ) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //does it matter where we put this? + wmma::store_matrix_sync( + scratch_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][k].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); + } + } + } + } + } else { + // #pragma unroll + __nv_bfloat16 tmp_real[2048]; + __nv_bfloat16 tmp_imag[2048]; + + for(int i = 0; i < N; i++) { + tmp_real[i] = __float2bfloat16(scratch_real[i]); + tmp_imag[i] = __float2bfloat16(scratch_imag[i]); + } + + __syncthreads(); + + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], tmp_real + a_idx, sqrt_N); + wmma::load_matrix_sync(a_frag[j_a][k][1], tmp_imag + a_idx, sqrt_N); + } + } + } +} + +// template +// __device__ __forceinline__ void load_a_frag_256( +// float *scratch_real, +// float *scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +// { +// int a_idx; + +// if (a_frag_from_acc) { +// // load up a_frag's from acc_frag_1 +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// // #pragma unroll +// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { +// // #pragma unroll +// for (int k = 0; k < 2; k++) { +// for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { +// a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); +// a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); +// } +// } +// } +// } +// } else { +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// // #pragma unroll +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; +// wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(scratch_real) + a_idx, 256); +// wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(scratch_imag) + a_idx, 256); +// } +// } +// } +// } + +template +__device__ __forceinline__ void load_b_frag_r2c( + const __nv_bfloat16* b_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + } + } +} + +// template +// __device__ __forceinline__ void load_b_frag( +// float* scratch_real, +// float* scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +// { +// int b_idx; +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// // #pragma unroll +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; +// wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); +// wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); +// } +// } +// } + +template +__device__ __forceinline__ void load_a_frag_r2c( + const __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + } + } + } +} + +// template +// __device__ __forceinline__ void load_a_frag_r2c_256( +// const __nv_bfloat16 *a_real, +// int sqrt_N, +// int N, +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +// { +// int a_idx; + +// if (a_frag_from_acc) { +// // load up a_frag's from acc_frag_1 +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// // #pragma unroll +// for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { +// // #pragma unroll +// for (int k = 0; k < 1; k++) { +// // #pragma unroll +// for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { +// a_frag[j_a][j_b][k].x[i] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); +// a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = __float2bfloat16(acc_frag_1[j_a][j_b][k].x[i]); +// } +// } +// } +// } +// } else { +// // #pragma unroll +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// // #pragma unroll +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; +// wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16 *>(a_real) + a_idx, 256); +// } +// } +// } +// } + +template +__device__ __forceinline__ void complex_matmul( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + + wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + _complex_matmul(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +// template +// __device__ __forceinline__ void complex_matmul_load_b( +// float* scratch_real, +// float* scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major) +// { +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; +// load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); + +// // __syncthreads(); +// _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// } + +// template +// __device__ __forceinline__ void complex_matmul_load_b( +// float* b_real, +// float* b_imag, +// int sqrt_N, +// int N, +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major) +// { +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; +// load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); + +// // __syncthreads(); +// // multiply b_frag by k_frag +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { +// complex_mul_bfloat162( +// __nv_bfloat162(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), +// __nv_bfloat162(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), +// __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), +// __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), +// &b_frag[j_a][k][0].x[2 * i], +// &b_frag[j_a][k][1].x[2 * i], +// &b_frag[j_a][k][0].x[2 * i + 1], +// &b_frag[j_a][k][1].x[2 * i + 1] +// ); +// } +// } +// } + +// // __syncthreads(); +// _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// } + +template +__device__ __forceinline__ void complex_matmul_r2c( + const __nv_bfloat16 *a_real_input, + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c(a_real_input, sqrt_N, N, acc_frag_1, a_frag); + + _complex_matmul_r2c(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_load_b( + const __nv_bfloat16 *b_real_input, + float* scratch_real, + float* scratch_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag_r2c(b_real_input, sqrt_N, N, acc_frag_1, b_frag); + + _complex_matmul_r2c_load_b(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +// template +// __device__ __forceinline__ void complex_matmul_r2c_256( +// const __nv_bfloat16 *a_real_input, +// float *scratch_real, +// float *scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major) +// { +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; +// load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_1, a_frag); + +// // __syncthreads(); + +// _complex_matmul_r2c_256(scratch_real, scratch_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// } + +template +__device__ __forceinline__ void complex_matmul_c2r( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); + + _complex_matmul_c2r(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +// template +// __device__ __forceinline__ void complex_matmul_c2r_256( +// float *scratch_real, +// float *scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major) +// { +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; +// load_a_frag_256(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); +// // __syncthreads(); + +// _complex_matmul_c2r_256(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// } + +// template +// __device__ __forceinline__ void complex_matmul_c2r_256( +// float *scratch_real, +// float *scratch_imag, +// int sqrt_N, +// int N, +// wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], +// wmma::layout_t out_layout = wmma::mem_row_major) +// { +// wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; +// load_a_frag_256(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); +// // __syncthreads(); + +// // multiply a_frag by k_frag +// for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { +// for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { +// for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { +// complex_mul_bfloat162( +// __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), +// __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), +// __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), +// __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), +// &a_frag[j_a][k][0].x[2 * i], +// &a_frag[j_a][k][1].x[2 * i], +// &a_frag[j_a][k][0].x[2 * i + 1], +// &a_frag[j_a][k][1].x[2 * i + 1] +// ); +// } +// } +// } + +// _complex_matmul_c2r_256(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// } + +template +__device__ __forceinline__ void complex_matmul_c2r( + float *scratch_real, + float *scratch_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(scratch_real, scratch_imag, sqrt_N, N, acc_frag_1, a_frag); + // __syncthreads(); + + //multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r(scratch_real, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +__device__ __forceinline__ void complex_mul(at::BFloat16 a_real, at::BFloat16 a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { + __nv_bfloat16 temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __nv_bfloat16(a_real * b_real - a_imag * b_imag); + temp_y = __hfma(__nv_bfloat16(a_imag), __nv_bfloat16(b_real), __nv_bfloat16(a_real * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_float_bfloat16(float a_real, float a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { + __nv_bfloat16 temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __nv_bfloat16(at::BFloat16(a_real) * b_real - at::BFloat16(a_imag) * b_imag); + temp_y = __hfma(__nv_bfloat16(at::BFloat16(a_imag)), __nv_bfloat16(b_real), __nv_bfloat16(at::BFloat16(a_real) * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat16 *c_real_0, __nv_bfloat16 *c_imag_0, __nv_bfloat16 *c_real_1, __nv_bfloat16 *c_imag_1) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real_0 = temp_x.x; + *c_imag_0 = temp_y.x; + *c_real_1 = temp_x.y; + *c_imag_1 = temp_y.y; +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, c10::complex<__nv_bfloat16> *c_0, c10::complex<__nv_bfloat16> *c_1) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_0 = c10::complex<__nv_bfloat16>(temp_x.x, temp_y.x); + *c_1 = c10::complex<__nv_bfloat16>(temp_x.y, temp_y.y); +} + +__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h index f6e8dcbdc1f02763043d99284739c352526f4f99..1ffef1eb2a01df4590988e25944ebd3af1967cfd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h @@ -1,471 +1,471 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "shared/monarch_cuda_shared_bf16_complex_mul.h" -#include "shared/monarch_cuda_shared_bf16_matmuls.h" -#include "shared/monarch_cuda_shared_bf16_load_frags.h" -using namespace nvcuda; - -using complex_bfloat16_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - - -#ifndef MONARCH_CUDA_BF16_ -#define MONARCH_CUDA_BF16_ - -template -__device__ __forceinline__ void complex_matmul( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - - wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_load_b( - __nv_bfloat16* b_real, - __nv_bfloat16* b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag(b_real, b_imag, sqrt_N, N, b_frag); - - // __syncthreads(); - _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_load_b( - __nv_bfloat16* b_real, - __nv_bfloat16* b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag(b_real, b_imag, sqrt_N, N, b_frag); - - // __syncthreads(); - // multiply b_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &b_frag[j_a][k][0].x[2 * i], - &b_frag[j_a][k][1].x[2 * i], - &b_frag[j_a][k][0].x[2 * i + 1], - &b_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - // __syncthreads(); - _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_load_b( - __nv_bfloat16 *b_real_input, - __nv_bfloat16* a_real, - __nv_bfloat16* a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag_r2c(b_real_input, sqrt_N, N, b_frag); - - _complex_matmul_r2c_load_b(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_256( - const __nv_bfloat16 *a_real_input, - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - - _complex_matmul_r2c_256(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_256( - const __nv_bfloat16 *a_real_inp, - const __nv_bfloat16 *a_imag_inp, - __nv_bfloat16 *a_real_out, - __nv_bfloat16 *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - - _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_256( - __nv_bfloat16 *a_real_inp, - __nv_bfloat16 *a_imag_inp, - __nv_bfloat16 *a_real_out, - __nv_bfloat16 *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_1024( - const __nv_bfloat16 *a_real_input, - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c_1024(a_real_input, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - - _complex_matmul_r2c_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - - _complex_matmul_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - const __nv_bfloat16 *a_real_inp, - const __nv_bfloat16 *a_imag_inp, - __nv_bfloat16 *a_real_out, - __nv_bfloat16 *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); - - // __syncthreads(); - - _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - __nv_bfloat16 *a_real_inp, - __nv_bfloat16 *a_imag_inp, - __nv_bfloat16 *a_real_out, - __nv_bfloat16 *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_256( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - __nv_bfloat16 *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - // __syncthreads(); - - _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_256( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - __nv_bfloat16 *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - // __syncthreads(); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_1024( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - __nv_bfloat16 *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - // __syncthreads(); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r_1024(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - __nv_bfloat16 *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); - // __syncthreads(); - - //multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_bfloat162( - __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "shared/monarch_cuda_shared_bf16_complex_mul.h" +#include "shared/monarch_cuda_shared_bf16_matmuls.h" +#include "shared/monarch_cuda_shared_bf16_load_frags.h" +using namespace nvcuda; + +using complex_bfloat16_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + + +#ifndef MONARCH_CUDA_BF16_ +#define MONARCH_CUDA_BF16_ + +template +__device__ __forceinline__ void complex_matmul( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + + wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_load_b( + __nv_bfloat16* b_real, + __nv_bfloat16* b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag(b_real, b_imag, sqrt_N, N, b_frag); + + // __syncthreads(); + _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_load_b( + __nv_bfloat16* b_real, + __nv_bfloat16* b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag(b_real, b_imag, sqrt_N, N, b_frag); + + // __syncthreads(); + // multiply b_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &b_frag[j_a][k][0].x[2 * i], + &b_frag[j_a][k][1].x[2 * i], + &b_frag[j_a][k][0].x[2 * i + 1], + &b_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + // __syncthreads(); + _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_load_b( + __nv_bfloat16 *b_real_input, + __nv_bfloat16* a_real, + __nv_bfloat16* a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag_r2c(b_real_input, sqrt_N, N, b_frag); + + _complex_matmul_r2c_load_b(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_256( + const __nv_bfloat16 *a_real_input, + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + + _complex_matmul_r2c_256(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_256( + const __nv_bfloat16 *a_real_inp, + const __nv_bfloat16 *a_imag_inp, + __nv_bfloat16 *a_real_out, + __nv_bfloat16 *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + + _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_256( + __nv_bfloat16 *a_real_inp, + __nv_bfloat16 *a_imag_inp, + __nv_bfloat16 *a_real_out, + __nv_bfloat16 *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_1024( + const __nv_bfloat16 *a_real_input, + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c_1024(a_real_input, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + + _complex_matmul_r2c_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + + _complex_matmul_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + const __nv_bfloat16 *a_real_inp, + const __nv_bfloat16 *a_imag_inp, + __nv_bfloat16 *a_real_out, + __nv_bfloat16 *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); + + // __syncthreads(); + + _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + __nv_bfloat16 *a_real_inp, + __nv_bfloat16 *a_imag_inp, + __nv_bfloat16 *a_real_out, + __nv_bfloat16 *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_half, a_frag); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_256( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + __nv_bfloat16 *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + // __syncthreads(); + + _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_256( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + __nv_bfloat16 *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + // __syncthreads(); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_1024( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + __nv_bfloat16 *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + // __syncthreads(); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r_1024(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + __nv_bfloat16 *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_half, a_frag); + // __syncthreads(); + + //multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_bfloat162( + __nv_bfloat162(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __nv_bfloat162(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, acc_frag_half, out_layout); +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_r2r_bf16.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_r2r_bf16.h index 9f97f1996dc3082c9b8fc4240b58abd2157193d3..6efc42b94a52943d3b52278ee9866a693f4665f8 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_r2r_bf16.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/monarch_cuda_shared_r2r_bf16.h @@ -1,316 +1,316 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared/monarch_cuda_shared_bf16_complex_mul.h" -using namespace nvcuda; - -using complex_bfloat16_t = typename c10::complex; - -#ifndef MONARCH_CUDA_SHARED_R2R_BF16_ -#define MONARCH_CUDA_SHARED_R2R_BF16_ - -__device__ __forceinline__ void negate_twid( - complex_bfloat16_t *twid_input_data, - complex_bfloat16_t *twid_output_data, - int items_per_thread -) { - for (int i = 0; i < items_per_thread; i++) { - twid_output_data[i] = conj(twid_input_data[i]); - } -} - -__device__ __forceinline__ void load_input( - at::BFloat16 *a_real, - at::BFloat16 *a_imag, - at::BFloat16 *x_input_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input / 4; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( - __nv_bfloat16(x_input_data[4 * i]), - __nv_bfloat16(x_input_data[4 * i + 2]) - ); - reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __nv_bfloat162( - __nv_bfloat16(x_input_data[4 * i + 1]), - __nv_bfloat16(x_input_data[4 * i + 3]) - ); - // a_imag[a_idx] = x_input_data[2 * i + 1]; - } -} - -__device__ __forceinline__ void load_output( - at::BFloat16 *a_real, - at::BFloat16 *a_imag, - at::BFloat16 *x_input_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input / 4; i++) - { - a_idx = i * num_threads + thread_id; - - x_input_data[4 * i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx].x; - x_input_data[4 * i + 2] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx].y; - x_input_data[4 * i + 1] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx].x; - x_input_data[4 * i + 3] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx].y; - } -} - -__device__ __forceinline__ void store_z_data( - at::BFloat16 *a_real, - at::BFloat16 *a_imag, - complex_bfloat16_t *z_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input; i++) - { - a_idx = i * num_threads + thread_id; - - a_real[a_idx] = z_data[i].real(); - a_imag[a_idx] = z_data[i].imag(); - } -} - -__device__ __forceinline__ void multiply_kf( - complex_bfloat16_t *z_data, - complex_bfloat16_t *kf_data, - complex_bfloat16_t *out_data, - int items_per_thread, - int num_threads, - int thread_id -) { - __nv_bfloat162 scratch; - for (int i = 0; i < items_per_thread / 2; i++) { - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // do pointwise - scratch = __hmul2( - __nv_bfloat162(__nv_bfloat16(z_data[0].real()), __nv_bfloat16(z_data[0].imag())), - __nv_bfloat162(__nv_bfloat16(kf_data[0].real()), __nv_bfloat16(kf_data[0].imag())) - ); - out_data[0] = complex_bfloat16_t(scratch.x, scratch.y); - complex_mul( - z_data[1], kf_data[1], - &out_data[1] - ); - } else { - complex_mul_bfloat162( - z_data[2*i], z_data[2*i+1], - kf_data[2*i], kf_data[2*i+1], - &out_data[2*i], &out_data[2*i+1] - ); - } - } -} - -__device__ __forceinline__ void multiply_kf_conj( - complex_bfloat16_t *z_data, - complex_bfloat16_t *kf_data, - complex_bfloat16_t *out_data, - int items_per_thread, - int num_threads, - int thread_id -) { - __nv_bfloat162 scratch; - for (int i = 0; i < items_per_thread / 2; i++) { - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // do pointwise - scratch = __hmul2( - __nv_bfloat162(__nv_bfloat16(z_data[0].real()), __nv_bfloat16(z_data[0].imag())), - __nv_bfloat162(__nv_bfloat16(kf_data[0].real()), __nv_bfloat16(kf_data[0].imag())) - ); - out_data[0] = complex_bfloat16_t(scratch.x, scratch.y); - complex_mul_conj( - z_data[1], kf_data[1], - &out_data[1] - ); - } else { - complex_mul_conj_bfloat162( - z_data[2*i], z_data[2*i+1], - kf_data[2*i], kf_data[2*i+1], - &out_data[2*i], &out_data[2*i+1] - ); - } - } -} - -__device__ __forceinline__ void process_zf( - at::BFloat16 *a_real, - at::BFloat16 *a_imag, - complex_bfloat16_t *z_data, - complex_bfloat16_t *twid_input_data, - int items_per_thread, - int num_threads, - int thread_id, - int N -) { - int a_idx1, a_idx2; - complex_bfloat16_t scratch_complex1, scratch_complex2, xe, xo; - __nv_bfloat162 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; - for (int i = 0; i < items_per_thread / 2; i++) { - a_idx1 = (2 * i * num_threads + thread_id); - a_idx2 = ((2 * i + 1) * num_threads + thread_id); - - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // xe = a_real[0] - // xo = a_imag[0] - // z.real = xe + xo * twid_real[0] = xe + xo - // z.imag = xe - xo - z_data[0] = complex_bfloat16_t( - a_real[0] + a_imag[0], - a_real[0] - a_imag[0] - ); - scratch_complex1 = complex_bfloat16_t(a_real[a_idx2], a_imag[a_idx2]); - scratch_complex2 = complex_bfloat16_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); - - xe = (scratch_complex1 + scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.5), __float2bfloat16(0.0)); - xo = (scratch_complex1 - scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.0), __float2bfloat16(-0.5)); - z_data[1] = xe + xo * twid_input_data[1]; - } else { - // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] - // xe = (a[a_idx] + a[N - a_idx]) / 2 - // xo = (a[a_idx] - a[N - a_idx]) / 2j - // z[i] = xe + xo * twid[a_idx] - a1_real2 = __nv_bfloat162(__nv_bfloat16(a_real[a_idx1]), __nv_bfloat16(a_real[a_idx2])); - a1_imag2 = __nv_bfloat162(__nv_bfloat16(a_imag[a_idx1]), __nv_bfloat16(a_imag[a_idx2])); - a2_real2 = __nv_bfloat162(__nv_bfloat16(a_real[N-a_idx1]), __nv_bfloat16(a_real[N-a_idx2])); - a2_imag2 = __nv_bfloat162(__nv_bfloat16(-a_imag[N-a_idx1]), __nv_bfloat16(-a_imag[N-a_idx2])); - - complex_mul_bfloat162( - __hadd2(a1_real2, a2_real2), - __hadd2(a1_imag2, a2_imag2), - __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), - __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), - &xe_real2, &xe_imag2 - ); - complex_mul_bfloat162( - __hsub2(a1_real2, a2_real2), - __hsub2(a1_imag2, a2_imag2), - __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), - __nv_bfloat162(__float2bfloat16(-0.5), __float2bfloat16(-0.5)), - &xo_real2, &xo_imag2 - ); - - complex_mul_bfloat162( - xo_real2, xo_imag2, - __nv_bfloat162(__nv_bfloat16(twid_input_data[2*i].real()), __nv_bfloat16(twid_input_data[2*i + 1].real())), - __nv_bfloat162(__nv_bfloat16(twid_input_data[2*i].imag()), __nv_bfloat16(twid_input_data[2*i + 1].imag())), - &z_real2, &z_imag2 - ); - - z_real2 = __hadd2(xe_real2, z_real2); - z_imag2 = __hadd2(xe_imag2, z_imag2); - - z_data[2*i] = complex_bfloat16_t(z_real2.x, z_imag2.x); - z_data[2*i + 1] = complex_bfloat16_t(z_real2.y, z_imag2.y); - } - } -} - -__device__ __forceinline__ void process_yf( - at::BFloat16 *a_real, - at::BFloat16 *a_imag, - complex_bfloat16_t *z_data, - complex_bfloat16_t *twid_input_data_conj, - int items_per_thread, - int num_threads, - int thread_id, - int N -) { - int a_idx1, a_idx2; - complex_bfloat16_t scratch_complex1, scratch_complex2, xe, xo; - - __nv_bfloat162 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; - for (int i = 0; i < items_per_thread / 2; i++) { - a_idx1 = (2 * i * num_threads + thread_id); - a_idx2 = ((2 * i + 1) * num_threads + thread_id); - // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] - // xe = (a[a_idx] + a[N - a_idx]) / 2 - // xo = (a[a_idx] - a[N - a_idx]) / 2 * twid[i].conj() - // z[i] = xe + xo * 1j - if (thread_id == 0 && i == 0) { - // special case - xe = complex_bfloat16_t( - (a_real[0] + a_imag[0]) / 2, - 0. - ); - xo = complex_bfloat16_t( - (a_real[0] - a_imag[0]) / 2, - 0. - ); - z_data[0] = xe + xo * complex_bfloat16_t(0., 1.); - - scratch_complex1 = complex_bfloat16_t(a_real[a_idx2], a_imag[a_idx2]); - scratch_complex2 = complex_bfloat16_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); - xe = (scratch_complex1 + scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.5), __float2bfloat16(0.0)); - xo = ((scratch_complex1 - scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.0), __float2bfloat16(0.5))) * twid_input_data_conj[1]; - - // z_data[1] = xe + xo * complex_bfloat16_t(0., 1.); - z_data[1] = xe + xo; - } else { - a1_real2 = __nv_bfloat162(__nv_bfloat16(a_real[a_idx1]), __nv_bfloat16(a_real[a_idx2])); - a1_imag2 = __nv_bfloat162(__nv_bfloat16(a_imag[a_idx1]), __nv_bfloat16(a_imag[a_idx2])); - a2_real2 = __nv_bfloat162(__nv_bfloat16(a_real[N-a_idx1]), __nv_bfloat16(a_real[N-a_idx2])); - a2_imag2 = __nv_bfloat162(__nv_bfloat16(-a_imag[N-a_idx1]), __nv_bfloat16(-a_imag[N-a_idx2])); - - complex_mul_bfloat162( - __hadd2(a1_real2, a2_real2), - __hadd2(a1_imag2, a2_imag2), - __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), - __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), - &xe_real2, &xe_imag2 - ); - complex_mul_bfloat162( - __hsub2(a1_real2, a2_real2), - __hsub2(a1_imag2, a2_imag2), - __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), - __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), - &xo_real2, &xo_imag2 - ); - - complex_mul_bfloat162( - xo_real2, xo_imag2, - __nv_bfloat162(__nv_bfloat16(twid_input_data_conj[2*i].real()), __nv_bfloat16(twid_input_data_conj[2*i + 1].real())), - __nv_bfloat162(__nv_bfloat16(twid_input_data_conj[2*i].imag()), __nv_bfloat16(twid_input_data_conj[2*i + 1].imag())), - &z_real2, &z_imag2 - ); - - z_real2 = __hadd2(xe_real2, z_real2); - z_imag2 = __hadd2(xe_imag2, z_imag2); - - z_data[2*i] = complex_bfloat16_t(z_real2.x, z_imag2.x); - z_data[2*i + 1] = complex_bfloat16_t(z_real2.y, z_imag2.y); - } - } -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared/monarch_cuda_shared_bf16_complex_mul.h" +using namespace nvcuda; + +using complex_bfloat16_t = typename c10::complex; + +#ifndef MONARCH_CUDA_SHARED_R2R_BF16_ +#define MONARCH_CUDA_SHARED_R2R_BF16_ + +__device__ __forceinline__ void negate_twid( + complex_bfloat16_t *twid_input_data, + complex_bfloat16_t *twid_output_data, + int items_per_thread +) { + for (int i = 0; i < items_per_thread; i++) { + twid_output_data[i] = conj(twid_input_data[i]); + } +} + +__device__ __forceinline__ void load_input( + at::BFloat16 *a_real, + at::BFloat16 *a_imag, + at::BFloat16 *x_input_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input / 4; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx] = __nv_bfloat162( + __nv_bfloat16(x_input_data[4 * i]), + __nv_bfloat16(x_input_data[4 * i + 2]) + ); + reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx] = __nv_bfloat162( + __nv_bfloat16(x_input_data[4 * i + 1]), + __nv_bfloat16(x_input_data[4 * i + 3]) + ); + // a_imag[a_idx] = x_input_data[2 * i + 1]; + } +} + +__device__ __forceinline__ void load_output( + at::BFloat16 *a_real, + at::BFloat16 *a_imag, + at::BFloat16 *x_input_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input / 4; i++) + { + a_idx = i * num_threads + thread_id; + + x_input_data[4 * i] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx].x; + x_input_data[4 * i + 2] = reinterpret_cast<__nv_bfloat162 *>(a_real)[a_idx].y; + x_input_data[4 * i + 1] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx].x; + x_input_data[4 * i + 3] = reinterpret_cast<__nv_bfloat162 *>(a_imag)[a_idx].y; + } +} + +__device__ __forceinline__ void store_z_data( + at::BFloat16 *a_real, + at::BFloat16 *a_imag, + complex_bfloat16_t *z_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input; i++) + { + a_idx = i * num_threads + thread_id; + + a_real[a_idx] = z_data[i].real(); + a_imag[a_idx] = z_data[i].imag(); + } +} + +__device__ __forceinline__ void multiply_kf( + complex_bfloat16_t *z_data, + complex_bfloat16_t *kf_data, + complex_bfloat16_t *out_data, + int items_per_thread, + int num_threads, + int thread_id +) { + __nv_bfloat162 scratch; + for (int i = 0; i < items_per_thread / 2; i++) { + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // do pointwise + scratch = __hmul2( + __nv_bfloat162(__nv_bfloat16(z_data[0].real()), __nv_bfloat16(z_data[0].imag())), + __nv_bfloat162(__nv_bfloat16(kf_data[0].real()), __nv_bfloat16(kf_data[0].imag())) + ); + out_data[0] = complex_bfloat16_t(scratch.x, scratch.y); + complex_mul( + z_data[1], kf_data[1], + &out_data[1] + ); + } else { + complex_mul_bfloat162( + z_data[2*i], z_data[2*i+1], + kf_data[2*i], kf_data[2*i+1], + &out_data[2*i], &out_data[2*i+1] + ); + } + } +} + +__device__ __forceinline__ void multiply_kf_conj( + complex_bfloat16_t *z_data, + complex_bfloat16_t *kf_data, + complex_bfloat16_t *out_data, + int items_per_thread, + int num_threads, + int thread_id +) { + __nv_bfloat162 scratch; + for (int i = 0; i < items_per_thread / 2; i++) { + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // do pointwise + scratch = __hmul2( + __nv_bfloat162(__nv_bfloat16(z_data[0].real()), __nv_bfloat16(z_data[0].imag())), + __nv_bfloat162(__nv_bfloat16(kf_data[0].real()), __nv_bfloat16(kf_data[0].imag())) + ); + out_data[0] = complex_bfloat16_t(scratch.x, scratch.y); + complex_mul_conj( + z_data[1], kf_data[1], + &out_data[1] + ); + } else { + complex_mul_conj_bfloat162( + z_data[2*i], z_data[2*i+1], + kf_data[2*i], kf_data[2*i+1], + &out_data[2*i], &out_data[2*i+1] + ); + } + } +} + +__device__ __forceinline__ void process_zf( + at::BFloat16 *a_real, + at::BFloat16 *a_imag, + complex_bfloat16_t *z_data, + complex_bfloat16_t *twid_input_data, + int items_per_thread, + int num_threads, + int thread_id, + int N +) { + int a_idx1, a_idx2; + complex_bfloat16_t scratch_complex1, scratch_complex2, xe, xo; + __nv_bfloat162 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; + for (int i = 0; i < items_per_thread / 2; i++) { + a_idx1 = (2 * i * num_threads + thread_id); + a_idx2 = ((2 * i + 1) * num_threads + thread_id); + + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // xe = a_real[0] + // xo = a_imag[0] + // z.real = xe + xo * twid_real[0] = xe + xo + // z.imag = xe - xo + z_data[0] = complex_bfloat16_t( + a_real[0] + a_imag[0], + a_real[0] - a_imag[0] + ); + scratch_complex1 = complex_bfloat16_t(a_real[a_idx2], a_imag[a_idx2]); + scratch_complex2 = complex_bfloat16_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); + + xe = (scratch_complex1 + scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.5), __float2bfloat16(0.0)); + xo = (scratch_complex1 - scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.0), __float2bfloat16(-0.5)); + z_data[1] = xe + xo * twid_input_data[1]; + } else { + // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] + // xe = (a[a_idx] + a[N - a_idx]) / 2 + // xo = (a[a_idx] - a[N - a_idx]) / 2j + // z[i] = xe + xo * twid[a_idx] + a1_real2 = __nv_bfloat162(__nv_bfloat16(a_real[a_idx1]), __nv_bfloat16(a_real[a_idx2])); + a1_imag2 = __nv_bfloat162(__nv_bfloat16(a_imag[a_idx1]), __nv_bfloat16(a_imag[a_idx2])); + a2_real2 = __nv_bfloat162(__nv_bfloat16(a_real[N-a_idx1]), __nv_bfloat16(a_real[N-a_idx2])); + a2_imag2 = __nv_bfloat162(__nv_bfloat16(-a_imag[N-a_idx1]), __nv_bfloat16(-a_imag[N-a_idx2])); + + complex_mul_bfloat162( + __hadd2(a1_real2, a2_real2), + __hadd2(a1_imag2, a2_imag2), + __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), + __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), + &xe_real2, &xe_imag2 + ); + complex_mul_bfloat162( + __hsub2(a1_real2, a2_real2), + __hsub2(a1_imag2, a2_imag2), + __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), + __nv_bfloat162(__float2bfloat16(-0.5), __float2bfloat16(-0.5)), + &xo_real2, &xo_imag2 + ); + + complex_mul_bfloat162( + xo_real2, xo_imag2, + __nv_bfloat162(__nv_bfloat16(twid_input_data[2*i].real()), __nv_bfloat16(twid_input_data[2*i + 1].real())), + __nv_bfloat162(__nv_bfloat16(twid_input_data[2*i].imag()), __nv_bfloat16(twid_input_data[2*i + 1].imag())), + &z_real2, &z_imag2 + ); + + z_real2 = __hadd2(xe_real2, z_real2); + z_imag2 = __hadd2(xe_imag2, z_imag2); + + z_data[2*i] = complex_bfloat16_t(z_real2.x, z_imag2.x); + z_data[2*i + 1] = complex_bfloat16_t(z_real2.y, z_imag2.y); + } + } +} + +__device__ __forceinline__ void process_yf( + at::BFloat16 *a_real, + at::BFloat16 *a_imag, + complex_bfloat16_t *z_data, + complex_bfloat16_t *twid_input_data_conj, + int items_per_thread, + int num_threads, + int thread_id, + int N +) { + int a_idx1, a_idx2; + complex_bfloat16_t scratch_complex1, scratch_complex2, xe, xo; + + __nv_bfloat162 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; + for (int i = 0; i < items_per_thread / 2; i++) { + a_idx1 = (2 * i * num_threads + thread_id); + a_idx2 = ((2 * i + 1) * num_threads + thread_id); + // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] + // xe = (a[a_idx] + a[N - a_idx]) / 2 + // xo = (a[a_idx] - a[N - a_idx]) / 2 * twid[i].conj() + // z[i] = xe + xo * 1j + if (thread_id == 0 && i == 0) { + // special case + xe = complex_bfloat16_t( + (a_real[0] + a_imag[0]) / 2, + 0. + ); + xo = complex_bfloat16_t( + (a_real[0] - a_imag[0]) / 2, + 0. + ); + z_data[0] = xe + xo * complex_bfloat16_t(0., 1.); + + scratch_complex1 = complex_bfloat16_t(a_real[a_idx2], a_imag[a_idx2]); + scratch_complex2 = complex_bfloat16_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); + xe = (scratch_complex1 + scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.5), __float2bfloat16(0.0)); + xo = ((scratch_complex1 - scratch_complex2) * complex_bfloat16_t(__float2bfloat16(0.0), __float2bfloat16(0.5))) * twid_input_data_conj[1]; + + // z_data[1] = xe + xo * complex_bfloat16_t(0., 1.); + z_data[1] = xe + xo; + } else { + a1_real2 = __nv_bfloat162(__nv_bfloat16(a_real[a_idx1]), __nv_bfloat16(a_real[a_idx2])); + a1_imag2 = __nv_bfloat162(__nv_bfloat16(a_imag[a_idx1]), __nv_bfloat16(a_imag[a_idx2])); + a2_real2 = __nv_bfloat162(__nv_bfloat16(a_real[N-a_idx1]), __nv_bfloat16(a_real[N-a_idx2])); + a2_imag2 = __nv_bfloat162(__nv_bfloat16(-a_imag[N-a_idx1]), __nv_bfloat16(-a_imag[N-a_idx2])); + + complex_mul_bfloat162( + __hadd2(a1_real2, a2_real2), + __hadd2(a1_imag2, a2_imag2), + __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), + __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), + &xe_real2, &xe_imag2 + ); + complex_mul_bfloat162( + __hsub2(a1_real2, a2_real2), + __hsub2(a1_imag2, a2_imag2), + __nv_bfloat162(__float2bfloat16(0.0), __float2bfloat16(0.0)), + __nv_bfloat162(__float2bfloat16(0.5), __float2bfloat16(0.5)), + &xo_real2, &xo_imag2 + ); + + complex_mul_bfloat162( + xo_real2, xo_imag2, + __nv_bfloat162(__nv_bfloat16(twid_input_data_conj[2*i].real()), __nv_bfloat16(twid_input_data_conj[2*i + 1].real())), + __nv_bfloat162(__nv_bfloat16(twid_input_data_conj[2*i].imag()), __nv_bfloat16(twid_input_data_conj[2*i + 1].imag())), + &z_real2, &z_imag2 + ); + + z_real2 = __hadd2(xe_real2, z_real2); + z_imag2 = __hadd2(xe_imag2, z_imag2); + + z_data[2*i] = complex_bfloat16_t(z_real2.x, z_imag2.x); + z_data[2*i + 1] = complex_bfloat16_t(z_real2.y, z_imag2.y); + } + } +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_complex_mul.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_complex_mul.h index ca572abaa213cb08690ab6c1518b37e1beb2daab..8459fa8365630d3b7587326a98f9a8893fa8801e 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_complex_mul.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_complex_mul.h @@ -1,220 +1,220 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -using namespace nvcuda; - -#ifndef MONARCH_CUDA_BF16_COMPLEX_MUL_ -#define MONARCH_CUDA_BF16_COMPLEX_MUL_ - -using complex_bfloat16_t = typename c10::complex; - -__device__ __forceinline__ void complex_mul(at::BFloat16 a_real, at::BFloat16 a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { - __nv_bfloat16 temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __nv_bfloat16(a_real * b_real - a_imag * b_imag); - temp_y = __hfma(__nv_bfloat16(a_imag), __nv_bfloat16(b_real), __nv_bfloat16(a_real * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul(complex_bfloat16_t a, complex_bfloat16_t b, complex_bfloat16_t *c) { - __nv_bfloat16 temp_x, temp_y; - __nv_bfloat162 temp2; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - // temp_x = __half(a.real() * b.real() - a.imag() * b.imag()); - temp2 = __hmul2( - __nv_bfloat162( - __nv_bfloat16(a.real()), - __nv_bfloat16(a.imag()) - ), - __nv_bfloat162( - __nv_bfloat16(b.real()), - __nv_bfloat16(b.imag()) - ) - ); - temp_x = __hsub(temp2.x, temp2.y); - temp_y = __hfma( - __nv_bfloat16(a.imag()), __nv_bfloat16(b.real()), - __nv_bfloat16(a.real() * b.imag()) - ); - *c = complex_bfloat16_t(temp_x, temp_y); -} - -__device__ __forceinline__ void complex_mul_float_bfloat16(float a_real, float a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { - __nv_bfloat16 temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __nv_bfloat16(at::BFloat16(a_real) * b_real - at::BFloat16(a_imag) * b_imag); - temp_y = __hfma(__nv_bfloat16(at::BFloat16(a_imag)), __nv_bfloat16(b_real), __nv_bfloat16(at::BFloat16(a_real) * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c1 = complex_bfloat16_t(temp_x.x, temp_y.x); - *c2 = complex_bfloat16_t(temp_x.y, temp_y.y); -} - -__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat16 *c_real_0, __nv_bfloat16 *c_imag_0, __nv_bfloat16 *c_real_1, __nv_bfloat16 *c_imag_1) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real_0 = temp_x.x; - *c_imag_0 = temp_y.x; - *c_real_1 = temp_x.y; - *c_imag_1 = temp_y.y; -} - -__device__ __forceinline__ void complex_mul_bfloat162(complex_bfloat16_t a1, complex_bfloat16_t a2, complex_bfloat16_t b1, complex_bfloat16_t b2, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { - __nv_bfloat162 a_real, a_imag, b_real, b_imag; - - a_real = __nv_bfloat162( - __nv_bfloat16(a1.real()), - __nv_bfloat16(a2.real()) - ); - a_imag = __nv_bfloat162( - __nv_bfloat16(a1.imag()), - __nv_bfloat16(a2.imag()) - ); - b_real = __nv_bfloat162( - __nv_bfloat16(b1.real()), - __nv_bfloat16(b2.real()) - ); - b_imag = __nv_bfloat162( - __nv_bfloat16(b1.imag()), - __nv_bfloat16(b2.imag()) - ); - - complex_mul_bfloat162(a_real, a_imag, b_real, b_imag, c1, c2); -} - -__device__ __forceinline__ void complex_mul_conj(complex_bfloat16_t a, complex_bfloat16_t b, complex_bfloat16_t *c) { - __nv_bfloat16 temp_x, temp_y; - __nv_bfloat162 temp2; - - temp_x = __hfma(__nv_bfloat16(a.real()), __nv_bfloat16(b.real()), __nv_bfloat16(a.imag() * b.imag())); - temp2 = __hmul2( - __nv_bfloat162( - __nv_bfloat16(a.imag()), - __nv_bfloat16(a.real()) - ), - __nv_bfloat162( - __nv_bfloat16(b.real()), - __nv_bfloat16(b.imag()) - ) - ); - temp_y = __hsub(temp2.x, temp2.y); - *c = complex_bfloat16_t(temp_x, temp_y); -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_bfloat162( - __nv_bfloat162 a_real, - __nv_bfloat162 a_imag, - __nv_bfloat162 b_real, - __nv_bfloat162 b_imag, - c10::complex<__nv_bfloat16> *c_0, - c10::complex<__nv_bfloat16> *c_1 -) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_0 = c10::complex<__nv_bfloat16>(temp_x.x, temp_y.x); - *c_1 = c10::complex<__nv_bfloat16>(temp_x.y, temp_y.y); -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, complex_bfloat16_t *c_0, complex_bfloat16_t *c_1) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_0 = complex_bfloat16_t(temp_x.x, temp_y.x); - *c_1 = complex_bfloat16_t(temp_x.y, temp_y.y); -} - -__device__ __forceinline__ void complex_mul_conj_bfloat162(complex_bfloat16_t a1, complex_bfloat16_t a2, complex_bfloat16_t b1, complex_bfloat16_t b2, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { - __nv_bfloat162 a_real, a_imag, b_real, b_imag; - - a_real = __nv_bfloat162( - __nv_bfloat16(a1.real()), - __nv_bfloat16(a2.real()) - ); - a_imag = __nv_bfloat162( - __nv_bfloat16(a1.imag()), - __nv_bfloat16(a2.imag()) - ); - b_real = __nv_bfloat162( - __nv_bfloat16(b1.real()), - __nv_bfloat16(b2.real()) - ); - b_imag = __nv_bfloat162( - __nv_bfloat16(b1.imag()), - __nv_bfloat16(b2.imag()) - ); - - complex_mul_conj_bfloat162(a_real, a_imag, b_real, b_imag, c1, c2); -} - -__device__ __forceinline__ void complex_mul_conj_bfloat162( - __nv_bfloat162 a_real, - __nv_bfloat162 a_imag, - __nv_bfloat162 b_real, - __nv_bfloat162 b_imag, - __nv_bfloat162 *c_real, - __nv_bfloat162 *c_imag -) { - __nv_bfloat162 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_conj_bfloat162( - __nv_bfloat162 a_real, - __nv_bfloat162 a_imag, - c10::complex<__nv_bfloat16> b_0, - c10::complex<__nv_bfloat16> b_1, - c10::complex<__nv_bfloat16> *c_0, - c10::complex<__nv_bfloat16> *c_1) { - __nv_bfloat162 b_real_h2, b_imag_h2; - - b_real_h2 = __nv_bfloat162(b_0.real(), b_1.real()); - b_imag_h2 = __nv_bfloat162(b_0.imag(), b_1.imag()); - complex_mul_conj_bfloat162(a_real, a_imag, b_real_h2, b_imag_h2, c_0, c_1); -} - -__device__ __forceinline__ complex_bfloat16_t conj(complex_bfloat16_t inp) { - return complex_bfloat16_t(inp.real(), -inp.imag()); -} - - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +using namespace nvcuda; + +#ifndef MONARCH_CUDA_BF16_COMPLEX_MUL_ +#define MONARCH_CUDA_BF16_COMPLEX_MUL_ + +using complex_bfloat16_t = typename c10::complex; + +__device__ __forceinline__ void complex_mul(at::BFloat16 a_real, at::BFloat16 a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { + __nv_bfloat16 temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __nv_bfloat16(a_real * b_real - a_imag * b_imag); + temp_y = __hfma(__nv_bfloat16(a_imag), __nv_bfloat16(b_real), __nv_bfloat16(a_real * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul(complex_bfloat16_t a, complex_bfloat16_t b, complex_bfloat16_t *c) { + __nv_bfloat16 temp_x, temp_y; + __nv_bfloat162 temp2; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + // temp_x = __half(a.real() * b.real() - a.imag() * b.imag()); + temp2 = __hmul2( + __nv_bfloat162( + __nv_bfloat16(a.real()), + __nv_bfloat16(a.imag()) + ), + __nv_bfloat162( + __nv_bfloat16(b.real()), + __nv_bfloat16(b.imag()) + ) + ); + temp_x = __hsub(temp2.x, temp2.y); + temp_y = __hfma( + __nv_bfloat16(a.imag()), __nv_bfloat16(b.real()), + __nv_bfloat16(a.real() * b.imag()) + ); + *c = complex_bfloat16_t(temp_x, temp_y); +} + +__device__ __forceinline__ void complex_mul_float_bfloat16(float a_real, float a_imag, at::BFloat16 b_real, at::BFloat16 b_imag, at::BFloat16 *c_real, at::BFloat16 *c_imag) { + __nv_bfloat16 temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __nv_bfloat16(at::BFloat16(a_real) * b_real - at::BFloat16(a_imag) * b_imag); + temp_y = __hfma(__nv_bfloat16(at::BFloat16(a_imag)), __nv_bfloat16(b_real), __nv_bfloat16(at::BFloat16(a_real) * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat162 *c_real, __nv_bfloat162 *c_imag) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c1 = complex_bfloat16_t(temp_x.x, temp_y.x); + *c2 = complex_bfloat16_t(temp_x.y, temp_y.y); +} + +__device__ __forceinline__ void complex_mul_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, __nv_bfloat16 *c_real_0, __nv_bfloat16 *c_imag_0, __nv_bfloat16 *c_real_1, __nv_bfloat16 *c_imag_1) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real_0 = temp_x.x; + *c_imag_0 = temp_y.x; + *c_real_1 = temp_x.y; + *c_imag_1 = temp_y.y; +} + +__device__ __forceinline__ void complex_mul_bfloat162(complex_bfloat16_t a1, complex_bfloat16_t a2, complex_bfloat16_t b1, complex_bfloat16_t b2, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { + __nv_bfloat162 a_real, a_imag, b_real, b_imag; + + a_real = __nv_bfloat162( + __nv_bfloat16(a1.real()), + __nv_bfloat16(a2.real()) + ); + a_imag = __nv_bfloat162( + __nv_bfloat16(a1.imag()), + __nv_bfloat16(a2.imag()) + ); + b_real = __nv_bfloat162( + __nv_bfloat16(b1.real()), + __nv_bfloat16(b2.real()) + ); + b_imag = __nv_bfloat162( + __nv_bfloat16(b1.imag()), + __nv_bfloat16(b2.imag()) + ); + + complex_mul_bfloat162(a_real, a_imag, b_real, b_imag, c1, c2); +} + +__device__ __forceinline__ void complex_mul_conj(complex_bfloat16_t a, complex_bfloat16_t b, complex_bfloat16_t *c) { + __nv_bfloat16 temp_x, temp_y; + __nv_bfloat162 temp2; + + temp_x = __hfma(__nv_bfloat16(a.real()), __nv_bfloat16(b.real()), __nv_bfloat16(a.imag() * b.imag())); + temp2 = __hmul2( + __nv_bfloat162( + __nv_bfloat16(a.imag()), + __nv_bfloat16(a.real()) + ), + __nv_bfloat162( + __nv_bfloat16(b.real()), + __nv_bfloat16(b.imag()) + ) + ); + temp_y = __hsub(temp2.x, temp2.y); + *c = complex_bfloat16_t(temp_x, temp_y); +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_bfloat162( + __nv_bfloat162 a_real, + __nv_bfloat162 a_imag, + __nv_bfloat162 b_real, + __nv_bfloat162 b_imag, + c10::complex<__nv_bfloat16> *c_0, + c10::complex<__nv_bfloat16> *c_1 +) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_0 = c10::complex<__nv_bfloat16>(temp_x.x, temp_y.x); + *c_1 = c10::complex<__nv_bfloat16>(temp_x.y, temp_y.y); +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_bfloat162(__nv_bfloat162 a_real, __nv_bfloat162 a_imag, __nv_bfloat162 b_real, __nv_bfloat162 b_imag, complex_bfloat16_t *c_0, complex_bfloat16_t *c_1) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_0 = complex_bfloat16_t(temp_x.x, temp_y.x); + *c_1 = complex_bfloat16_t(temp_x.y, temp_y.y); +} + +__device__ __forceinline__ void complex_mul_conj_bfloat162(complex_bfloat16_t a1, complex_bfloat16_t a2, complex_bfloat16_t b1, complex_bfloat16_t b2, complex_bfloat16_t *c1, complex_bfloat16_t *c2) { + __nv_bfloat162 a_real, a_imag, b_real, b_imag; + + a_real = __nv_bfloat162( + __nv_bfloat16(a1.real()), + __nv_bfloat16(a2.real()) + ); + a_imag = __nv_bfloat162( + __nv_bfloat16(a1.imag()), + __nv_bfloat16(a2.imag()) + ); + b_real = __nv_bfloat162( + __nv_bfloat16(b1.real()), + __nv_bfloat16(b2.real()) + ); + b_imag = __nv_bfloat162( + __nv_bfloat16(b1.imag()), + __nv_bfloat16(b2.imag()) + ); + + complex_mul_conj_bfloat162(a_real, a_imag, b_real, b_imag, c1, c2); +} + +__device__ __forceinline__ void complex_mul_conj_bfloat162( + __nv_bfloat162 a_real, + __nv_bfloat162 a_imag, + __nv_bfloat162 b_real, + __nv_bfloat162 b_imag, + __nv_bfloat162 *c_real, + __nv_bfloat162 *c_imag +) { + __nv_bfloat162 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_conj_bfloat162( + __nv_bfloat162 a_real, + __nv_bfloat162 a_imag, + c10::complex<__nv_bfloat16> b_0, + c10::complex<__nv_bfloat16> b_1, + c10::complex<__nv_bfloat16> *c_0, + c10::complex<__nv_bfloat16> *c_1) { + __nv_bfloat162 b_real_h2, b_imag_h2; + + b_real_h2 = __nv_bfloat162(b_0.real(), b_1.real()); + b_imag_h2 = __nv_bfloat162(b_0.imag(), b_1.imag()); + complex_mul_conj_bfloat162(a_real, a_imag, b_real_h2, b_imag_h2, c_0, c_1); +} + +__device__ __forceinline__ complex_bfloat16_t conj(complex_bfloat16_t inp) { + return complex_bfloat16_t(inp.real(), -inp.imag()); +} + + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_load_frags.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_load_frags.h index 4967a836bfe83031d859f29c9aad52233d41ae2c..e7354f851ecc2f6dd38e3ed1ec818b857c4d7fa6 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_load_frags.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_load_frags.h @@ -1,373 +1,373 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_bfloat16_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_BF16_LOAD_ -#define MONARCH_CUDA_BF16_LOAD_ - -template -__device__ __forceinline__ void accfrag2afrag( - wmma::fragment *acc_frag, - wmma::fragment *a_frag -) { - for (int i = 0; i < acc_frag->num_elements; i++) { - a_frag->x[i] = __float2bfloat16(acc_frag->x[i]); - a_frag->x[i + acc_frag->num_elements] = __float2bfloat16(acc_frag->x[i]); - } -} - -template -__device__ __forceinline__ void accfrag2afrag( - wmma::fragment *acc_frag, - wmma::fragment *a_frag -) { - // assume that the acc_frag is already converted to bf16! - // for (int i = 0; i < acc_frag->num_elements; i++) { - // a_frag->x[i] = reinterpret_cast<__nv_bfloat16 *>(acc_frag->x)[i]; - // a_frag->x[i + acc_frag->num_elements] = reinterpret_cast<__nv_bfloat16 *>(acc_frag->x)[i]; - // } - for (int i = 0; i < acc_frag->num_elements / 2; i++) { - reinterpret_cast<__half2 *>(a_frag->x)[i] = reinterpret_cast<__half2 *>(acc_frag->x)[i]; - reinterpret_cast<__half2 *>(a_frag->x)[i + acc_frag->num_elements / 2] = reinterpret_cast<__half2 *>(acc_frag->x)[i]; - } -} - -template -__device__ __forceinline__ void load_a_frag( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_256( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(a_real) + a_idx, 256); - wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(a_imag) + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_256( - const __nv_bfloat16 *a_real, - const __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 256); - wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast(a_imag) + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_1024( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(a_real) + a_idx, 1024); - wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(a_imag) + a_idx, 1024); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_1024( - const __nv_bfloat16 *a_real, - const __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 1024); - wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast(a_imag) + a_idx, 1024); - } - } - } -} - -template -__device__ __forceinline__ void load_b_frag_r2c( - __nv_bfloat16* b_real, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - } - } -} - -template -__device__ __forceinline__ void load_b_frag( - __nv_bfloat16* b_real, - __nv_bfloat16* b_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c( - const __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c_256( - const __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c_1024( - const __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_half - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); - } - } - } -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_bfloat16_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_BF16_LOAD_ +#define MONARCH_CUDA_BF16_LOAD_ + +template +__device__ __forceinline__ void accfrag2afrag( + wmma::fragment *acc_frag, + wmma::fragment *a_frag +) { + for (int i = 0; i < acc_frag->num_elements; i++) { + a_frag->x[i] = __float2bfloat16(acc_frag->x[i]); + a_frag->x[i + acc_frag->num_elements] = __float2bfloat16(acc_frag->x[i]); + } +} + +template +__device__ __forceinline__ void accfrag2afrag( + wmma::fragment *acc_frag, + wmma::fragment *a_frag +) { + // assume that the acc_frag is already converted to bf16! + // for (int i = 0; i < acc_frag->num_elements; i++) { + // a_frag->x[i] = reinterpret_cast<__nv_bfloat16 *>(acc_frag->x)[i]; + // a_frag->x[i + acc_frag->num_elements] = reinterpret_cast<__nv_bfloat16 *>(acc_frag->x)[i]; + // } + for (int i = 0; i < acc_frag->num_elements / 2; i++) { + reinterpret_cast<__half2 *>(a_frag->x)[i] = reinterpret_cast<__half2 *>(acc_frag->x)[i]; + reinterpret_cast<__half2 *>(a_frag->x)[i + acc_frag->num_elements / 2] = reinterpret_cast<__half2 *>(acc_frag->x)[i]; + } +} + +template +__device__ __forceinline__ void load_a_frag( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_256( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(a_real) + a_idx, 256); + wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(a_imag) + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_256( + const __nv_bfloat16 *a_real, + const __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 256); + wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast(a_imag) + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_1024( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast<__nv_bfloat16*>(a_real) + a_idx, 1024); + wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast<__nv_bfloat16*>(a_imag) + a_idx, 1024); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_1024( + const __nv_bfloat16 *a_real, + const __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 1024); + wmma::load_matrix_sync(a_frag[j_a][k][1], reinterpret_cast(a_imag) + a_idx, 1024); + } + } + } +} + +template +__device__ __forceinline__ void load_b_frag_r2c( + __nv_bfloat16* b_real, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + } + } +} + +template +__device__ __forceinline__ void load_b_frag( + __nv_bfloat16* b_real, + __nv_bfloat16* b_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c( + const __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c_256( + const __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], reinterpret_cast(a_real) + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c_1024( + const __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_half + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + accfrag2afrag(& acc_frag_half[j_a][j_b][k], &a_frag[j_a][j_b][k]); + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); + } + } + } +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_matmuls.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_matmuls.h index 622a34ba04282bc0df2261706cd5935d77dfab49..ad286b40e0c457e1a54930889e87d215e23f476b 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_matmuls.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_bf16/shared/monarch_cuda_shared_bf16_matmuls.h @@ -1,680 +1,680 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_bfloat16_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_BF16_MATMULS_ -#define MONARCH_CUDA_BF16_MATMULS_ - -__device__ __forceinline__ void floatacc2bfloatacc( - wmma::fragment *float_acc, - wmma::fragment *bfloat_acc -) { - for (int i = 0; i < float_acc->num_elements; i++) { - reinterpret_cast<__nv_bfloat16 *>(bfloat_acc->x)[i] = __float2bfloat16(float_acc->x[i]); - } - // for (int i = 0; i < float_acc->num_elements / 2; i++) { - // reinterpret_cast<__nv_bfloat162 *>(bfloat_acc->x)[i] = __float22bfloat162_rn(reinterpret_cast(float_acc->x)[i]); - // } -} - -template -__device__ __forceinline__ void _complex_matmul( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast ( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast ( - a_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_load_b( - __nv_bfloat16* a_real, - __nv_bfloat16* a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - - //does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast( - a_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_256( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast ( - a_real + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 256, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast ( - a_imag + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_256( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory - //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory - //does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast( - a_real + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 256, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast ( - a_imag + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_1024( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast ( - a_real + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 1024, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast ( - a_imag + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], 1024, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_1024( - __nv_bfloat16 *a_real, - __nv_bfloat16 *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast( - a_real + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 1024, out_layout - ); - - wmma::store_matrix_sync( - reinterpret_cast( - a_imag + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][1], 1024, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r( - __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory - //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory - - //does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r_256( - __nv_bfloat16 *a_real, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - //does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast ( - a_real + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r_1024( - __nv_bfloat16 *a_real_out, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - reinterpret_cast ( - a_real_out + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N) - ), - acc_frag_half[j_a][j_b][0], 1024, out_layout - ); - } - } - } -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_bfloat16_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_BF16_MATMULS_ +#define MONARCH_CUDA_BF16_MATMULS_ + +__device__ __forceinline__ void floatacc2bfloatacc( + wmma::fragment *float_acc, + wmma::fragment *bfloat_acc +) { + for (int i = 0; i < float_acc->num_elements; i++) { + reinterpret_cast<__nv_bfloat16 *>(bfloat_acc->x)[i] = __float2bfloat16(float_acc->x[i]); + } + // for (int i = 0; i < float_acc->num_elements / 2; i++) { + // reinterpret_cast<__nv_bfloat162 *>(bfloat_acc->x)[i] = __float22bfloat162_rn(reinterpret_cast(float_acc->x)[i]); + // } +} + +template +__device__ __forceinline__ void _complex_matmul( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast ( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast ( + a_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_load_b( + __nv_bfloat16* a_real, + __nv_bfloat16* a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + + //does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast( + a_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_256( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast ( + a_real + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 256, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast ( + a_imag + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_256( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory + //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory + //does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast( + a_real + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 256, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast ( + a_imag + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_1024( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast ( + a_real + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 1024, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast ( + a_imag + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], 1024, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_1024( + __nv_bfloat16 *a_real, + __nv_bfloat16 *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], 0.0f); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][1], &acc_frag_half[j_a][j_b][1]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast( + a_real + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 1024, out_layout + ); + + wmma::store_matrix_sync( + reinterpret_cast( + a_imag + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][1], 1024, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r( + __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //accumlator fragments are not supporte for bfloat16, so we cannot directly cast or store the values to shared memory + //of type bfloat 16. We need to move the values to the a_fragment which supports bfloat16 and then store it to shared memory + + //does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r_256( + __nv_bfloat16 *a_real, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + //does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast ( + a_real + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r_1024( + __nv_bfloat16 *a_real_out, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_half[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], 0.0f); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = -acc_frag_1[j_a][j_b][0].x[i]; + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + floatacc2bfloatacc(&acc_frag_1[j_a][j_b][0], &acc_frag_half[j_a][j_b][0]); + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + reinterpret_cast ( + a_real_out + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N) + ), + acc_frag_half[j_a][j_b][0], 1024, out_layout + ); + } + } + } +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h index bb45b4fdcb3359866b74a261bd109f0fe12cb7ee..46d6b197155b00486e265c23a2f12e78ab196ccd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h @@ -1,615 +1,615 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_complex_kernel( - const at::Half *__restrict__ dout_real_inp, - const at::Half *__restrict__ dout_imag_inp, - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *dx_out_real, - at::Half *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + 256]; - at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT(dout) - complex_matmul_c2c_256( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // multiply dout by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __half2 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_complex_kernel( + const at::Half *__restrict__ dout_real_inp, + const at::Half *__restrict__ dout_imag_inp, + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *dx_out_real, + at::Half *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + 256]; + at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT(dout) + complex_matmul_c2c_256( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // multiply dout by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __half2 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h index fd33228d5d0a73df2ac1f41e5ad6975c9a0702dc..1fd5cf18007e26c7831024d0b5984976ef73458c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h @@ -1,742 +1,742 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + 256]; - at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input gates - at::Half dgate_data[items_per_thread_input]; - at::Half dout_data[items_per_thread_input]; - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT(dout) - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_256( - reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("dout @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("x @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { - // printf("DFT(dout)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("DFT(x)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // multiply dout by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __half2 real, imag; - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + 256]; + at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input gates + at::Half dgate_data[items_per_thread_input]; + at::Half dout_data[items_per_thread_input]; + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT(dout) + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_256( + reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("dout @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("x @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { + // printf("DFT(dout)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("DFT(x)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // multiply dout by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __half2 real, imag; + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h index d6a64c2da84880154b3ff99b9e977ff8c05cb85b..81c67ea6520ad436587c09c1d522a3194742f882 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h @@ -1,728 +1,728 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -#define ADJUST_FACTOR 1000 - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::BFloat16 *__restrict__ dout, - const at::BFloat16 *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *dx_out, - c10::complex *dk_f_out, - const at::BFloat16 *__restrict__ in_gate, - const at::BFloat16 *__restrict__ out_gate, - at::BFloat16 *din_gate, - at::BFloat16 *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + 256]; - at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / ADJUST_FACTOR), - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / ADJUST_FACTOR) - ); - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __half2( - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i])), - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1])) - ); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT(dout) - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_256( - reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("dout @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("x @ f_sqrt_N_fft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { - // printf("DFT(dout)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("DFT(x)\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // // x = x * N - // for (int i = 0; i < 256 / 32 / 2; i++) - // { - // a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - // reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - // reinterpret_cast<__half2 *>(a_real_2)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - // reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Values in a_real, a_imag before mul\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("Values in a_real_2, a_imag_2 before mul\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Values in a_real, a_imag\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // printf("Values in a_real_2, a_imag_2\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); - // } - // printf("\n"); - // } - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // multiply dout by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x)); - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y)); - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __half2 real, imag; - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = __hmul2(reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N) * ADJUST_FACTOR), __float2half(float(N) * ADJUST_FACTOR))); - imag = __hmul2(reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N) * ADJUST_FACTOR), __float2half(float(N) * ADJUST_FACTOR))); - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - } // b_tile_id - - // for(int i = 0; i < items_per_thread_input; i++) { - // reinterpret_cast<__half2 *>(temp)[i] = __hmul2(reinterpret_cast<__half2 *>(temp)[i], __half2(__float2half(float(N)), __float2half(float(N)))); - // } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +#define ADJUST_FACTOR 1000 + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::BFloat16 *__restrict__ dout, + const at::BFloat16 *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *dx_out, + c10::complex *dk_f_out, + const at::BFloat16 *__restrict__ in_gate, + const at::BFloat16 *__restrict__ out_gate, + at::BFloat16 *din_gate, + at::BFloat16 *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + 256]; + at::Half *b_real_2 = &a_real[4 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / ADJUST_FACTOR), + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / ADJUST_FACTOR) + ); + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __half2( + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i])), + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1])) + ); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT(dout) + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_256( + reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("dout @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("x @ f_sqrt_N_fft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx == 15) { + // printf("DFT(dout)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("DFT(x)\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // // x = x * N + // for (int i = 0; i < 256 / 32 / 2; i++) + // { + // a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + // reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + // reinterpret_cast<__half2 *>(a_real_2)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + // reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Values in a_real, a_imag before mul\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("Values in a_real_2, a_imag_2 before mul\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Values in a_real, a_imag\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // printf("Values in a_real_2, a_imag_2\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real_2[a_idx]), __half2float(a_imag_2[a_idx])); + // } + // printf("\n"); + // } + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // multiply dout by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x)); + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y)); + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __half2 real, imag; + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = __hmul2(reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N) * ADJUST_FACTOR), __float2half(float(N) * ADJUST_FACTOR))); + imag = __hmul2(reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N) * ADJUST_FACTOR), __float2half(float(N) * ADJUST_FACTOR))); + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + } // b_tile_id + + // for(int i = 0; i < items_per_thread_input; i++) { + // reinterpret_cast<__half2 *>(temp)[i] = __hmul2(reinterpret_cast<__half2 *>(temp)[i], __half2(__float2half(float(N)), __float2half(float(N)))); + // } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h index 74b669da89109e9556625a67b2ed4bb0aec3419a..790eb348de72e64383420658b7b48bc8bc1fa113 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h @@ -1,536 +1,536 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_complex_kernel( - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *out_real, - at::Half *out_imag, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + 256]; - at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); - // } - - // __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - // x_input_data[2 * i] = scratch.x; - // x_input_data[2 * i + 1] = scratch.y; - // } - - // // store a_real - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_complex_kernel( + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *out_real, + at::Half *out_imag, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + 256]; + at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); + // } + + // __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + // x_input_data[2 * i] = scratch.x; + // x_input_data[2 * i + 1] = scratch.y; + // } + + // // store a_real + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel.h index cd0a76fd463fea340a9f43a72d69ad4cca4383bf..d914b7dedb4375800b7b1d073e79132c279192cf 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel.h @@ -1,568 +1,568 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + 256]; - at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the gates - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != NULL) { - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - if(out_gate != NULL) { - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(gate_data)[i], - reinterpret_cast<__half2 *>(a_real)[a_idx] - ); - }else{ - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // store a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + 256]; + at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the gates + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != NULL) { + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + if(out_gate != NULL) { + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(gate_data)[i], + reinterpret_cast<__half2 *>(a_real)[a_idx] + ); + }else{ + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // store a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h index 7da76b081d1e1554ef852af3d0ce6e83244edb29..5d59fdd917ac1c2e59c8a1f40b3e27ebaa931d17 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h @@ -1,541 +1,541 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + 256]; - at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for 256 twiddle - wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // // for twiddles - // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load twiddle_256_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Matrix().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load 256 twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), - DFT_SIZE * DFT_SIZE / 2); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), - DFT_SIZE * DFT_SIZE / 2); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load 256 twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); - } - } - } - - __syncthreads(); - - // load twiddle_256_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_256_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load 256 ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load 256 idft twiddle factors into registers - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / N), - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / N) - ); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - -#pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x) * N); - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y) * N); - } - - // store a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_fft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_256_ifft, // 4096 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + 256]; + at::Half *b_real_2 = &a_real[2 * N + 2 * 256]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * 256]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = num_threads <= 128 ? DFT_SIZE * DFT_SIZE / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for 256 twiddle + wmma::fragment twiddle_256_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // // for twiddles + // wmma::fragment twiddle_256_dft_frag[N / (DFT_SIZE * DFT_SIZE)][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load twiddle_256_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Matrix().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load 256 twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data), + DFT_SIZE * DFT_SIZE / 2); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2), + DFT_SIZE * DFT_SIZE / 2); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load 256 twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N); + } + } + } + + __syncthreads(); + + // load twiddle_256_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_256_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load 256 ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load 256 idft twiddle factors into registers + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = j_b * WMMA_N * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / N), + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / N) + ); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE + warp_id * DFT_SIZE; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + +#pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x) * N); + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y) * N); + } + + // store a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h index 7fc12bd8b1cb03401853c9c4c3f150adcd3a82a3..615576a9c4123b639c6e1fd4d843a84712056754 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h @@ -1,669 +1,669 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_16_32_32_complex_kernel( - const at::Half *__restrict__ dout_real_inp, - const at::Half *__restrict__ dout_imag_inp, - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *dx_out_real, - at::Half *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + N_2]; - at::Half *b_real_2 = &a_real[4 * N + 2 * N_2]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_1024( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_32_idft_frag, - wmma::mem_col_major); - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_imag)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - // put dk_f into a_input_data, and udpate temp - __half2 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_16_32_32_complex_kernel( + const at::Half *__restrict__ dout_real_inp, + const at::Half *__restrict__ dout_imag_inp, + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *dx_out_real, + at::Half *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + N_2]; + at::Half *b_real_2 = &a_real[4 * N + 2 * N_2]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_1024( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_32_idft_frag, + wmma::mem_col_major); + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_imag)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + // put dk_f into a_input_data, and udpate temp + __half2 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h index 9b150060b74882a7145a95d5692a0d249243ee88..c463a79341db90062ee94235d386662b6bd5006e 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h @@ -1,792 +1,792 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_16_32_32_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + N_2]; - at::Half *b_real_2 = &a_real[4 * N + 2 * N_2]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input gates - at::Half dgate_data[items_per_thread_input]; - at::Half dout_data[items_per_thread_input]; - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_1024( - reinterpret_cast(a_real + k_idx_offset), // read from HBM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_1024( - reinterpret_cast(a_real_2 + k_idx_offset), // read from HBM - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - // dk_f = dout * x.conj() - for (int i = 0; i < 1024 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // finish iFFT dout - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and udpate temp - __half2 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_16_32_32_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + N_2]; + at::Half *b_real_2 = &a_real[4 * N + 2 * N_2]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input gates + at::Half dgate_data[items_per_thread_input]; + at::Half dout_data[items_per_thread_input]; + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_1024( + reinterpret_cast(a_real + k_idx_offset), // read from HBM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_1024( + reinterpret_cast(a_real_2 + k_idx_offset), // read from HBM + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + // dk_f = dout * x.conj() + for (int i = 0; i < 1024 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // finish iFFT dout + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and udpate temp + __half2 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h index 16abf649d8524187fbe0b4c936671aa99b528b41..89ea2da0dc2086614037733523e7951cd517c9e9 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h @@ -1,637 +1,637 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_16_32_32_complex_kernel( - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *out_real, - at::Half *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + N_2]; - at::Half *b_real_2 = &a_real[2 * N + 2 * N_2]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); - // } - - // __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // reinterpret_cast(a_input_data)[i] = reinterpret_cast(a_real)[a_idx]; - // } - - // // HACK - // // for now, just output the a_real output - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(a_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_16_32_32_complex_kernel( + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *out_real, + at::Half *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + N_2]; + at::Half *b_real_2 = &a_real[2 * N + 2 * N_2]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); + // } + + // __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // reinterpret_cast(a_input_data)[i] = reinterpret_cast(a_real)[a_idx]; + // } + + // // HACK + // // for now, just output the a_real output + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(a_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_kernel.h index 40d09a60b144d7cf85ef0be2af3130f72f293dc9..35d819e3a3e47a3159514f788409c32f0a8e5cee 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_16_32_32_kernel.h @@ -1,673 +1,673 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_16_32_32_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_16, // 32 x 32 - const c10::complex *__restrict__ b_32, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_16_ifft, // 32 x 32 - const c10::complex *__restrict__ b_32_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 16; - const uint sqrt_N_2 = 32; - const uint N_1 = 256; - const uint N_2 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + N_2]; - at::Half *b_real_2 = &a_real[2 * N + 2 * N_2]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * N_2]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; - const int items_per_thread_matrix_N_2 = N_2 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the gate - complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 16 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_16 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_16_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) - { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 16x16 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 16x16 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // start loading 32x32 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 32x32 iDFT matrices - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + - warp_id * sqrt_N_2 * sqrt_N_2; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - // load input gate into gate_data - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - - } - - //read the output gate into gate_data - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_1024( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 16 times (32, 32) - for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 16 = 64 - for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_16_32_32_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_16, // 32 x 32 + const c10::complex *__restrict__ b_32, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_16_ifft, // 32 x 32 + const c10::complex *__restrict__ b_32_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 16; + const uint sqrt_N_2 = 32; + const uint N_1 = 256; + const uint N_2 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + N_2]; + at::Half *b_real_2 = &a_real[2 * N + 2 * N_2]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * N_2]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = num_threads <= 128 ? N_1 / num_threads : 2; + const int items_per_thread_matrix_N_2 = N_2 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the gate + complex_half_t b_input_data[items_per_thread_matrix_N_2]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_2]; // another place for storing matrices + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 16 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[64 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[16 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_16 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_16_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) + { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 16x16 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 16x16 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // start loading 32x32 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 32x32 iDFT matrices + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load the 32x32 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + + warp_id * sqrt_N_2 * sqrt_N_2; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + // load input gate into gate_data + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + + } + + //read the output gate into gate_data + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_1024( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 16 times (32, 32) + for (int k_idx = 0; k_idx < 16 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_2 * sqrt_N_2 + warp_id * sqrt_N_2 * sqrt_N_2; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 16 = 64 + for (int k_idx = 0; k_idx < 64 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h index 5f1af7d87e981ba5564775a7d36dd8d4c67d2952..fa59b129bc71e7a932a4fb9e1e8a26f631647ed2 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h @@ -1,684 +1,684 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_complex_kernel( - const at::Half *__restrict__ dout_real_inp, - const at::Half *__restrict__ dout_imag_inp, - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *dx_out_real, - at::Half *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + N_1]; - at::Half *b_real_2 = &a_real[4 * N + 2 * N_1]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_256( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // 256 / 32 = 8 - // finish iFFT dout - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __half2 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_complex_kernel( + const at::Half *__restrict__ dout_real_inp, + const at::Half *__restrict__ dout_imag_inp, + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *dx_out_real, + at::Half *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + N_1]; + at::Half *b_real_2 = &a_real[4 * N + 2 * N_1]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_256( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // 256 / 32 = 8 + // finish iFFT dout + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __half2 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h index 76f1b40aeae67df51222c13c874a8c6653899e09..07cef3b4b15dffef6bb5fd0702ed11a9e751d59c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h @@ -1,811 +1,811 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[4 * N + N_1]; - at::Half *b_real_2 = &a_real[4 * N + 2 * N_1]; - at::Half *b_imag_2 = &a_real[4 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input gates - at::Half dgate_data[items_per_thread_input]; - at::Half dout_data[items_per_thread_input]; - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - // outer DFT(x) - complex_matmul_r2c_256( - reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2 + k_idx_offset), // this is the output - reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real_2 + k_idx_offset), - reinterpret_cast(a_imag_2 + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // x = x * N - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - // dk_f = dout * x.conj() - for (int i = 0; i < 256 / 32 / 2; i++) - { - a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - &reinterpret_cast<__half2 *>(a_real_2)[a_idx], - &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); - } - - __syncthreads(); - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - // finish iFFT dout - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - // put dk_f into a_input_data, and write to HBM - __half2 real, imag; - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - __syncthreads(); - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[4 * N + N_1]; + at::Half *b_real_2 = &a_real[4 * N + 2 * N_1]; + at::Half *b_imag_2 = &a_real[4 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input gates + at::Half dgate_data[items_per_thread_input]; + at::Half dout_data[items_per_thread_input]; + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + // outer DFT(x) + complex_matmul_r2c_256( + reinterpret_cast(a_real_2 + k_idx_offset), // read from SRAM + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2 + k_idx_offset), // this is the output + reinterpret_cast(a_imag_2 + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real_2 + k_idx_offset), + reinterpret_cast(a_imag_2 + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // x = x * N + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + // dk_f = dout * x.conj() + for (int i = 0; i < 256 / 32 / 2; i++) + { + a_idx = k_idx_offset / 2 + i * 32 + thread_id % 32; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + &reinterpret_cast<__half2 *>(a_real_2)[a_idx], + &reinterpret_cast<__half2 *>(a_imag_2)[a_idx]); + } + + __syncthreads(); + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + // finish iFFT dout + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + // put dk_f into a_input_data, and write to HBM + __half2 real, imag; + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + imag = reinterpret_cast<__half2 *>(a_imag_2)[a_idx]; + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + __syncthreads(); + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h index 1b1c80994476eb50774a0b9a34fc5496af350cdb..d6ecf307d453f61fe6ca96aee130fef82e5ccbcd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h @@ -1,652 +1,652 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_complex_kernel( - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *out_real, - at::Half *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + N_1]; - at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // load input into a_real - // BlockLoad_Input().Load( - // reinterpret_cast(a + input_offset), - // reinterpret_cast(x_input_data), - // signal_size / 2, 0. - // ); - - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); - // } - - // __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 1) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 1) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // reinterpret_cast(a_input_data)[i] = reinterpret_cast(a_real)[a_idx]; - // } - - // // HACK - // // for now, just output the a_real output - // BlockStore_Sequence().Store( - // reinterpret_cast(out + input_offset), - // reinterpret_cast(a_input_data), - // signal_size / 2 - // ); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_complex_kernel( + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *out_real, + at::Half *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + N_1]; + at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // load input into a_real + // BlockLoad_Input().Load( + // reinterpret_cast(a + input_offset), + // reinterpret_cast(x_input_data), + // signal_size / 2, 0. + // ); + + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(x_input_data[2 * i], x_input_data[2 * i + 1]); + // } + + // __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 1) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 1) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // reinterpret_cast(a_input_data)[i] = reinterpret_cast(a_real)[a_idx]; + // } + + // // HACK + // // for now, just output the a_real output + // BlockStore_Sequence().Store( + // reinterpret_cast(out + input_offset), + // reinterpret_cast(a_input_data), + // signal_size / 2 + // ); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel.h index 5480bb1df25debcede5b8e9ce44da6aedfb68cde..6f440c765a7e52883eddfc99d628872d9c2eb085 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel.h @@ -1,688 +1,688 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + N_1]; - at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the gates - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - // load input gate into gate_data - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - - } - - //read the output gate into gate_data - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from HBM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + N_1]; + at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the gates + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + // load input gate into gate_data + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + + } + + //read the output gate into gate_data + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from HBM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h index 651fb624252d9d9a06e708964636a2c458e4c243..8a3451cb4c80fc586e343f8d435cd15909ba045c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h @@ -1,661 +1,661 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::BFloat16 *__restrict__ a, - const at::BFloat16 *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ b_16, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ b_16_ifft, // 16 x 16 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 - const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 - at::BFloat16 *out, - const at::BFloat16 *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint sqrt_N_2 = 16; - const uint N_1 = 1024; - const uint N_2 = 256; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[2 * N + N_1]; - at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; - at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 16 x 16 dft - wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 idft - wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for the 16 x 16 dft - wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for 16 x 16 twiddles - wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 16 x 16 twiddles - wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for the 32 x 256 twiddle - wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - // for 32 x 256 idft twiddle - wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 32 x 32 and 16 x 16 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load in 16x16 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(twiddle_factors_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); - } - } - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - // load 16x16 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // start loading 16x16 DFT matrices - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), - N_2 / 2); - - // start loading 16x16 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_2().Load( - reinterpret_cast *>(b_16_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), - N_2 / 2); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 256 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); - wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); - } - } - } - - // load 16x16 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - __syncthreads(); - - // load the 16x16 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - if (num_threads <= 128) { - for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } else { - if (thread_id < 128) { - b_idx = thread_id; - - scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - } - - __syncthreads(); - - // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); - wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_2 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + - warp_id * DFT_SIZE * DFT_SIZE; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / N), - __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / N) - ); - } - - __syncthreads(); - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_256( - reinterpret_cast(a_real + k_idx_offset), // read from HBM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (16, 16) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_2, - N, - a_frag_dft_N_2, - acc_frag_2, - twiddle_256_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_dft_N_2, - acc_frag_2, - twiddle_16_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_2, - N, - b_frag_idft_N_2, - acc_frag_2, - twiddle_16_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 256 / 32 = 8 - for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_256( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_256_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x) * N); - reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y) * N); - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::BFloat16 *__restrict__ a, + const at::BFloat16 *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ b_16, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_fft, // 256 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ b_16_ifft, // 16 x 16 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 8192 + const c10::complex *__restrict__ twiddle_factors_16_ifft, // 256 + at::BFloat16 *out, + const at::BFloat16 *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint sqrt_N_2 = 16; + const uint N_1 = 1024; + const uint N_2 = 256; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[2 * N + N_1]; + at::Half *b_real_2 = &a_real[2 * N + 2 * N_1]; + at::Half *b_imag_2 = &a_real[2 * N + 3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int items_per_thread_matrix_N_2 = num_threads <= 128 ? N_2 / num_threads : 2; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockLoad_Matrix_N_2 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_2 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 16 x 16 dft + wmma::fragment b_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 idft + wmma::fragment b_frag_idft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for the 16 x 16 dft + wmma::fragment a_frag_dft_N_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for 16 x 16 twiddles + wmma::fragment twiddle_16_dft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 16 x 16 twiddles + wmma::fragment twiddle_16_idft_frag[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for the 32 x 256 twiddle + wmma::fragment twiddle_256_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + // for 32 x 256 idft twiddle + wmma::fragment twiddle_256_idft_frag[8 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 32 x 32 and 16 x 16 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + wmma::fragment acc_frag_2[MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // for kernels - note that there are 32 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_2][MATMUL_WARP_WIDTH_2][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load in 16x16 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(twiddle_factors_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_256_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_2); + } + } + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + // load 16x16 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // start loading 16x16 DFT matrices + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data), + N_2 / 2); + + // start loading 16x16 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_2().Load( + reinterpret_cast *>(b_16_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_2 / 2]>(b_input_data_2), + N_2 / 2); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 256 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 256); + wmma::load_matrix_sync(twiddle_256_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 256); + } + } + } + + // load 16x16 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(twiddle_16_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + __syncthreads(); + + // load the 16x16 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + if (num_threads <= 128) { + for (int i = 0; i < items_per_thread_matrix_N_2 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } else { + if (thread_id < 128) { + b_idx = thread_id; + + scratch = __half2(b_input_data[0].real(), b_input_data[1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[0].imag(), b_input_data[1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[0].real(), b_input_data_2[1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[0].imag(), b_input_data_2[1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + } + + __syncthreads(); + + // load the 16x16 DFT matrices into b_frag_dft_N_2, b_frag_idft_N_2 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(a_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_dft_N_2[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_2; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_2 + k * WMMA_K : k * WMMA_K * sqrt_N_2 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_2); + wmma::load_matrix_sync(b_frag_idft_N_2[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_2); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_2; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_2; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_2 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + + warp_id * DFT_SIZE * DFT_SIZE; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_2); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_2); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i]) / N), + __float2half(__bfloat162float(reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1]) / N) + ); + } + + __syncthreads(); + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_256( + reinterpret_cast(a_real + k_idx_offset), // read from HBM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (16, 16) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * DFT_SIZE * DFT_SIZE + warp_id * DFT_SIZE * DFT_SIZE; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_2, + N, + a_frag_dft_N_2, + acc_frag_2, + twiddle_256_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_dft_N_2, + acc_frag_2, + twiddle_16_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_2, + N, + b_frag_idft_N_2, + acc_frag_2, + twiddle_16_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 256 / 32 = 8 + for (int k_idx = 0; k_idx < 8 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_256( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_256_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i] = __float2bfloat16(__half2float(scratch.x) * N); + reinterpret_cast<__nv_bfloat16 *>(x_input_data)[2 * i + 1] = __float2bfloat16(__half2float(scratch.y) * N); + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h index 74314e87326bbdd3a8354c5f44e898d24cd24904..511efe42fd421d85c3a364c2e0d9a8f24d384e23 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h @@ -1,608 +1,608 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_32_32_32_complex_kernel( - const at::Half *__restrict__ dout_real_inp, - const at::Half *__restrict__ dout_imag_inp, - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *dx_out_real, - at::Half *dx_out_imag, - c10::complex *dk_f_out, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[0]; - at::Half *b_imag = &a_real[N_1]; - at::Half *b_real_2 = &a_real[2 * N_1]; - at::Half *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * N * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } -__syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(x) - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - __half2 real, imag; - // write DFT(x) in a_real, a_imag to a_input_data - // todo: try doing this as a_real, a_imag? - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N))) - ); - imag = __hmul2( - reinterpret_cast<__half2 *>(a_imag)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N))) - ); - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_c2c_1024( - reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - // TODO: compute a_input_data = a * a_input_data.conj() - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast *>(a_input_data)[2 * i], - reinterpret_cast *>(a_input_data)[2 * i + 1], - &reinterpret_cast *>(a_input_data)[2 * i], - &reinterpret_cast *>(a_input_data)[2 * i + 1]); - // update temp - temp[2 * i] += a_input_data[2 * i]; - temp[2 * i + 1] += a_input_data[2 * i + 1]; - } - - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_imag)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_real + input_offset), - reinterpret_cast(a_input_data) - ); - BlockStore_Sequence().Store( - reinterpret_cast(dx_out_imag + input_offset), - reinterpret_cast(x_input_data) - ); - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_32_32_32_complex_kernel( + const at::Half *__restrict__ dout_real_inp, + const at::Half *__restrict__ dout_imag_inp, + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *dx_out_real, + at::Half *dx_out_imag, + c10::complex *dk_f_out, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[0]; + at::Half *b_imag = &a_real[N_1]; + at::Half *b_real_2 = &a_real[2 * N_1]; + at::Half *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * N * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } +__syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(x) + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + __half2 real, imag; + // write DFT(x) in a_real, a_imag to a_input_data + // todo: try doing this as a_real, a_imag? + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N))) + ); + imag = __hmul2( + reinterpret_cast<__half2 *>(a_imag)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N))) + ); + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_c2c_1024( + reinterpret_cast(dout_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(dout_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + // TODO: compute a_input_data = a * a_input_data.conj() + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast *>(a_input_data)[2 * i], + reinterpret_cast *>(a_input_data)[2 * i + 1], + &reinterpret_cast *>(a_input_data)[2 * i], + &reinterpret_cast *>(a_input_data)[2 * i + 1]); + // update temp + temp[2 * i] += a_input_data[2 * i]; + temp[2 * i + 1] += a_input_data[2 * i + 1]; + } + + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_imag)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_real + input_offset), + reinterpret_cast(a_input_data) + ); + BlockStore_Sequence().Store( + reinterpret_cast(dx_out_imag + input_offset), + reinterpret_cast(x_input_data) + ); + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h index d21a1e4ceb5bff69e985c96bceff2f3e0ad517e1..37e139a865ca852f6f39f82d21b2efbcebe62d1b 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h @@ -1,709 +1,709 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_32_32_32_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[0]; - at::Half *b_imag = &a_real[N_1]; - at::Half *b_real_2 = &a_real[2 * N_1]; - at::Half *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input gates - at::Half dgate_data[items_per_thread_input]; - at::Half dout_data[items_per_thread_input]; - complex_half_t temp[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments for the 16 x 16 and 32 x 32 - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } -__syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f.conj() into registers in k_frag - // in the inner loop, so treat as 32 x 256 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); - } - } - } - - for(int i = 0; i < items_per_thread_input; i++) { - temp[i] = complex_half_t(0.0f, 0.0f); - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(x) - complex_matmul_r2c_1024( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(x) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - __half2 real, imag; - // write DFT(x) in a_real, a_imag to a_input_data - // todo: try doing this as a_real, a_imag? - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - real = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N))) - ); - imag = __hmul2( - reinterpret_cast<__half2 *>(a_imag)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N))) - ); - reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - } - - __syncthreads(); - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT(dout) - complex_matmul_r2c_1024( - reinterpret_cast(a_real + k_idx_offset), // read from HBM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // first DFT, output is NOT written to shared memory - // DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - // DFT(dout) - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - } - - __syncthreads(); - - // TODO: compute a_input_data = a * a_input_data.conj() - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - // // dout = dout / N - // reinterpret_cast<__half2 *>(a_real)[a_idx] = __h2div( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - // reinterpret_cast<__half2 *>(a_imag)[a_idx] = __h2div( - // reinterpret_cast<__half2 *>(a_imag)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast *>(a_input_data)[2 * i], - reinterpret_cast *>(a_input_data)[2 * i + 1], - &reinterpret_cast *>(a_input_data)[2 * i], - &reinterpret_cast *>(a_input_data)[2 * i + 1]); - // update temp - temp[2 * i] += a_input_data[2 * i]; - temp[2 * i + 1] += a_input_data[2 * i + 1]; - } - - __syncthreads(); - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // start computing iFFT(dout) - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // __syncthreads(); - - // second iFFT dout - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - } - - __syncthreads(); - - // finish iFFT dout - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - // reinterpret_cast<__half2 *>(a_real)[a_idx], - // __half2(__float2half(float(N)), __float2half(float(N)))); - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - __syncthreads(); - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_32_32_32_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[0]; + at::Half *b_imag = &a_real[N_1]; + at::Half *b_real_2 = &a_real[2 * N_1]; + at::Half *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input gates + at::Half dgate_data[items_per_thread_input]; + at::Half dout_data[items_per_thread_input]; + complex_half_t temp[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 16 x 1024 idft twiddle - split into 64 x (16 x 16) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments for the 16 x 16 and 32 x 32 + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } +__syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __hneg2(__half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag())); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f.conj() into registers in k_frag + // in the inner loop, so treat as 32 x 256 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); + } + } + } + + for(int i = 0; i < items_per_thread_input; i++) { + temp[i] = complex_half_t(0.0f, 0.0f); + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(x) + complex_matmul_r2c_1024( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(x) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + __half2 real, imag; + // write DFT(x) in a_real, a_imag to a_input_data + // todo: try doing this as a_real, a_imag? + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + real = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N))) + ); + imag = __hmul2( + reinterpret_cast<__half2 *>(a_imag)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N))) + ); + reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + } + + __syncthreads(); + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT(dout) + complex_matmul_r2c_1024( + reinterpret_cast(a_real + k_idx_offset), // read from HBM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // first DFT, output is NOT written to shared memory + // DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + // DFT(dout) + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + } + + __syncthreads(); + + // TODO: compute a_input_data = a * a_input_data.conj() + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + // // dout = dout / N + // reinterpret_cast<__half2 *>(a_real)[a_idx] = __h2div( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + // reinterpret_cast<__half2 *>(a_imag)[a_idx] = __h2div( + // reinterpret_cast<__half2 *>(a_imag)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast *>(a_input_data)[2 * i], + reinterpret_cast *>(a_input_data)[2 * i + 1], + &reinterpret_cast *>(a_input_data)[2 * i], + &reinterpret_cast *>(a_input_data)[2 * i + 1]); + // update temp + temp[2 * i] += a_input_data[2 * i]; + temp[2 * i + 1] += a_input_data[2 * i + 1]; + } + + __syncthreads(); + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // start computing iFFT(dout) + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // __syncthreads(); + + // second iFFT dout + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + } + + __syncthreads(); + + // finish iFFT dout + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + // reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + // reinterpret_cast<__half2 *>(a_real)[a_idx], + // __half2(__float2half(float(N)), __float2half(float(N)))); + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + __syncthreads(); + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h index 248823b6389fef6b15bec4e926ba99cf0ef93c0d..e8a396fbd52ede5cf2af1c29f4c0f9d9731b9850 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h @@ -1,564 +1,564 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_32_32_32_complex_kernel( - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *out_real, - at::Half *out_imag, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[0]; - at::Half *b_imag = &a_real[N_1]; - at::Half *b_real_2 = &a_real[2 * N_1]; - at::Half *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // start loading a - // // NOTE(danfu): this load from HBM costs about 60 us - // BlockLoad_Sequence().Load( - // reinterpret_cast *>(a + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // // load a into shared memory - // // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - // reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - // scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - // reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - // } - - // __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // __half2 real, imag; - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // real = reinterpret_cast<__half2 *>(a_real)[a_idx]; - // imag = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - // } - - // // store the complex output - // BlockStore_Sequence().Store( - // reinterpret_cast *>(out + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_32_32_32_complex_kernel( + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *out_real, + at::Half *out_imag, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[0]; + at::Half *b_imag = &a_real[N_1]; + at::Half *b_real_2 = &a_real[2 * N_1]; + at::Half *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // start loading a + // // NOTE(danfu): this load from HBM costs about 60 us + // BlockLoad_Sequence().Load( + // reinterpret_cast *>(a + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // // load a into shared memory + // // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + // reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + // scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + // reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + // } + + // __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // __half2 real, imag; + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // real = reinterpret_cast<__half2 *>(a_real)[a_idx]; + // imag = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + // } + + // // store the complex output + // BlockStore_Sequence().Store( + // reinterpret_cast *>(out + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h index 03ec07a8588ed85aa80e7e8556951cc1dec3b58f..39687ff6633a90452bfb696e8c59fd6da1ed630b 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h @@ -1,567 +1,567 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -#include "monarch_cuda_shared_truncated.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_32_32_32_complex_kernel_truncated( - const at::Half *__restrict__ a_real_inp, - const at::Half *__restrict__ a_imag_inp, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *out_real, - at::Half *out_imag, - uint B, - uint H, - uint signal_size, - uint kernel_trunc) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[0]; - at::Half *b_imag = &a_real[N_1]; - at::Half *b_real_2 = &a_real[2 * N_1]; - at::Half *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * N * B_TILE_SIZE; - // index into the H - int h_offset = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; - - int k_idx_offset; - - // // start loading a - // // NOTE(danfu): this load from HBM costs about 60 us - // BlockLoad_Sequence().Load( - // reinterpret_cast *>(a + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // // load a into shared memory - // // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - - // scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - // reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - // scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - // reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - // } - - // __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < (32 - kernel_trunc) / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b_truncated( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul_truncated( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul_truncated( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul_truncated( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2c_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output - reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - // __half2 real, imag; - - // #pragma unroll - // for (int i = 0; i < items_per_thread_input / 2; i++) - // { - // a_idx = i * num_threads + thread_id; - // real = reinterpret_cast<__half2 *>(a_real)[a_idx]; - // imag = reinterpret_cast<__half2 *>(a_imag)[a_idx]; - // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); - // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); - // } - - // // store the complex output - // BlockStore_Sequence().Store( - // reinterpret_cast *>(out + input_offset), - // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +#include "monarch_cuda_shared_truncated.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_32_32_32_complex_kernel_truncated( + const at::Half *__restrict__ a_real_inp, + const at::Half *__restrict__ a_imag_inp, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *out_real, + at::Half *out_imag, + uint B, + uint H, + uint signal_size, + uint kernel_trunc) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[0]; + at::Half *b_imag = &a_real[N_1]; + at::Half *b_real_2 = &a_real[2 * N_1]; + at::Half *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * N * B_TILE_SIZE; + // index into the H + int h_offset = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset + b_offset + h_tile_id * N + b_tile_id * H * N; + + int k_idx_offset; + + // // start loading a + // // NOTE(danfu): this load from HBM costs about 60 us + // BlockLoad_Sequence().Load( + // reinterpret_cast *>(a + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // // load a into shared memory + // // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + + // scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + // reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + // scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + // reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + // } + + // __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < (32 - kernel_trunc) / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_imag_inp + input_offset + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b_truncated( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul_truncated( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul_truncated( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul_truncated( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2c_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(out_real + input_offset + k_idx_offset), // this is the output + reinterpret_cast(out_imag + input_offset + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + // __half2 real, imag; + + // #pragma unroll + // for (int i = 0; i < items_per_thread_input / 2; i++) + // { + // a_idx = i * num_threads + thread_id; + // real = reinterpret_cast<__half2 *>(a_real)[a_idx]; + // imag = reinterpret_cast<__half2 *>(a_imag)[a_idx]; + // reinterpret_cast *>(a_input_data)[2 * i] = c10::complex<__half>(real.x, imag.x); + // reinterpret_cast *>(a_input_data)[2 * i + 1] = c10::complex<__half>(real.y, imag.y); + // } + + // // store the complex output + // BlockStore_Sequence().Store( + // reinterpret_cast *>(out + input_offset), + // reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_kernel.h index 5416b3c9b9f6f4562a896de50a6575d2de49397c..33d004cbc7ed3a18cf835e8b781c41f18e386f22 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_32_32_32_kernel.h @@ -1,593 +1,593 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_32_32_32_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b_32, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 - const c10::complex *__restrict__ b_32_ifft, // 32 x 32 - const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K - const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size) -{ - - const uint sqrt_N_1 = 32; - const uint N_1 = 1024; - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[0]; - at::Half *b_imag = &a_real[N_1]; - at::Half *b_real_2 = &a_real[2 * N_1]; - at::Half *b_imag_2 = &a_real[3 * N_1]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix_N_1 = N_1 / num_threads; - const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the gates - complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices - complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices - - // for the 32 x 32 dft - wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 idft - wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for the 32 x 32 dft - wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for 32 x 32 twiddles - wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 32 twiddles - wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for the 32 x 1024 twiddle - wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) - wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // accumulator fragments - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! - wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; - - // load twiddle_N_dft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_fft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // loads b_32 into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); // hopefully this interleaves things correctly - - // loads b_32_ifft into b - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(b_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the 32x32 DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - bool a_trans = true; - bool b_trans = false; - - // load 32x32 DFT matrix into b_frag_dft_N_1 - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load 32x32 iDFT matrix into b_frag_idft_N_1 - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load in 32x32 twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_fft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), - N_1 / 2); - - // start loading 32x32 ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Matrix_N_1().Load( - reinterpret_cast *>(twiddle_factors_32_ifft), - reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), - N_1 / 2); - - // load N twiddle into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load twiddle_N_idft - BlockLoad_Sequence().Load( - reinterpret_cast *>(twiddle_factors_N_ifft), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load N twiddle factors into registers - // these will be loaded into the inner loop, so treat them as 32 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); - } - } - } - - __syncthreads(); - - // load 32x32 twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load 32x32 DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); - wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); - } - } - - __syncthreads(); - - // load N ifft twiddle factors into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load N idft twiddle factors into registers - // these will be used in the last iFFT, so treat them as 32 x 32 x 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); - wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - __syncthreads(); - - // load k_f into registers in k_frag - // in the inner loop, so treat as 16 x 1024 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) - { - // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - a_idx = j_a * WMMA_K * sqrt_N_1 + - k * WMMA_K + - k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + - warp_id * sqrt_N_1 * sqrt_N_1; - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); - wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); - } - } - } - - __syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; - - int k_idx_offset; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - if(out_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_r2c_1024( - reinterpret_cast(a_real + k_idx_offset), // read from SRAM - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After first DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 32 times (32, 32) - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); - // } - - // first DFT, output is NOT written to shared memory - complex_matmul_load_b( - reinterpret_cast(a_real + k_idx_offset), // this is the output - reinterpret_cast(a_imag + k_idx_offset), // this is the output - sqrt_N_1, - N, - a_frag_dft_N_1, - acc_frag_1, - twiddle_1024_dft_frag[k_idx], - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After first DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 32; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_dft_N_1, - acc_frag_1, - twiddle_32_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After second DFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - k_frag[k_idx], - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real + k_idx_offset), - reinterpret_cast(a_imag + k_idx_offset), - // reinterpret_cast(out + input_offset + k_idx_offset), - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_32_idft_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { - // printf("After 2nd iDFT in the conv, %d\n", k_idx); - // for (int i = 0; i < 8; i++) { - // a_idx = i * num_threads + thread_id + k_idx_offset; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - } - - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After inner conv\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // 1024 / 32 = 32 - for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) - { - // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; - k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; - // outer DFT - complex_matmul_c2r_1024( - reinterpret_cast(a_real + k_idx_offset), // this is the input - reinterpret_cast(a_imag + k_idx_offset), // this is the input - reinterpret_cast(a_real + k_idx_offset), // write to SRAM - sqrt_N_1, - N, - b_frag_idft_N_1, - acc_frag_1, - twiddle_1024_idft_frag[k_idx], - wmma::mem_col_major); - } - __syncthreads(); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("Before output\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f, ", __half2float(a_real[a_idx])); - // } - // printf("\n"); - // } - - #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - - } - - // HACK - // for now, just output the a_real output - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - __syncthreads(); - } // b_tile_id - } // h_tile_id -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_32_32_32_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b_32, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_fft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_fft, // 1024 + const c10::complex *__restrict__ b_32_ifft, // 32 x 32 + const c10::complex *__restrict__ twiddle_factors_N_ifft, // 16K + const c10::complex *__restrict__ twiddle_factors_32_ifft, // 1024 + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size) +{ + + const uint sqrt_N_1 = 32; + const uint N_1 = 1024; + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[0]; + at::Half *b_imag = &a_real[N_1]; + at::Half *b_real_2 = &a_real[2 * N_1]; + at::Half *b_imag_2 = &a_real[3 * N_1]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix_N_1 = N_1 / num_threads; + const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Matrix_N_1 = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix_N_1 / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the gates + complex_half_t b_input_data[items_per_thread_matrix_N_1]; // for storing matrices + complex_half_t b_input_data_2[items_per_thread_matrix_N_1]; // another place for storing matrices + + // for the 32 x 32 dft + wmma::fragment b_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 idft + wmma::fragment b_frag_idft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for the 32 x 32 dft + wmma::fragment a_frag_dft_N_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for 32 x 32 twiddles + wmma::fragment twiddle_32_dft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 32 twiddles + wmma::fragment twiddle_32_idft_frag[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for the 32 x 1024 twiddle + wmma::fragment twiddle_1024_dft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + // for 32 x 1024 idft twiddle - split into 32 x (32 x 32) + wmma::fragment twiddle_1024_idft_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // accumulator fragments + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // for kernels - note that there are 16 / WARP_TILE_SIZE of these now! + wmma::fragment k_frag[32 / WARP_TILE_SIZE][MATMUL_WARP_WIDTH_1][MATMUL_WARP_WIDTH_1][2]; + + // load twiddle_N_dft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_fft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // loads b_32 into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); // hopefully this interleaves things correctly + + // loads b_32_ifft into b + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(b_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the 32x32 DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + bool a_trans = true; + bool b_trans = false; + + // load 32x32 DFT matrix into b_frag_dft_N_1 + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(a_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_dft_N_1[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load 32x32 iDFT matrix into b_frag_idft_N_1 + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(b_frag_idft_N_1[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load in 32x32 twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_fft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data), + N_1 / 2); + + // start loading 32x32 ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Matrix_N_1().Load( + reinterpret_cast *>(twiddle_factors_32_ifft), + reinterpret_cast(&)[items_per_thread_matrix_N_1 / 2]>(b_input_data_2), + N_1 / 2); + + // load N twiddle into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load twiddle_N_idft + BlockLoad_Sequence().Load( + reinterpret_cast *>(twiddle_factors_N_ifft), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load N twiddle factors into registers + // these will be loaded into the inner loop, so treat them as 32 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_1024_dft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, sqrt_N_1); + } + } + } + + __syncthreads(); + + // load 32x32 twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix_N_1 / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load 32x32 DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N_1); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N_1 + k * WMMA_K : k * WMMA_K * sqrt_N_1 + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N_1); + wmma::load_matrix_sync(twiddle_32_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N_1); + } + } + + __syncthreads(); + + // load N ifft twiddle factors into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load N idft twiddle factors into registers + // these will be used in the last iFFT, so treat them as 32 x 32 x 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + int k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH_1; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + b_idx = j_b * WMMA_N * 1024 + k * WMMA_K; + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][0], reinterpret_cast(a_real) + k_idx_offset + b_idx, 1024); + wmma::load_matrix_sync(twiddle_1024_idft_frag[k_idx][k][j_b][1], reinterpret_cast(a_imag) + k_idx_offset + b_idx, 1024); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + __syncthreads(); + + // load k_f into registers in k_frag + // in the inner loop, so treat as 16 x 1024 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH_1; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH_1; k++) + { + // a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K + k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + a_idx = j_a * WMMA_K * sqrt_N_1 + + k * WMMA_K + + k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + + warp_id * sqrt_N_1 * sqrt_N_1; + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N_1); + wmma::load_matrix_sync(k_frag[k_idx][j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N_1); + } + } + } + + __syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset + h_tile_id * signal_size + b_tile_id * H * signal_size; + + int k_idx_offset; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + if(out_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_r2c_1024( + reinterpret_cast(a_real + k_idx_offset), // read from SRAM + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After first DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 32 times (32, 32) + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 * sqrt_N_1 + warp_id * sqrt_N_1 * sqrt_N_1; + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("k_idx %d, k_idx_offset %d\n", k_idx, k_idx_offset); + // } + + // first DFT, output is NOT written to shared memory + complex_matmul_load_b( + reinterpret_cast(a_real + k_idx_offset), // this is the output + reinterpret_cast(a_imag + k_idx_offset), // this is the output + sqrt_N_1, + N, + a_frag_dft_N_1, + acc_frag_1, + twiddle_1024_dft_frag[k_idx], + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After first DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 32; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_dft_N_1, + acc_frag_1, + twiddle_32_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After second DFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + k_frag[k_idx], + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real + k_idx_offset), + reinterpret_cast(a_imag + k_idx_offset), + // reinterpret_cast(out + input_offset + k_idx_offset), + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_32_idft_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0 && k_idx < 2) { + // printf("After 2nd iDFT in the conv, %d\n", k_idx); + // for (int i = 0; i < 8; i++) { + // a_idx = i * num_threads + thread_id + k_idx_offset; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + } + + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After inner conv\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // 1024 / 32 = 32 + for (int k_idx = 0; k_idx < 32 / WARP_TILE_SIZE; k_idx++) + { + // k_idx_offset = k_idx * DFT_SIZE + warp_id * (16 / WARP_TILE_SIZE) * DFT_SIZE; + k_idx_offset = k_idx * WARP_TILE_SIZE * sqrt_N_1 + warp_id * sqrt_N_1; + // outer DFT + complex_matmul_c2r_1024( + reinterpret_cast(a_real + k_idx_offset), // this is the input + reinterpret_cast(a_imag + k_idx_offset), // this is the input + reinterpret_cast(a_real + k_idx_offset), // write to SRAM + sqrt_N_1, + N, + b_frag_idft_N_1, + acc_frag_1, + twiddle_1024_idft_frag[k_idx], + wmma::mem_col_major); + } + __syncthreads(); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("Before output\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f, ", __half2float(a_real[a_idx])); + // } + // printf("\n"); + // } + + #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + + } + + // HACK + // for now, just output the a_real output + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + __syncthreads(); + } // b_tile_id + } // h_tile_id +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel.h index dc3846d5c73a75c52e64b96de89ae26c57c45465..f029fa648b504421082d16f8f068b5b39454ed57 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel.h @@ -1,547 +1,547 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[5 * N]; - at::Half *b_real_2 = &a_real[6 * N]; - at::Half *b_imag_2 = &a_real[7 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - complex_half_t temp[items_per_thread_input]; - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input gates - at::Half dgate_data[items_per_thread_input]; - at::Half dout_data[items_per_thread_input]; - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f.conj() into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - - reinterpret_cast<__half2 *>(a_imag)[a_idx] = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - } - - __syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - - __syncthreads(); - - for(int i=0; i< items_per_thread_input; i++) { - temp[i] = complex_half_t(__float2half(0.0f), __float2half(0.0f)); - } - - __syncthreads(); - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - // int output_offset_kernel = h_offset_kernel + b_offset_kernel + h_tile_id * N + b_tile_id * H * N; - - // load dout into a_real - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - - if(out_gate != nullptr){ - // load output gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - __syncthreads(); - - // load a into a_real_2 - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - if(in_gate != nullptr){ - // load input gate into gate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - } - - // first DFT(dout) - complex_matmul_r2c_load_b( - reinterpret_cast(a_real), // read from SRAM - reinterpret_cast(a_real), // this is the output - reinterpret_cast(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // second DFT(dout), with twiddle - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_row_major); - - // first DFT(x) - complex_matmul_r2c_load_b( - reinterpret_cast(a_real_2), // read from HBM - reinterpret_cast(a_real_2), // this is the output - reinterpret_cast(a_imag_2), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT(x), with twiddle - complex_matmul( - reinterpret_cast(a_real_2), - reinterpret_cast(a_imag_2), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_row_major); - - //x = x * N - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - reinterpret_cast<__half2 *>(b_real_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_real_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - reinterpret_cast<__half2 *>(b_imag_2)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(a_imag_2)[a_idx], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real_2), - reinterpret_cast(a_imag_2), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag, - wmma::mem_col_major); - - complex_matmul_c2r( - reinterpret_cast(a_real_2), - reinterpret_cast(a_imag_2), - reinterpret_cast(a_real_2), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++){ - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - for(int i=0; i < k_frag[j_a][k][1].num_elements; i++){ - k_frag[j_a][k][1].x[i] = __hneg(k_frag[j_a][k][1].x[i]); - } - } - } - - if(out_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; - - } - - __syncthreads(); - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(dout_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - __syncthreads(); - - // dk_f = dout * x.conj() - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - complex_mul_conj_half2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(a_imag)[a_idx], - reinterpret_cast<__half2 *>(b_real_2)[a_idx], - reinterpret_cast<__half2 *>(b_imag_2)[a_idx], - &reinterpret_cast *>(a_input_data)[2 * i], - &reinterpret_cast *>(a_input_data)[2 * i + 1]); - } - - __syncthreads(); - - for(int i=0; i< items_per_thread_input; i++) { - temp[i] += a_input_data[i]; - } - - __syncthreads(); - - // start computing iFFT(dout), and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag, - wmma::mem_col_major); - - // second iFFT dout, and multiply by twiddle - complex_matmul_c2r( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - reinterpret_cast(a_real), - // reinterpret_cast(out + input_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - if(in_gate != nullptr){ - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dgate_data), - signal_size / 2 - ); - } - - // multiply by N, and prepare for writing to HBM - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(a_input_data), - signal_size / 2 - ); - - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++){ - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - for(int i=0; i < k_frag[j_a][k][1].num_elements; i++){ - k_frag[j_a][k][1].x[i] = __hneg(k_frag[j_a][k][1].x[i]); - } - } - } - } // b_tile_id - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[5 * N]; + at::Half *b_real_2 = &a_real[6 * N]; + at::Half *b_imag_2 = &a_real[7 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_STORE_STRIPED, BLOCK_DIM_Y>; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + complex_half_t temp[items_per_thread_input]; + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input gates + at::Half dgate_data[items_per_thread_input]; + at::Half dout_data[items_per_thread_input]; + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f.conj() into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + + reinterpret_cast<__half2 *>(a_imag)[a_idx] = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + } + + __syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + + __syncthreads(); + + for(int i=0; i< items_per_thread_input; i++) { + temp[i] = complex_half_t(__float2half(0.0f), __float2half(0.0f)); + } + + __syncthreads(); + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + // int output_offset_kernel = h_offset_kernel + b_offset_kernel + h_tile_id * N + b_tile_id * H * N; + + // load dout into a_real + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + + if(out_gate != nullptr){ + // load output gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dout_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + __syncthreads(); + + // load a into a_real_2 + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + if(in_gate != nullptr){ + // load input gate into gate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real_2)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + } + + // first DFT(dout) + complex_matmul_r2c_load_b( + reinterpret_cast(a_real), // read from SRAM + reinterpret_cast(a_real), // this is the output + reinterpret_cast(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // second DFT(dout), with twiddle + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_row_major); + + // first DFT(x) + complex_matmul_r2c_load_b( + reinterpret_cast(a_real_2), // read from HBM + reinterpret_cast(a_real_2), // this is the output + reinterpret_cast(a_imag_2), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT(x), with twiddle + complex_matmul( + reinterpret_cast(a_real_2), + reinterpret_cast(a_imag_2), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_row_major); + + //x = x * N + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + reinterpret_cast<__half2 *>(b_real_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_real_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + reinterpret_cast<__half2 *>(b_imag_2)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(a_imag_2)[a_idx], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real_2), + reinterpret_cast(a_imag_2), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag, + wmma::mem_col_major); + + complex_matmul_c2r( + reinterpret_cast(a_real_2), + reinterpret_cast(a_imag_2), + reinterpret_cast(a_real_2), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++){ + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + for(int i=0; i < k_frag[j_a][k][1].num_elements; i++){ + k_frag[j_a][k][1].x[i] = __hneg(k_frag[j_a][k][1].x[i]); + } + } + } + + if(out_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = reinterpret_cast<__half2 *>(a_real_2)[a_idx]; + + } + + __syncthreads(); + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(dout_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + __syncthreads(); + + // dk_f = dout * x.conj() + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + complex_mul_conj_half2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(a_imag)[a_idx], + reinterpret_cast<__half2 *>(b_real_2)[a_idx], + reinterpret_cast<__half2 *>(b_imag_2)[a_idx], + &reinterpret_cast *>(a_input_data)[2 * i], + &reinterpret_cast *>(a_input_data)[2 * i + 1]); + } + + __syncthreads(); + + for(int i=0; i< items_per_thread_input; i++) { + temp[i] += a_input_data[i]; + } + + __syncthreads(); + + // start computing iFFT(dout), and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag, + wmma::mem_col_major); + + // second iFFT dout, and multiply by twiddle + complex_matmul_c2r( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + reinterpret_cast(a_real), + // reinterpret_cast(out + input_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + if(in_gate != nullptr){ + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(dgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dgate_data), + signal_size / 2 + ); + } + + // multiply by N, and prepare for writing to HBM + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(a_input_data), + signal_size / 2 + ); + + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++){ + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + for(int i=0; i < k_frag[j_a][k][1].num_elements; i++){ + k_frag[j_a][k][1].x[i] = __hneg(k_frag[j_a][k][1].x[i]); + } + } + } + } // b_tile_id + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast *>(dk_f_out + h_offset_kernel + blockIdx.x * H * N + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(temp)); + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel_r2r.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel_r2r.h index 686d4b6fedd22997815bbd9736cccf1d73a40ef6..518bb821e55dfc2751adebf6f18b7f6a10aece45 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel_r2r.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_bwd_kernel_r2r.h @@ -1,569 +1,569 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -#include "monarch_cuda_shared_r2r.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_bwd_cuda_kernel( - const at::Half *__restrict__ dout, - const at::Half *__restrict__ a, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ twid_r2r, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::Half *dx_out, - c10::complex *dk_f_out, - const at::Half *__restrict__ in_gate, - const at::Half *__restrict__ out_gate, - at::Half *din_gate, - at::Half *dout_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *a_real_2 = &a_real[2 * N]; - at::Half *a_imag_2 = &a_real[3 * N]; - at::Half *b_real = &a_real[4 * N]; - at::Half *b_imag = &a_real[5 * N]; - at::Half *b_real_2 = &a_real[6 * N]; - at::Half *b_imag_2 = &a_real[7 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = 2 * N / num_threads; - const int items_per_thread_kf = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Filter = cub::BlockLoad; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - using BlockStore_Sequence_Complex = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input - complex_half_t kf_input_data[items_per_thread_input]; // for storing the kf - complex_half_t z_data[items_per_thread_kf]; // for storing the intermediates - complex_half_t temp[items_per_thread_input]; - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half orig_input_data[items_per_thread_input]; // for storing the input - at::Half ingate_data[items_per_thread_input]; // for storing the gates - at::Half outgate_data[items_per_thread_input]; // for storing the gates - at::Half dingate_data[items_per_thread_input]; // for storing the dgate - at::Half doutgate_data[items_per_thread_input]; // for storing the dgate - complex_half_t twid_input_data[items_per_thread_kf]; // for storing the input - complex_half_t twid_input_data_conj[items_per_thread_kf]; // for storing the input - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // load DFT matrix into b_frag - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // load twid into twid_input_data - BlockLoad_Filter().Load( - reinterpret_cast(twid_r2r), - reinterpret_cast(twid_input_data) - ); - - negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - - BlockLoad_Filter().Load( - reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), - reinterpret_cast(kf_input_data)); - - if (thread_id == 0) - { - // load in the pivot into the imag position - kf_input_data[0] = complex_half_t(kf_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); - } - - for(int i=0; i< items_per_thread_input; i++) { - temp[i] = complex_half_t(__float2half(0.0f), __float2half(0.0f)); - } - - // __syncthreads(); - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load a into x_input_data - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - if(in_gate != nullptr) { - // load in_gate into ingate_data - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(ingate_data), - signal_size / 4, 0. - ); - - // put orig a into orig_input_data, and compute a = in_gate * a - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(orig_input_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(ingate_data)[i] - ); - } - } - - // load a into a_real_2 - load_input( - &a_real_2[0], &a_imag_2[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - __syncthreads(); - - // first DFT(x) - complex_matmul_load_b( - reinterpret_cast(a_real_2), // this is the output - reinterpret_cast(a_imag_2), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT(x), with twiddle - complex_matmul( - reinterpret_cast(a_real_2), - reinterpret_cast(a_imag_2), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // load dout into x_input_data - BlockLoad_Input().Load( - reinterpret_cast(dout + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - // put DFT(x) into a_input_data - process_zf( - &a_real_2[0], &a_imag_2[0], &a_input_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - if (out_gate != nullptr) { // compute dout_gate - - // multiply by kf, and put it into z_data - multiply_kf( - &a_input_data[0], &kf_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // put it into a_real - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - // process yf from a_real and put it into z_data - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - // put it back into a_real - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // compute ifft - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - // k_frag, - wmma::mem_col_major); - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - // put result into doutgate_data - load_output( - &a_real[0], &a_imag[0], &doutgate_data[0], - items_per_thread_input, num_threads, thread_id); - - // load out_gate - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(outgate_data), - signal_size / 4, 0. - ); - - // compute dout_gate = dout_gate * dout - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(doutgate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(doutgate_data)[i] - ); - } - - // compute dout = dout * out_gate - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(outgate_data)[i] - ); - } - - __syncthreads(); - } - - // put dout from x_input_data into a_real - load_input( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - __syncthreads(); - - // first DFT(dout) - complex_matmul_load_b( - reinterpret_cast(a_real), // this is the output - reinterpret_cast(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // second DFT(dout), with twiddle - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_col_major); - - __syncthreads(); - - // put DFT(dout) into z_data - process_zf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - // DFT(x) = DFT(x) * N is in a_input_data - for (int i = 0; i < items_per_thread_kf; i++) - { - reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_input_data)[i], - __half2(__float2half(float(N)), __float2half(float(N)))); - } - - // dk_f = dout * x.conj() - multiply_kf_conj( - &z_data[0], &a_input_data[0], &a_input_data[0], items_per_thread_kf, num_threads, thread_id); - - if (thread_id == 0) { - reinterpret_cast<__half2 *>(a_input_data)[0] = __hmul2( - __half2(__half(a_input_data[0].real()), __half(a_input_data[0].imag())), - __half2(__float2half(0.5), __float2half(0.5)) - ); - } - - for(int i=0; i< items_per_thread_kf; i++) { - temp[i] += a_input_data[i]; - } - - // multiply z_data by kf.conj() - multiply_kf_conj( - &z_data[0], &kf_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - // start computing iFFT(dout), and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - // k_frag, - wmma::mem_col_major); - - // second iFFT dout, and multiply by twiddle - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - // reinterpret_cast(a_real), - // reinterpret_cast(out + input_offset), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - load_output( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - if (in_gate != nullptr) { - // din_gate = dx * u, du = dx * ingate - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(dingate_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(orig_input_data)[i] - ); - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(ingate_data)[i] - ); - } - BlockStore_Sequence().Store( - reinterpret_cast(din_gate + input_offset), - reinterpret_cast(dingate_data), - signal_size / 4 - ); - } - - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(dx_out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4 - ); - - if (out_gate != nullptr) { - // write to HBM - BlockStore_Sequence().Store( - reinterpret_cast(dout_gate + input_offset), - reinterpret_cast(doutgate_data), - signal_size / 4 - ); - } - - // __syncthreads(); - } // b_tile_id - - if (thread_id == 0) { - complex_half_t pivot = complex_half_t(temp[0].imag(), 0.); - temp[0] = complex_half_t(temp[0].real(), 0.); - (dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1))[N] = pivot; - } - - // store dk_f - BlockStore_Sequence_Complex().Store( - reinterpret_cast(dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1)), - reinterpret_cast(temp)); - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +#include "monarch_cuda_shared_r2r.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_bwd_cuda_kernel( + const at::Half *__restrict__ dout, + const at::Half *__restrict__ a, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ twid_r2r, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::Half *dx_out, + c10::complex *dk_f_out, + const at::Half *__restrict__ in_gate, + const at::Half *__restrict__ out_gate, + at::Half *din_gate, + at::Half *dout_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *a_real_2 = &a_real[2 * N]; + at::Half *a_imag_2 = &a_real[3 * N]; + at::Half *b_real = &a_real[4 * N]; + at::Half *b_imag = &a_real[5 * N]; + at::Half *b_real_2 = &a_real[6 * N]; + at::Half *b_imag_2 = &a_real[7 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = 2 * N / num_threads; + const int items_per_thread_kf = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Filter = cub::BlockLoad; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + using BlockStore_Sequence_Complex = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input + complex_half_t kf_input_data[items_per_thread_input]; // for storing the kf + complex_half_t z_data[items_per_thread_kf]; // for storing the intermediates + complex_half_t temp[items_per_thread_input]; + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half orig_input_data[items_per_thread_input]; // for storing the input + at::Half ingate_data[items_per_thread_input]; // for storing the gates + at::Half outgate_data[items_per_thread_input]; // for storing the gates + at::Half dingate_data[items_per_thread_input]; // for storing the dgate + at::Half doutgate_data[items_per_thread_input]; // for storing the dgate + complex_half_t twid_input_data[items_per_thread_kf]; // for storing the input + complex_half_t twid_input_data_conj[items_per_thread_kf]; // for storing the input + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // load DFT matrix into b_frag + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // load twid into twid_input_data + BlockLoad_Filter().Load( + reinterpret_cast(twid_r2r), + reinterpret_cast(twid_input_data) + ); + + negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + + BlockLoad_Filter().Load( + reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), + reinterpret_cast(kf_input_data)); + + if (thread_id == 0) + { + // load in the pivot into the imag position + kf_input_data[0] = complex_half_t(kf_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); + } + + for(int i=0; i< items_per_thread_input; i++) { + temp[i] = complex_half_t(__float2half(0.0f), __float2half(0.0f)); + } + + // __syncthreads(); + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load a into x_input_data + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + if(in_gate != nullptr) { + // load in_gate into ingate_data + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(ingate_data), + signal_size / 4, 0. + ); + + // put orig a into orig_input_data, and compute a = in_gate * a + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(orig_input_data)[i] = reinterpret_cast<__half2 *>(x_input_data)[i]; + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(ingate_data)[i] + ); + } + } + + // load a into a_real_2 + load_input( + &a_real_2[0], &a_imag_2[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + __syncthreads(); + + // first DFT(x) + complex_matmul_load_b( + reinterpret_cast(a_real_2), // this is the output + reinterpret_cast(a_imag_2), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT(x), with twiddle + complex_matmul( + reinterpret_cast(a_real_2), + reinterpret_cast(a_imag_2), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // load dout into x_input_data + BlockLoad_Input().Load( + reinterpret_cast(dout + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + // put DFT(x) into a_input_data + process_zf( + &a_real_2[0], &a_imag_2[0], &a_input_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + if (out_gate != nullptr) { // compute dout_gate + + // multiply by kf, and put it into z_data + multiply_kf( + &a_input_data[0], &kf_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // put it into a_real + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + // process yf from a_real and put it into z_data + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + // put it back into a_real + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // compute ifft + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + // k_frag, + wmma::mem_col_major); + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + // put result into doutgate_data + load_output( + &a_real[0], &a_imag[0], &doutgate_data[0], + items_per_thread_input, num_threads, thread_id); + + // load out_gate + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(outgate_data), + signal_size / 4, 0. + ); + + // compute dout_gate = dout_gate * dout + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(doutgate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(doutgate_data)[i] + ); + } + + // compute dout = dout * out_gate + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(outgate_data)[i] + ); + } + + __syncthreads(); + } + + // put dout from x_input_data into a_real + load_input( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + __syncthreads(); + + // first DFT(dout) + complex_matmul_load_b( + reinterpret_cast(a_real), // this is the output + reinterpret_cast(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // second DFT(dout), with twiddle + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_col_major); + + __syncthreads(); + + // put DFT(dout) into z_data + process_zf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + // DFT(x) = DFT(x) * N is in a_input_data + for (int i = 0; i < items_per_thread_kf; i++) + { + reinterpret_cast<__half2 *>(a_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_input_data)[i], + __half2(__float2half(float(N)), __float2half(float(N)))); + } + + // dk_f = dout * x.conj() + multiply_kf_conj( + &z_data[0], &a_input_data[0], &a_input_data[0], items_per_thread_kf, num_threads, thread_id); + + if (thread_id == 0) { + reinterpret_cast<__half2 *>(a_input_data)[0] = __hmul2( + __half2(__half(a_input_data[0].real()), __half(a_input_data[0].imag())), + __half2(__float2half(0.5), __float2half(0.5)) + ); + } + + for(int i=0; i< items_per_thread_kf; i++) { + temp[i] += a_input_data[i]; + } + + // multiply z_data by kf.conj() + multiply_kf_conj( + &z_data[0], &kf_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + // start computing iFFT(dout), and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + // k_frag, + wmma::mem_col_major); + + // second iFFT dout, and multiply by twiddle + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + // reinterpret_cast(a_real), + // reinterpret_cast(out + input_offset), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + load_output( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + if (in_gate != nullptr) { + // din_gate = dx * u, du = dx * ingate + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(dingate_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(orig_input_data)[i] + ); + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(ingate_data)[i] + ); + } + BlockStore_Sequence().Store( + reinterpret_cast(din_gate + input_offset), + reinterpret_cast(dingate_data), + signal_size / 4 + ); + } + + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(dx_out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4 + ); + + if (out_gate != nullptr) { + // write to HBM + BlockStore_Sequence().Store( + reinterpret_cast(dout_gate + input_offset), + reinterpret_cast(doutgate_data), + signal_size / 4 + ); + } + + // __syncthreads(); + } // b_tile_id + + if (thread_id == 0) { + complex_half_t pivot = complex_half_t(temp[0].imag(), 0.); + temp[0] = complex_half_t(temp[0].real(), 0.); + (dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1))[N] = pivot; + } + + // store dk_f + BlockStore_Sequence_Complex().Store( + reinterpret_cast(dk_f_out + h_offset_kernel + blockIdx.x * H * (N + 1) + h_tile_id * (N+1)), + reinterpret_cast(temp)); + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel.h index b30c60febfff498712e3654f6695a5425d77d700..c4522eea4bfddc3ee29572aeadc16ad725ef1840 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel.h @@ -1,396 +1,396 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[3 * N]; - at::Half *b_real_2 = &a_real[4 * N]; - at::Half *b_imag_2 = &a_real[5 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the gates - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Sequence().Load( - reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), - reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); - - // load k_f into shared memory - // #pragma unroll - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; - - scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; - } - - //__syncthreads(); - - // load k_f into registers in k_frag - // NOTE(danfu): this loop costs 60 us - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); - wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); - } - } - - //__syncthreads(); - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load input into a_real - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2, 0. - ); - - // load input gate into gate_data - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - - if(in_gate != nullptr){ - reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( - reinterpret_cast<__half2 *>(x_input_data)[i], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; - } - - } - - - //read the output gate into gate_data - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 2, 0. - ); - } - - __syncthreads(); - - // first DFT - complex_matmul_r2c_load_b( - reinterpret_cast(a_real), // read from HBM - reinterpret_cast(a_real), // this is the output - reinterpret_cast(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output is NOT written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_row_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After second DFT\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); - // } - // printf("\n"); - // } - - // __syncthreads(); - - // load the input from acc_frag_1, and multiply by k_frag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - k_frag, - wmma::mem_col_major); - - // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { - // printf("After ifft\n"); - // for (int i = 0; i < items_per_thread_input; i++) { - // a_idx = i * num_threads + thread_id; - // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); - // } - // printf("\n"); - // } - - // __syncthreads(); - - complex_matmul_c2r( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - reinterpret_cast(a_real), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - - for (int i = 0; i < items_per_thread_input / 2; i++) - { - a_idx = i * num_threads + thread_id; - scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; - - if(out_gate != nullptr){ - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(a_real)[a_idx], - reinterpret_cast<__half2 *>(gate_data)[i] - ); - }else{ - reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; - } - } - - // load input into a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 2 - ); - - //__syncthreads(); - - } // b_tile_id - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[3 * N]; + at::Half *b_real_2 = &a_real[4 * N]; + at::Half *b_imag_2 = &a_real[5 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * N * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing the input, also used for k_f + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the gates + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Sequence().Load( + reinterpret_cast *>(k_f + h_offset_kernel + h_tile_id * N), + reinterpret_cast(&)[items_per_thread_input / 2]>(a_input_data)); + + // load k_f into shared memory + // #pragma unroll + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + scratch = __half2(a_input_data[2 * i].real(), a_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(a_real)[a_idx] = scratch; + + scratch = __half2(a_input_data[2 * i].imag(), a_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = scratch; + } + + //__syncthreads(); + + // load k_f into registers in k_frag + // NOTE(danfu): this loop costs 60 us + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(k_frag[j_a][k][0], reinterpret_cast(a_real + a_idx), sqrt_N); + wmma::load_matrix_sync(k_frag[j_a][k][1], reinterpret_cast(a_imag + a_idx), sqrt_N); + } + } + + //__syncthreads(); + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load input into a_real + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2, 0. + ); + + // load input gate into gate_data + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + + if(in_gate != nullptr){ + reinterpret_cast<__half2 *>(a_real)[a_idx] = __hmul2( + reinterpret_cast<__half2 *>(x_input_data)[i], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(a_real)[a_idx] = reinterpret_cast<__half2 *>(x_input_data)[i]; + } + + } + + + //read the output gate into gate_data + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 2, 0. + ); + } + + __syncthreads(); + + // first DFT + complex_matmul_r2c_load_b( + reinterpret_cast(a_real), // read from HBM + reinterpret_cast(a_real), // this is the output + reinterpret_cast(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output is NOT written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_row_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After second DFT\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", __half2float(a_real[a_idx]), __half2float(a_imag[a_idx])); + // } + // printf("\n"); + // } + + // __syncthreads(); + + // load the input from acc_frag_1, and multiply by k_frag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + k_frag, + wmma::mem_col_major); + + // if (threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0) { + // printf("After ifft\n"); + // for (int i = 0; i < items_per_thread_input; i++) { + // a_idx = i * num_threads + thread_id; + // printf("%f + %fi, ", scratch_real[a_idx], scratch_imag[a_idx]); + // } + // printf("\n"); + // } + + // __syncthreads(); + + complex_matmul_c2r( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + reinterpret_cast(a_real), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + + for (int i = 0; i < items_per_thread_input / 2; i++) + { + a_idx = i * num_threads + thread_id; + scratch = reinterpret_cast<__half2 *>(a_real)[a_idx]; + + if(out_gate != nullptr){ + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(a_real)[a_idx], + reinterpret_cast<__half2 *>(gate_data)[i] + ); + }else{ + reinterpret_cast<__half2 *>(x_input_data)[i] = reinterpret_cast<__half2 *>(a_real)[a_idx]; + } + } + + // load input into a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 2 + ); + + //__syncthreads(); + + } // b_tile_id + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel_r2r.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel_r2r.h index dc421612c5486a31bf01802d15f865fc898ab592..b9b08183aef306ddb8268f59dff0211a2145114c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel_r2r.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_kernel_r2r.h @@ -1,381 +1,381 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "monarch_cuda_shared.h" -#include "monarch_cuda_shared_r2r.h" -using namespace nvcuda; - -template -__global__ void monarch_conv_cuda_kernel( - const at::Half *__restrict__ a, - const at::Half *__restrict__ in_gate, - const c10::complex *__restrict__ k_f, - const c10::complex *__restrict__ b, - const c10::complex *__restrict__ twiddle_factors_fft, - const c10::complex *__restrict__ twid_r2r, - const c10::complex *__restrict__ b_ifft, - const c10::complex *__restrict__ twiddle_factors_ifft, - at::Half *out, - const at::Half *__restrict__ out_gate, - uint B, - uint H, - uint signal_size, - uint sqrt_N) -{ - - extern __shared__ at::Half a_real[]; - at::Half *a_imag = &a_real[N]; - at::Half *b_real = &a_real[2 * N]; - at::Half *b_imag = &a_real[3 * N]; - at::Half *b_real_2 = &a_real[4 * N]; - at::Half *b_imag_2 = &a_real[5 * N]; - - const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; - const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; - // const int thread_id = threadIdx.x; - const int items_per_thread_input = 2 * N / num_threads; - const int items_per_thread_kf = N / num_threads; - // this is for reading in the DFT matrix or twiddle factors - const int items_per_thread_matrix = N / num_threads; - // const int warp_id = thread_id / WARP_SIZE; - - // NOTE - we are loading and storing data in a STRIPED FORMAT - // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input - using BlockLoad_Input = cub::BlockLoad; - using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; - using BlockLoad_Filter = cub::BlockLoad; - using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc - using BlockStore_Sequence = cub::BlockStore; - - // index into block blockIdx.x - int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; - // index into the H - int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; - int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; - - complex_half_t a_input_data[items_per_thread_input]; // for storing k_f - complex_half_t z_data[items_per_thread_kf]; // for storing the intermediates - at::Half x_input_data[items_per_thread_input]; // for storing the input - at::Half gate_data[items_per_thread_input]; // for storing the input - complex_half_t twid_input_data[items_per_thread_kf]; // for storing the input - complex_half_t twid_input_data_conj[items_per_thread_kf]; // for storing the input - complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors - complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors - - // for the dft - wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the dft - wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for the idft - // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for kernels - // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - // for twiddles - wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly - - // loads SEQUENCE_SIZE into b - BlockLoad_Shared().Load( - reinterpret_cast *>(b_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly - - int a_idx, b_idx; - __half2 scratch; - // complex_half_t scratch_complex1, scratch_complex2, xe, xo; - - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load into twiddle factors - // NOTE(danfu): this takes about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_fft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); - - // start loading ifft twiddle factors - // TODO(danfu): this costs about 60 us - BlockLoad_Shared().Load( - reinterpret_cast *>(twiddle_factors_ifft), - reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); - - bool a_trans = true; - bool b_trans = false; - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - -// load DFT matrix into b_frag -#pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT matrix into b_frag_idft - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); - wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // __syncthreads(); - - // load twiddles into shared memory - // load the DFT matrix into b_real, b_imag - // this costs about 60 us - // #pragma unroll - for (int i = 0; i < items_per_thread_matrix / 2; i++) - { - b_idx = i * num_threads + thread_id; - - scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; - scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; - - scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); - reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; - scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); - reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; - } - - // __syncthreads(); - - // load DFT twiddles into twiddle_dft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); - } - } - - // load iDFT twiddles into twiddle_idft_frag - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) - { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) - { - b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); - wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); - } - } - - // __syncthreads(); - - // load twid into twid_input_data - BlockLoad_Filter().Load( - reinterpret_cast(twid_r2r), - reinterpret_cast(twid_input_data) - ); - - negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); - - // #pragma unroll - for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) - { - - // start loading k_f - // NOTE(danfu): this load from HBM costs about 60 us - BlockLoad_Filter().Load( - reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), - reinterpret_cast(a_input_data)); - - if (thread_id == 0) - { - // load in the pivot into the imag position - a_input_data[0] = complex_half_t(a_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); - } - - // #pragma unroll - for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) - { - - int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; - - // load input into a_real and a_imag - BlockLoad_Input().Load( - reinterpret_cast(a + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4, 0. - ); - - // load input gate into gate_data - if(in_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(in_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 4, 0. - ); - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(gate_data)[i], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - } - - //read the output gate into gate_data - if(out_gate != nullptr){ - BlockLoad_Input().Load( - reinterpret_cast(out_gate + input_offset), - reinterpret_cast(gate_data), - signal_size / 4, 0. - ); - } - - load_input( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - //__syncthreads(); - - // first DFT - complex_matmul_load_b( - reinterpret_cast(a_real), // this is the output - reinterpret_cast(a_imag), // this is the output - sqrt_N, - N, - a_frag_dft, - acc_frag_1, - wmma::mem_row_major); - - // __syncthreads(); - - // second DFT, output IS written to a_real, a_imag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_dft, - acc_frag_1, - twiddle_dft_frag, - wmma::mem_col_major); - - process_zf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], - items_per_thread_kf, num_threads, thread_id, N); - - multiply_kf( - &z_data[0], &a_input_data[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - __syncthreads(); - - process_yf( - &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], - items_per_thread_kf, num_threads, thread_id, N); - - store_z_data( - &a_real[0], &a_imag[0], &z_data[0], - items_per_thread_kf, num_threads, thread_id); - - // load the input from acc_frag_1, DO NOT multiply by k_frag - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - // k_frag, - wmma::mem_col_major); - // __syncthreads(); - - complex_matmul( - reinterpret_cast(a_real), - reinterpret_cast(a_imag), - sqrt_N, - N, - b_frag_idft, - acc_frag_1, - twiddle_idft_frag, - wmma::mem_col_major); - - // __syncthreads(); - - load_output( - &a_real[0], &a_imag[0], &x_input_data[0], - items_per_thread_input, num_threads, thread_id); - - if (out_gate != nullptr) { - for (int i = 0; i < items_per_thread_input / 2; i++) { - reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( - reinterpret_cast<__half2 *>(gate_data)[i], - reinterpret_cast<__half2 *>(x_input_data)[i] - ); - } - } - - // load input into a_real - BlockStore_Sequence().Store( - reinterpret_cast(out + input_offset), - reinterpret_cast(x_input_data), - signal_size / 4 - ); - - //__syncthreads(); - - } // b_tile_id - } // h_tile_id +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "monarch_cuda_shared.h" +#include "monarch_cuda_shared_r2r.h" +using namespace nvcuda; + +template +__global__ void monarch_conv_cuda_kernel( + const at::Half *__restrict__ a, + const at::Half *__restrict__ in_gate, + const c10::complex *__restrict__ k_f, + const c10::complex *__restrict__ b, + const c10::complex *__restrict__ twiddle_factors_fft, + const c10::complex *__restrict__ twid_r2r, + const c10::complex *__restrict__ b_ifft, + const c10::complex *__restrict__ twiddle_factors_ifft, + at::Half *out, + const at::Half *__restrict__ out_gate, + uint B, + uint H, + uint signal_size, + uint sqrt_N) +{ + + extern __shared__ at::Half a_real[]; + at::Half *a_imag = &a_real[N]; + at::Half *b_real = &a_real[2 * N]; + at::Half *b_imag = &a_real[3 * N]; + at::Half *b_real_2 = &a_real[4 * N]; + at::Half *b_imag_2 = &a_real[5 * N]; + + const int num_threads = BLOCK_DIM_X * BLOCK_DIM_Y; + const int thread_id = threadIdx.x + blockDim.x * threadIdx.y; + // const int thread_id = threadIdx.x; + const int items_per_thread_input = 2 * N / num_threads; + const int items_per_thread_kf = N / num_threads; + // this is for reading in the DFT matrix or twiddle factors + const int items_per_thread_matrix = N / num_threads; + // const int warp_id = thread_id / WARP_SIZE; + + // NOTE - we are loading and storing data in a STRIPED FORMAT + // SEQUENCE_SIZE * TILE_SIZE items, WARP_SIZE * TILE_SIZE threads -> items_per_thread_input + using BlockLoad_Input = cub::BlockLoad; + using BlockLoad_Complex_Input = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_input / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Sequence = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_kf / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; + using BlockLoad_Filter = cub::BlockLoad; + using BlockLoad_Shared = cub::BlockLoad, BLOCK_DIM_X, items_per_thread_matrix / 2, cub::BLOCK_LOAD_STRIPED, BLOCK_DIM_Y>; // for the DFT / Twiddle, etc + using BlockStore_Sequence = cub::BlockStore; + + // index into block blockIdx.x + int b_offset_signal = blockIdx.x * H * signal_size * B_TILE_SIZE; + // index into the H + int h_offset_signal = blockIdx.y * signal_size * H_TILE_SIZE; + int h_offset_kernel = blockIdx.y * (N + 1) * H_TILE_SIZE; + + complex_half_t a_input_data[items_per_thread_input]; // for storing k_f + complex_half_t z_data[items_per_thread_kf]; // for storing the intermediates + at::Half x_input_data[items_per_thread_input]; // for storing the input + at::Half gate_data[items_per_thread_input]; // for storing the input + complex_half_t twid_input_data[items_per_thread_kf]; // for storing the input + complex_half_t twid_input_data_conj[items_per_thread_kf]; // for storing the input + complex_half_t b_input_data[items_per_thread_matrix]; // for storing matrices, twiddle factors + complex_half_t b_input_data_2[items_per_thread_matrix]; // another place for storing matrices, twiddle factors + + // for the dft + wmma::fragment b_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + wmma::fragment b_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the dft + wmma::fragment a_frag_dft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for the idft + // wmma::fragment a_frag_idft[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for kernels + // wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_dft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + // for twiddles + wmma::fragment twiddle_idft_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); // hopefully this interleaves things correctly + + // loads SEQUENCE_SIZE into b + BlockLoad_Shared().Load( + reinterpret_cast *>(b_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); // hopefully this interleaves things correctly + + int a_idx, b_idx; + __half2 scratch; + // complex_half_t scratch_complex1, scratch_complex2, xe, xo; + + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load into twiddle factors + // NOTE(danfu): this takes about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_fft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data)); + + // start loading ifft twiddle factors + // TODO(danfu): this costs about 60 us + BlockLoad_Shared().Load( + reinterpret_cast *>(twiddle_factors_ifft), + reinterpret_cast(&)[items_per_thread_matrix / 2]>(b_input_data_2)); + + bool a_trans = true; + bool b_trans = false; + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + +// load DFT matrix into b_frag +#pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(a_frag_dft[k][j_b][0], reinterpret_cast(b_real) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(a_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_dft[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT matrix into b_frag_idft + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + // a_idx = a_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + // wmma::load_matrix_sync(a_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + // wmma::load_matrix_sync(a_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + a_idx, sqrt_N); + wmma::load_matrix_sync(b_frag_idft[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // __syncthreads(); + + // load twiddles into shared memory + // load the DFT matrix into b_real, b_imag + // this costs about 60 us + // #pragma unroll + for (int i = 0; i < items_per_thread_matrix / 2; i++) + { + b_idx = i * num_threads + thread_id; + + scratch = __half2(b_input_data[2 * i].real(), b_input_data[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real)[b_idx] = scratch; + scratch = __half2(b_input_data[2 * i].imag(), b_input_data[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag)[b_idx] = scratch; + + scratch = __half2(b_input_data_2[2 * i].real(), b_input_data_2[2 * i + 1].real()); + reinterpret_cast<__half2 *>(b_real_2)[b_idx] = scratch; + scratch = __half2(b_input_data_2[2 * i].imag(), b_input_data_2[2 * i + 1].imag()); + reinterpret_cast<__half2 *>(b_imag_2)[b_idx] = scratch; + } + + // __syncthreads(); + + // load DFT twiddles into twiddle_dft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][0], reinterpret_cast(b_real) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_dft_frag[k][j_b][1], reinterpret_cast(b_imag) + b_idx, sqrt_N); + } + } + + // load iDFT twiddles into twiddle_idft_frag + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) + { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) + { + b_idx = b_trans ? j_b * WMMA_N * sqrt_N + k * WMMA_K : k * WMMA_K * sqrt_N + j_b * WMMA_N; + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][0], reinterpret_cast(b_real_2) + b_idx, sqrt_N); + wmma::load_matrix_sync(twiddle_idft_frag[k][j_b][1], reinterpret_cast(b_imag_2) + b_idx, sqrt_N); + } + } + + // __syncthreads(); + + // load twid into twid_input_data + BlockLoad_Filter().Load( + reinterpret_cast(twid_r2r), + reinterpret_cast(twid_input_data) + ); + + negate_twid(&twid_input_data[0], &twid_input_data_conj[0], items_per_thread_kf); + + // #pragma unroll + for (int h_tile_id = 0; h_tile_id < H_TILE_SIZE; h_tile_id++) + { + + // start loading k_f + // NOTE(danfu): this load from HBM costs about 60 us + BlockLoad_Filter().Load( + reinterpret_cast(k_f + h_offset_kernel + h_tile_id * (N + 1)), + reinterpret_cast(a_input_data)); + + if (thread_id == 0) + { + // load in the pivot into the imag position + a_input_data[0] = complex_half_t(a_input_data[0].real(), (k_f + h_offset_kernel + h_tile_id * (N + 1))[N].real()); + } + + // #pragma unroll + for (int b_tile_id = 0; b_tile_id < B_TILE_SIZE; b_tile_id++) + { + + int input_offset = h_offset_signal + b_offset_signal + h_tile_id * signal_size + b_tile_id * H * signal_size; + + // load input into a_real and a_imag + BlockLoad_Input().Load( + reinterpret_cast(a + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4, 0. + ); + + // load input gate into gate_data + if(in_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(in_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 4, 0. + ); + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(gate_data)[i], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + } + + //read the output gate into gate_data + if(out_gate != nullptr){ + BlockLoad_Input().Load( + reinterpret_cast(out_gate + input_offset), + reinterpret_cast(gate_data), + signal_size / 4, 0. + ); + } + + load_input( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + //__syncthreads(); + + // first DFT + complex_matmul_load_b( + reinterpret_cast(a_real), // this is the output + reinterpret_cast(a_imag), // this is the output + sqrt_N, + N, + a_frag_dft, + acc_frag_1, + wmma::mem_row_major); + + // __syncthreads(); + + // second DFT, output IS written to a_real, a_imag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_dft, + acc_frag_1, + twiddle_dft_frag, + wmma::mem_col_major); + + process_zf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data[0], + items_per_thread_kf, num_threads, thread_id, N); + + multiply_kf( + &z_data[0], &a_input_data[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + __syncthreads(); + + process_yf( + &a_real[0], &a_imag[0], &z_data[0], &twid_input_data_conj[0], + items_per_thread_kf, num_threads, thread_id, N); + + store_z_data( + &a_real[0], &a_imag[0], &z_data[0], + items_per_thread_kf, num_threads, thread_id); + + // load the input from acc_frag_1, DO NOT multiply by k_frag + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + // k_frag, + wmma::mem_col_major); + // __syncthreads(); + + complex_matmul( + reinterpret_cast(a_real), + reinterpret_cast(a_imag), + sqrt_N, + N, + b_frag_idft, + acc_frag_1, + twiddle_idft_frag, + wmma::mem_col_major); + + // __syncthreads(); + + load_output( + &a_real[0], &a_imag[0], &x_input_data[0], + items_per_thread_input, num_threads, thread_id); + + if (out_gate != nullptr) { + for (int i = 0; i < items_per_thread_input / 2; i++) { + reinterpret_cast<__half2 *>(x_input_data)[i] = __hmul2( + reinterpret_cast<__half2 *>(gate_data)[i], + reinterpret_cast<__half2 *>(x_input_data)[i] + ); + } + } + + // load input into a_real + BlockStore_Sequence().Store( + reinterpret_cast(out + input_offset), + reinterpret_cast(x_input_data), + signal_size / 4 + ); + + //__syncthreads(); + + } // b_tile_id + } // h_tile_id } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared.h index 6855fd8cfea22b9b69c95bacd96e19246084737d..69d318895728459da0ccf162640a37caf776e130 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared.h @@ -1,487 +1,487 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared/monarch_cuda_shared_fp16_complex_mul.h" -#include "shared/monarch_cuda_shared_fp16_matmuls.h" -#include "shared/monarch_cuda_shared_fp16_load_frags.h" -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_H_ -#define MONARCH_CUDA_H_ - -template -__device__ __forceinline__ void complex_matmul( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - - wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_load_b( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); - - // __syncthreads(); - _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_load_b( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); - - // __syncthreads(); - // multiply b_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), - __half2(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &b_frag[j_a][k][0].x[2 * i], - &b_frag[j_a][k][1].x[2 * i], - &b_frag[j_a][k][0].x[2 * i + 1], - &b_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - // __syncthreads(); - _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c( - const half *a_real_input, - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c(a_real_input, sqrt_N, N, acc_frag_1, a_frag); - - _complex_matmul_r2c(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_load_b( - const half *b_real_input, - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag_r2c(b_real_input, sqrt_N, N, acc_frag_1, b_frag); - - _complex_matmul_r2c_load_b(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_256( - const half *a_real_input, - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - - _complex_matmul_r2c_256(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_r2c_1024( - const half *a_real_input, - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_r2c_1024(a_real_input, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - - _complex_matmul_r2c_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - - _complex_matmul_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_256( - const half *a_real_inp, - const half *a_imag_inp, - half *a_real_out, - half *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - - _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_256( - half *a_real_inp, - half *a_imag_inp, - half *a_real_out, - half *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - const half *a_real_inp, - const half *a_imag_inp, - half *a_real_out, - half *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - - _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2c_1024( - half *a_real_inp, - half *a_imag_inp, - half *a_real_out, - half *a_imag_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r( - half *a_real, - half *a_imag, - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_256( - half *a_real, - half *a_imag, - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - // __syncthreads(); - - _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_256( - half *a_real, - half *a_imag, - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - // __syncthreads(); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r_1024( - half *a_real, - half *a_imag, - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - // __syncthreads(); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r_1024(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_c2r( - half *a_real, - half *a_imag, - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - // __syncthreads(); - - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared/monarch_cuda_shared_fp16_complex_mul.h" +#include "shared/monarch_cuda_shared_fp16_matmuls.h" +#include "shared/monarch_cuda_shared_fp16_load_frags.h" +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_H_ +#define MONARCH_CUDA_H_ + +template +__device__ __forceinline__ void complex_matmul( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + + wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + _complex_matmul(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_load_b( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); + + // __syncthreads(); + _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_load_b( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); + + // __syncthreads(); + // multiply b_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), + __half2(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &b_frag[j_a][k][0].x[2 * i], + &b_frag[j_a][k][1].x[2 * i], + &b_frag[j_a][k][0].x[2 * i + 1], + &b_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + // __syncthreads(); + _complex_matmul(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c( + const half *a_real_input, + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c(a_real_input, sqrt_N, N, acc_frag_1, a_frag); + + _complex_matmul_r2c(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_load_b( + const half *b_real_input, + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag_r2c(b_real_input, sqrt_N, N, acc_frag_1, b_frag); + + _complex_matmul_r2c_load_b(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_256( + const half *a_real_input, + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c_256(a_real_input, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + + _complex_matmul_r2c_256(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_r2c_1024( + const half *a_real_input, + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_r2c_1024(a_real_input, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + + _complex_matmul_r2c_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + + _complex_matmul_1024(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_256( + const half *a_real_inp, + const half *a_imag_inp, + half *a_real_out, + half *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + + _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_256( + half *a_real_inp, + half *a_imag_inp, + half *a_real_out, + half *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_256(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + const half *a_real_inp, + const half *a_imag_inp, + half *a_real_out, + half *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + + _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2c_1024( + half *a_real_inp, + half *a_imag_inp, + half *a_real_out, + half *a_imag_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real_inp, a_imag_inp, sqrt_N, N, acc_frag_1, a_frag); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_1024(a_real_out, a_imag_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r( + half *a_real, + half *a_imag, + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_256( + half *a_real, + half *a_imag, + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + // __syncthreads(); + + _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_256( + half *a_real, + half *a_imag, + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_256(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + // __syncthreads(); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r_256(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r_1024( + half *a_real, + half *a_imag, + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_1024(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + // __syncthreads(); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r_1024(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_c2r( + half *a_real, + half *a_imag, + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + // __syncthreads(); + + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_c2r(a_real_out, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_r2r.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_r2r.h index fab7b3568f6348bc9f8c96724083b3d8cd551299..2fab061bd448b739e84817e0ad2f19a1d7f2bb54 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_r2r.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_r2r.h @@ -1,311 +1,311 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include "shared/monarch_cuda_shared_fp16_complex_mul.h" -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -__device__ __forceinline__ void negate_twid( - complex_half_t *twid_input_data, - complex_half_t *twid_output_data, - int items_per_thread -) { - for (int i = 0; i < items_per_thread; i++) { - twid_output_data[i] = conj(twid_input_data[i]); - } -} - -__device__ __forceinline__ void load_input( - at::Half *a_real, - at::Half *a_imag, - at::Half *x_input_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input / 4; i++) - { - a_idx = i * num_threads + thread_id; - - reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( - __half(x_input_data[4 * i]), - __half(x_input_data[4 * i + 2]) - ); - reinterpret_cast<__half2 *>(a_imag)[a_idx] = __half2( - __half(x_input_data[4 * i + 1]), - __half(x_input_data[4 * i + 3]) - ); - // a_imag[a_idx] = x_input_data[2 * i + 1]; - } -} - -__device__ __forceinline__ void load_output( - at::Half *a_real, - at::Half *a_imag, - at::Half *x_input_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input / 4; i++) - { - a_idx = i * num_threads + thread_id; - - x_input_data[4 * i] = reinterpret_cast<__half2 *>(a_real)[a_idx].x; - x_input_data[4 * i + 2] = reinterpret_cast<__half2 *>(a_real)[a_idx].y; - x_input_data[4 * i + 1] = reinterpret_cast<__half2 *>(a_imag)[a_idx].x; - x_input_data[4 * i + 3] = reinterpret_cast<__half2 *>(a_imag)[a_idx].y; - } -} - -__device__ __forceinline__ void store_z_data( - at::Half *a_real, - at::Half *a_imag, - complex_half_t *z_data, - int items_per_thread_input, - int num_threads, - int thread_id -) { - int a_idx; - for (int i = 0; i < items_per_thread_input; i++) - { - a_idx = i * num_threads + thread_id; - - a_real[a_idx] = z_data[i].real(); - a_imag[a_idx] = z_data[i].imag(); - } -} - -__device__ __forceinline__ void multiply_kf( - complex_half_t *z_data, - complex_half_t *kf_data, - complex_half_t *out_data, - int items_per_thread, - int num_threads, - int thread_id -) { - __half2 scratch; - for (int i = 0; i < items_per_thread / 2; i++) { - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // do pointwise - scratch = __hmul2( - __half2(__half(z_data[0].real()), __half(z_data[0].imag())), - __half2(__half(kf_data[0].real()), __half(kf_data[0].imag())) - ); - out_data[0] = complex_half_t(scratch.x, scratch.y); - complex_mul( - z_data[1], kf_data[1], - &out_data[1] - ); - } else { - complex_mul_half2( - z_data[2*i], z_data[2*i+1], - kf_data[2*i], kf_data[2*i+1], - &out_data[2*i], &out_data[2*i+1] - ); - } - } -} - -__device__ __forceinline__ void multiply_kf_conj( - complex_half_t *z_data, - complex_half_t *kf_data, - complex_half_t *out_data, - int items_per_thread, - int num_threads, - int thread_id -) { - __half2 scratch; - for (int i = 0; i < items_per_thread / 2; i++) { - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // do pointwise - scratch = __hmul2( - __half2(__half(z_data[0].real()), __half(z_data[0].imag())), - __half2(__half(kf_data[0].real()), __half(kf_data[0].imag())) - ); - out_data[0] = complex_half_t(scratch.x, scratch.y); - complex_mul_conj( - z_data[1], kf_data[1], - &out_data[1] - ); - } else { - complex_mul_conj_half2( - z_data[2*i], z_data[2*i+1], - kf_data[2*i], kf_data[2*i+1], - &out_data[2*i], &out_data[2*i+1] - ); - } - } -} - -__device__ __forceinline__ void process_zf( - at::Half *a_real, - at::Half *a_imag, - complex_half_t *z_data, - complex_half_t *twid_input_data, - int items_per_thread, - int num_threads, - int thread_id, - int N -) { - int a_idx1, a_idx2; - complex_half_t scratch_complex1, scratch_complex2, xe, xo; - __half2 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; - for (int i = 0; i < items_per_thread / 2; i++) { - a_idx1 = (2 * i * num_threads + thread_id); - a_idx2 = ((2 * i + 1) * num_threads + thread_id); - - // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] - // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] - - if (thread_id == 0 && i == 0) { - // special case - // xe = a_real[0] - // xo = a_imag[0] - // z.real = xe + xo * twid_real[0] = xe + xo - // z.imag = xe - xo - z_data[0] = complex_half_t( - a_real[0] + a_imag[0], - a_real[0] - a_imag[0] - ); - scratch_complex1 = complex_half_t(a_real[a_idx2], a_imag[a_idx2]); - scratch_complex2 = complex_half_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); - - xe = (scratch_complex1 + scratch_complex2) * complex_half_t(__float2half(0.5), __float2half(0.0)); - xo = (scratch_complex1 - scratch_complex2) * complex_half_t(__float2half(0.0), __float2half(-0.5)); - z_data[1] = xe + xo * twid_input_data[1]; - } else { - // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] - // xe = (a[a_idx] + a[N - a_idx]) / 2 - // xo = (a[a_idx] - a[N - a_idx]) / 2j - // z[i] = xe + xo * twid[a_idx] - a1_real2 = __half2(__half(a_real[a_idx1]), __half(a_real[a_idx2])); - a1_imag2 = __half2(__half(a_imag[a_idx1]), __half(a_imag[a_idx2])); - a2_real2 = __half2(__half(a_real[N-a_idx1]), __half(a_real[N-a_idx2])); - a2_imag2 = __half2(__half(-a_imag[N-a_idx1]), __half(-a_imag[N-a_idx2])); - - complex_mul_half2( - __hadd2(a1_real2, a2_real2), - __hadd2(a1_imag2, a2_imag2), - __half2(__float2half(0.5), __float2half(0.5)), - __half2(__float2half(0.0), __float2half(0.0)), - &xe_real2, &xe_imag2 - ); - complex_mul_half2( - __hsub2(a1_real2, a2_real2), - __hsub2(a1_imag2, a2_imag2), - __half2(__float2half(0.0), __float2half(0.0)), - __half2(__float2half(-0.5), __float2half(-0.5)), - &xo_real2, &xo_imag2 - ); - - complex_mul_half2( - xo_real2, xo_imag2, - __half2(__half(twid_input_data[2*i].real()), __half(twid_input_data[2*i + 1].real())), - __half2(__half(twid_input_data[2*i].imag()), __half(twid_input_data[2*i + 1].imag())), - &z_real2, &z_imag2 - ); - - z_real2 = __hadd2(xe_real2, z_real2); - z_imag2 = __hadd2(xe_imag2, z_imag2); - - z_data[2*i] = complex_half_t(z_real2.x, z_imag2.x); - z_data[2*i + 1] = complex_half_t(z_real2.y, z_imag2.y); - } - } -} - -__device__ __forceinline__ void process_yf( - at::Half *a_real, - at::Half *a_imag, - complex_half_t *z_data, - complex_half_t *twid_input_data_conj, - int items_per_thread, - int num_threads, - int thread_id, - int N -) { - int a_idx1, a_idx2; - complex_half_t scratch_complex1, scratch_complex2, xe, xo; - - __half2 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; - for (int i = 0; i < items_per_thread / 2; i++) { - a_idx1 = (2 * i * num_threads + thread_id); - a_idx2 = ((2 * i + 1) * num_threads + thread_id); - // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] - // xe = (a[a_idx] + a[N - a_idx]) / 2 - // xo = (a[a_idx] - a[N - a_idx]) / 2 * twid[i].conj() - // z[i] = xe + xo * 1j - if (thread_id == 0 && i == 0) { - // special case - xe = complex_half_t( - (a_real[0] + a_imag[0]) / 2, - 0. - ); - xo = complex_half_t( - (a_real[0] - a_imag[0]) / 2, - 0. - ); - z_data[0] = xe + xo * complex_half_t(0., 1.); - - scratch_complex1 = complex_half_t(a_real[a_idx2], a_imag[a_idx2]); - scratch_complex2 = complex_half_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); - xe = (scratch_complex1 + scratch_complex2) * complex_half_t(__float2half(0.5), __float2half(0.0)); - xo = ((scratch_complex1 - scratch_complex2) * complex_half_t(__float2half(0.0), __float2half(0.5))) * twid_input_data_conj[1]; - - // z_data[1] = xe + xo * complex_half_t(0., 1.); - z_data[1] = xe + xo; - } else { - a1_real2 = __half2(__half(a_real[a_idx1]), __half(a_real[a_idx2])); - a1_imag2 = __half2(__half(a_imag[a_idx1]), __half(a_imag[a_idx2])); - a2_real2 = __half2(__half(a_real[N-a_idx1]), __half(a_real[N-a_idx2])); - a2_imag2 = __half2(__half(-a_imag[N-a_idx1]), __half(-a_imag[N-a_idx2])); - - complex_mul_half2( - __hadd2(a1_real2, a2_real2), - __hadd2(a1_imag2, a2_imag2), - __half2(__float2half(0.5), __float2half(0.5)), - __half2(__float2half(0.0), __float2half(0.0)), - &xe_real2, &xe_imag2 - ); - complex_mul_half2( - __hsub2(a1_real2, a2_real2), - __hsub2(a1_imag2, a2_imag2), - __half2(__float2half(0.0), __float2half(0.0)), - __half2(__float2half(0.5), __float2half(0.5)), - &xo_real2, &xo_imag2 - ); - - complex_mul_half2( - xo_real2, xo_imag2, - __half2(__half(twid_input_data_conj[2*i].real()), __half(twid_input_data_conj[2*i + 1].real())), - __half2(__half(twid_input_data_conj[2*i].imag()), __half(twid_input_data_conj[2*i + 1].imag())), - &z_real2, &z_imag2 - ); - - z_real2 = __hadd2(xe_real2, z_real2); - z_imag2 = __hadd2(xe_imag2, z_imag2); - - z_data[2*i] = complex_half_t(z_real2.x, z_imag2.x); - z_data[2*i + 1] = complex_half_t(z_real2.y, z_imag2.y); - } - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include "shared/monarch_cuda_shared_fp16_complex_mul.h" +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +__device__ __forceinline__ void negate_twid( + complex_half_t *twid_input_data, + complex_half_t *twid_output_data, + int items_per_thread +) { + for (int i = 0; i < items_per_thread; i++) { + twid_output_data[i] = conj(twid_input_data[i]); + } +} + +__device__ __forceinline__ void load_input( + at::Half *a_real, + at::Half *a_imag, + at::Half *x_input_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input / 4; i++) + { + a_idx = i * num_threads + thread_id; + + reinterpret_cast<__half2 *>(a_real)[a_idx] = __half2( + __half(x_input_data[4 * i]), + __half(x_input_data[4 * i + 2]) + ); + reinterpret_cast<__half2 *>(a_imag)[a_idx] = __half2( + __half(x_input_data[4 * i + 1]), + __half(x_input_data[4 * i + 3]) + ); + // a_imag[a_idx] = x_input_data[2 * i + 1]; + } +} + +__device__ __forceinline__ void load_output( + at::Half *a_real, + at::Half *a_imag, + at::Half *x_input_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input / 4; i++) + { + a_idx = i * num_threads + thread_id; + + x_input_data[4 * i] = reinterpret_cast<__half2 *>(a_real)[a_idx].x; + x_input_data[4 * i + 2] = reinterpret_cast<__half2 *>(a_real)[a_idx].y; + x_input_data[4 * i + 1] = reinterpret_cast<__half2 *>(a_imag)[a_idx].x; + x_input_data[4 * i + 3] = reinterpret_cast<__half2 *>(a_imag)[a_idx].y; + } +} + +__device__ __forceinline__ void store_z_data( + at::Half *a_real, + at::Half *a_imag, + complex_half_t *z_data, + int items_per_thread_input, + int num_threads, + int thread_id +) { + int a_idx; + for (int i = 0; i < items_per_thread_input; i++) + { + a_idx = i * num_threads + thread_id; + + a_real[a_idx] = z_data[i].real(); + a_imag[a_idx] = z_data[i].imag(); + } +} + +__device__ __forceinline__ void multiply_kf( + complex_half_t *z_data, + complex_half_t *kf_data, + complex_half_t *out_data, + int items_per_thread, + int num_threads, + int thread_id +) { + __half2 scratch; + for (int i = 0; i < items_per_thread / 2; i++) { + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // do pointwise + scratch = __hmul2( + __half2(__half(z_data[0].real()), __half(z_data[0].imag())), + __half2(__half(kf_data[0].real()), __half(kf_data[0].imag())) + ); + out_data[0] = complex_half_t(scratch.x, scratch.y); + complex_mul( + z_data[1], kf_data[1], + &out_data[1] + ); + } else { + complex_mul_half2( + z_data[2*i], z_data[2*i+1], + kf_data[2*i], kf_data[2*i+1], + &out_data[2*i], &out_data[2*i+1] + ); + } + } +} + +__device__ __forceinline__ void multiply_kf_conj( + complex_half_t *z_data, + complex_half_t *kf_data, + complex_half_t *out_data, + int items_per_thread, + int num_threads, + int thread_id +) { + __half2 scratch; + for (int i = 0; i < items_per_thread / 2; i++) { + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // do pointwise + scratch = __hmul2( + __half2(__half(z_data[0].real()), __half(z_data[0].imag())), + __half2(__half(kf_data[0].real()), __half(kf_data[0].imag())) + ); + out_data[0] = complex_half_t(scratch.x, scratch.y); + complex_mul_conj( + z_data[1], kf_data[1], + &out_data[1] + ); + } else { + complex_mul_conj_half2( + z_data[2*i], z_data[2*i+1], + kf_data[2*i], kf_data[2*i+1], + &out_data[2*i], &out_data[2*i+1] + ); + } + } +} + +__device__ __forceinline__ void process_zf( + at::Half *a_real, + at::Half *a_imag, + complex_half_t *z_data, + complex_half_t *twid_input_data, + int items_per_thread, + int num_threads, + int thread_id, + int N +) { + int a_idx1, a_idx2; + complex_half_t scratch_complex1, scratch_complex2, xe, xo; + __half2 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; + for (int i = 0; i < items_per_thread / 2; i++) { + a_idx1 = (2 * i * num_threads + thread_id); + a_idx2 = ((2 * i + 1) * num_threads + thread_id); + + // z_data[2*i] corresponds to a_real[a_idx], a_imag[a_idx] + // z_data[2*i + 1] corresponds to a_real[a_idx + 1], a_imag[a_idx + 1] + + if (thread_id == 0 && i == 0) { + // special case + // xe = a_real[0] + // xo = a_imag[0] + // z.real = xe + xo * twid_real[0] = xe + xo + // z.imag = xe - xo + z_data[0] = complex_half_t( + a_real[0] + a_imag[0], + a_real[0] - a_imag[0] + ); + scratch_complex1 = complex_half_t(a_real[a_idx2], a_imag[a_idx2]); + scratch_complex2 = complex_half_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); + + xe = (scratch_complex1 + scratch_complex2) * complex_half_t(__float2half(0.5), __float2half(0.0)); + xo = (scratch_complex1 - scratch_complex2) * complex_half_t(__float2half(0.0), __float2half(-0.5)); + z_data[1] = xe + xo * twid_input_data[1]; + } else { + // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] + // xe = (a[a_idx] + a[N - a_idx]) / 2 + // xo = (a[a_idx] - a[N - a_idx]) / 2j + // z[i] = xe + xo * twid[a_idx] + a1_real2 = __half2(__half(a_real[a_idx1]), __half(a_real[a_idx2])); + a1_imag2 = __half2(__half(a_imag[a_idx1]), __half(a_imag[a_idx2])); + a2_real2 = __half2(__half(a_real[N-a_idx1]), __half(a_real[N-a_idx2])); + a2_imag2 = __half2(__half(-a_imag[N-a_idx1]), __half(-a_imag[N-a_idx2])); + + complex_mul_half2( + __hadd2(a1_real2, a2_real2), + __hadd2(a1_imag2, a2_imag2), + __half2(__float2half(0.5), __float2half(0.5)), + __half2(__float2half(0.0), __float2half(0.0)), + &xe_real2, &xe_imag2 + ); + complex_mul_half2( + __hsub2(a1_real2, a2_real2), + __hsub2(a1_imag2, a2_imag2), + __half2(__float2half(0.0), __float2half(0.0)), + __half2(__float2half(-0.5), __float2half(-0.5)), + &xo_real2, &xo_imag2 + ); + + complex_mul_half2( + xo_real2, xo_imag2, + __half2(__half(twid_input_data[2*i].real()), __half(twid_input_data[2*i + 1].real())), + __half2(__half(twid_input_data[2*i].imag()), __half(twid_input_data[2*i + 1].imag())), + &z_real2, &z_imag2 + ); + + z_real2 = __hadd2(xe_real2, z_real2); + z_imag2 = __hadd2(xe_imag2, z_imag2); + + z_data[2*i] = complex_half_t(z_real2.x, z_imag2.x); + z_data[2*i + 1] = complex_half_t(z_real2.y, z_imag2.y); + } + } +} + +__device__ __forceinline__ void process_yf( + at::Half *a_real, + at::Half *a_imag, + complex_half_t *z_data, + complex_half_t *twid_input_data_conj, + int items_per_thread, + int num_threads, + int thread_id, + int N +) { + int a_idx1, a_idx2; + complex_half_t scratch_complex1, scratch_complex2, xe, xo; + + __half2 xe_real2, xe_imag2, xo_real2, xo_imag2, a1_real2, a1_imag2, a2_real2, a2_imag2, z_real2, z_imag2; + for (int i = 0; i < items_per_thread / 2; i++) { + a_idx1 = (2 * i * num_threads + thread_id); + a_idx2 = ((2 * i + 1) * num_threads + thread_id); + // to compute z[i], we need a[a_idx], a[N - a_idx], and twid[a_idx] + // xe = (a[a_idx] + a[N - a_idx]) / 2 + // xo = (a[a_idx] - a[N - a_idx]) / 2 * twid[i].conj() + // z[i] = xe + xo * 1j + if (thread_id == 0 && i == 0) { + // special case + xe = complex_half_t( + (a_real[0] + a_imag[0]) / 2, + 0. + ); + xo = complex_half_t( + (a_real[0] - a_imag[0]) / 2, + 0. + ); + z_data[0] = xe + xo * complex_half_t(0., 1.); + + scratch_complex1 = complex_half_t(a_real[a_idx2], a_imag[a_idx2]); + scratch_complex2 = complex_half_t(a_real[N-a_idx2], -a_imag[N-a_idx2]); + xe = (scratch_complex1 + scratch_complex2) * complex_half_t(__float2half(0.5), __float2half(0.0)); + xo = ((scratch_complex1 - scratch_complex2) * complex_half_t(__float2half(0.0), __float2half(0.5))) * twid_input_data_conj[1]; + + // z_data[1] = xe + xo * complex_half_t(0., 1.); + z_data[1] = xe + xo; + } else { + a1_real2 = __half2(__half(a_real[a_idx1]), __half(a_real[a_idx2])); + a1_imag2 = __half2(__half(a_imag[a_idx1]), __half(a_imag[a_idx2])); + a2_real2 = __half2(__half(a_real[N-a_idx1]), __half(a_real[N-a_idx2])); + a2_imag2 = __half2(__half(-a_imag[N-a_idx1]), __half(-a_imag[N-a_idx2])); + + complex_mul_half2( + __hadd2(a1_real2, a2_real2), + __hadd2(a1_imag2, a2_imag2), + __half2(__float2half(0.5), __float2half(0.5)), + __half2(__float2half(0.0), __float2half(0.0)), + &xe_real2, &xe_imag2 + ); + complex_mul_half2( + __hsub2(a1_real2, a2_real2), + __hsub2(a1_imag2, a2_imag2), + __half2(__float2half(0.0), __float2half(0.0)), + __half2(__float2half(0.5), __float2half(0.5)), + &xo_real2, &xo_imag2 + ); + + complex_mul_half2( + xo_real2, xo_imag2, + __half2(__half(twid_input_data_conj[2*i].real()), __half(twid_input_data_conj[2*i + 1].real())), + __half2(__half(twid_input_data_conj[2*i].imag()), __half(twid_input_data_conj[2*i + 1].imag())), + &z_real2, &z_imag2 + ); + + z_real2 = __hadd2(xe_real2, z_real2); + z_imag2 = __hadd2(xe_imag2, z_imag2); + + z_data[2*i] = complex_half_t(z_real2.x, z_imag2.x); + z_data[2*i + 1] = complex_half_t(z_real2.y, z_imag2.y); + } + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_truncated.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_truncated.h index 019629d7ebd060f2c53ea0b20a47843bfc217ad5..29346aa4405640ce2ed628bdeb37590c80dc68b3 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_truncated.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/monarch_cuda_shared_truncated.h @@ -1,256 +1,256 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -template -__device__ __forceinline__ void _complex_matmul_truncated( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH/2; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH/2; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - - - - -template -__device__ __forceinline__ void load_a_frag_truncated( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); - } - } - } -} - - -template -__device__ __forceinline__ void load_b_frag_truncated( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); - } - } -} - - -template -__device__ __forceinline__ void complex_matmul_truncated( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - - wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_truncated(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - // multiply a_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), - __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &a_frag[j_a][k][0].x[2 * i], - &a_frag[j_a][k][1].x[2 * i], - &a_frag[j_a][k][0].x[2 * i + 1], - &a_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - _complex_matmul_truncated(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - -template -__device__ __forceinline__ void complex_matmul_truncated( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_a_frag_truncated(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); - - // __syncthreads(); - _complex_matmul_truncated(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); -} - - -template -__device__ __forceinline__ void complex_matmul_load_b_truncated( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; - load_b_frag_truncated(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); - - // __syncthreads(); - // multiply b_frag by k_frag - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { - complex_mul_half2( - __half2(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), - __half2(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), - __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), - __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), - &b_frag[j_a][k][0].x[2 * i], - &b_frag[j_a][k][1].x[2 * i], - &b_frag[j_a][k][0].x[2 * i + 1], - &b_frag[j_a][k][1].x[2 * i + 1] - ); - } - } - } - - // __syncthreads(); - _complex_matmul_truncated(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +template +__device__ __forceinline__ void _complex_matmul_truncated( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH/2; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH/2; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + + + + +template +__device__ __forceinline__ void load_a_frag_truncated( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); + } + } + } +} + + +template +__device__ __forceinline__ void load_b_frag_truncated( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); + } + } +} + + +template +__device__ __forceinline__ void complex_matmul_truncated( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + + wmma::fragment a_frag [MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_truncated(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + // multiply a_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(a_frag[j_a][k][0].x[2 * i], a_frag[j_a][k][0].x[2 * i + 1]), + __half2(a_frag[j_a][k][1].x[2 * i], a_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &a_frag[j_a][k][0].x[2 * i], + &a_frag[j_a][k][1].x[2 * i], + &a_frag[j_a][k][0].x[2 * i + 1], + &a_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + _complex_matmul_truncated(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + +template +__device__ __forceinline__ void complex_matmul_truncated( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_a_frag_truncated(a_real, a_imag, sqrt_N, N, acc_frag_1, a_frag); + + // __syncthreads(); + _complex_matmul_truncated(a_real, a_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); +} + + +template +__device__ __forceinline__ void complex_matmul_load_b_truncated( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment k_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]; + load_b_frag_truncated(b_real, b_imag, sqrt_N, N, acc_frag_1, b_frag); + + // __syncthreads(); + // multiply b_frag by k_frag + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH/2; j_a++) { + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + for (int i = 0; i < acc_frag_1[j_a][k][0].num_elements / 2; i++) { + complex_mul_half2( + __half2(b_frag[j_a][k][0].x[2 * i], b_frag[j_a][k][0].x[2 * i + 1]), + __half2(b_frag[j_a][k][1].x[2 * i], b_frag[j_a][k][1].x[2 * i + 1]), + __half2(k_frag[j_a][k][0].x[2 * i], k_frag[j_a][k][0].x[2 * i + 1]), + __half2(k_frag[j_a][k][1].x[2 * i], k_frag[j_a][k][1].x[2 * i + 1]), + &b_frag[j_a][k][0].x[2 * i], + &b_frag[j_a][k][1].x[2 * i], + &b_frag[j_a][k][0].x[2 * i + 1], + &b_frag[j_a][k][1].x[2 * i + 1] + ); + } + } + } + + // __syncthreads(); + _complex_matmul_truncated(b_real, b_imag, sqrt_N, N, a_frag, b_frag, acc_frag_1, out_layout); } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_complex_mul.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_complex_mul.h index 9a2d6cce8630ab8c32715542444a53b8a8a3f65b..3de9c98226ec3bac1ee370215df1af1c084f3cbd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_complex_mul.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_complex_mul.h @@ -1,159 +1,159 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -#ifndef MONARCH_CUDA_FP16_COMPLEX_MUL_ -#define MONARCH_CUDA_FP16_COMPLEX_MUL_ - -__device__ __forceinline__ void complex_mul(at::Half a_real, at::Half a_imag, at::Half b_real, at::Half b_imag, at::Half *c_real, at::Half *c_imag) { - __half temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __half(a_real * b_real - a_imag * b_imag); - temp_y = __hfma(__half(a_imag), __half(b_real), __half(a_real * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul(complex_half_t a, complex_half_t b, complex_half_t *c) { - __half temp_x, temp_y; - __half2 temp2; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - // temp_x = __half(a.real() * b.real() - a.imag() * b.imag()); - temp2 = __hmul2(__half2(a.real(), a.imag()), __half2(b.real(), b.imag())); - temp_x = __hsub(temp2.x, temp2.y); - temp_y = __hfma(__half(a.imag()), __half(b.real()), __half(a.real() * b.imag())); - *c = complex_half_t(temp_x, temp_y); -} - -__device__ __forceinline__ void complex_mul_float_half(float a_real, float a_imag, at::Half b_real, at::Half b_imag, at::Half *c_real, at::Half *c_imag) { - __half temp_x, temp_y; - // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); - // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); - temp_x = __half(at::Half(a_real) * b_real - at::Half(a_imag) * b_imag); - temp_y = __hfma(__half(at::Half(a_imag)), __half(b_real), __half(at::Half(a_real) * b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half2 *c_real, __half2 *c_imag) { - __half2 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, complex_half_t *c1, complex_half_t *c2) { - __half2 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c1 = complex_half_t(temp_x.x, temp_y.x); - *c2 = complex_half_t(temp_x.y, temp_y.y); -} - -__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half *c_real_0, __half *c_imag_0, __half *c_real_1, __half *c_imag_1) { - __half2 temp_x, temp_y; - - temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real_0 = temp_x.x; - *c_imag_0 = temp_y.x; - *c_real_1 = temp_x.y; - *c_imag_1 = temp_y.y; -} - -__device__ __forceinline__ void complex_mul_half2(complex_half_t a1, complex_half_t a2, complex_half_t b1, complex_half_t b2, complex_half_t *c1, complex_half_t *c2) { - __half2 a_real, a_imag, b_real, b_imag; - - a_real = __half2(a1.real(), a2.real()); - a_imag = __half2(a1.imag(), a2.imag()); - b_real = __half2(b1.real(), b2.real()); - b_imag = __half2(b1.imag(), b2.imag()); - - complex_mul_half2(a_real, a_imag, b_real, b_imag, c1, c2); -} - -__device__ __forceinline__ void complex_mul_conj(complex_half_t a, complex_half_t b, complex_half_t *c) { - __half temp_x, temp_y; - __half2 temp2; - - temp_x = __hfma(__half(a.real()), __half(b.real()), __half(a.imag() * b.imag())); - temp2 = __hmul2(__half2(a.imag(), a.real()), __half2(__half(b.real()), __half(b.imag()))); - temp_y = __hsub(temp2.x, temp2.y); - *c = complex_half_t(temp_x, temp_y); -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, c10::complex<__half> *c_0, c10::complex<__half> *c_1) { - __half2 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_0 = c10::complex<__half>(temp_x.x, temp_y.x); - *c_1 = c10::complex<__half>(temp_x.y, temp_y.y); -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, complex_half_t *c_0, complex_half_t *c_1) { - __half2 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_0 = complex_half_t(temp_x.x, temp_y.x); - *c_1 = complex_half_t(temp_x.y, temp_y.y); -} - -__device__ __forceinline__ void complex_mul_conj_half2(complex_half_t a1, complex_half_t a2, complex_half_t b1, complex_half_t b2, complex_half_t *c1, complex_half_t *c2) { - __half2 a_real, a_imag, b_real, b_imag; - - a_real = __half2(a1.real(), a2.real()); - a_imag = __half2(a1.imag(), a2.imag()); - b_real = __half2(b1.real(), b2.real()); - b_imag = __half2(b1.imag(), b2.imag()); - - complex_mul_conj_half2(a_real, a_imag, b_real, b_imag, c1, c2); -} - -// negates b_imag -__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, c10::complex<__half> b_0, c10::complex<__half> b_1, c10::complex<__half> *c_0, c10::complex<__half> *c_1) { - __half2 b_real_h2, b_imag_h2; - - b_real_h2 = __half2(b_0.real(), b_1.real()); - b_imag_h2 = __half2(b_0.imag(), b_1.imag()); - complex_mul_conj_half2(a_real, a_imag, b_real_h2, b_imag_h2, c_0, c_1); -} - -__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half2 *c_real, __half2 *c_imag) { - __half2 temp_x, temp_y; - - temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); - // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); - temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); - // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); - *c_real = temp_x; - *c_imag = temp_y; -} - -__device__ __forceinline__ complex_half_t conj(complex_half_t inp) { - return complex_half_t(inp.real(), -inp.imag()); -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +#ifndef MONARCH_CUDA_FP16_COMPLEX_MUL_ +#define MONARCH_CUDA_FP16_COMPLEX_MUL_ + +__device__ __forceinline__ void complex_mul(at::Half a_real, at::Half a_imag, at::Half b_real, at::Half b_imag, at::Half *c_real, at::Half *c_imag) { + __half temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __half(a_real * b_real - a_imag * b_imag); + temp_y = __hfma(__half(a_imag), __half(b_real), __half(a_real * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul(complex_half_t a, complex_half_t b, complex_half_t *c) { + __half temp_x, temp_y; + __half2 temp2; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + // temp_x = __half(a.real() * b.real() - a.imag() * b.imag()); + temp2 = __hmul2(__half2(a.real(), a.imag()), __half2(b.real(), b.imag())); + temp_x = __hsub(temp2.x, temp2.y); + temp_y = __hfma(__half(a.imag()), __half(b.real()), __half(a.real() * b.imag())); + *c = complex_half_t(temp_x, temp_y); +} + +__device__ __forceinline__ void complex_mul_float_half(float a_real, float a_imag, at::Half b_real, at::Half b_imag, at::Half *c_real, at::Half *c_imag) { + __half temp_x, temp_y; + // temp_x = __hsub(__hmul(a_real, b_real), __hmul(a_imag, b_imag)); + // temp_y = __hadd(__hmul(a_imag, b_real), __hmul(a_real, b_imag)); + temp_x = __half(at::Half(a_real) * b_real - at::Half(a_imag) * b_imag); + temp_y = __hfma(__half(at::Half(a_imag)), __half(b_real), __half(at::Half(a_real) * b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half2 *c_real, __half2 *c_imag) { + __half2 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, complex_half_t *c1, complex_half_t *c2) { + __half2 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c1 = complex_half_t(temp_x.x, temp_y.x); + *c2 = complex_half_t(temp_x.y, temp_y.y); +} + +__device__ __forceinline__ void complex_mul_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half *c_real_0, __half *c_imag_0, __half *c_real_1, __half *c_imag_1) { + __half2 temp_x, temp_y; + + temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real_0 = temp_x.x; + *c_imag_0 = temp_y.x; + *c_real_1 = temp_x.y; + *c_imag_1 = temp_y.y; +} + +__device__ __forceinline__ void complex_mul_half2(complex_half_t a1, complex_half_t a2, complex_half_t b1, complex_half_t b2, complex_half_t *c1, complex_half_t *c2) { + __half2 a_real, a_imag, b_real, b_imag; + + a_real = __half2(a1.real(), a2.real()); + a_imag = __half2(a1.imag(), a2.imag()); + b_real = __half2(b1.real(), b2.real()); + b_imag = __half2(b1.imag(), b2.imag()); + + complex_mul_half2(a_real, a_imag, b_real, b_imag, c1, c2); +} + +__device__ __forceinline__ void complex_mul_conj(complex_half_t a, complex_half_t b, complex_half_t *c) { + __half temp_x, temp_y; + __half2 temp2; + + temp_x = __hfma(__half(a.real()), __half(b.real()), __half(a.imag() * b.imag())); + temp2 = __hmul2(__half2(a.imag(), a.real()), __half2(__half(b.real()), __half(b.imag()))); + temp_y = __hsub(temp2.x, temp2.y); + *c = complex_half_t(temp_x, temp_y); +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, c10::complex<__half> *c_0, c10::complex<__half> *c_1) { + __half2 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_0 = c10::complex<__half>(temp_x.x, temp_y.x); + *c_1 = c10::complex<__half>(temp_x.y, temp_y.y); +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, complex_half_t *c_0, complex_half_t *c_1) { + __half2 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_0 = complex_half_t(temp_x.x, temp_y.x); + *c_1 = complex_half_t(temp_x.y, temp_y.y); +} + +__device__ __forceinline__ void complex_mul_conj_half2(complex_half_t a1, complex_half_t a2, complex_half_t b1, complex_half_t b2, complex_half_t *c1, complex_half_t *c2) { + __half2 a_real, a_imag, b_real, b_imag; + + a_real = __half2(a1.real(), a2.real()); + a_imag = __half2(a1.imag(), a2.imag()); + b_real = __half2(b1.real(), b2.real()); + b_imag = __half2(b1.imag(), b2.imag()); + + complex_mul_conj_half2(a_real, a_imag, b_real, b_imag, c1, c2); +} + +// negates b_imag +__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, c10::complex<__half> b_0, c10::complex<__half> b_1, c10::complex<__half> *c_0, c10::complex<__half> *c_1) { + __half2 b_real_h2, b_imag_h2; + + b_real_h2 = __half2(b_0.real(), b_1.real()); + b_imag_h2 = __half2(b_0.imag(), b_1.imag()); + complex_mul_conj_half2(a_real, a_imag, b_real_h2, b_imag_h2, c_0, c_1); +} + +__device__ __forceinline__ void complex_mul_conj_half2(__half2 a_real, __half2 a_imag, __half2 b_real, __half2 b_imag, __half2 *c_real, __half2 *c_imag) { + __half2 temp_x, temp_y; + + temp_x = __hfma2(a_real, b_real, __hmul2(a_imag, b_imag)); + // temp_x = __hsub2(__hmul2(a_real, b_real), __hmul2(a_imag, b_imag)); + temp_y = __hsub2(__hmul2(a_imag, b_real), __hmul2(a_real, b_imag)); + // temp_y = __hfma2(a_imag, b_real, __hmul2(a_real, b_imag)); + *c_real = temp_x; + *c_imag = temp_y; +} + +__device__ __forceinline__ complex_half_t conj(complex_half_t inp) { + return complex_half_t(inp.real(), -inp.imag()); +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_load_frags.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_load_frags.h index 8be253267f4b5860e7b7e400501ceb6e6a4c3b5f..0e6bc630c207a711cb7114b563ebf31775dcd9c4 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_load_frags.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_load_frags.h @@ -1,373 +1,373 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_LOAD_ -#define MONARCH_CUDA_LOAD_ - -template -__device__ __forceinline__ void load_a_frag( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_256( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_256( - const half *a_real, - const half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_1024( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 1024); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_1024( - const half *a_real, - const half *a_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 2; k++) { - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); - wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 1024); - } - } - } -} - -template -__device__ __forceinline__ void load_b_frag_r2c( - const half *b_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - } - } -} - -template -__device__ __forceinline__ void load_b_frag( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int b_idx; - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); - wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c( - const half *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c_256( - const half *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); - } - } - } -} - -template -__device__ __forceinline__ void load_a_frag_r2c_1024( - const half *a_real, - int sqrt_N, - int N, - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) -{ - int a_idx; - - if (a_frag_from_acc) { - // load up a_frag's from acc_frag_1 - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int k = 0; k < 1; k++) { - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; - a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; - } - } - } - } - } else { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; - wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); - } - } - } -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_LOAD_ +#define MONARCH_CUDA_LOAD_ + +template +__device__ __forceinline__ void load_a_frag( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, sqrt_N); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_256( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_256( + const half *a_real, + const half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_1024( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 1024); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_1024( + const half *a_real, + const half *a_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 2; k++) { + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); + wmma::load_matrix_sync(a_frag[j_a][k][1], a_imag + a_idx, 1024); + } + } + } +} + +template +__device__ __forceinline__ void load_b_frag_r2c( + const half *b_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + } + } +} + +template +__device__ __forceinline__ void load_b_frag( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int b_idx; + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + b_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(b_frag[j_a][k][0], b_real + b_idx, sqrt_N); + wmma::load_matrix_sync(b_frag[j_a][k][1], b_imag + b_idx, sqrt_N); + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c( + const half *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * sqrt_N + j_a * WMMA_K : j_a * WMMA_K * sqrt_N + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, sqrt_N); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c_256( + const half *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 256 + j_a * WMMA_K : j_a * WMMA_K * 256 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 256); + } + } + } +} + +template +__device__ __forceinline__ void load_a_frag_r2c_1024( + const half *a_real, + int sqrt_N, + int N, + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2]) +{ + int a_idx; + + if (a_frag_from_acc) { + // load up a_frag's from acc_frag_1 + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int k = 0; k < 1; k++) { + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + a_frag[j_a][j_b][k].x[i] = acc_frag_1[j_a][j_b][k].x[i]; + a_frag[j_a][j_b][k].x[i + acc_frag_1[j_a][j_b][k].num_elements] = acc_frag_1[j_a][j_b][k].x[i]; + } + } + } + } + } else { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + a_idx = a_trans ? k * WMMA_K * 1024 + j_a * WMMA_K : j_a * WMMA_K * 1024 + k * WMMA_K; + wmma::load_matrix_sync(a_frag[j_a][k][0], a_real + a_idx, 1024); + } + } + } +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_matmuls.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_matmuls.h index 258e3225af7c1d442664968f5972c88a4ba2715e..2a930b8cf37f09c5a6ab3cbf52dabc1fcd1c72d6 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_matmuls.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/kernels_fp16/shared/monarch_cuda_shared_fp16_matmuls.h @@ -1,651 +1,651 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -using namespace nvcuda; - -using complex_half_t = typename c10::complex; - -#define WMMA_M 16 -#define WMMA_N 16 -#define WMMA_K 16 -// #define TILE_SIZE 4 -// #define SHMEM_SIZE 256 * TILE_SIZE -// #define SEQUENCE_SIZE 256 -#define WARP_SIZE 32 - -#ifndef MONARCH_CUDA_MATMULS_ -#define MONARCH_CUDA_MATMULS_ - -template -__device__ __forceinline__ void _complex_matmul( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_load_b( - half *b_real, - half *b_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - b_real + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - - wmma::store_matrix_sync( - b_imag + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_256( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 256, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_256( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 256, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_1024( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - // #pragma unroll - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - // bc - // #pragma unroll - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 1024, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], 1024, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_r2c_1024( - half *a_real, - half *a_imag, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); - - // imag - // ad - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 1024, out_layout - ); - - wmma::store_matrix_sync( - a_imag + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][1], 1024, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r( - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real_out + (out_trans ? - j_b * WMMA_M * sqrt_N + j_a * WMMA_N: - j_a * WMMA_M * sqrt_N + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], sqrt_N, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r_256( - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real_out + (out_trans ? - j_b * WMMA_M * 256 + j_a * WMMA_N: - j_a * WMMA_M * 256 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 256, out_layout - ); - } - } - } -} - -template -__device__ __forceinline__ void _complex_matmul_c2r_1024( - half *a_real_out, - int sqrt_N, - int N, - wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], - wmma::layout_t out_layout = wmma::mem_row_major) -{ - #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); - - // real - // bd - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); - } - - // bd -> -bd - for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { - acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); - } - - // ac - for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { - wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); - } - - } - } - - if (output_to_shmem) { - // #pragma unroll - for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { - // #pragma unroll - for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { - // does it matter where we put this? - wmma::store_matrix_sync( - a_real_out + (out_trans ? - j_b * WMMA_M * 1024 + j_a * WMMA_N: - j_a * WMMA_M * 1024 + j_b * WMMA_N), - acc_frag_1[j_a][j_b][0], 1024, out_layout - ); - } - } - } -} - +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +using namespace nvcuda; + +using complex_half_t = typename c10::complex; + +#define WMMA_M 16 +#define WMMA_N 16 +#define WMMA_K 16 +// #define TILE_SIZE 4 +// #define SHMEM_SIZE 256 * TILE_SIZE +// #define SEQUENCE_SIZE 256 +#define WARP_SIZE 32 + +#ifndef MONARCH_CUDA_MATMULS_ +#define MONARCH_CUDA_MATMULS_ + +template +__device__ __forceinline__ void _complex_matmul( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_load_b( + half *b_real, + half *b_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + b_real + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + + wmma::store_matrix_sync( + b_imag + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_256( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 256, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_256( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 256, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_1024( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + // #pragma unroll + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + // bc + // #pragma unroll + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][1], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 1024, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], 1024, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_r2c_1024( + half *a_real, + half *a_imag, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + wmma::fill_fragment(acc_frag_1[j_a][j_b][1], __float2half(0.0f)); + + // imag + // ad + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][1], a_frag[j_a][k][0], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][1]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 1024, out_layout + ); + + wmma::store_matrix_sync( + a_imag + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][1], 1024, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r( + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real_out + (out_trans ? + j_b * WMMA_M * sqrt_N + j_a * WMMA_N: + j_a * WMMA_M * sqrt_N + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], sqrt_N, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r_256( + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real_out + (out_trans ? + j_b * WMMA_M * 256 + j_a * WMMA_N: + j_a * WMMA_M * 256 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 256, out_layout + ); + } + } + } +} + +template +__device__ __forceinline__ void _complex_matmul_c2r_1024( + half *a_real_out, + int sqrt_N, + int N, + wmma::fragment a_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment b_frag[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::fragment acc_frag_1[MATMUL_WARP_WIDTH][MATMUL_WARP_WIDTH][2], + wmma::layout_t out_layout = wmma::mem_row_major) +{ + #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + wmma::fill_fragment(acc_frag_1[j_a][j_b][0], __float2half(0.0f)); + + // real + // bd + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][1], b_frag[k][j_b][1], acc_frag_1[j_a][j_b][0]); + } + + // bd -> -bd + for (int i = 0; i < acc_frag_1[j_a][j_b][0].num_elements; i++) { + acc_frag_1[j_a][j_b][0].x[i] = __hneg(acc_frag_1[j_a][j_b][0].x[i]); + } + + // ac + for (int k = 0; k < MATMUL_WARP_WIDTH; k++) { + wmma::mma_sync(acc_frag_1[j_a][j_b][0], a_frag[j_a][k][0], b_frag[k][j_b][0], acc_frag_1[j_a][j_b][0]); + } + + } + } + + if (output_to_shmem) { + // #pragma unroll + for (int j_b = 0; j_b < MATMUL_WARP_WIDTH; j_b++) { + // #pragma unroll + for (int j_a = 0; j_a < MATMUL_WARP_WIDTH; j_a++) { + // does it matter where we put this? + wmma::store_matrix_sync( + a_real_out + (out_trans ? + j_b * WMMA_M * 1024 + j_a * WMMA_N: + j_a * WMMA_M * 1024 + j_b * WMMA_N), + acc_frag_1[j_a][j_b][0], 1024, out_layout + ); + } + } + } +} + #endif \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd.h index 10f2e34d380a8539fe06b83e7301c4371c4deeca..3f030271d44d971cd10a9f9a832a992decf0f2f1 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd.h @@ -1,537 +1,537 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - -std::vector -monarch_conv_bwd_cuda( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_16_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_16_16_16_bf16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_16_16_16_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_32_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector -monarch_conv_bwd_cuda_32_16_16_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector -monarch_conv_bwd_cuda_16_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector -monarch_conv_bwd_cuda_16_32_32_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector -monarch_conv_bwd_cuda_32_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector -monarch_conv_bwd_cuda_32_32_32_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - - -std::vector -monarch_conv_bwd( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N) -{ - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_fft); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_ifft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::vector -monarch_conv_bwd_16_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N_256, - uint sqrt_N_16) -{ - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_16_16_16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } - else if (x.dtype() == torch::kBFloat16) - { - if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { - return monarch_conv_bwd_cuda_16_16_16_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } else { - return monarch_conv_bwd_cuda_16_16_16_bf16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::vector -monarch_conv_bwd_32_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_32_16_16( - dout, x, k_f, - f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - // if (true) { - return monarch_conv_bwd_cuda_32_16_16_bf16_all( - dout, x, k_f, - f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N); - // } else { - // return monarch_conv_bwd_cuda_32_16_16_bf16( - // dout, x, k_f, - // f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); - // } - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::vector -monarch_conv_bwd_16_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(f_16_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(f_16_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_16_32_32( - dout, x, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_16_32_32_bf16_all( - dout, x, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::vector -monarch_conv_bwd_32_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_32_32_32( - dout, x, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_32_32_32_bf16_all( - dout, x, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + +std::vector +monarch_conv_bwd_cuda( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_16_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_16_16_16_bf16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_16_16_16_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_32_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector +monarch_conv_bwd_cuda_32_16_16_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector +monarch_conv_bwd_cuda_16_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector +monarch_conv_bwd_cuda_16_32_32_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector +monarch_conv_bwd_cuda_32_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector +monarch_conv_bwd_cuda_32_32_32_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + + +std::vector +monarch_conv_bwd( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N) +{ + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_fft); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_ifft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::vector +monarch_conv_bwd_16_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N_256, + uint sqrt_N_16) +{ + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_16_16_16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } + else if (x.dtype() == torch::kBFloat16) + { + if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { + return monarch_conv_bwd_cuda_16_16_16_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } else { + return monarch_conv_bwd_cuda_16_16_16_bf16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::vector +monarch_conv_bwd_32_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_32_16_16( + dout, x, k_f, + f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + // if (true) { + return monarch_conv_bwd_cuda_32_16_16_bf16_all( + dout, x, k_f, + f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N); + // } else { + // return monarch_conv_bwd_cuda_32_16_16_bf16( + // dout, x, k_f, + // f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); + // } + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::vector +monarch_conv_bwd_16_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(f_16_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(f_16_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_16_32_32( + dout, x, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_16_32_32_bf16_all( + dout, x, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::vector +monarch_conv_bwd_32_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_32_32_32( + dout, x, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_32_32_32_bf16_all( + dout, x, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_complex.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_complex.h index 4bdcda0947a6b3b04a58a30a398c9dfa18acdef9..76d22ad658c75557a32684418a6b649318d7999d 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_complex.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_complex.h @@ -1,449 +1,449 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - -std::tuple -monarch_conv_bwd_cuda_16_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_32_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_16_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_32_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::tuple -monarch_conv_bwd_16_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(dout_real); - CHECK_INPUT(dout_imag); - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(dout_real, B, H, N); - CHECK_SHAPE(dout_imag, B, H, N); - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_256_fft, 16, 256, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_256_ifft, 16, 256, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_16_16_16_complex( - dout_real, dout_imag, x_real, x_imag, k_f, - f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( - dout_real, dout_imag, x_real, x_imag, k_f, - f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::tuple -monarch_conv_bwd_32_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(dout_real); - CHECK_INPUT(dout_imag); - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(dout_real, B, H, N); - CHECK_SHAPE(dout_imag, B, H, N); - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_32_16_16_complex( - dout_real, dout_imag, x_real, x_imag, k_f, - f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( - dout_real, dout_imag, x_real, x_imag, k_f, - f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::tuple -monarch_conv_bwd_16_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N) -{ - - CHECK_INPUT(dout_real); - CHECK_INPUT(dout_imag); - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(dout_real.is_contiguous()); - TORCH_CHECK(dout_imag.is_contiguous()); - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(f_16_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(f_16_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(dout_real, B, H, N); - CHECK_SHAPE(dout_imag, B, H, N); - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_16_32_32_complex( - dout_real, dout_imag, x_real, x_imag, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( - dout_real, dout_imag, x_real, x_imag, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::tuple -monarch_conv_bwd_32_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(dout_real); - CHECK_INPUT(dout_imag); - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(dout_real.is_contiguous()); - TORCH_CHECK(dout_imag.is_contiguous()); - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(dout_real, B, H, N); - CHECK_SHAPE(dout_imag, B, H, N); - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_32_32_32_complex( - dout_real, dout_imag, x_real, x_imag, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( - dout_real, dout_imag, x_real, x_imag, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + +std::tuple +monarch_conv_bwd_cuda_16_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_32_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_16_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_32_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::tuple +monarch_conv_bwd_16_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(dout_real); + CHECK_INPUT(dout_imag); + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(dout_real, B, H, N); + CHECK_SHAPE(dout_imag, B, H, N); + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_256_fft, 16, 256, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_256_ifft, 16, 256, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_16_16_16_complex( + dout_real, dout_imag, x_real, x_imag, k_f, + f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( + dout_real, dout_imag, x_real, x_imag, k_f, + f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::tuple +monarch_conv_bwd_32_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(dout_real); + CHECK_INPUT(dout_imag); + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(dout_real, B, H, N); + CHECK_SHAPE(dout_imag, B, H, N); + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_32_16_16_complex( + dout_real, dout_imag, x_real, x_imag, k_f, + f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( + dout_real, dout_imag, x_real, x_imag, k_f, + f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::tuple +monarch_conv_bwd_16_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N) +{ + + CHECK_INPUT(dout_real); + CHECK_INPUT(dout_imag); + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(dout_real.is_contiguous()); + TORCH_CHECK(dout_imag.is_contiguous()); + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(f_16_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(f_16_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(dout_real, B, H, N); + CHECK_SHAPE(dout_imag, B, H, N); + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_16_32_32_complex( + dout_real, dout_imag, x_real, x_imag, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( + dout_real, dout_imag, x_real, x_imag, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::tuple +monarch_conv_bwd_32_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(dout_real); + CHECK_INPUT(dout_imag); + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(dout_real.is_contiguous()); + TORCH_CHECK(dout_imag.is_contiguous()); + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(dout_real, B, H, N); + CHECK_SHAPE(dout_imag, B, H, N); + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_32_32_32_complex( + dout_real, dout_imag, x_real, x_imag, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( + dout_real, dout_imag, x_real, x_imag, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_r2r.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_r2r.h index c49160a2e727c138dc80fd6821584ef9e39eb04d..a9c844c7953dca87c7f1f9110286596a15b6bbeb 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_r2r.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_bwd_r2r.h @@ -1,526 +1,526 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - -std::vector -monarch_conv_bwd_cuda_r2r( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -std::vector -monarch_conv_bwd_cuda_r2r_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -// std::pair -// monarch_conv_bwd_cuda_bf16_all( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_sqrt_N_fft, -// torch::Tensor twiddle_factors_fft, -// torch::Tensor f_sqrt_N_ifft, -// torch::Tensor twiddle_factors_ifft, -// uint fftsize, -// uint N, -// uint sqrt_N); - -// std::pair -// monarch_conv_bwd_cuda_16_16_16( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_256_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_256_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N, -// uint sqrt_N); - -// std::pair -// monarch_conv_bwd_cuda_16_16_16_bf16( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_256_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_256_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N, -// uint sqrt_N); - -// std::pair -// monarch_conv_bwd_cuda_16_16_16_bf16_all( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_256_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_256_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N, -// uint sqrt_N); - -// std::pair -// monarch_conv_bwd_cuda_32_16_16( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N); - -// std::pair -// monarch_conv_bwd_cuda_32_16_16_bf16_all( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N); - -// std::pair -// monarch_conv_bwd_cuda_16_32_32( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N); - -// std::pair -// monarch_conv_bwd_cuda_16_32_32_bf16_all( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N); - -// std::pair -// monarch_conv_bwd_cuda_32_32_32( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N); - -// std::pair -// monarch_conv_bwd_cuda_32_32_32_bf16_all( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N); - - -std::vector -monarch_conv_bwd_r2r( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N) -{ - CHECK_INPUT(dout); - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_fft); - CHECK_INPUT(twid_r2r); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_ifft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(dout, B, H, N); - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize + 1, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twid_r2r, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_bwd_cuda_r2r(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, - in_gate, out_gate, fftsize, N, sqrt_N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_bwd_cuda_r2r_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -// std::pair -// monarch_conv_bwd_16_16_16( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_sqrt_N_fft, -// torch::Tensor twiddle_factors_256_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_sqrt_N_ifft, -// torch::Tensor twiddle_factors_256_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N, -// uint sqrt_N_256, -// uint sqrt_N_16) -// { -// CHECK_INPUT(dout); -// CHECK_INPUT(x); -// CHECK_INPUT(k_f); -// CHECK_INPUT(f_sqrt_N_fft); -// CHECK_INPUT(twiddle_factors_256_fft); -// CHECK_INPUT(twiddle_factors_16_fft); -// CHECK_INPUT(f_sqrt_N_ifft); -// CHECK_INPUT(twiddle_factors_256_fft); -// CHECK_INPUT(twiddle_factors_16_fft); - -// const int B = x.size(0); -// const int H = x.size(1); - -// CHECK_SHAPE(dout, B, H, N); -// CHECK_SHAPE(x, B, H, N); -// CHECK_SHAPE(k_f, H, fftsize, 2); -// CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); -// CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); -// CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); -// CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); -// CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); -// CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); - -// if (x.dtype() == torch::kFloat16) -// { -// return monarch_conv_bwd_cuda_16_16_16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); -// } -// else if (x.dtype() == torch::kBFloat16) -// { -// if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { -// return monarch_conv_bwd_cuda_16_16_16_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); -// } else { -// return monarch_conv_bwd_cuda_16_16_16_bf16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); -// } -// } -// else -// { -// TORCH_CHECK(false, "Unsupported dtype"); -// } -// } - -// std::pair -// monarch_conv_bwd_32_16_16( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor f_16_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_16_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor f_16_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_16_ifft, -// uint fftsize, -// uint N) -// { -// CHECK_INPUT(dout); -// CHECK_INPUT(x); -// CHECK_INPUT(k_f); -// CHECK_INPUT(f_32_fft); -// CHECK_INPUT(f_16_fft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_16_fft); -// CHECK_INPUT(f_32_ifft); -// CHECK_INPUT(f_16_ifft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_16_fft); - -// const int B = x.size(0); -// const int H = x.size(1); - -// CHECK_SHAPE(dout, B, H, N); -// CHECK_SHAPE(x, B, H, N); -// CHECK_SHAPE(k_f, H, fftsize, 2); -// CHECK_SHAPE(f_32_fft, 32, 32, 2); -// CHECK_SHAPE(f_16_fft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); -// CHECK_SHAPE(f_32_ifft, 32, 32, 2); -// CHECK_SHAPE(f_16_ifft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); - -// if (x.dtype() == torch::kFloat16) -// { -// return monarch_conv_bwd_cuda_32_16_16( -// dout, x, k_f, -// f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); -// } -// else if (x.dtype() == torch::kBFloat16) -// { -// // if (true) { -// return monarch_conv_bwd_cuda_32_16_16_bf16_all( -// dout, x, k_f, -// f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); -// // } else { -// // return monarch_conv_bwd_cuda_32_16_16_bf16( -// // dout, x, k_f, -// // f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); -// // } -// } -// else -// { -// TORCH_CHECK(false, "Unsupported dtype"); -// } -// } - -// std::pair -// monarch_conv_bwd_16_32_32( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_16_fft, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_16_ifft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N) -// { - -// CHECK_INPUT(dout); -// CHECK_INPUT(x); -// CHECK_INPUT(k_f); -// CHECK_INPUT(f_32_fft); -// CHECK_INPUT(f_16_fft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_32_fft); -// CHECK_INPUT(f_32_ifft); -// CHECK_INPUT(f_16_ifft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_32_fft); - -// TORCH_CHECK(x.is_contiguous()); -// TORCH_CHECK(k_f.is_contiguous()); -// TORCH_CHECK(f_32_fft.is_contiguous()); -// TORCH_CHECK(f_16_fft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); -// TORCH_CHECK(f_32_ifft.is_contiguous()); -// TORCH_CHECK(f_16_ifft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - -// const int B = x.size(0); -// const int H = x.size(1); - -// CHECK_SHAPE(dout, B, H, N); -// CHECK_SHAPE(x, B, H, N); -// CHECK_SHAPE(k_f, H, fftsize, 2); -// CHECK_SHAPE(f_32_fft, 32, 32, 2); -// CHECK_SHAPE(f_16_fft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); -// CHECK_SHAPE(f_32_ifft, 32, 32, 2); -// CHECK_SHAPE(f_16_ifft, 16, 16, 2); -// CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); - -// if (x.dtype() == torch::kFloat16) -// { -// return monarch_conv_bwd_cuda_16_32_32( -// dout, x, k_f, -// f_16_fft, f_32_fft, -// twiddle_factors_N_fft, twiddle_factors_32_fft, -// f_16_ifft, f_32_ifft, -// twiddle_factors_N_ifft, twiddle_factors_32_ifft, -// fftsize, N); -// } -// else if (x.dtype() == torch::kBFloat16) -// { -// return monarch_conv_bwd_cuda_16_32_32_bf16_all( -// dout, x, k_f, -// f_16_fft, f_32_fft, -// twiddle_factors_N_fft, twiddle_factors_32_fft, -// f_16_ifft, f_32_ifft, -// twiddle_factors_N_ifft, twiddle_factors_32_ifft, -// fftsize, N); -// } -// else -// { -// TORCH_CHECK(false, "Unsupported dtype"); -// } -// } - -// std::pair -// monarch_conv_bwd_32_32_32( -// torch::Tensor dout, -// torch::Tensor x, -// torch::Tensor k_f, -// torch::Tensor f_32_fft, -// torch::Tensor twiddle_factors_N_fft, -// torch::Tensor twiddle_factors_32_fft, -// torch::Tensor f_32_ifft, -// torch::Tensor twiddle_factors_N_ifft, -// torch::Tensor twiddle_factors_32_ifft, -// uint fftsize, -// uint N) -// { -// CHECK_INPUT(dout); -// CHECK_INPUT(x); -// CHECK_INPUT(k_f); -// CHECK_INPUT(f_32_fft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_32_fft); -// CHECK_INPUT(f_32_ifft); -// CHECK_INPUT(twiddle_factors_N_fft); -// CHECK_INPUT(twiddle_factors_32_fft); - -// TORCH_CHECK(x.is_contiguous()); -// TORCH_CHECK(k_f.is_contiguous()); -// TORCH_CHECK(f_32_fft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); -// TORCH_CHECK(f_32_ifft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); -// TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - -// const int B = x.size(0); -// const int H = x.size(1); - -// CHECK_SHAPE(dout, B, H, N); -// CHECK_SHAPE(x, B, H, N); -// CHECK_SHAPE(k_f, H, fftsize, 2); -// CHECK_SHAPE(f_32_fft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); -// CHECK_SHAPE(f_32_ifft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); -// CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - -// if (x.dtype() == torch::kFloat16) -// { -// return monarch_conv_bwd_cuda_32_32_32( -// dout, x, k_f, -// f_32_fft, -// twiddle_factors_N_fft, twiddle_factors_32_fft, -// f_32_ifft, -// twiddle_factors_N_ifft, twiddle_factors_32_ifft, -// fftsize, N); -// } -// else if (x.dtype() == torch::kBFloat16) -// { -// return monarch_conv_bwd_cuda_32_32_32_bf16_all( -// dout, x, k_f, -// f_32_fft, -// twiddle_factors_N_fft, twiddle_factors_32_fft, -// f_32_ifft, -// twiddle_factors_N_ifft, twiddle_factors_32_ifft, -// fftsize, N); -// } -// else -// { -// TORCH_CHECK(false, "Unsupported dtype"); -// } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + +std::vector +monarch_conv_bwd_cuda_r2r( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +std::vector +monarch_conv_bwd_cuda_r2r_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +// std::pair +// monarch_conv_bwd_cuda_bf16_all( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_sqrt_N_fft, +// torch::Tensor twiddle_factors_fft, +// torch::Tensor f_sqrt_N_ifft, +// torch::Tensor twiddle_factors_ifft, +// uint fftsize, +// uint N, +// uint sqrt_N); + +// std::pair +// monarch_conv_bwd_cuda_16_16_16( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_256_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_256_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N, +// uint sqrt_N); + +// std::pair +// monarch_conv_bwd_cuda_16_16_16_bf16( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_256_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_256_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N, +// uint sqrt_N); + +// std::pair +// monarch_conv_bwd_cuda_16_16_16_bf16_all( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_256_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_256_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N, +// uint sqrt_N); + +// std::pair +// monarch_conv_bwd_cuda_32_16_16( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N); + +// std::pair +// monarch_conv_bwd_cuda_32_16_16_bf16_all( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N); + +// std::pair +// monarch_conv_bwd_cuda_16_32_32( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N); + +// std::pair +// monarch_conv_bwd_cuda_16_32_32_bf16_all( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N); + +// std::pair +// monarch_conv_bwd_cuda_32_32_32( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N); + +// std::pair +// monarch_conv_bwd_cuda_32_32_32_bf16_all( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N); + + +std::vector +monarch_conv_bwd_r2r( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N) +{ + CHECK_INPUT(dout); + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_fft); + CHECK_INPUT(twid_r2r); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_ifft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(dout, B, H, N); + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize + 1, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twid_r2r, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_bwd_cuda_r2r(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, + in_gate, out_gate, fftsize, N, sqrt_N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_bwd_cuda_r2r_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +// std::pair +// monarch_conv_bwd_16_16_16( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_sqrt_N_fft, +// torch::Tensor twiddle_factors_256_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_sqrt_N_ifft, +// torch::Tensor twiddle_factors_256_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N, +// uint sqrt_N_256, +// uint sqrt_N_16) +// { +// CHECK_INPUT(dout); +// CHECK_INPUT(x); +// CHECK_INPUT(k_f); +// CHECK_INPUT(f_sqrt_N_fft); +// CHECK_INPUT(twiddle_factors_256_fft); +// CHECK_INPUT(twiddle_factors_16_fft); +// CHECK_INPUT(f_sqrt_N_ifft); +// CHECK_INPUT(twiddle_factors_256_fft); +// CHECK_INPUT(twiddle_factors_16_fft); + +// const int B = x.size(0); +// const int H = x.size(1); + +// CHECK_SHAPE(dout, B, H, N); +// CHECK_SHAPE(x, B, H, N); +// CHECK_SHAPE(k_f, H, fftsize, 2); +// CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); +// CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); +// CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); +// CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); +// CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); +// CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); + +// if (x.dtype() == torch::kFloat16) +// { +// return monarch_conv_bwd_cuda_16_16_16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); +// } +// else if (x.dtype() == torch::kBFloat16) +// { +// if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { +// return monarch_conv_bwd_cuda_16_16_16_bf16_all(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); +// } else { +// return monarch_conv_bwd_cuda_16_16_16_bf16(dout, x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, fftsize, N, sqrt_N_16); +// } +// } +// else +// { +// TORCH_CHECK(false, "Unsupported dtype"); +// } +// } + +// std::pair +// monarch_conv_bwd_32_16_16( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor f_16_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_16_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor f_16_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_16_ifft, +// uint fftsize, +// uint N) +// { +// CHECK_INPUT(dout); +// CHECK_INPUT(x); +// CHECK_INPUT(k_f); +// CHECK_INPUT(f_32_fft); +// CHECK_INPUT(f_16_fft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_16_fft); +// CHECK_INPUT(f_32_ifft); +// CHECK_INPUT(f_16_ifft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_16_fft); + +// const int B = x.size(0); +// const int H = x.size(1); + +// CHECK_SHAPE(dout, B, H, N); +// CHECK_SHAPE(x, B, H, N); +// CHECK_SHAPE(k_f, H, fftsize, 2); +// CHECK_SHAPE(f_32_fft, 32, 32, 2); +// CHECK_SHAPE(f_16_fft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); +// CHECK_SHAPE(f_32_ifft, 32, 32, 2); +// CHECK_SHAPE(f_16_ifft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); + +// if (x.dtype() == torch::kFloat16) +// { +// return monarch_conv_bwd_cuda_32_16_16( +// dout, x, k_f, +// f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); +// } +// else if (x.dtype() == torch::kBFloat16) +// { +// // if (true) { +// return monarch_conv_bwd_cuda_32_16_16_bf16_all( +// dout, x, k_f, +// f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); +// // } else { +// // return monarch_conv_bwd_cuda_32_16_16_bf16( +// // dout, x, k_f, +// // f_32_fft, f_16_fft, twiddle_factors_N_fft, twiddle_factors_16_fft, f_32_ifft, f_16_ifft, twiddle_factors_N_ifft, twiddle_factors_16_ifft, fftsize, N); +// // } +// } +// else +// { +// TORCH_CHECK(false, "Unsupported dtype"); +// } +// } + +// std::pair +// monarch_conv_bwd_16_32_32( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_16_fft, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_16_ifft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N) +// { + +// CHECK_INPUT(dout); +// CHECK_INPUT(x); +// CHECK_INPUT(k_f); +// CHECK_INPUT(f_32_fft); +// CHECK_INPUT(f_16_fft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_32_fft); +// CHECK_INPUT(f_32_ifft); +// CHECK_INPUT(f_16_ifft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_32_fft); + +// TORCH_CHECK(x.is_contiguous()); +// TORCH_CHECK(k_f.is_contiguous()); +// TORCH_CHECK(f_32_fft.is_contiguous()); +// TORCH_CHECK(f_16_fft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); +// TORCH_CHECK(f_32_ifft.is_contiguous()); +// TORCH_CHECK(f_16_ifft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + +// const int B = x.size(0); +// const int H = x.size(1); + +// CHECK_SHAPE(dout, B, H, N); +// CHECK_SHAPE(x, B, H, N); +// CHECK_SHAPE(k_f, H, fftsize, 2); +// CHECK_SHAPE(f_32_fft, 32, 32, 2); +// CHECK_SHAPE(f_16_fft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); +// CHECK_SHAPE(f_32_ifft, 32, 32, 2); +// CHECK_SHAPE(f_16_ifft, 16, 16, 2); +// CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); + +// if (x.dtype() == torch::kFloat16) +// { +// return monarch_conv_bwd_cuda_16_32_32( +// dout, x, k_f, +// f_16_fft, f_32_fft, +// twiddle_factors_N_fft, twiddle_factors_32_fft, +// f_16_ifft, f_32_ifft, +// twiddle_factors_N_ifft, twiddle_factors_32_ifft, +// fftsize, N); +// } +// else if (x.dtype() == torch::kBFloat16) +// { +// return monarch_conv_bwd_cuda_16_32_32_bf16_all( +// dout, x, k_f, +// f_16_fft, f_32_fft, +// twiddle_factors_N_fft, twiddle_factors_32_fft, +// f_16_ifft, f_32_ifft, +// twiddle_factors_N_ifft, twiddle_factors_32_ifft, +// fftsize, N); +// } +// else +// { +// TORCH_CHECK(false, "Unsupported dtype"); +// } +// } + +// std::pair +// monarch_conv_bwd_32_32_32( +// torch::Tensor dout, +// torch::Tensor x, +// torch::Tensor k_f, +// torch::Tensor f_32_fft, +// torch::Tensor twiddle_factors_N_fft, +// torch::Tensor twiddle_factors_32_fft, +// torch::Tensor f_32_ifft, +// torch::Tensor twiddle_factors_N_ifft, +// torch::Tensor twiddle_factors_32_ifft, +// uint fftsize, +// uint N) +// { +// CHECK_INPUT(dout); +// CHECK_INPUT(x); +// CHECK_INPUT(k_f); +// CHECK_INPUT(f_32_fft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_32_fft); +// CHECK_INPUT(f_32_ifft); +// CHECK_INPUT(twiddle_factors_N_fft); +// CHECK_INPUT(twiddle_factors_32_fft); + +// TORCH_CHECK(x.is_contiguous()); +// TORCH_CHECK(k_f.is_contiguous()); +// TORCH_CHECK(f_32_fft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); +// TORCH_CHECK(f_32_ifft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); +// TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + +// const int B = x.size(0); +// const int H = x.size(1); + +// CHECK_SHAPE(dout, B, H, N); +// CHECK_SHAPE(x, B, H, N); +// CHECK_SHAPE(k_f, H, fftsize, 2); +// CHECK_SHAPE(f_32_fft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); +// CHECK_SHAPE(f_32_ifft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); +// CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + +// if (x.dtype() == torch::kFloat16) +// { +// return monarch_conv_bwd_cuda_32_32_32( +// dout, x, k_f, +// f_32_fft, +// twiddle_factors_N_fft, twiddle_factors_32_fft, +// f_32_ifft, +// twiddle_factors_N_ifft, twiddle_factors_32_ifft, +// fftsize, N); +// } +// else if (x.dtype() == torch::kBFloat16) +// { +// return monarch_conv_bwd_cuda_32_32_32_bf16_all( +// dout, x, k_f, +// f_32_fft, +// twiddle_factors_N_fft, twiddle_factors_32_fft, +// f_32_ifft, +// twiddle_factors_N_ifft, twiddle_factors_32_ifft, +// fftsize, N); +// } +// else +// { +// TORCH_CHECK(false, "Unsupported dtype"); +// } // } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd.cu index 82c7c8e9196be72c10326a6fe564e3585f1386b9..f2bd69ed89dc333b21663c052cf6a3c63a0ee6ed 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd.cu @@ -1,1055 +1,1055 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_bwd_kernel.h" -#include "kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h" -#include "kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h" -#include "kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastFP16Bwd(__FILE__, __LINE__) -void checkLastFP16Bwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda_16_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - - -torch::Tensor monarch_conv_cuda_32_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_16_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector monarch_conv_bwd_cuda( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - } - - switch (fftsize) { - case 256: - if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 4) == 0) { - gridDim.x = B; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B/8, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector monarch_conv_bwd_cuda_16_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_16_16_16( - x, - k_f, - f_16_fft, - twiddle_factors_256_fft, - twiddle_factors_16_fft, - f_16_ifft, - twiddle_factors_256_ifft, - twiddle_factors_16_ifft, - in_gate, - {}, - fftsize, - N, - sqrt_N); - } - - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - - -std::vector monarch_conv_bwd_cuda_32_16_16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_32_16_16( - x, - k_f, - f_32_fft, - f_16_fft, - twiddle_factors_N_fft, - twiddle_factors_16_fft, - f_32_ifft, - f_16_ifft, - twiddle_factors_N_ifft, - twiddle_factors_16_ifft, - in_gate, - {}, - fftsize, - N); - } - - switch (fftsize) { - case 8192: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector monarch_conv_bwd_cuda_16_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_16_32_32( - x, - k_f, - f_16_fft, - f_32_fft, - twiddle_factors_N_fft, - twiddle_factors_32_fft, - f_16_ifft, - f_32_ifft, - twiddle_factors_N_ifft, - twiddle_factors_32_ifft, - in_gate, - {}, - fftsize, - N); - } - - switch (fftsize) { - case 16384: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector monarch_conv_bwd_cuda_32_32_32( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_32_32_32( - x, - k_f, - f_32_fft, - twiddle_factors_N_fft, - twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, - twiddle_factors_32_ifft, - in_gate, - {}, - fftsize, - N); - } - - switch (fftsize) { - case 32768: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_bwd_kernel.h" +#include "kernels_fp16/monarch_cuda_16_16_16_bwd_kernel.h" +#include "kernels_fp16/monarch_cuda_32_16_16_bwd_kernel.h" +#include "kernels_fp16/monarch_cuda_16_32_32_bwd_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_bwd_kernel.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastFP16Bwd(__FILE__, __LINE__) +void checkLastFP16Bwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda_16_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + + +torch::Tensor monarch_conv_cuda_32_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_16_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector monarch_conv_bwd_cuda( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + } + + switch (fftsize) { + case 256: + if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 4) == 0) { + gridDim.x = B; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B/8, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector monarch_conv_bwd_cuda_16_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_16_16_16( + x, + k_f, + f_16_fft, + twiddle_factors_256_fft, + twiddle_factors_16_fft, + f_16_ifft, + twiddle_factors_256_ifft, + twiddle_factors_16_ifft, + in_gate, + {}, + fftsize, + N, + sqrt_N); + } + + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + + +std::vector monarch_conv_bwd_cuda_32_16_16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_32_16_16( + x, + k_f, + f_32_fft, + f_16_fft, + twiddle_factors_N_fft, + twiddle_factors_16_fft, + f_32_ifft, + f_16_ifft, + twiddle_factors_N_ifft, + twiddle_factors_16_ifft, + in_gate, + {}, + fftsize, + N); + } + + switch (fftsize) { + case 8192: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector monarch_conv_bwd_cuda_16_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_16_32_32( + x, + k_f, + f_16_fft, + f_32_fft, + twiddle_factors_N_fft, + twiddle_factors_32_fft, + f_16_ifft, + f_32_ifft, + twiddle_factors_N_ifft, + twiddle_factors_32_ifft, + in_gate, + {}, + fftsize, + N); + } + + switch (fftsize) { + case 16384: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector monarch_conv_bwd_cuda_32_32_32( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_32_32_32( + x, + k_f, + f_32_fft, + twiddle_factors_N_fft, + twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, + twiddle_factors_32_ifft, + in_gate, + {}, + fftsize, + N); + } + + switch (fftsize) { + case 32768: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16.cu index 0d5f7a6bff440234de159d24618a17c765ea3ad0..183d39b04b051d4326bffa6a4014e6786490bf6c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16.cu @@ -1,1266 +1,1266 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" -#include "kernels_bf16/monarch_cuda_bwd_kernel_bf16.h" -#include "kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h" -#include "kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16Bwd(__FILE__, __LINE__) -void checkLastBF16Bwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_16_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -std::vector monarch_conv_bwd_cuda_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, {}, fftsize, N, sqrt_N); - } - - switch (fftsize) { - case 256: - if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 4) == 0) { - gridDim.x = B; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector -monarch_conv_bwd_cuda_16_16_16_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_16_16_16_bf16_all(x, k_f, f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, {}, fftsize, N, sqrt_N); - } - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B/2, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector -monarch_conv_bwd_cuda_16_16_16_bf16( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_16_16_16_bf16(x, k_f, f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, {}, fftsize, N, sqrt_N); - } - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B/2, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - - -std::vector monarch_conv_bwd_cuda_32_16_16_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_32_16_16_bf16_all( - x, k_f, - f_32_fft, f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - in_gate, {}, - fftsize, N); - } - - switch (fftsize) { - case 8192: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector monarch_conv_bwd_cuda_16_32_32_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_16_32_32_bf16_all( - x, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, {}, - fftsize, N); - } - - switch (fftsize) { - case 16384: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} - -std::vector monarch_conv_bwd_cuda_32_32_32_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - - torch::Tensor din_gate; - torch::Tensor dout_gate; - torch::Tensor out; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - out = monarch_conv_cuda_32_32_32_bf16_all(x, k_f, f_32_fft, twiddle_factors_N_fft, twiddle_factors_32_fft, f_32_ifft, twiddle_factors_N_ifft, twiddle_factors_32_ifft, in_gate, {}, fftsize, N); - } - - switch (fftsize) { - case 32768: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - }else{ - return {dx_out, dk_f_out.sum(0)}; - } -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" +#include "kernels_bf16/monarch_cuda_bwd_kernel_bf16.h" +#include "kernels_fp16/monarch_cuda_16_16_16_bwd_kernel_fp16_bf16_inp.h" +#include "kernels_bf16/monarch_cuda_16_16_16_bwd_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_16_16_bwd_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_16_32_32_bwd_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_32_32_bwd_kernel_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16Bwd(__FILE__, __LINE__) +void checkLastBF16Bwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_16_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +std::vector monarch_conv_bwd_cuda_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, {}, fftsize, N, sqrt_N); + } + + switch (fftsize) { + case 256: + if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B/2, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 4) == 0) { + gridDim.x = B; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector +monarch_conv_bwd_cuda_16_16_16_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_16_16_16_bf16_all(x, k_f, f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, {}, fftsize, N, sqrt_N); + } + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B/2, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector +monarch_conv_bwd_cuda_16_16_16_bf16( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_16_16_16_bf16(x, k_f, f_16_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_16_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, {}, fftsize, N, sqrt_N); + } + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B/2, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + monarch_conv_bwd_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + + +std::vector monarch_conv_bwd_cuda_32_16_16_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_32_16_16_bf16_all( + x, k_f, + f_32_fft, f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + in_gate, {}, + fftsize, N); + } + + switch (fftsize) { + case 8192: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B/4, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + monarch_conv_bwd_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector monarch_conv_bwd_cuda_16_32_32_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_16_32_32_bf16_all( + x, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, {}, + fftsize, N); + } + + switch (fftsize) { + case 16384: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} + +std::vector monarch_conv_bwd_cuda_32_32_32_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + + torch::Tensor din_gate; + torch::Tensor dout_gate; + torch::Tensor out; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + out = monarch_conv_cuda_32_32_32_bf16_all(x, k_f, f_32_fft, twiddle_factors_N_fft, twiddle_factors_32_fft, f_32_ifft, twiddle_factors_N_ifft, twiddle_factors_32_ifft, in_gate, {}, fftsize, N); + } + + switch (fftsize) { + case 32768: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, out.mul(dout)}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + }else{ + return {dx_out, dk_f_out.sum(0)}; + } +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu index eca07c328680ea939fa12077d16fb9ed41d9a5d2..ff03404fbc6aea539f2c9d54dd94e2401dd16850 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu @@ -1,661 +1,661 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" -#include "kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16ComplexBwd(__FILE__, __LINE__) -void checkLastBF16ComplexBwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::tuple -monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 4096: - // if (true) { - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } - else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B / 2, H, fftsize, 2}, k_f.options()); - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } else { - gridDim.x = B; - gridDim.y = H; - - dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - -std::tuple -monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 8192: - // if (true) { - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - -std::tuple -monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 16384: - // if (true) { - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - -std::tuple -monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 32768: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" +#include "kernels_bf16/monarch_cuda_16_16_16_bwd_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_16_16_bwd_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_16_32_32_bwd_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_32_32_bwd_complex_kernel_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16ComplexBwd(__FILE__, __LINE__) +void checkLastBF16ComplexBwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::tuple +monarch_conv_bwd_cuda_16_16_16_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 4096: + // if (true) { + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, k_f.options()); + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } + else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B / 2, H, fftsize, 2}, k_f.options()); + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } else { + gridDim.x = B; + gridDim.y = H; + + dk_f_out = torch::empty({B, H, fftsize, 2}, k_f.options()); + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_bwd_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + +std::tuple +monarch_conv_bwd_cuda_32_16_16_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 8192: + // if (true) { + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + +std::tuple +monarch_conv_bwd_cuda_16_32_32_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 16384: + // if (true) { + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + +std::tuple +monarch_conv_bwd_cuda_32_32_32_complex_bf16_all( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 32768: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_complex.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_complex.cu index 7ce0759287db2ba08f7d687859916f142f355611..6f7d7a16ecd1160cd6f970c85f3999315aee7e69 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_complex.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_complex.cu @@ -1,627 +1,627 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16BwdComplex(__FILE__, __LINE__) -void checkLastBF16BwdComplex(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::tuple -monarch_conv_bwd_cuda_16_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 4, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } - else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 2, H, fftsize, 2}, x_real.options()); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 2, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N, - 16); - } - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - - -std::tuple -monarch_conv_bwd_cuda_32_16_16_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 8192: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - -std::tuple -monarch_conv_bwd_cuda_16_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 16384: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); - - monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); -} - - -std::tuple -monarch_conv_bwd_cuda_32_32_32_complex( - torch::Tensor dout_real, - torch::Tensor dout_imag, - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); - - torch::Tensor dk_f_out; - - switch (fftsize) { - case 32768: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(dout_real.data_ptr()), - static_cast(dout_imag.data_ptr()), - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(dx_out_real.data_ptr()), - static_cast(dx_out_imag.data_ptr()), - static_cast(dk_f_out.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_16_16_16_bwd_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_16_16_bwd_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_16_32_32_bwd_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_bwd_complex_kernel.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16BwdComplex(__FILE__, __LINE__) +void checkLastBF16BwdComplex(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::tuple +monarch_conv_bwd_cuda_16_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 4, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } + else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 2, H, fftsize, 2}, x_real.options()); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 2, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 4096, 1, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N, + 16); + } + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + + +std::tuple +monarch_conv_bwd_cuda_32_16_16_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 8192: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 4, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + monarch_conv_bwd_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + +std::tuple +monarch_conv_bwd_cuda_16_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 16384: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 140000)); + + monarch_conv_bwd_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); +} + + +std::tuple +monarch_conv_bwd_cuda_32_32_32_complex( + torch::Tensor dout_real, + torch::Tensor dout_imag, + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor dx_out_imag = torch::empty({B, H, N}, x_imag.options()); + + torch::Tensor dk_f_out; + + switch (fftsize) { + case 32768: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B / 8, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 8, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + dk_f_out = torch::empty({B, H, fftsize, 2}, x_real.options()); + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_bwd_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(dout_real.data_ptr()), + static_cast(dout_imag.data_ptr()), + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(dx_out_real.data_ptr()), + static_cast(dx_out_imag.data_ptr()), + static_cast(dk_f_out.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_tuple(dx_out_real, dx_out_imag, dk_f_out.sum(/*dim=*/0)); } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r.cu index 6671d787b357276d65f601ee1eed4ab367ff16e5..fdcd19be14e21512a529259dd5096b1b04f6410c 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r.cu @@ -1,326 +1,326 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_bwd_kernel_r2r.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastFP16BwdR2R(__FILE__, __LINE__) -void checkLastFP16BwdR2R(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::vector -monarch_conv_bwd_cuda_r2r( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - torch::Tensor din_gate; - torch::Tensor dout_gate; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - } - - switch (fftsize) { - case 256: - // if (true) { - if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B / 2, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - else if ((H % 4) == 0) { - gridDim.x = B; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - // if (true) { - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - // gridDim.x = B; - // gridDim.y = H; - - dk_f_out = torch::empty({B / 8, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B / 4, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - } else{ - return {dx_out, dk_f_out.sum(0)}; - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_bwd_kernel_r2r.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastFP16BwdR2R(__FILE__, __LINE__) +void checkLastFP16BwdR2R(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::vector +monarch_conv_bwd_cuda_r2r( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + torch::Tensor din_gate; + torch::Tensor dout_gate; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + } + + switch (fftsize) { + case 256: + // if (true) { + if (B >= 2 && (B % 8) == 0 && (H % 4) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B / 2, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + else if ((H % 4) == 0) { + gridDim.x = B; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + // if (true) { + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + // gridDim.x = B; + // gridDim.y = H; + + dk_f_out = torch::empty({B / 8, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B / 4, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + } else{ + return {dx_out, dk_f_out.sum(0)}; + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu index 9a3b36f29f9d7343ac6a1df7abb3d36244a993d5..6d0b4ef594dfe54ff3db9b1f2f0126de5b885fcb 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu @@ -1,329 +1,329 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h" -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16BwdR2R(__FILE__, __LINE__) -void checkLastBF16BwdR2R(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::vector -monarch_conv_bwd_cuda_r2r_bf16_all( - torch::Tensor dout, - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); - torch::Tensor dk_f_out; - torch::Tensor din_gate; - torch::Tensor dout_gate; - - if(in_gate.has_value()){ - din_gate = torch::empty_like(in_gate.value()); - } - - if(out_gate.has_value()){ - dout_gate = torch::empty_like(out_gate.value()); - } - - switch (fftsize) { - case 256: - // if (true) { - if (B >= 2 && (B % 2) == 0 && (H % 4) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 4; - // gridDim.x = B; - // gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B / 2, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - else if ((H % 4) == 0) { - gridDim.x = B; - gridDim.y = H / 4; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - // if (true) { - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - // gridDim.x = B; - // gridDim.y = H; - - dk_f_out = torch::empty({B / 8, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B / 4, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); - - blockDim.x = 32; - blockDim.y = 1; - monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(dout.data_ptr()), - static_cast(x.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(dx_out.data_ptr()), - static_cast(dk_f_out.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, - out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - default: - AT_ERROR("Monarch backward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - if (in_gate.has_value() && out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; - } else if (in_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), din_gate}; - } else if (out_gate.has_value()) { - return {dx_out, dk_f_out.sum(0), dout_gate}; - } else{ - return {dx_out, dk_f_out.sum(0)}; - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_bf16/monarch_cuda_bwd_kernel_r2r_bf16.h" +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16BwdR2R(__FILE__, __LINE__) +void checkLastBF16BwdR2R(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::vector +monarch_conv_bwd_cuda_r2r_bf16_all( + torch::Tensor dout, + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor dx_out = torch::empty({B, H, N}, x.options()); + torch::Tensor dk_f_out; + torch::Tensor din_gate; + torch::Tensor dout_gate; + + if(in_gate.has_value()){ + din_gate = torch::empty_like(in_gate.value()); + } + + if(out_gate.has_value()){ + dout_gate = torch::empty_like(out_gate.value()); + } + + switch (fftsize) { + case 256: + // if (true) { + if (B >= 2 && (B % 2) == 0 && (H % 4) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 4; + // gridDim.x = B; + // gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B / 2, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 2, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + else if ((H % 4) == 0) { + gridDim.x = B; + gridDim.y = H / 4; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 4><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + monarch_conv_bwd_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + // if (true) { + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + // gridDim.x = B; + // gridDim.y = H; + + dk_f_out = torch::empty({B / 8, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B / 4, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + dk_f_out = torch::empty({B, H, fftsize + 1, 2}, x.options()); + + blockDim.x = 32; + blockDim.y = 1; + monarch_conv_bwd_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(dout.data_ptr()), + static_cast(x.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(dx_out.data_ptr()), + static_cast(dk_f_out.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + in_gate.has_value() ? static_cast(din_gate.data_ptr()) : nullptr, + out_gate.has_value() ? static_cast(dout_gate.data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + default: + AT_ERROR("Monarch backward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + if (in_gate.has_value() && out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate, dout_gate}; + } else if (in_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), din_gate}; + } else if (out_gate.has_value()) { + return {dx_out, dk_f_out.sum(0), dout_gate}; + } else{ + return {dx_out, dk_f_out.sum(0)}; + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd.cu index efb2838c937354ab0cd72de8fcb36e7bb95dfeb3..91c3bc8601b0e1531535068d4382451d8432e953 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd.cu @@ -1,776 +1,776 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_kernel.h" -#include "kernels_fp16/monarch_cuda_16_16_16_kernel.h" -#include "kernels_fp16/monarch_cuda_32_16_16_kernel.h" -#include "kernels_fp16/monarch_cuda_16_32_32_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastFP16Fwd(__FILE__, __LINE__) -void checkLastFP16Fwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 256: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - case 1024: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_16_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - - } else if (B == 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_32_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 8192: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_16_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 16384: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_32_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 32768: - if (B >= 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_kernel.h" +#include "kernels_fp16/monarch_cuda_16_16_16_kernel.h" +#include "kernels_fp16/monarch_cuda_32_16_16_kernel.h" +#include "kernels_fp16/monarch_cuda_16_32_32_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastFP16Fwd(__FILE__, __LINE__) +void checkLastFP16Fwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 256: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + case 1024: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_16_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + + } else if (B == 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_32_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 8192: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_16_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 16384: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_32_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 32768: + if (B >= 2 && (B % 2) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16.cu index ab4a5f0412c5b0f6f10853db793776dd6790d1b2..16011f8cdfed61c2adfdff2f13d79dd7dc226eb2 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16.cu @@ -1,1043 +1,1043 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" -#include "kernels_bf16/monarch_cuda_kernel_bf16.h" -#include "kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h" -#include "kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h" -#include "kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h" -#include "kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16Fwd(__FILE__, __LINE__) -void checkLastBF16Fwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 256: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - - break; - case 1024: - if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_16_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - - } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 4096: - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - - } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - - -torch::Tensor monarch_conv_cuda_32_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 8192: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 8192: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 16384: - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} - -torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 32768: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1,8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" +#include "kernels_bf16/monarch_cuda_kernel_bf16.h" +#include "kernels_fp16/monarch_cuda_16_16_16_kernel_fp16_bf16_inp.h" +#include "kernels_bf16/monarch_cuda_16_16_16_kernel_bf16.h" +#include "kernels_fp16/monarch_cuda_32_16_16_kernel_fp16_bf16_inp.h" +#include "kernels_bf16/monarch_cuda_32_16_16_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_16_32_32_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_32_32_kernel_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16Fwd(__FILE__, __LINE__) +void checkLastBF16Fwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 256: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + + break; + case 1024: + if (B >= 8 && (B % 8) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0 && H >= 8 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_16_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + + } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 4096: + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + + } else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + + +torch::Tensor monarch_conv_cuda_32_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 8192: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 4, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 8192: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 16384: + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 8, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 4, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} + +torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 32768: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 2, 8,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 8,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_kernel<32, 8, 32768, 2, 16, false, 1, 1,8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu index be5020d41587d8a323785055dc8de58a598d1584..79a62e1b50d89825fc79dae01b6e7100f19f7c83 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu @@ -1,549 +1,549 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" -#include "kernels_bf16/monarch_cuda_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h" -#include "kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16ComplexFwd(__FILE__, __LINE__) -void checkLastBF16ComplexFwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::pair -monarch_conv_cuda_16_16_16_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 4096: - // if (true) { - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } - else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_32_16_16_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 8192: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_16_32_32_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 16384: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_32_32_32_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 32768: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16_no_float_shm.h" +#include "kernels_bf16/monarch_cuda_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_16_16_16_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_16_16_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_16_32_32_complex_kernel_bf16.h" +#include "kernels_bf16/monarch_cuda_32_32_32_complex_kernel_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16ComplexFwd(__FILE__, __LINE__) +void checkLastBF16ComplexFwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::pair +monarch_conv_cuda_16_16_16_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 4096: + // if (true) { + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } + else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_32_16_16_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 8192: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_16_32_32_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 16384: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_32_32_32_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 32768: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_complex.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_complex.cu index 1651a3b8b7ac7d957ec86690b3646b0dddac9dcf..0830e1f04db9fc47b266060dc95df90a4c083e03 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_complex.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_complex.cu @@ -1,665 +1,665 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_kernel.h" -#include "kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h" -#include "kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastComplexFP16Fwd(__FILE__, __LINE__) -void checkLastComplexFP16Fwd(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -std::pair -monarch_conv_cuda_16_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 4096: - // if (true) { - if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { - gridDim.x = B / 4; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } - else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 4; - - monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_256_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_256_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - 16); - } - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_32_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 8192: - // if (true) { - if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_16_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_16_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_16_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 16384: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - // if (true) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); - - monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_16_fft.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_16_ifft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_32_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - switch (fftsize) { - case 32768: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} - -std::pair -monarch_conv_cuda_32_32_32_complex_truncated( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N, - uint trunc, - uint kernel_trunc -){ - - uint B = x_real.size(0); - uint H = x_real.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); - torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); - torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); - - H = H - 128 * trunc; - - switch (fftsize) { - case 32768: - if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { - gridDim.x = B / 2; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - kernel_trunc); - } else if ((H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - kernel_trunc); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 8; - - CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); - - monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( - static_cast(x_real.data_ptr()), - static_cast(x_imag.data_ptr()), - static_cast(k_f.data_ptr()), - static_cast(f_32_fft.data_ptr()), - static_cast(twiddle_factors_N_fft.data_ptr()), - static_cast(twiddle_factors_32_fft.data_ptr()), - static_cast(f_32_ifft.data_ptr()), - static_cast(twiddle_factors_N_ifft.data_ptr()), - static_cast(twiddle_factors_32_ifft.data_ptr()), - static_cast(out_real.data_ptr()), - static_cast(out_imag.data_ptr()), - B, - H, - N, - kernel_trunc); - } - - break; - default: - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - - CHECK_LAST_CUDA_ERROR(); - return std::make_pair(out_real, out_imag); -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_kernel.h" +#include "kernels_fp16/monarch_cuda_16_16_16_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_16_16_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_16_32_32_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_complex_kernel.h" +#include "kernels_fp16/monarch_cuda_32_32_32_complex_truncated_kernel.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastComplexFP16Fwd(__FILE__, __LINE__) +void checkLastComplexFP16Fwd(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +std::pair +monarch_conv_cuda_16_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 4096: + // if (true) { + if (B >= 4 && (B % 4) == 0 && (H % 8) == 0) { + gridDim.x = B / 4; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 4, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } + else if (B == 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 2, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 8, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 4; + + monarch_conv_cuda_complex_kernel<32, 4, 4096, 1, 16, false, 1, 1, 4><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_256_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_256_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + 16); + } + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_32_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 8192: + // if (true) { + if (B >= 8 && (B % 8) == 0 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 8, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + monarch_conv_cuda_complex_kernel<32, 8, 8192, 2, 1, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_16_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_16_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_16_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 16384: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + // if (true) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 102400)); + + monarch_conv_cuda_16_32_32_complex_kernel<32, 8, 16384, 1, 2, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_16_fft.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_16_ifft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_32_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + switch (fftsize) { + case 32768: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} + +std::pair +monarch_conv_cuda_32_32_32_complex_truncated( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N, + uint trunc, + uint kernel_trunc +){ + + uint B = x_real.size(0); + uint H = x_real.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + // printf("gridDim.x = %d, gridDim.y = %d\n", gridDim.x, gridDim.y); + torch::Tensor out_real = torch::empty({B, H, N}, x_real.options()); + torch::Tensor out_imag = torch::empty({B, H, N}, x_real.options()); + + H = H - 128 * trunc; + + switch (fftsize) { + case 32768: + if (B >= 2 && (B % 2) == 0 && (H % 8) == 0) { + gridDim.x = B / 2; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 2, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 2, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + kernel_trunc); + } else if ((H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 8, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 8, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + kernel_trunc); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 8; + + CUDA_RT_CALL(cudaFuncSetAttribute(&monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 1, 8>, cudaFuncAttributeMaxDynamicSharedMemorySize, 135168)); + + monarch_conv_cuda_32_32_32_complex_kernel_truncated<32, 8, 32768, 2, 16, false, 1, 1, 8><<>>( + static_cast(x_real.data_ptr()), + static_cast(x_imag.data_ptr()), + static_cast(k_f.data_ptr()), + static_cast(f_32_fft.data_ptr()), + static_cast(twiddle_factors_N_fft.data_ptr()), + static_cast(twiddle_factors_32_fft.data_ptr()), + static_cast(f_32_ifft.data_ptr()), + static_cast(twiddle_factors_N_ifft.data_ptr()), + static_cast(twiddle_factors_32_ifft.data_ptr()), + static_cast(out_real.data_ptr()), + static_cast(out_imag.data_ptr()), + B, + H, + N, + kernel_trunc); + } + + break; + default: + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + + CHECK_LAST_CUDA_ERROR(); + return std::make_pair(out_real, out_imag); +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r.cu index c7b6ff4aff5d6ba3e096a8a51c7136b250a28ce0..9fa7edf5bbae38010491ec3af5e43de0b8c80a2e 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r.cu @@ -1,260 +1,260 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_fp16/monarch_cuda_kernel_r2r.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastFP16FwdR2R(__FILE__, __LINE__) -void checkLastFP16FwdR2R(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda_r2r( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 256: - // if (B >= 8 && (B % 8) == 0) { - if (B >= 8 && (B % 8) == 0 & H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - if (B >= 8 && (B % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 1; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0) { - gridDim.x = B / 4; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - printf("fftsize = %d\n", fftsize); - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - CHECK_LAST_CUDA_ERROR(); - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_fp16/monarch_cuda_kernel_r2r.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastFP16FwdR2R(__FILE__, __LINE__) +void checkLastFP16FwdR2R(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda_r2r( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 256: + // if (B >= 8 && (B % 8) == 0) { + if (B >= 8 && (B % 8) == 0 & H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + if (B >= 8 && (B % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 1; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0) { + gridDim.x = B / 4; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + printf("fftsize = %d\n", fftsize); + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + CHECK_LAST_CUDA_ERROR(); + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu index 4cc4200e17a15e23ab44b7b1850cad3826755595..f02b05a2c5f42e163fe490275221aaeea9979941 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu @@ -1,265 +1,265 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include -#include -#include -#include -#include -#include -#include -#include "kernels_bf16/monarch_cuda_kernel_r2r_bf16.h" -#include "kernels_fp16/monarch_cuda_shared.h" -#include "kernels_bf16/monarch_cuda_shared_bf16.h" -using namespace nvcuda; - -// *************** FOR ERROR CHECKING ******************* -#ifndef CUDA_RT_CALL -#define CUDA_RT_CALL( call ) \ - { \ - auto status = static_cast( call ); \ - if ( status != cudaSuccess ) \ - fprintf( stderr, \ - "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ - "with " \ - "%s (%d).\n", \ - #call, \ - __LINE__, \ - __FILE__, \ - cudaGetErrorString( status ), \ - status ); \ - } -#endif // CUDA_RT_CALL -// *************** FOR ERROR CHECKING ******************* - -#ifndef CUDA_CHECK_ERROR -// Define some error checking macros. -#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) -template -void check(T err, const char* const func, const char* const file, - const int line) -{ - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << " " << func << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CUDA_CHECK_ERROR - -#ifndef CHECK_LAST_CUDA_ERROR -#define CHECK_LAST_CUDA_ERROR() checkLastBF16FwdR2R(__FILE__, __LINE__) -void checkLastBF16FwdR2R(const char* const file, const int line) -{ - cudaError_t err{cudaGetLastError()}; - if (err != cudaSuccess) - { - std::cerr << "CUDA Runtime Error at: " << file << ":" << line - << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl; - // We don't exit when we encounter CUDA errors in this example. - // std::exit(EXIT_FAILURE); - } -} -#endif // CHECK_LAST_CUDA_ERROR - -torch::Tensor monarch_conv_cuda_r2r_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N -){ - - uint B = x.size(0); - uint H = x.size(1); - // First: using WMMA - dim3 gridDim; - dim3 blockDim; - - torch::Tensor out = torch::empty({B, H, N}, x.options()); - - switch (fftsize) { - case 256: - // if (B >= 8 && (B % 8) == 0) { - // if (true) { - if (B >= 8 && (B % 8) == 0 & H >= 8 && (H % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 8; - // gridDim.x = B; - // gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - case 1024: - if (B >= 8 && (B % 8) == 0) { - gridDim.x = B / 8; - gridDim.y = H / 1; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (B >= 4 && (B % 4) == 0) { - gridDim.x = B / 4; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else if (H >= 8 && (H % 8) == 0) { - gridDim.x = B; - gridDim.y = H / 8; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } else { - gridDim.x = B; - gridDim.y = H; - - blockDim.x = 32; - blockDim.y = 1; - - monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( - static_cast(x.data_ptr()), - in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, - static_cast(k_f.data_ptr()), - static_cast(f_sqrt_N_fft.data_ptr()), - static_cast(twiddle_factors_fft.data_ptr()), - static_cast(twid_r2r.data_ptr()), - static_cast(f_sqrt_N_ifft.data_ptr()), - static_cast(twiddle_factors_ifft.data_ptr()), - static_cast(out.data_ptr()), - out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, - B, - H, - N, - sqrt_N); - } - break; - default: - printf("fftsize = %d\n", fftsize); - AT_ERROR("Monarch forward not implemented for this sequence length"); - } - CHECK_LAST_CUDA_ERROR(); - return out; -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include +#include +#include +#include +#include +#include +#include +#include "kernels_bf16/monarch_cuda_kernel_r2r_bf16.h" +#include "kernels_fp16/monarch_cuda_shared.h" +#include "kernels_bf16/monarch_cuda_shared_bf16.h" +using namespace nvcuda; + +// *************** FOR ERROR CHECKING ******************* +#ifndef CUDA_RT_CALL +#define CUDA_RT_CALL( call ) \ + { \ + auto status = static_cast( call ); \ + if ( status != cudaSuccess ) \ + fprintf( stderr, \ + "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \ + "with " \ + "%s (%d).\n", \ + #call, \ + __LINE__, \ + __FILE__, \ + cudaGetErrorString( status ), \ + status ); \ + } +#endif // CUDA_RT_CALL +// *************** FOR ERROR CHECKING ******************* + +#ifndef CUDA_CHECK_ERROR +// Define some error checking macros. +#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) +template +void check(T err, const char* const func, const char* const file, + const int line) +{ + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << " " << func << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CUDA_CHECK_ERROR + +#ifndef CHECK_LAST_CUDA_ERROR +#define CHECK_LAST_CUDA_ERROR() checkLastBF16FwdR2R(__FILE__, __LINE__) +void checkLastBF16FwdR2R(const char* const file, const int line) +{ + cudaError_t err{cudaGetLastError()}; + if (err != cudaSuccess) + { + std::cerr << "CUDA Runtime Error at: " << file << ":" << line + << std::endl; + std::cerr << cudaGetErrorString(err) << std::endl; + // We don't exit when we encounter CUDA errors in this example. + // std::exit(EXIT_FAILURE); + } +} +#endif // CHECK_LAST_CUDA_ERROR + +torch::Tensor monarch_conv_cuda_r2r_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N +){ + + uint B = x.size(0); + uint H = x.size(1); + // First: using WMMA + dim3 gridDim; + dim3 blockDim; + + torch::Tensor out = torch::empty({B, H, N}, x.options()); + + switch (fftsize) { + case 256: + // if (B >= 8 && (B % 8) == 0) { + // if (true) { + if (B >= 8 && (B % 8) == 0 & H >= 8 && (H % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 8; + // gridDim.x = B; + // gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 8, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 256, 1, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + case 1024: + if (B >= 8 && (B % 8) == 0) { + gridDim.x = B / 8; + gridDim.y = H / 1; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 8, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (B >= 4 && (B % 4) == 0) { + gridDim.x = B / 4; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 4, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else if (H >= 8 && (H % 8) == 0) { + gridDim.x = B; + gridDim.y = H / 8; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 8><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } else { + gridDim.x = B; + gridDim.y = H; + + blockDim.x = 32; + blockDim.y = 1; + + monarch_conv_cuda_kernel<32, 1, 1024, 2, false, 1, 1><<>>( + static_cast(x.data_ptr()), + in_gate.has_value() ? static_cast(in_gate.value().data_ptr()) : nullptr, + static_cast(k_f.data_ptr()), + static_cast(f_sqrt_N_fft.data_ptr()), + static_cast(twiddle_factors_fft.data_ptr()), + static_cast(twid_r2r.data_ptr()), + static_cast(f_sqrt_N_ifft.data_ptr()), + static_cast(twiddle_factors_ifft.data_ptr()), + static_cast(out.data_ptr()), + out_gate.has_value() ? static_cast(out_gate.value().data_ptr()) : nullptr, + B, + H, + N, + sqrt_N); + } + break; + default: + printf("fftsize = %d\n", fftsize); + AT_ERROR("Monarch forward not implemented for this sequence length"); + } + CHECK_LAST_CUDA_ERROR(); + return out; +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd.h index 3d86bca61dc5393fcfd13f5b88a21050abec0949..19d5101ef80862aa016f46adc296a6d3890a2d1f 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd.h @@ -1,528 +1,528 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - - -torch::Tensor monarch_conv_cuda( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_16_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_16_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_32_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_16_16_bf16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_16_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N); - -torch::Tensor monarch_conv( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_fft); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_ifft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -torch::Tensor monarch_conv_16_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N_256, - uint sqrt_N_16) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); - CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_16_16_16(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } - else if (x.dtype() == torch::kBFloat16) - { - if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { - return monarch_conv_cuda_16_16_16_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } else { - return monarch_conv_cuda_16_16_16_bf16(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); - } - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -torch::Tensor monarch_conv_32_16_16( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_32_16_16( - x, k_f, - f_32_fft, f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - in_gate, out_gate, - fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - // if (false) { - if (f_32_fft.dtype() == torch::kBFloat16) { - return monarch_conv_cuda_32_16_16_bf16_all( - x, k_f, - f_32_fft, f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - in_gate, out_gate, - fftsize, N); - } - else { - return monarch_conv_cuda_32_16_16_bf16( - x, k_f, - f_32_fft, f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - in_gate, out_gate, - fftsize, N); - } - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -torch::Tensor monarch_conv_16_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(f_16_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(f_16_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_16_32_32( - x, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_16_32_32_bf16_all( - x, k_f, - f_16_fft, f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -torch::Tensor monarch_conv_32_32_32( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_32_32_32( - x, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_32_32_32_bf16_all( - x, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - in_gate, out_gate, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + + +torch::Tensor monarch_conv_cuda( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_16_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_16_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_16_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_32_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_16_16_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_16_16_bf16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_16_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_16_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv_cuda_32_32_32_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N); + +torch::Tensor monarch_conv( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_fft); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_ifft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +torch::Tensor monarch_conv_16_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N_256, + uint sqrt_N_16) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_256_fft, sqrt_N_16, sqrt_N_256, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, sqrt_N_16, sqrt_N_16, 2); + CHECK_SHAPE(twiddle_factors_256_ifft, sqrt_N_16, sqrt_N_256, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_16_16_16(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } + else if (x.dtype() == torch::kBFloat16) + { + if (f_sqrt_N_fft.dtype() == torch::kBFloat16) { + return monarch_conv_cuda_16_16_16_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } else { + return monarch_conv_cuda_16_16_16_bf16(x, k_f, f_sqrt_N_fft, twiddle_factors_256_fft, twiddle_factors_16_fft, f_sqrt_N_ifft, twiddle_factors_256_ifft, twiddle_factors_16_ifft, in_gate, out_gate, fftsize, N, sqrt_N_16); + } + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +torch::Tensor monarch_conv_32_16_16( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_32_16_16( + x, k_f, + f_32_fft, f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + in_gate, out_gate, + fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + // if (false) { + if (f_32_fft.dtype() == torch::kBFloat16) { + return monarch_conv_cuda_32_16_16_bf16_all( + x, k_f, + f_32_fft, f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + in_gate, out_gate, + fftsize, N); + } + else { + return monarch_conv_cuda_32_16_16_bf16( + x, k_f, + f_32_fft, f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + in_gate, out_gate, + fftsize, N); + } + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +torch::Tensor monarch_conv_16_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(f_16_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(f_16_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_16_32_32( + x, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_16_32_32_bf16_all( + x, k_f, + f_16_fft, f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +torch::Tensor monarch_conv_32_32_32( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_32_32_32( + x, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_32_32_32_bf16_all( + x, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + in_gate, out_gate, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_complex.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_complex.h index eb0a56d21abd31a9f9f3dc5764e62b1cd4f3a3b5..c7fc0d6cf3581f7fa3aee97b62b459d0446f87de 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_complex.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_complex.h @@ -1,529 +1,529 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - -std::pair -monarch_conv_cuda_16_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_16_16_16_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_32_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_32_16_16_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_16_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_16_32_32_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_32_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::pair -monarch_conv_cuda_32_32_32_complex_truncated( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N, - uint trunc, - uint kernel_trunc); - -std::pair -monarch_conv_cuda_32_32_32_complex_bf16_all( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N); - -std::pair monarch_conv_16_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_256_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_256_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_256_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_sqrt_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_256_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_16_fft.is_contiguous()); - TORCH_CHECK(f_sqrt_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_256_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_16_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_256_fft, 16, 256, 2); - CHECK_SHAPE(f_sqrt_N_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_256_ifft, 16, 256, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_16_16_16_complex( - x_real, x_imag, k_f, - f_sqrt_N_fft, - twiddle_factors_256_fft, twiddle_factors_16_fft, - f_sqrt_N_ifft, - twiddle_factors_256_ifft, twiddle_factors_16_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_16_16_16_complex_bf16_all( - x_real, x_imag, k_f, - f_sqrt_N_fft, - twiddle_factors_256_fft, twiddle_factors_16_fft, - f_sqrt_N_ifft, - twiddle_factors_256_ifft, twiddle_factors_16_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::pair monarch_conv_32_16_16_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor f_16_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_16_fft, - torch::Tensor f_32_ifft, - torch::Tensor f_16_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_16_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_16_fft); - - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_16_fft.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_16_fft.is_contiguous()); - TORCH_CHECK(f_16_ifft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_16_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_32_16_16_complex( - x_real, x_imag, k_f, - f_32_fft, - f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, - f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_32_16_16_complex_bf16_all( - x_real, x_imag, k_f, - f_32_fft, - f_16_fft, - twiddle_factors_N_fft, twiddle_factors_16_fft, - f_32_ifft, - f_16_ifft, - twiddle_factors_N_ifft, twiddle_factors_16_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::pair monarch_conv_16_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_16_fft, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_16_ifft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_16_fft); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_16_ifft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_16_fft.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_16_ifft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_16_fft, 16, 16, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); - CHECK_SHAPE(f_16_ifft, 16, 16, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_16_32_32_complex( - x_real, x_imag, k_f, - f_16_fft, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_16_32_32_complex_bf16_all( - x_real, x_imag, k_f, - f_16_fft, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_16_ifft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - -std::pair monarch_conv_32_32_32_complex( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N) -{ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_32_32_32_complex( - x_real, x_imag, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else if (x_real.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_32_32_32_complex_bf16_all( - x_real, x_imag, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} - - -std::pair monarch_conv_32_32_32_complex_truncated( - torch::Tensor x_real, - torch::Tensor x_imag, - torch::Tensor k_f, - torch::Tensor f_32_fft, - torch::Tensor twiddle_factors_N_fft, - torch::Tensor twiddle_factors_32_fft, - torch::Tensor f_32_ifft, - torch::Tensor twiddle_factors_N_ifft, - torch::Tensor twiddle_factors_32_ifft, - uint fftsize, - uint N, - uint trunc, - uint kernel_trunc) -{ - CHECK_INPUT(x_real); - CHECK_INPUT(x_imag); - CHECK_INPUT(k_f); - CHECK_INPUT(f_32_fft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - CHECK_INPUT(f_32_ifft); - CHECK_INPUT(twiddle_factors_N_fft); - CHECK_INPUT(twiddle_factors_32_fft); - - TORCH_CHECK(x_real.is_contiguous()); - TORCH_CHECK(x_imag.is_contiguous()); - TORCH_CHECK(k_f.is_contiguous()); - TORCH_CHECK(f_32_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); - TORCH_CHECK(f_32_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); - TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); - - const int B = x_real.size(0); - const int H = x_real.size(1); - - CHECK_SHAPE(x_real, B, H, N); - CHECK_SHAPE(x_imag, B, H, N); - CHECK_SHAPE(k_f, H, fftsize, 2); - CHECK_SHAPE(f_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); - CHECK_SHAPE(f_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); - CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); - - if (x_real.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_32_32_32_complex_truncated( - x_real, x_imag, k_f, - f_32_fft, - twiddle_factors_N_fft, twiddle_factors_32_fft, - f_32_ifft, - twiddle_factors_N_ifft, twiddle_factors_32_ifft, - fftsize, N, - trunc, - kernel_trunc); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + +std::pair +monarch_conv_cuda_16_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_16_16_16_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_32_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_32_16_16_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_16_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_16_32_32_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_32_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::pair +monarch_conv_cuda_32_32_32_complex_truncated( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N, + uint trunc, + uint kernel_trunc); + +std::pair +monarch_conv_cuda_32_32_32_complex_bf16_all( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N); + +std::pair monarch_conv_16_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_256_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_256_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_256_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_sqrt_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_256_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_16_fft.is_contiguous()); + TORCH_CHECK(f_sqrt_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_256_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_16_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_256_fft, 16, 256, 2); + CHECK_SHAPE(f_sqrt_N_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_256_ifft, 16, 256, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_16_16_16_complex( + x_real, x_imag, k_f, + f_sqrt_N_fft, + twiddle_factors_256_fft, twiddle_factors_16_fft, + f_sqrt_N_ifft, + twiddle_factors_256_ifft, twiddle_factors_16_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_16_16_16_complex_bf16_all( + x_real, x_imag, k_f, + f_sqrt_N_fft, + twiddle_factors_256_fft, twiddle_factors_16_fft, + f_sqrt_N_ifft, + twiddle_factors_256_ifft, twiddle_factors_16_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::pair monarch_conv_32_16_16_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor f_16_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_16_fft, + torch::Tensor f_32_ifft, + torch::Tensor f_16_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_16_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_16_fft); + + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_16_fft.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_16_fft.is_contiguous()); + TORCH_CHECK(f_16_ifft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_16_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_fft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 256, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_16_ifft, 16, 16, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 256, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_32_16_16_complex( + x_real, x_imag, k_f, + f_32_fft, + f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, + f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_32_16_16_complex_bf16_all( + x_real, x_imag, k_f, + f_32_fft, + f_16_fft, + twiddle_factors_N_fft, twiddle_factors_16_fft, + f_32_ifft, + f_16_ifft, + twiddle_factors_N_ifft, twiddle_factors_16_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::pair monarch_conv_16_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_16_fft, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_16_ifft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_16_fft); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_16_ifft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_16_fft.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_16_ifft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_16_fft, 16, 16, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 16, 1024, 2); + CHECK_SHAPE(f_16_ifft, 16, 16, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 16, 1024, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_16_32_32_complex( + x_real, x_imag, k_f, + f_16_fft, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_16_32_32_complex_bf16_all( + x_real, x_imag, k_f, + f_16_fft, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_16_ifft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + +std::pair monarch_conv_32_32_32_complex( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N) +{ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_32_32_32_complex( + x_real, x_imag, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else if (x_real.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_32_32_32_complex_bf16_all( + x_real, x_imag, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} + + +std::pair monarch_conv_32_32_32_complex_truncated( + torch::Tensor x_real, + torch::Tensor x_imag, + torch::Tensor k_f, + torch::Tensor f_32_fft, + torch::Tensor twiddle_factors_N_fft, + torch::Tensor twiddle_factors_32_fft, + torch::Tensor f_32_ifft, + torch::Tensor twiddle_factors_N_ifft, + torch::Tensor twiddle_factors_32_ifft, + uint fftsize, + uint N, + uint trunc, + uint kernel_trunc) +{ + CHECK_INPUT(x_real); + CHECK_INPUT(x_imag); + CHECK_INPUT(k_f); + CHECK_INPUT(f_32_fft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + CHECK_INPUT(f_32_ifft); + CHECK_INPUT(twiddle_factors_N_fft); + CHECK_INPUT(twiddle_factors_32_fft); + + TORCH_CHECK(x_real.is_contiguous()); + TORCH_CHECK(x_imag.is_contiguous()); + TORCH_CHECK(k_f.is_contiguous()); + TORCH_CHECK(f_32_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_fft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_fft.is_contiguous()); + TORCH_CHECK(f_32_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_N_ifft.is_contiguous()); + TORCH_CHECK(twiddle_factors_32_ifft.is_contiguous()); + + const int B = x_real.size(0); + const int H = x_real.size(1); + + CHECK_SHAPE(x_real, B, H, N); + CHECK_SHAPE(x_imag, B, H, N); + CHECK_SHAPE(k_f, H, fftsize, 2); + CHECK_SHAPE(f_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_fft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_fft, 32, 1024, 2); + CHECK_SHAPE(f_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_32_ifft, 32, 32, 2); + CHECK_SHAPE(twiddle_factors_N_ifft, 32, 1024, 2); + + if (x_real.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_32_32_32_complex_truncated( + x_real, x_imag, k_f, + f_32_fft, + twiddle_factors_N_fft, twiddle_factors_32_fft, + f_32_ifft, + twiddle_factors_N_ifft, twiddle_factors_32_ifft, + fftsize, N, + trunc, + kernel_trunc); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } } \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_r2r.h b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_r2r.h index f6df7fff1121191ba0cb0e1498d9619667a32d4c..907c3aaab66a5665bfeceb95b862896248273f53 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_r2r.h +++ b/overlay/kernels/cuda/flashfftconv/csrc/monarch_cuda/monarch_fwd_r2r.h @@ -1,90 +1,90 @@ -// Copyright (c) 2023 Dan Fu, Hermann Kumbong - -#include - -#include - -#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x); \ - CHECK_IS_HALF_OR_BFLOAT(x) -#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") - - -torch::Tensor monarch_conv_cuda_r2r( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_cuda_r2r_bf16_all( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N); - -torch::Tensor monarch_conv_r2r( - torch::Tensor x, - torch::Tensor k_f, - torch::Tensor f_sqrt_N_fft, - torch::Tensor twiddle_factors_fft, - torch::Tensor twid_r2r, - torch::Tensor f_sqrt_N_ifft, - torch::Tensor twiddle_factors_ifft, - c10::optional in_gate, - c10::optional out_gate, - uint fftsize, - uint N, - uint sqrt_N) -{ - CHECK_INPUT(x); - CHECK_INPUT(k_f); - CHECK_INPUT(f_sqrt_N_fft); - CHECK_INPUT(twiddle_factors_fft); - CHECK_INPUT(twid_r2r); - CHECK_INPUT(f_sqrt_N_ifft); - CHECK_INPUT(twiddle_factors_ifft); - - const int B = x.size(0); - const int H = x.size(1); - - CHECK_SHAPE(x, B, H, N); - CHECK_SHAPE(k_f, H, fftsize + 1, 2); - CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twid_r2r, fftsize, 2); - CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); - CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); - - if (x.dtype() == torch::kFloat16) - { - return monarch_conv_cuda_r2r(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else if (x.dtype() == torch::kBFloat16) - { - return monarch_conv_cuda_r2r_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); - } - else - { - TORCH_CHECK(false, "Unsupported dtype"); - } -} +// Copyright (c) 2023 Dan Fu, Hermann Kumbong + +#include + +#include + +#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_IS_HALF_OR_BFLOAT(x) TORCH_CHECK(x.dtype() == torch::kFloat16 || x.dtype() == torch::kBFloat16, #x " must be float16 or bfloat16") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x); \ + CHECK_IS_HALF_OR_BFLOAT(x) +#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")") + + +torch::Tensor monarch_conv_cuda_r2r( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_cuda_r2r_bf16_all( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N); + +torch::Tensor monarch_conv_r2r( + torch::Tensor x, + torch::Tensor k_f, + torch::Tensor f_sqrt_N_fft, + torch::Tensor twiddle_factors_fft, + torch::Tensor twid_r2r, + torch::Tensor f_sqrt_N_ifft, + torch::Tensor twiddle_factors_ifft, + c10::optional in_gate, + c10::optional out_gate, + uint fftsize, + uint N, + uint sqrt_N) +{ + CHECK_INPUT(x); + CHECK_INPUT(k_f); + CHECK_INPUT(f_sqrt_N_fft); + CHECK_INPUT(twiddle_factors_fft); + CHECK_INPUT(twid_r2r); + CHECK_INPUT(f_sqrt_N_ifft); + CHECK_INPUT(twiddle_factors_ifft); + + const int B = x.size(0); + const int H = x.size(1); + + CHECK_SHAPE(x, B, H, N); + CHECK_SHAPE(k_f, H, fftsize + 1, 2); + CHECK_SHAPE(f_sqrt_N_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_fft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twid_r2r, fftsize, 2); + CHECK_SHAPE(f_sqrt_N_ifft, sqrt_N, sqrt_N, 2); + CHECK_SHAPE(twiddle_factors_ifft, sqrt_N, sqrt_N, 2); + + if (x.dtype() == torch::kFloat16) + { + return monarch_conv_cuda_r2r(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else if (x.dtype() == torch::kBFloat16) + { + return monarch_conv_cuda_r2r_bf16_all(x, k_f, f_sqrt_N_fft, twiddle_factors_fft, twid_r2r, f_sqrt_N_ifft, twiddle_factors_ifft, in_gate, out_gate, fftsize, N, sqrt_N); + } + else + { + TORCH_CHECK(false, "Unsupported dtype"); + } +} diff --git a/overlay/kernels/cuda/flashfftconv/csrc/setup.py b/overlay/kernels/cuda/flashfftconv/csrc/setup.py index 94a467d364e5135ce1fe699b0fe9afdc92a9be78..12d94743cc8a2e8275eee8e1ceb6bb261705b7dd 100644 --- a/overlay/kernels/cuda/flashfftconv/csrc/setup.py +++ b/overlay/kernels/cuda/flashfftconv/csrc/setup.py @@ -1,76 +1,76 @@ -import torch -from setuptools import setup -from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME -import subprocess - -def get_last_arch_torch(): - arch = torch.cuda.get_arch_list()[-1] - print(f"Found arch: {arch} from existing torch installation") - return arch - -def get_cuda_bare_metal_version(cuda_dir): - raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True) - output = raw_output.split() - release_idx = output.index("release") + 1 - release = output[release_idx].split(".") - bare_metal_major = release[0] - bare_metal_minor = release[1][0] - - return raw_output, bare_metal_major, bare_metal_minor - -def append_nvcc_threads(nvcc_extra_args): - _, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME) - if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2: - return nvcc_extra_args + ["--threads", "4"] - return nvcc_extra_args - -arch = get_last_arch_torch() -# [MP] make install more flexible here -sm_num = arch[-2:] -# Auto-detect compute capability from torch's detected arch string (e.g. "sm_86" -> "compute_86") -cc_flag = [f'--generate-code=arch=compute_{sm_num},code=compute_{sm_num}'] - - -setup( - name='monarch_cuda', - ext_modules=[ - CUDAExtension('monarch_cuda', [ - 'monarch.cpp', - 'monarch_cuda/monarch_cuda_interface_fwd.cu', - 'monarch_cuda/monarch_cuda_interface_fwd_complex.cu', - 'monarch_cuda/monarch_cuda_interface_fwd_bf16.cu', - 'monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu', - 'monarch_cuda/monarch_cuda_interface_fwd_r2r.cu', - 'monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu', - 'monarch_cuda/monarch_cuda_interface_bwd.cu', - 'monarch_cuda/monarch_cuda_interface_bwd_complex.cu', - 'monarch_cuda/monarch_cuda_interface_bwd_bf16.cu', - 'monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu', - 'monarch_cuda/monarch_cuda_interface_bwd_r2r.cu', - 'monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu', - 'butterfly/butterfly_cuda.cu', - 'butterfly/butterfly_padded_cuda.cu', - 'butterfly/butterfly_padded_cuda_bf16.cu', - 'butterfly/butterfly_ifft_cuda.cu', - 'butterfly/butterfly_cuda_bf16.cu', - 'butterfly/butterfly_ifft_cuda_bf16.cu', - 'butterfly/butterfly_padded_ifft_cuda.cu', - 'butterfly/butterfly_padded_ifft_cuda_bf16.cu', - 'conv1d/conv1d_bhl.cu', - 'conv1d/conv1d_blh.cu', - 'conv1d/conv1d_bwd_cuda_bhl.cu', - 'conv1d/conv1d_bwd_cuda_blh.cu', - ], - extra_compile_args={'cxx': ['-O3'], - 'nvcc': append_nvcc_threads(['-O3', '-lineinfo', '--use_fast_math', '-std=c++17'] + cc_flag) - }) - ], - cmdclass={ - 'build_ext': BuildExtension - }, - version='0.0.0', - description='Fast FFT algorithms for convolutions', - url='https://github.com/HazyResearch/flash-fft-conv', - author='Dan Fu, Hermann Kumbong', - author_email='danfu@cs.stanford.edu', +import torch +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME +import subprocess + +def get_last_arch_torch(): + arch = torch.cuda.get_arch_list()[-1] + print(f"Found arch: {arch} from existing torch installation") + return arch + +def get_cuda_bare_metal_version(cuda_dir): + raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True) + output = raw_output.split() + release_idx = output.index("release") + 1 + release = output[release_idx].split(".") + bare_metal_major = release[0] + bare_metal_minor = release[1][0] + + return raw_output, bare_metal_major, bare_metal_minor + +def append_nvcc_threads(nvcc_extra_args): + _, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME) + if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2: + return nvcc_extra_args + ["--threads", "4"] + return nvcc_extra_args + +arch = get_last_arch_torch() +# [MP] make install more flexible here +sm_num = arch[-2:] +# Auto-detect compute capability from torch's detected arch string (e.g. "sm_86" -> "compute_86") +cc_flag = [f'--generate-code=arch=compute_{sm_num},code=compute_{sm_num}'] + + +setup( + name='monarch_cuda', + ext_modules=[ + CUDAExtension('monarch_cuda', [ + 'monarch.cpp', + 'monarch_cuda/monarch_cuda_interface_fwd.cu', + 'monarch_cuda/monarch_cuda_interface_fwd_complex.cu', + 'monarch_cuda/monarch_cuda_interface_fwd_bf16.cu', + 'monarch_cuda/monarch_cuda_interface_fwd_bf16_complex.cu', + 'monarch_cuda/monarch_cuda_interface_fwd_r2r.cu', + 'monarch_cuda/monarch_cuda_interface_fwd_r2r_bf16.cu', + 'monarch_cuda/monarch_cuda_interface_bwd.cu', + 'monarch_cuda/monarch_cuda_interface_bwd_complex.cu', + 'monarch_cuda/monarch_cuda_interface_bwd_bf16.cu', + 'monarch_cuda/monarch_cuda_interface_bwd_bf16_complex.cu', + 'monarch_cuda/monarch_cuda_interface_bwd_r2r.cu', + 'monarch_cuda/monarch_cuda_interface_bwd_r2r_bf16.cu', + 'butterfly/butterfly_cuda.cu', + 'butterfly/butterfly_padded_cuda.cu', + 'butterfly/butterfly_padded_cuda_bf16.cu', + 'butterfly/butterfly_ifft_cuda.cu', + 'butterfly/butterfly_cuda_bf16.cu', + 'butterfly/butterfly_ifft_cuda_bf16.cu', + 'butterfly/butterfly_padded_ifft_cuda.cu', + 'butterfly/butterfly_padded_ifft_cuda_bf16.cu', + 'conv1d/conv1d_bhl.cu', + 'conv1d/conv1d_blh.cu', + 'conv1d/conv1d_bwd_cuda_bhl.cu', + 'conv1d/conv1d_bwd_cuda_blh.cu', + ], + extra_compile_args={'cxx': ['-O3'], + 'nvcc': append_nvcc_threads(['-O3', '-lineinfo', '--use_fast_math', '-std=c++17'] + cc_flag) + }) + ], + cmdclass={ + 'build_ext': BuildExtension + }, + version='0.0.0', + description='Fast FFT algorithms for convolutions', + url='https://github.com/HazyResearch/flash-fft-conv', + author='Dan Fu, Hermann Kumbong', + author_email='danfu@cs.stanford.edu', license='Apache 2.0') \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/flashfftconv/__init__.py b/overlay/kernels/cuda/flashfftconv/flashfftconv/__init__.py index c5bdcb0303fdb992cb0b74f49eb3465a55d05944..5b129fce2b0461f4bb94701f4c6bf0af41c419f2 100644 --- a/overlay/kernels/cuda/flashfftconv/flashfftconv/__init__.py +++ b/overlay/kernels/cuda/flashfftconv/flashfftconv/__init__.py @@ -1,2 +1,2 @@ -from .conv import FlashFFTConv +from .conv import FlashFFTConv from .depthwise_1d import FlashDepthWiseConv1d \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/flashfftconv/conv.py b/overlay/kernels/cuda/flashfftconv/flashfftconv/conv.py index 45d1126175d44a5b37f62cff3e7728a074571acd..6f7c63437d0eb03601a8eb8ee892ccd65b5e9e34 100644 --- a/overlay/kernels/cuda/flashfftconv/flashfftconv/conv.py +++ b/overlay/kernels/cuda/flashfftconv/flashfftconv/conv.py @@ -1,4958 +1,4958 @@ -# Copyright (c) 2023, Dan Fu and Hermann Kumbong. -import math - -import torch -import torch.nn.functional as F - -from einops import rearrange - -from monarch_cuda import monarch_conv_forward, monarch_conv_backward, \ - monarch_conv_forward_r2r, monarch_conv_backward_r2r, \ - monarch_conv_forward_16_16_16, monarch_conv_backward_16_16_16, \ - monarch_conv_forward_32_16_16, monarch_conv_backward_32_16_16, \ - monarch_conv_forward_16_32_32, monarch_conv_backward_16_32_32, \ - monarch_conv_forward_32_32_32, monarch_conv_backward_32_32_32, \ - monarch_conv_forward_16_16_16_complex, monarch_conv_backward_16_16_16_complex, \ - monarch_conv_forward_32_16_16_complex, monarch_conv_backward_32_16_16_complex, \ - monarch_conv_forward_16_32_32_complex, monarch_conv_backward_16_32_32_complex, \ - monarch_conv_forward_32_32_32_complex, monarch_conv_backward_32_32_32_complex -from monarch_cuda import butterfly_forward, butterfly_ifft_forward, butterfly_padded_forward, butterfly_ifft_padded_forward, butterfly_padded_gated_forward, butterfly_ifft_padded_gated_forward -from monarch_cuda import butterfly_bf16_forward, butterfly_ifft_bf16_forward, butterfly_padded_bf16_forward, butterfly_ifft_padded_bf16_forward, butterfly_padded_gated_bf16_forward, butterfly_ifft_padded_gated_bf16_forward - -def fft_matrix(N): - n = torch.arange(N) - k = n.view(-1, 1) - M = torch.exp(-2j * torch.pi * n * k / N) - return M - -def compute_twiddle_factors_fft(n, m): - """Compute the twiddle factors of size n x m""" - # n_a = torch.arange(n).view(-1, 1) - # m_a = torch.arange(m) - n_a = torch.arange(n).view(-1, 1) - m_a = torch.arange(m) - N = n * m - M = torch.exp(-2j * torch.pi * n_a * m_a / N) - return M - -def ifft_matrix(N): - n = torch.arange(N) - k = n.view(-1, 1) - M = torch.exp(2j * torch.pi * n * k / N) - return M - -def compute_twiddle_factors_ifft(n, m): - """Compute the twiddle factors of size n x m""" - # n_a = torch.arange(n).view(-1, 1) - # m_a = torch.arange(m) - n_a = torch.arange(n).view(-1, 1) - m_a = torch.arange(m) - N = n * m - M = torch.exp(2j * torch.pi * n_a * m_a / N) - return M - -def monarch_outer_dft(x, f_sqrt_N_fft, twiddle_factors_fft, sqrt_N): - x = x.transpose(-1, -2) # 32K, 32 - x = x @ f_sqrt_N_fft # 32K, 32 - x = x.transpose(-1, -2) # 32, 32K - # x = (f_sqrt_N_fft.T @ x) * twiddle_factors_fft # (32, 32K) * (32, 32K), pointwise - - return (x * twiddle_factors_fft).contiguous() - -def monarch_outer_idft(x, f_sqrt_N_ifft, twiddle_factors_ifft, sqrt_N): - # x = f_sqrt_N_ifft.T @ (x * twiddle_factors_ifft) # (32, 32K) * (32, 32K), pointwise - x = x * twiddle_factors_ifft - x = x.transpose(-1, -2) # 32K, 32 - x = x @ f_sqrt_N_ifft - x = x.transpose(-1, -2) # 32, 32K - - return x.contiguous() - -class FlashFFTConv(torch.nn.Module): - def __init__(self, seqlen, dtype=torch.float16, use_32_butterfly=True): - super().__init__() - assert dtype == torch.bfloat16 or dtype == torch.float16 - self.seqlen = seqlen - self.dtype = dtype - self.use_32_butterfly=use_32_butterfly - if seqlen in [256, 1024]: - N = seqlen - sqrt_N = int(math.sqrt(seqlen)) - self.N = N - self.sqrt_N = sqrt_N - f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) - f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) - - twiddle_factors_fft = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N) / N).to(dtype) - twiddle_factors_ifft = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) - - self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) - self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) - self.register_buffer('twiddle_factors_fft', twiddle_factors_fft) - self.register_buffer('twiddle_factors_ifft', twiddle_factors_ifft) - elif seqlen in [512, 2048]: - N = seqlen // 2 - sqrt_N = int(math.sqrt(seqlen // 2)) - self.N = seqlen // 2 - self.sqrt_N = sqrt_N - f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) - f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) - - twiddle_factors_fft = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N) / N).to(dtype) - twiddle_factors_ifft = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) - - twid = torch.view_as_real(torch.exp(-2j * torch.pi * torch.arange(seqlen // 2) / seqlen)).to(dtype) - - self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) - self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) - self.register_buffer('twiddle_factors_fft', twiddle_factors_fft) - self.register_buffer('twiddle_factors_ifft', twiddle_factors_ifft) - self.register_buffer('twid', twid) - elif seqlen == 4096: - N = seqlen - sqrt_N = 16 - sqrt_N_256 = 256 - self.N = N - self.sqrt_N = sqrt_N - self.sqrt_N_256 = sqrt_N_256 - f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) - f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) - - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) - twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N_256) / N).to(dtype) - twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N_256)).to(dtype) - - self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) - self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) - self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) - elif seqlen == 8192: - N = seqlen - N1 = 32 - N2 = 16 - self.N = N - self.N1 = N1 - self.N2 = N2 - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) - twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / N).to(dtype) - twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) - self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) - elif seqlen == 16384: - N = seqlen - N1 = 16 - N2 = 32 - self.N = N - self.N1 = N1 - self.N2 = N2 - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / N).to(dtype) - twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) - - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) - self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) - elif seqlen == 32768: - N = seqlen - N1 = 32 - N2 = 32 - self.N = N - self.N1 = N1 - self.N2 = N2 - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / N).to(dtype) - twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) - self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) - elif seqlen == 16 * 4096: #65K - N = seqlen - self.N = N - - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - - if dtype == torch.bfloat16: - f_16_fft_real = fft_matrix(16).real.to(dtype) - f_16_ifft_real = ifft_matrix(16).real.to(dtype) - f_16_fft_imag = fft_matrix(16).imag.to(dtype) - f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) - - self.register_buffer('f_16_fft_real', f_16_fft_real) - self.register_buffer('f_16_ifft_real', f_16_ifft_real) - self.register_buffer('f_16_fft_imag', f_16_fft_imag) - self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) - - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) - twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(16, 256) / 4096).to(dtype) - twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(16, 256)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(16, 4096) / 16 - twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 4096) - - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) - self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 16 * 8192: #131K - N = seqlen - self.N = N - - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - - if self.use_32_butterfly: - if dtype == torch.bfloat16: - f_32_fft_real = fft_matrix(32).real.to(dtype) - f_32_ifft_real = ifft_matrix(32).real.to(dtype) - f_32_fft_imag = fft_matrix(32).imag.to(dtype) - f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) - - self.register_buffer('f_32_fft_real', f_32_fft_real) - self.register_buffer('f_32_ifft_real', f_32_ifft_real) - self.register_buffer('f_32_fft_imag', f_32_fft_imag) - self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) - else: - if dtype == torch.bfloat16: - f_16_fft_real = fft_matrix(16).real.to(dtype) - f_16_ifft_real = ifft_matrix(16).real.to(dtype) - f_16_fft_imag = fft_matrix(16).imag.to(dtype) - f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) - - self.register_buffer('f_16_fft_real', f_16_fft_real) - self.register_buffer('f_16_ifft_real', f_16_ifft_real) - self.register_buffer('f_16_fft_imag', f_16_fft_imag) - self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - - if self.use_32_butterfly: - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) - twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(16, 256) / 4096).to(dtype) - twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(16, 256)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(32, 4096) / 32 - twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 4096) - else: - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) - twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / 8192).to(dtype) - twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(16, 8192) / 16 - twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 8192) - - if self.use_32_butterfly: - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) - self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) - else: - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) - self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 16 * 16384: #262K - N = seqlen - self.N = N - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - - if self.use_32_butterfly: - if dtype == torch.bfloat16: - f_32_fft_real = fft_matrix(32).real.to(dtype) - f_32_ifft_real = ifft_matrix(32).real.to(dtype) - f_32_fft_imag = fft_matrix(32).imag.to(dtype) - f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) - - self.register_buffer('f_32_fft_real', f_32_fft_real) - self.register_buffer('f_32_ifft_real', f_32_ifft_real) - self.register_buffer('f_32_fft_imag', f_32_fft_imag) - self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) - else: - if dtype == torch.bfloat16: - f_16_fft_real = fft_matrix(16).real.to(dtype) - f_16_ifft_real = ifft_matrix(16).real.to(dtype) - f_16_fft_imag = fft_matrix(16).imag.to(dtype) - f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) - - self.register_buffer('f_16_fft_real', f_16_fft_real) - self.register_buffer('f_16_ifft_real', f_16_ifft_real) - self.register_buffer('f_16_fft_imag', f_16_fft_imag) - self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) - - if self.use_32_butterfly: - twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) - twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) - twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / 8192).to(dtype) - twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(32, 8192) / 32 - twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 8192) - else: - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / 16384).to(dtype) - twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(16, 16384) / 16 - twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 16384) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - if self.use_32_butterfly: - self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) - self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) - self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) - self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) - else: - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) - self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 16 * 32768: #524K - N = seqlen - self.N = N - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) - f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) - - if self.use_32_butterfly: - if dtype == torch.bfloat16: - f_32_fft_real = fft_matrix(32).real.to(dtype) - f_32_ifft_real = ifft_matrix(32).real.to(dtype) - f_32_fft_imag = fft_matrix(32).imag.to(dtype) - f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) - - self.register_buffer('f_32_fft_real', f_32_fft_real) - self.register_buffer('f_32_ifft_real', f_32_ifft_real) - self.register_buffer('f_32_fft_imag', f_32_fft_imag) - self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) - else: - if dtype == torch.bfloat16: - f_16_fft_real = fft_matrix(16).real.to(dtype) - f_16_ifft_real = ifft_matrix(16).real.to(dtype) - f_16_fft_imag = fft_matrix(16).imag.to(dtype) - f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) - - self.register_buffer('f_16_fft_real', f_16_fft_real) - self.register_buffer('f_16_ifft_real', f_16_ifft_real) - self.register_buffer('f_16_fft_imag', f_16_fft_imag) - self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - - if self.use_32_butterfly: - twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / 16384).to(dtype) - twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(32, 16384) / 32 - twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 16384) - else: - twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) - twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(16, 32768) / 16 - twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 32768) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_16_fft', f_16_fft) - self.register_buffer('f_16_ifft', f_16_ifft) - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - if self.use_32_butterfly: - self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) - self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) - else: - self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) - self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 32 * 32768: #1M - N = seqlen - self.N = N - - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - if dtype == torch.bfloat16: - f_32_fft_real = fft_matrix(32).real.to(dtype) - f_32_ifft_real = ifft_matrix(32).real.to(dtype) - f_32_fft_imag = fft_matrix(32).imag.to(dtype) - f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) - - self.register_buffer('f_32_fft_real', f_32_fft_real) - self.register_buffer('f_32_ifft_real', f_32_ifft_real) - self.register_buffer('f_32_fft_imag', f_32_fft_imag) - self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) - twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(32, 32768) / 32 - twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 32768) - - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) - self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 64 * 32768: #2M - N = seqlen - self.N = N - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_64_fft = torch.view_as_real(fft_matrix(64)).to(dtype) - f_64_ifft = torch.view_as_real(ifft_matrix(64)).to(dtype) - - if dtype == torch.bfloat16: - f_64_fft_real = fft_matrix(64).real.to(dtype) - f_64_ifft_real = ifft_matrix(64).real.to(dtype) - f_64_fft_imag = fft_matrix(64).imag.to(dtype) - f_64_ifft_imag = ifft_matrix(64).imag.to(dtype) - - self.register_buffer('f_64_fft_real', f_64_fft_real) - self.register_buffer('f_64_ifft_real', f_64_ifft_real) - self.register_buffer('f_64_fft_imag', f_64_fft_imag) - self.register_buffer('f_64_ifft_imag', f_64_ifft_imag) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) - twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(64, 32768) / 64 - twiddle_factors_ifft = compute_twiddle_factors_ifft(64, 32768) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_64_fft', f_64_fft) - self.register_buffer('f_64_ifft', f_64_ifft) - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) - self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - elif seqlen == 128 * 32768: #4M - N = seqlen - self.N = N - f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) - f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) - f_128_fft = torch.view_as_real(fft_matrix(128)).to(dtype) - f_128_ifft = torch.view_as_real(ifft_matrix(128)).to(dtype) - - if dtype == torch.bfloat16: - f_128_fft_real = fft_matrix(128).real.to(dtype) - f_128_ifft_real = ifft_matrix(128).real.to(dtype) - f_128_fft_imag = fft_matrix(128).imag.to(dtype) - f_128_ifft_imag = ifft_matrix(128).imag.to(dtype) - - self.register_buffer('f_128_fft_real', f_128_fft_real) - self.register_buffer('f_128_ifft_real', f_128_ifft_real) - self.register_buffer('f_128_fft_imag', f_128_fft_imag) - self.register_buffer('f_128_ifft_imag', f_128_ifft_imag) - - twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) - twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) - twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) - twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) - - twiddle_factors_fft = compute_twiddle_factors_fft(128, 32768) / 128 - twiddle_factors_ifft = compute_twiddle_factors_ifft(128, 32768) - - self.register_buffer('f_32_fft', f_32_fft) - self.register_buffer('f_32_ifft', f_32_ifft) - self.register_buffer('f_128_fft', f_128_fft) - self.register_buffer('f_128_ifft', f_128_ifft) - self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) - self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) - self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) - self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) - self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) - self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) - else: - raise NotImplementedError(f'seqlen {seqlen} not supported') - - def forward(self, u, k, pregate=None, postgate=None): - # orig_dtype = u.dtype - # if (u.dtype != self.dtype): - # u = u.to(self.dtype).contiguous() - if pregate is not None or postgate is not None: - assert pregate is not None and postgate is not None - return GatedFlashFFTConvFunc.apply(u, k, self, pregate, postgate) - return FlashFFTConvFunc.apply(u, k, self) - - -class FlashFFTConvFunc(torch.autograd.Function): - - @staticmethod - def forward(ctx, u, k, fftconv_data): - # assert(u.dtype == fftconv_data.dtype) - - B, H, L = u.shape - - # replace this with a kernel - if fftconv_data.seqlen in [512, 2048]: - k_f = torch.fft.rfft(k, n=fftconv_data.seqlen) - else: - k_f = torch.fft.fft(k, n=fftconv_data.seqlen) - - ctx.fftconv_data = fftconv_data - ctx.k_len = k.shape[-1] - - if fftconv_data.seqlen in [256, 1024]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted) - - return monarch_conv_forward( - u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - None, None, - N, L, sqrt_N - ) - elif fftconv_data.seqlen in [512, 2048]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - k_f = torch.view_as_real(k_f).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f) - - return monarch_conv_forward_r2r( - u, k_f, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.twid, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - None, None, - N, L, sqrt_N - ) - elif fftconv_data.seqlen == 4096: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - sqrt_N_256 = fftconv_data.sqrt_N_256 - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N_256, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted) - - out = monarch_conv_forward_16_16_16( - u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, - fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_sqrt_N_ifft, - fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, - None, None, - N, L, sqrt_N_256, sqrt_N - ) - - return out - elif fftconv_data.seqlen == 8192: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 256, 32).transpose(-1, -2).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted) - - return monarch_conv_forward_32_16_16( - u, k_f_permuted, - fftconv_data.f_32_fft, fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, - None, None, - N, L - ) - elif fftconv_data.seqlen == 16384: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 16).transpose(-1, -2).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted) - - return monarch_conv_forward_16_32_32( - u, k_f_permuted, - fftconv_data.f_16_fft, fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, - None, None, - N, L - ) - elif fftconv_data.seqlen == 32768: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted) - - return monarch_conv_forward_32_32_32( - u, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - None, None, - N, L - ) - elif fftconv_data.seqlen == 16 * 4096: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 4096, 16).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 16).transpose(-1, -2).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H * 16, 4096)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x = u.reshape(B, H, 16, 4096) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 4096) - x_half_imag = x_half_imag.reshape(B, H * 16, 4096) - - out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 16, 4096) - out_half_imag = out_half_imag.reshape(B, H, 16, 4096) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 16 * 8192: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - - k_f_permuted = k_f.reshape(H, 4096, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 16).transpose(-1, -2).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H * 32, 4096)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x = u.reshape(B, H, 32, 4096) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 4096) - x_half_imag = x_half_imag.reshape(B, H * 32, 4096) - - out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 32, 4096) - out_half_imag = out_half_imag.reshape(B, H, 32, 4096) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - else: - - k_f_permuted = k_f.reshape(H, 8192, 16).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 32).transpose(-1, -2).reshape(H, 16, 32, 16, 16).transpose(-1, -2).reshape(H * 16, 8192)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x = u.reshape(B, H, 16, 8192) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 8192) - x_half_imag = x_half_imag.reshape(B, H * 16, 8192) - - out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 16, 8192) - out_half_imag = out_half_imag.reshape(B, H, 16, 8192) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 16 * 16384: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - - k_f_permuted = k_f.reshape(H, 8192, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 32).transpose(-1, -2).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H * 32, 8192)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x = u.reshape(B, H, 32, 8192) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 8192) - x_half_imag = x_half_imag.reshape(B, H * 32, 8192) - - out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 32, 8192) - out_half_imag = out_half_imag.reshape(B, H, 32, 8192) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - else: - - k_f_permuted = k_f.reshape(H, 16384, 16).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 1024, 16).transpose(-1, -2).reshape(H, 16, 16, 32, 32).transpose(-1, -2).reshape(H * 16, 16384)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x = u.reshape(B, H, 16, 16384) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 16384) - x_half_imag = x_half_imag.reshape(B, H * 16, 16384) - - out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 16, 16384) - out_half_imag = out_half_imag.reshape(B, H, 16, 16384) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 16 * 32768: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - k_f_permuted = k_f.reshape(H, 16384, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 16).transpose(-1, -2).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H * 32, 16384)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x = u.reshape(B, H, 32, 16384) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 16384) - x_half_imag = x_half_imag.reshape(B, H * 32, 16384) - - out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 32, 16384) - out_half_imag = out_half_imag.reshape(B, H, 32, 16384) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - else: - k_f_permuted = k_f.reshape(H, 32768, 16).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 1024, 32).transpose(-1, -2).reshape(H, 16, 32, 32, 32).transpose(-1, -2).reshape(H * 16, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 16, 32768) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 32768) - x_half_imag = x_half_imag.reshape(B, H * 16, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 16, 32768) - out_half_imag = out_half_imag.reshape(B, H, 16, 32768) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 32 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H * 32, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 32, 32768) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 32768) - x_half_imag = x_half_imag.reshape(B, H * 32, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 32, 32768) - out_half_imag = out_half_imag.reshape(B, H, 32, 32768) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 64 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 64).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 64, 1024, 32).transpose(-1, -2).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H * 64, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 64, 32768) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 64, 32768) - x_half_imag = x_half_imag.reshape(B, H * 64, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 64, 32768) - out_half_imag = out_half_imag.reshape(B, H, 64, 32768) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - elif fftconv_data.seqlen == 128 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 128).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 128, 1024, 32).transpose(-1, -2).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H * 128, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted) - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 128, 32768) - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - else: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 128, 32768) - x_half_imag = x_half_imag.reshape(B, H * 128, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - x = butterfly_ifft_padded_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - out_half_real = out_half_real.reshape(B, H, 128, 32768) - out_half_imag = out_half_imag.reshape(B, H, 128, 32768) - - if x.dtype == torch.float16: - out_half = butterfly_ifft_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - else: - out_half = butterfly_ifft_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - x = out_half.reshape(B, H, N) - - return x[..., :L] - else: - raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for FlashFFTConv fwd') - - @staticmethod - def backward(ctx, dout): - fftconv_data = ctx.fftconv_data - # assert(dout.dtype == fftconv_data.dtype) - - B, H, L = dout.shape - dout = dout.contiguous() - - u, k_f_permuted = ctx.saved_tensors - k_len = ctx.k_len - - if fftconv_data.seqlen in [256, 1024]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - du, dk_f_permuted = monarch_conv_backward( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - None, None, - N, L, sqrt_N - ) - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None - elif fftconv_data.seqlen in [512, 2048]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - du, dk_f = monarch_conv_backward_r2r( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.twid, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - None, None, - N, L, sqrt_N - ) - dk_f = torch.fft.irfft( - torch.view_as_complex(dk_f.to(torch.float32)), n=fftconv_data.seqlen, norm='forward' - ).real[..., :k_len] / 2 - - return du, dk_f, None - elif fftconv_data.seqlen == 4096: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - sqrt_N_256 = fftconv_data.sqrt_N_256 - - du, dk_f_permuted = monarch_conv_backward_16_16_16( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, - fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_sqrt_N_ifft, - fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, - None, None, - N, L, sqrt_N_256, sqrt_N - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N_256).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None - elif fftconv_data.seqlen == 8192: - N = fftconv_data.N - - # assert(L == N) - - du, dk_f_permuted = monarch_conv_backward_32_16_16( - dout, u, k_f_permuted, - fftconv_data.f_32_fft, fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, - None, None, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 256).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None - elif fftconv_data.seqlen == 16384: - N = fftconv_data.N - - # assert(L == N) - - du, dk_f_permuted = monarch_conv_backward_16_32_32( - dout, u, k_f_permuted, - fftconv_data.f_16_fft, fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, - None, None, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 1024).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None - elif fftconv_data.seqlen == 32768: - N = fftconv_data.N - - # assert(L == N) - - du, dk_f_permuted = monarch_conv_backward_32_32_32( - dout, u, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - None, None, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 1024).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None - elif fftconv_data.seqlen == 16 * 4096: - N = fftconv_data.N - - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x = u.reshape(B, H, 16, 4096) - dout = dout.reshape(B, H, 16, 4096) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 4096) - x_half_imag = x_half_imag.reshape(B, H * 16, 4096) - - dout_half_real = dout_half_real.reshape(B, H * 16, 4096) - dout_half_imag = dout_half_imag.reshape(B, H * 16, 4096) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 16, 4096) - dx_half_imag = dx_half_imag.reshape(B, H, 16, 4096) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H, 16, 16, 256).transpose(-1, -2).reshape(H, 16, 4096).transpose(-1, -2).reshape(H, N) * 16, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 16 * 8192: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096 - ) - else: - x = u.reshape(B, H, 32, 4096) - dout = dout.reshape(B, H, 32, 4096) - - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 4096) - x_half_imag = x_half_imag.reshape(B, H * 32, 4096) - - dout_half_real = dout_half_real.reshape(B, H * 32, 4096) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 4096) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 32, 4096) - dx_half_imag = dx_half_imag.reshape(B, H, 32, 4096) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H, 32, 16, 256).transpose(-1, -2).reshape(H, 32, 4096).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - else: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x = u.reshape(B, H, 16, 8192) - dout = dout.reshape(B, H, 16, 8192) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 8192) - x_half_imag = x_half_imag.reshape(B, H * 16, 8192) - - dout_half_real = dout_half_real.reshape(B, H * 16, 8192) - dout_half_imag = dout_half_imag.reshape(B, H * 16, 8192) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 16, 8192) - dx_half_imag = dx_half_imag.reshape(B, H, 16, 8192) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 16, 16).transpose(-1, -2).reshape(H, 16, 32, 256).transpose(-1, -2).reshape(H, 16, 8192).transpose(-1, -2).reshape(H, N) * 16, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 16 * 16384: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192 - ) - else: - x = u.reshape(B, H, 32, 8192) - dout = dout.reshape(B, H, 32, 8192) - - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 8192) - x_half_imag = x_half_imag.reshape(B, H * 32, 8192) - - dout_half_real = dout_half_real.reshape(B, H * 32, 8192) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 8192) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 32, 8192) - dx_half_imag = dx_half_imag.reshape(B, H, 32, 8192) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 32, 256).transpose(-1, -2).reshape(H, 32, 8192).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - else: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x = u.reshape(B, H, 16, 16384) - dout = dout.reshape(B, H, 16, 16384) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 16384) - x_half_imag = x_half_imag.reshape(B, H * 16, 16384) - - dout_half_real = dout_half_real.reshape(B, H * 16, 16384) - dout_half_imag = dout_half_imag.reshape(B, H * 16, 16384) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 16, 16384) - dx_half_imag = dx_half_imag.reshape(B, H, 16, 16384) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 16, 1024).transpose(-1, -2).reshape(H, 16, 16384).transpose(-1, -2).reshape(H, N) * 16, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 16 * 32768: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384 - ) - else: - x = u.reshape(B, H, 32, 16384) - dout = dout.reshape(B, H, 32, 16384) - - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 16384) - x_half_imag = x_half_imag.reshape(B, H * 32, 16384) - - dout_half_real = dout_half_real.reshape(B, H * 32, 16384) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 16384) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 32, 16384) - dx_half_imag = dx_half_imag.reshape(B, H, 32, 16384) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H, 32, 16, 1024).transpose(-1, -2).reshape(H, 32, 16384).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - else: - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 16, 32768) - dout = dout.reshape(B, H, 16, 32768) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 16, 32768) - x_half_imag = x_half_imag.reshape(B, H * 16, 32768) - - dout_half_real = dout_half_real.reshape(B, H * 16, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 16, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 16, 32768) - dx_half_imag = dx_half_imag.reshape(B, H, 16, 32768) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32, 32).transpose(-1, -2).reshape(H, 16, 32, 1024).transpose(-1, -2).reshape(H, 16, 32768).transpose(-1, -2).reshape(H, N) * 16, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 32 * 32768: - N = fftconv_data.N - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 32, 32768) - dout = dout.reshape(B, H, 32, 32768) - - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 32, 32768) - x_half_imag = x_half_imag.reshape(B, H * 32, 32768) - - dout_half_real = dout_half_real.reshape(B, H * 32, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 32, 32768) - dx_half_imag = dx_half_imag.reshape(B, H, 32, 32768) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 32, 1024).transpose(-1, -2).reshape(H, 32, 32768).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 64 * 32768: - N = fftconv_data.N - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 64, 32768) - dout = dout.reshape(B, H, 64, 32768) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 64, 32768) - x_half_imag = x_half_imag.reshape(B, H * 64, 32768) - - dout_half_real = dout_half_real.reshape(B, H * 64, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 64, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 64, 32768) - dx_half_imag = dx_half_imag.reshape(B, H, 64, 32768) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H, 64, 32, 1024).transpose(-1, -2).reshape(H, 64, 32768).transpose(-1, -2).reshape(H, N) * 64, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - elif fftconv_data.seqlen == 128 * 32768: - N = fftconv_data.N - - # assert(N == L) - if L < N: - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_forward( - u, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_forward( - dout, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x_half_real, x_half_imag = butterfly_padded_bf16_forward( - u, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( - dout, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768 - ) - else: - x = u.reshape(B, H, 128, 32768) - dout = dout.reshape(B, H, 128, 32768) - - if x.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_forward( - x, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_forward( - dout, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - elif x.dtype == torch.bfloat16: - x_half_real, x_half_imag = butterfly_bf16_forward( - x, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - dout_half_real, dout_half_imag = butterfly_bf16_forward( - dout, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag - ) - - x_half_real = x_half_real.reshape(B, H * 128, 32768) - x_half_imag = x_half_imag.reshape(B, H * 128, 32768) - - dout_half_real = dout_half_real.reshape(B, H * 128, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 128, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - x_half_real, x_half_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - if L < N: - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dx = butterfly_ifft_padded_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx = butterfly_ifft_padded_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L - ) - else: - dx_half_real = dx_half_real.reshape(B, H, 128, 32768) - dx_half_imag = dx_half_imag.reshape(B, H, 128, 32768) - - if x.dtype == torch.float16: - dx_half = butterfly_ifft_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - elif x.dtype == torch.bfloat16: - dx_half = butterfly_ifft_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag - ) - - dx = dx_half.reshape(B, H, N) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H, 128, 32, 1024).transpose(-1, -2).reshape(H, 128, 32768).transpose(-1, -2).reshape(H, N) * 128, - norm='forward', n=N - ).real[..., :k_len] - - return dx[..., :L], dk_f, None - else: - raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for FlashFFTConv bwd') - -class GatedFlashFFTConvFunc(torch.autograd.Function): - - @staticmethod - def forward(ctx, u, k, fftconv_data, pregate, postgate): - # assert(u.dtype == fftconv_data.dtype) - - B, H, L = u.shape - - if fftconv_data.seqlen in [512, 2048]: - k_f = torch.fft.rfft(k, n=fftconv_data.seqlen) - else: - k_f = torch.fft.fft(k, n=fftconv_data.seqlen) - - ctx.fftconv_data = fftconv_data - ctx.k_len = k.shape[-1] - - if fftconv_data.seqlen in [256, 1024]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted, pregate, postgate) - - return monarch_conv_forward( - u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - pregate, postgate, - N, L, sqrt_N - ) - elif fftconv_data.seqlen in [512, 2048]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - k_f = torch.view_as_real(k_f).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f, pregate, postgate) - - return monarch_conv_forward_r2r( - u, k_f, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.twid, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - pregate, postgate, - N, L, sqrt_N - ) - elif fftconv_data.seqlen == 4096: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - sqrt_N_256 = fftconv_data.sqrt_N_256 - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N_256, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted, pregate, postgate) - - out = monarch_conv_forward_16_16_16( - u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, - fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_sqrt_N_ifft, - fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, - pregate, postgate, - N, L, sqrt_N_256, sqrt_N - ) - - return out - elif fftconv_data.seqlen == 8192: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 256, 32).transpose(-1, -2).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted, pregate, postgate) - - return monarch_conv_forward_32_16_16( - u, k_f_permuted, - fftconv_data.f_32_fft, fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, - pregate, postgate, - N, L - ) - elif fftconv_data.seqlen == 16384: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 16).transpose(-1, -2).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted, pregate, postgate) - - return monarch_conv_forward_16_32_32( - u, k_f_permuted, - fftconv_data.f_16_fft, fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, - pregate, postgate, - N, L - ) - elif fftconv_data.seqlen == 32768: - N = fftconv_data.N - - # assert(L == N) - k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_permuted, pregate, postgate) - - return monarch_conv_forward_32_32_32( - u, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - pregate, postgate, - N, L - ) - if fftconv_data.seqlen == 16 * 4096: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 4096, 16).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 16).transpose(-1, -2).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H * 16, 4096)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 16, 4096) - x_half_imag = x_half_imag.reshape(B, H * 16, 4096) - - out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - - return x[..., :L] - if fftconv_data.seqlen == 16 * 8192: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - k_f_permuted = k_f.reshape(H, 4096, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 16).transpose(-1, -2).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H * 32, 4096)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - # assert(N == L) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 32, 4096) - x_half_imag = x_half_imag.reshape(B, H * 32, 4096) - - out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - raise NotImplementedError - - return x[..., :L] - elif fftconv_data.seqlen == 16 * 16384: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - - k_f_permuted = k_f.reshape(H, 8192, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 32).transpose(-1, -2).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H * 32, 8192)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 32, 8192) - x_half_imag = x_half_imag.reshape(B, H * 32, 8192) - - out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - raise NotImplementedError - - return x[..., :L] - elif fftconv_data.seqlen == 16 * 32768: - N = fftconv_data.N - - if fftconv_data.use_32_butterfly: - k_f_permuted = k_f.reshape(H, 16384, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 16).transpose(-1, -2).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H * 32, 16384)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - # assert(N == L) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 32, 16384) - x_half_imag = x_half_imag.reshape(B, H * 32, 16384) - - out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - raise NotImplementedError - - return x[..., :L] - elif fftconv_data.seqlen == 32 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 32).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H * 32, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - # assert(N == L) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 32, 32768) - x_half_imag = x_half_imag.reshape(B, H * 32, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - - return x[..., :L] - elif fftconv_data.seqlen == 64 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 64).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 64, 1024, 32).transpose(-1, -2).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H * 64, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - # assert(N == L) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 64, 32768) - x_half_imag = x_half_imag.reshape(B, H * 64, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - - return x[..., :L] - elif fftconv_data.seqlen == 128 * 32768: - N = fftconv_data.N - - k_f_permuted = k_f.reshape(H, 32768, 128).transpose(-1, -2).reshape(H, N) - k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 128, 1024, 32).transpose(-1, -2).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H * 128, 32768)).contiguous().to(fftconv_data.dtype) - - if fftconv_data.training: - ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) - - # assert(N == L) - if u.dtype == torch.float16: - x_half_real, x_half_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - else: - x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - - x_half_real = x_half_real.reshape(B, H * 128, 32768) - x_half_imag = x_half_imag.reshape(B, H * 128, 32768) - - out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( - x_half_real, x_half_imag, k_f_double_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - out_half_real = out_half_real.reshape(B, H, N) - out_half_imag = out_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - x = butterfly_ifft_padded_gated_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - else: - x = butterfly_ifft_padded_gated_bf16_forward( - out_half_real, out_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - postgate - ) - - return x[..., :L] - else: - raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for GatedFlashFFTConv fwd') - - @staticmethod - def backward(ctx, dout): - fftconv_data = ctx.fftconv_data - # assert(dout.dtype == fftconv_data.dtype) - - B, H, L = dout.shape - dout = dout.contiguous() - - u, k_f_permuted, pregate, postgate = ctx.saved_tensors - k_len = ctx.k_len - - if fftconv_data.seqlen in [256, 1024]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - pregate, postgate, - N, L, sqrt_N - ) - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen in [512, 2048]: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - - du, dk_f, dpregate, dpostgate = monarch_conv_backward_r2r( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, - fftconv_data.twid, - fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, - pregate, postgate, - N, L, sqrt_N - ) - dk_f = torch.fft.irfft( - torch.view_as_complex(dk_f.to(torch.float32)), n=fftconv_data.seqlen, norm='forward' - ).real[..., :k_len] / 2 - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen == 4096: - N = fftconv_data.N - sqrt_N = fftconv_data.sqrt_N - sqrt_N_256 = fftconv_data.sqrt_N_256 - - du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_16_16_16( - dout, u, k_f_permuted, - fftconv_data.f_sqrt_N_fft, - fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_sqrt_N_ifft, - fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, - pregate, postgate, - N, L, sqrt_N_256, sqrt_N - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N_256).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen == 8192: - N = fftconv_data.N - - du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_32_16_16( - dout, u, k_f_permuted, - fftconv_data.f_32_fft, fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, - pregate, postgate, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 256).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen == 16384: - N = fftconv_data.N - - du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_16_32_32( - dout, u, k_f_permuted, - fftconv_data.f_16_fft, fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, - pregate, postgate, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 1024).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen == 32768: - N = fftconv_data.N - - du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_32_32_32( - dout, u, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - pregate, postgate, - N, L - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 1024).transpose(-1, -2).reshape(H, N), - norm='forward', n=N - ).real[..., :k_len] - - return du, dk_f, None, dpregate, dpostgate - elif fftconv_data.seqlen == 16 * 4096: - N = fftconv_data.N - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_16_fft_real, - fftconv_data.f_16_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 16, 4096) - u_gate1_imag = u_gate1_imag.reshape(B, H * 16, 4096) - - y_half_real, y_half_imag = monarch_conv_forward_16_16_16_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 16, 4096) - dout_half_imag = dout_half_imag.reshape(B, H * 16, 4096) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_16_ifft_real, - fftconv_data.f_16_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H, 16, 16, 256).transpose(-1, -2).reshape(H, 16, 4096).transpose(-1, -2).reshape(H, N) * 16, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 16 * 8192: - N = fftconv_data.N - assert fftconv_data.use_32_butterfly - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 4096, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 32, 4096) - u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 4096) - - y_half_real, y_half_imag = monarch_conv_forward_16_16_16_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 32, 4096) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 4096) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_16_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_16_256, - fftconv_data.twiddle_factors_ifft_16_16, - 4096, 4096 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H, 32, 16, 256).transpose(-1, -2).reshape(H, 32, 4096).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 16 * 16384: - N = fftconv_data.N - assert fftconv_data.use_32_butterfly - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 8192, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 32, 8192) - u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 8192) - - y_half_real, y_half_imag = monarch_conv_forward_32_16_16_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 32, 8192) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 8192) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.f_16_fft, - fftconv_data.twiddle_factors_fft_32_256, - fftconv_data.twiddle_factors_fft_16_16, - fftconv_data.f_32_ifft, - fftconv_data.f_16_ifft, - fftconv_data.twiddle_factors_ifft_32_256, - fftconv_data.twiddle_factors_ifft_16_16, - 8192, 8192 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 32, 256).transpose(-1, -2).reshape(H, 32, 8192).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 16 * 32768: - N = fftconv_data.N - assert fftconv_data.use_32_butterfly - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 16384, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 32, 16384) - u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 16384) - - y_half_real, y_half_imag = monarch_conv_forward_16_32_32_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 32, 16384) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 16384) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_16_fft, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_16_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_16_ifft, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_16_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 16384, 16384 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H, 32, 16, 1024).transpose(-1, -2).reshape(H, 32, 16384).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 32 * 32768: - N = fftconv_data.N - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_32_fft_real, - fftconv_data.f_32_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 32, 32768) - u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 32768) - - y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 32, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 32, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_32_ifft_real, - fftconv_data.f_32_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 32, 1024).transpose(-1, -2).reshape(H, 32, 32768).transpose(-1, -2).reshape(H, N) * 32, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 64 * 32768: - N = fftconv_data.N - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_64_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_64_fft_real, - fftconv_data.f_64_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 64, 32768) - u_gate1_imag = u_gate1_imag.reshape(B, H * 64, 32768) - - y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 64, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 64, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_64_ifft_real, - fftconv_data.f_64_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H, 64, 32, 1024).transpose(-1, -2).reshape(H, 64, 32768).transpose(-1, -2).reshape(H, N) * 64, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - elif fftconv_data.seqlen == 128 * 32768: - N = fftconv_data.N - - if u.dtype == torch.float16: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( - u, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_forward( - dout, - fftconv_data.f_128_fft, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - else: - u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( - u, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - pregate - ) - dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( - dout, - fftconv_data.f_128_fft_real, - fftconv_data.f_128_fft_imag, - fftconv_data.twiddle_factors_fft_real, - fftconv_data.twiddle_factors_fft_imag, - 32768, - postgate - ) - - u_gate1_real = u_gate1_real.reshape(B, H * 128, 32768) - u_gate1_imag = u_gate1_imag.reshape(B, H * 128, 32768) - - y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - y_half_real = y_half_real.reshape(B, H, N) - y_half_imag = y_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - dpostgate = butterfly_ifft_padded_gated_forward( - y_half_real, y_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - else: - dpostgate = butterfly_ifft_padded_gated_bf16_forward( - y_half_real, y_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - dout - ) - - dout_half_real = dout_half_real.reshape(B, H * 128, 32768) - dout_half_imag = dout_half_imag.reshape(B, H * 128, 32768) - - dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( - dout_half_real, dout_half_imag, - u_gate1_real, u_gate1_imag, k_f_permuted, - fftconv_data.f_32_fft, - fftconv_data.twiddle_factors_fft_32_1K, - fftconv_data.twiddle_factors_fft_32_32, - fftconv_data.f_32_ifft, - fftconv_data.twiddle_factors_ifft_32_1K, - fftconv_data.twiddle_factors_ifft_32_32, - 32768, 32768 - ) - - dx_half_real = dx_half_real.reshape(B, H, N) - dx_half_imag = dx_half_imag.reshape(B, H, N) - - if u.dtype == torch.float16: - du = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - else: - du = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - pregate - ) - dpregate = butterfly_ifft_padded_gated_bf16_forward( - dx_half_real, dx_half_imag, - fftconv_data.f_128_ifft_real, - fftconv_data.f_128_ifft_imag, - fftconv_data.twiddle_factors_ifft_real, - fftconv_data.twiddle_factors_ifft_imag, - L, - u - ) - - dk_f = torch.fft.ifft( - torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H, 128, 32, 1024).transpose(-1, -2).reshape(H, 128, 32768).transpose(-1, -2).reshape(H, N) * 128, - norm='forward', n=N - ).real[..., :k_len] - - return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] - else: - raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for GatedFlashFFTConv bwd') +# Copyright (c) 2023, Dan Fu and Hermann Kumbong. +import math + +import torch +import torch.nn.functional as F + +from einops import rearrange + +from monarch_cuda import monarch_conv_forward, monarch_conv_backward, \ + monarch_conv_forward_r2r, monarch_conv_backward_r2r, \ + monarch_conv_forward_16_16_16, monarch_conv_backward_16_16_16, \ + monarch_conv_forward_32_16_16, monarch_conv_backward_32_16_16, \ + monarch_conv_forward_16_32_32, monarch_conv_backward_16_32_32, \ + monarch_conv_forward_32_32_32, monarch_conv_backward_32_32_32, \ + monarch_conv_forward_16_16_16_complex, monarch_conv_backward_16_16_16_complex, \ + monarch_conv_forward_32_16_16_complex, monarch_conv_backward_32_16_16_complex, \ + monarch_conv_forward_16_32_32_complex, monarch_conv_backward_16_32_32_complex, \ + monarch_conv_forward_32_32_32_complex, monarch_conv_backward_32_32_32_complex +from monarch_cuda import butterfly_forward, butterfly_ifft_forward, butterfly_padded_forward, butterfly_ifft_padded_forward, butterfly_padded_gated_forward, butterfly_ifft_padded_gated_forward +from monarch_cuda import butterfly_bf16_forward, butterfly_ifft_bf16_forward, butterfly_padded_bf16_forward, butterfly_ifft_padded_bf16_forward, butterfly_padded_gated_bf16_forward, butterfly_ifft_padded_gated_bf16_forward + +def fft_matrix(N): + n = torch.arange(N) + k = n.view(-1, 1) + M = torch.exp(-2j * torch.pi * n * k / N) + return M + +def compute_twiddle_factors_fft(n, m): + """Compute the twiddle factors of size n x m""" + # n_a = torch.arange(n).view(-1, 1) + # m_a = torch.arange(m) + n_a = torch.arange(n).view(-1, 1) + m_a = torch.arange(m) + N = n * m + M = torch.exp(-2j * torch.pi * n_a * m_a / N) + return M + +def ifft_matrix(N): + n = torch.arange(N) + k = n.view(-1, 1) + M = torch.exp(2j * torch.pi * n * k / N) + return M + +def compute_twiddle_factors_ifft(n, m): + """Compute the twiddle factors of size n x m""" + # n_a = torch.arange(n).view(-1, 1) + # m_a = torch.arange(m) + n_a = torch.arange(n).view(-1, 1) + m_a = torch.arange(m) + N = n * m + M = torch.exp(2j * torch.pi * n_a * m_a / N) + return M + +def monarch_outer_dft(x, f_sqrt_N_fft, twiddle_factors_fft, sqrt_N): + x = x.transpose(-1, -2) # 32K, 32 + x = x @ f_sqrt_N_fft # 32K, 32 + x = x.transpose(-1, -2) # 32, 32K + # x = (f_sqrt_N_fft.T @ x) * twiddle_factors_fft # (32, 32K) * (32, 32K), pointwise + + return (x * twiddle_factors_fft).contiguous() + +def monarch_outer_idft(x, f_sqrt_N_ifft, twiddle_factors_ifft, sqrt_N): + # x = f_sqrt_N_ifft.T @ (x * twiddle_factors_ifft) # (32, 32K) * (32, 32K), pointwise + x = x * twiddle_factors_ifft + x = x.transpose(-1, -2) # 32K, 32 + x = x @ f_sqrt_N_ifft + x = x.transpose(-1, -2) # 32, 32K + + return x.contiguous() + +class FlashFFTConv(torch.nn.Module): + def __init__(self, seqlen, dtype=torch.float16, use_32_butterfly=True): + super().__init__() + assert dtype == torch.bfloat16 or dtype == torch.float16 + self.seqlen = seqlen + self.dtype = dtype + self.use_32_butterfly=use_32_butterfly + if seqlen in [256, 1024]: + N = seqlen + sqrt_N = int(math.sqrt(seqlen)) + self.N = N + self.sqrt_N = sqrt_N + f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) + f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) + + twiddle_factors_fft = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N) / N).to(dtype) + twiddle_factors_ifft = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) + + self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) + self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) + self.register_buffer('twiddle_factors_fft', twiddle_factors_fft) + self.register_buffer('twiddle_factors_ifft', twiddle_factors_ifft) + elif seqlen in [512, 2048]: + N = seqlen // 2 + sqrt_N = int(math.sqrt(seqlen // 2)) + self.N = seqlen // 2 + self.sqrt_N = sqrt_N + f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) + f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) + + twiddle_factors_fft = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N) / N).to(dtype) + twiddle_factors_ifft = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) + + twid = torch.view_as_real(torch.exp(-2j * torch.pi * torch.arange(seqlen // 2) / seqlen)).to(dtype) + + self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) + self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) + self.register_buffer('twiddle_factors_fft', twiddle_factors_fft) + self.register_buffer('twiddle_factors_ifft', twiddle_factors_ifft) + self.register_buffer('twid', twid) + elif seqlen == 4096: + N = seqlen + sqrt_N = 16 + sqrt_N_256 = 256 + self.N = N + self.sqrt_N = sqrt_N + self.sqrt_N_256 = sqrt_N_256 + f_sqrt_N_fft = torch.view_as_real(fft_matrix(sqrt_N)).to(dtype) + f_sqrt_N_ifft = torch.view_as_real(ifft_matrix(sqrt_N)).to(dtype) + + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N)).to(dtype) + twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(sqrt_N, sqrt_N_256) / N).to(dtype) + twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(sqrt_N, sqrt_N_256)).to(dtype) + + self.register_buffer('f_sqrt_N_fft', f_sqrt_N_fft) + self.register_buffer('f_sqrt_N_ifft', f_sqrt_N_ifft) + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) + self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) + elif seqlen == 8192: + N = seqlen + N1 = 32 + N2 = 16 + self.N = N + self.N1 = N1 + self.N2 = N2 + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) + twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / N).to(dtype) + twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) + self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) + elif seqlen == 16384: + N = seqlen + N1 = 16 + N2 = 32 + self.N = N + self.N1 = N1 + self.N2 = N2 + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / N).to(dtype) + twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) + + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) + self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) + elif seqlen == 32768: + N = seqlen + N1 = 32 + N2 = 32 + self.N = N + self.N1 = N1 + self.N2 = N2 + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / N).to(dtype) + twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) + self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) + elif seqlen == 16 * 4096: #65K + N = seqlen + self.N = N + + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + + if dtype == torch.bfloat16: + f_16_fft_real = fft_matrix(16).real.to(dtype) + f_16_ifft_real = ifft_matrix(16).real.to(dtype) + f_16_fft_imag = fft_matrix(16).imag.to(dtype) + f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) + + self.register_buffer('f_16_fft_real', f_16_fft_real) + self.register_buffer('f_16_ifft_real', f_16_ifft_real) + self.register_buffer('f_16_fft_imag', f_16_fft_imag) + self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) + + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) + twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(16, 256) / 4096).to(dtype) + twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(16, 256)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(16, 4096) / 16 + twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 4096) + + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) + self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 16 * 8192: #131K + N = seqlen + self.N = N + + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + + if self.use_32_butterfly: + if dtype == torch.bfloat16: + f_32_fft_real = fft_matrix(32).real.to(dtype) + f_32_ifft_real = ifft_matrix(32).real.to(dtype) + f_32_fft_imag = fft_matrix(32).imag.to(dtype) + f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) + + self.register_buffer('f_32_fft_real', f_32_fft_real) + self.register_buffer('f_32_ifft_real', f_32_ifft_real) + self.register_buffer('f_32_fft_imag', f_32_fft_imag) + self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) + else: + if dtype == torch.bfloat16: + f_16_fft_real = fft_matrix(16).real.to(dtype) + f_16_ifft_real = ifft_matrix(16).real.to(dtype) + f_16_fft_imag = fft_matrix(16).imag.to(dtype) + f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) + + self.register_buffer('f_16_fft_real', f_16_fft_real) + self.register_buffer('f_16_ifft_real', f_16_ifft_real) + self.register_buffer('f_16_fft_imag', f_16_fft_imag) + self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + + if self.use_32_butterfly: + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) + twiddle_factors_fft_16_256 = torch.view_as_real(compute_twiddle_factors_fft(16, 256) / 4096).to(dtype) + twiddle_factors_ifft_16_256 = torch.view_as_real(compute_twiddle_factors_ifft(16, 256)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(32, 4096) / 32 + twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 4096) + else: + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) + twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / 8192).to(dtype) + twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(16, 8192) / 16 + twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 8192) + + if self.use_32_butterfly: + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_16_256', twiddle_factors_fft_16_256) + self.register_buffer('twiddle_factors_ifft_16_256', twiddle_factors_ifft_16_256) + else: + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) + self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 16 * 16384: #262K + N = seqlen + self.N = N + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + + if self.use_32_butterfly: + if dtype == torch.bfloat16: + f_32_fft_real = fft_matrix(32).real.to(dtype) + f_32_ifft_real = ifft_matrix(32).real.to(dtype) + f_32_fft_imag = fft_matrix(32).imag.to(dtype) + f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) + + self.register_buffer('f_32_fft_real', f_32_fft_real) + self.register_buffer('f_32_ifft_real', f_32_ifft_real) + self.register_buffer('f_32_fft_imag', f_32_fft_imag) + self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) + else: + if dtype == torch.bfloat16: + f_16_fft_real = fft_matrix(16).real.to(dtype) + f_16_ifft_real = ifft_matrix(16).real.to(dtype) + f_16_fft_imag = fft_matrix(16).imag.to(dtype) + f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) + + self.register_buffer('f_16_fft_real', f_16_fft_real) + self.register_buffer('f_16_ifft_real', f_16_ifft_real) + self.register_buffer('f_16_fft_imag', f_16_fft_imag) + self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) + + if self.use_32_butterfly: + twiddle_factors_fft_16_16 = torch.view_as_real(compute_twiddle_factors_fft(16, 16)).to(dtype) + twiddle_factors_ifft_16_16 = torch.view_as_real(compute_twiddle_factors_ifft(16, 16)).to(dtype) + twiddle_factors_fft_32_256 = torch.view_as_real(compute_twiddle_factors_fft(32, 256) / 8192).to(dtype) + twiddle_factors_ifft_32_256 = torch.view_as_real(compute_twiddle_factors_ifft(32, 256)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(32, 8192) / 32 + twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 8192) + else: + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / 16384).to(dtype) + twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(16, 16384) / 16 + twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 16384) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + if self.use_32_butterfly: + self.register_buffer('twiddle_factors_fft_16_16', twiddle_factors_fft_16_16) + self.register_buffer('twiddle_factors_ifft_16_16', twiddle_factors_ifft_16_16) + self.register_buffer('twiddle_factors_fft_32_256', twiddle_factors_fft_32_256) + self.register_buffer('twiddle_factors_ifft_32_256', twiddle_factors_ifft_32_256) + else: + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) + self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 16 * 32768: #524K + N = seqlen + self.N = N + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_16_fft = torch.view_as_real(fft_matrix(16)).to(dtype) + f_16_ifft = torch.view_as_real(ifft_matrix(16)).to(dtype) + + if self.use_32_butterfly: + if dtype == torch.bfloat16: + f_32_fft_real = fft_matrix(32).real.to(dtype) + f_32_ifft_real = ifft_matrix(32).real.to(dtype) + f_32_fft_imag = fft_matrix(32).imag.to(dtype) + f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) + + self.register_buffer('f_32_fft_real', f_32_fft_real) + self.register_buffer('f_32_ifft_real', f_32_ifft_real) + self.register_buffer('f_32_fft_imag', f_32_fft_imag) + self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) + else: + if dtype == torch.bfloat16: + f_16_fft_real = fft_matrix(16).real.to(dtype) + f_16_ifft_real = ifft_matrix(16).real.to(dtype) + f_16_fft_imag = fft_matrix(16).imag.to(dtype) + f_16_ifft_imag = ifft_matrix(16).imag.to(dtype) + + self.register_buffer('f_16_fft_real', f_16_fft_real) + self.register_buffer('f_16_ifft_real', f_16_ifft_real) + self.register_buffer('f_16_fft_imag', f_16_fft_imag) + self.register_buffer('f_16_ifft_imag', f_16_ifft_imag) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + + if self.use_32_butterfly: + twiddle_factors_fft_16_1K = torch.view_as_real(compute_twiddle_factors_fft(16, 1024) / 16384).to(dtype) + twiddle_factors_ifft_16_1K = torch.view_as_real(compute_twiddle_factors_ifft(16, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(32, 16384) / 32 + twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 16384) + else: + twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) + twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(16, 32768) / 16 + twiddle_factors_ifft = compute_twiddle_factors_ifft(16, 32768) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_16_fft', f_16_fft) + self.register_buffer('f_16_ifft', f_16_ifft) + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + if self.use_32_butterfly: + self.register_buffer('twiddle_factors_fft_16_1K', twiddle_factors_fft_16_1K) + self.register_buffer('twiddle_factors_ifft_16_1K', twiddle_factors_ifft_16_1K) + else: + self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) + self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 32 * 32768: #1M + N = seqlen + self.N = N + + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + if dtype == torch.bfloat16: + f_32_fft_real = fft_matrix(32).real.to(dtype) + f_32_ifft_real = ifft_matrix(32).real.to(dtype) + f_32_fft_imag = fft_matrix(32).imag.to(dtype) + f_32_ifft_imag = ifft_matrix(32).imag.to(dtype) + + self.register_buffer('f_32_fft_real', f_32_fft_real) + self.register_buffer('f_32_ifft_real', f_32_ifft_real) + self.register_buffer('f_32_fft_imag', f_32_fft_imag) + self.register_buffer('f_32_ifft_imag', f_32_ifft_imag) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) + twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(32, 32768) / 32 + twiddle_factors_ifft = compute_twiddle_factors_ifft(32, 32768) + + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) + self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 64 * 32768: #2M + N = seqlen + self.N = N + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_64_fft = torch.view_as_real(fft_matrix(64)).to(dtype) + f_64_ifft = torch.view_as_real(ifft_matrix(64)).to(dtype) + + if dtype == torch.bfloat16: + f_64_fft_real = fft_matrix(64).real.to(dtype) + f_64_ifft_real = ifft_matrix(64).real.to(dtype) + f_64_fft_imag = fft_matrix(64).imag.to(dtype) + f_64_ifft_imag = ifft_matrix(64).imag.to(dtype) + + self.register_buffer('f_64_fft_real', f_64_fft_real) + self.register_buffer('f_64_ifft_real', f_64_ifft_real) + self.register_buffer('f_64_fft_imag', f_64_fft_imag) + self.register_buffer('f_64_ifft_imag', f_64_ifft_imag) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) + twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(64, 32768) / 64 + twiddle_factors_ifft = compute_twiddle_factors_ifft(64, 32768) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_64_fft', f_64_fft) + self.register_buffer('f_64_ifft', f_64_ifft) + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) + self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + elif seqlen == 128 * 32768: #4M + N = seqlen + self.N = N + f_32_fft = torch.view_as_real(fft_matrix(32)).to(dtype) + f_32_ifft = torch.view_as_real(ifft_matrix(32)).to(dtype) + f_128_fft = torch.view_as_real(fft_matrix(128)).to(dtype) + f_128_ifft = torch.view_as_real(ifft_matrix(128)).to(dtype) + + if dtype == torch.bfloat16: + f_128_fft_real = fft_matrix(128).real.to(dtype) + f_128_ifft_real = ifft_matrix(128).real.to(dtype) + f_128_fft_imag = fft_matrix(128).imag.to(dtype) + f_128_ifft_imag = ifft_matrix(128).imag.to(dtype) + + self.register_buffer('f_128_fft_real', f_128_fft_real) + self.register_buffer('f_128_ifft_real', f_128_ifft_real) + self.register_buffer('f_128_fft_imag', f_128_fft_imag) + self.register_buffer('f_128_ifft_imag', f_128_ifft_imag) + + twiddle_factors_fft_32_32 = torch.view_as_real(compute_twiddle_factors_fft(32, 32)).to(dtype) + twiddle_factors_ifft_32_32 = torch.view_as_real(compute_twiddle_factors_ifft(32, 32)).to(dtype) + twiddle_factors_fft_32_1K = torch.view_as_real(compute_twiddle_factors_fft(32, 1024) / 32768).to(dtype) + twiddle_factors_ifft_32_1K = torch.view_as_real(compute_twiddle_factors_ifft(32, 1024)).to(dtype) + + twiddle_factors_fft = compute_twiddle_factors_fft(128, 32768) / 128 + twiddle_factors_ifft = compute_twiddle_factors_ifft(128, 32768) + + self.register_buffer('f_32_fft', f_32_fft) + self.register_buffer('f_32_ifft', f_32_ifft) + self.register_buffer('f_128_fft', f_128_fft) + self.register_buffer('f_128_ifft', f_128_ifft) + self.register_buffer('twiddle_factors_fft_32_32', twiddle_factors_fft_32_32) + self.register_buffer('twiddle_factors_ifft_32_32', twiddle_factors_ifft_32_32) + self.register_buffer('twiddle_factors_fft_32_1K', twiddle_factors_fft_32_1K) + self.register_buffer('twiddle_factors_ifft_32_1K', twiddle_factors_ifft_32_1K) + self.register_buffer('twiddle_factors_fft_real', twiddle_factors_fft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_real', twiddle_factors_ifft.real.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_fft_imag', twiddle_factors_fft.imag.to(dtype).contiguous()) + self.register_buffer('twiddle_factors_ifft_imag', twiddle_factors_ifft.imag.to(dtype).contiguous()) + else: + raise NotImplementedError(f'seqlen {seqlen} not supported') + + def forward(self, u, k, pregate=None, postgate=None): + # orig_dtype = u.dtype + # if (u.dtype != self.dtype): + # u = u.to(self.dtype).contiguous() + if pregate is not None or postgate is not None: + assert pregate is not None and postgate is not None + return GatedFlashFFTConvFunc.apply(u, k, self, pregate, postgate) + return FlashFFTConvFunc.apply(u, k, self) + + +class FlashFFTConvFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, u, k, fftconv_data): + # assert(u.dtype == fftconv_data.dtype) + + B, H, L = u.shape + + # replace this with a kernel + if fftconv_data.seqlen in [512, 2048]: + k_f = torch.fft.rfft(k, n=fftconv_data.seqlen) + else: + k_f = torch.fft.fft(k, n=fftconv_data.seqlen) + + ctx.fftconv_data = fftconv_data + ctx.k_len = k.shape[-1] + + if fftconv_data.seqlen in [256, 1024]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted) + + return monarch_conv_forward( + u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + None, None, + N, L, sqrt_N + ) + elif fftconv_data.seqlen in [512, 2048]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + k_f = torch.view_as_real(k_f).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f) + + return monarch_conv_forward_r2r( + u, k_f, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.twid, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + None, None, + N, L, sqrt_N + ) + elif fftconv_data.seqlen == 4096: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + sqrt_N_256 = fftconv_data.sqrt_N_256 + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N_256, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted) + + out = monarch_conv_forward_16_16_16( + u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, + fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_sqrt_N_ifft, + fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, + None, None, + N, L, sqrt_N_256, sqrt_N + ) + + return out + elif fftconv_data.seqlen == 8192: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 256, 32).transpose(-1, -2).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted) + + return monarch_conv_forward_32_16_16( + u, k_f_permuted, + fftconv_data.f_32_fft, fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, + None, None, + N, L + ) + elif fftconv_data.seqlen == 16384: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 16).transpose(-1, -2).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted) + + return monarch_conv_forward_16_32_32( + u, k_f_permuted, + fftconv_data.f_16_fft, fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, + None, None, + N, L + ) + elif fftconv_data.seqlen == 32768: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted) + + return monarch_conv_forward_32_32_32( + u, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + None, None, + N, L + ) + elif fftconv_data.seqlen == 16 * 4096: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 4096, 16).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 16).transpose(-1, -2).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H * 16, 4096)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x = u.reshape(B, H, 16, 4096) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 4096) + x_half_imag = x_half_imag.reshape(B, H * 16, 4096) + + out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 16, 4096) + out_half_imag = out_half_imag.reshape(B, H, 16, 4096) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 16 * 8192: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + + k_f_permuted = k_f.reshape(H, 4096, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 16).transpose(-1, -2).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H * 32, 4096)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x = u.reshape(B, H, 32, 4096) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 4096) + x_half_imag = x_half_imag.reshape(B, H * 32, 4096) + + out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 32, 4096) + out_half_imag = out_half_imag.reshape(B, H, 32, 4096) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + else: + + k_f_permuted = k_f.reshape(H, 8192, 16).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 32).transpose(-1, -2).reshape(H, 16, 32, 16, 16).transpose(-1, -2).reshape(H * 16, 8192)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x = u.reshape(B, H, 16, 8192) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 8192) + x_half_imag = x_half_imag.reshape(B, H * 16, 8192) + + out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 16, 8192) + out_half_imag = out_half_imag.reshape(B, H, 16, 8192) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 16 * 16384: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + + k_f_permuted = k_f.reshape(H, 8192, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 32).transpose(-1, -2).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H * 32, 8192)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x = u.reshape(B, H, 32, 8192) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 8192) + x_half_imag = x_half_imag.reshape(B, H * 32, 8192) + + out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 32, 8192) + out_half_imag = out_half_imag.reshape(B, H, 32, 8192) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + else: + + k_f_permuted = k_f.reshape(H, 16384, 16).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 1024, 16).transpose(-1, -2).reshape(H, 16, 16, 32, 32).transpose(-1, -2).reshape(H * 16, 16384)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x = u.reshape(B, H, 16, 16384) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 16384) + x_half_imag = x_half_imag.reshape(B, H * 16, 16384) + + out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 16, 16384) + out_half_imag = out_half_imag.reshape(B, H, 16, 16384) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 16 * 32768: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + k_f_permuted = k_f.reshape(H, 16384, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 16).transpose(-1, -2).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H * 32, 16384)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x = u.reshape(B, H, 32, 16384) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 16384) + x_half_imag = x_half_imag.reshape(B, H * 32, 16384) + + out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 32, 16384) + out_half_imag = out_half_imag.reshape(B, H, 32, 16384) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + else: + k_f_permuted = k_f.reshape(H, 32768, 16).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 1024, 32).transpose(-1, -2).reshape(H, 16, 32, 32, 32).transpose(-1, -2).reshape(H * 16, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 16, 32768) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 32768) + x_half_imag = x_half_imag.reshape(B, H * 16, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 16, 32768) + out_half_imag = out_half_imag.reshape(B, H, 16, 32768) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 32 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H * 32, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 32, 32768) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 32768) + x_half_imag = x_half_imag.reshape(B, H * 32, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 32, 32768) + out_half_imag = out_half_imag.reshape(B, H, 32, 32768) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 64 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 64).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 64, 1024, 32).transpose(-1, -2).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H * 64, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 64, 32768) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 64, 32768) + x_half_imag = x_half_imag.reshape(B, H * 64, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 64, 32768) + out_half_imag = out_half_imag.reshape(B, H, 64, 32768) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + elif fftconv_data.seqlen == 128 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 128).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 128, 1024, 32).transpose(-1, -2).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H * 128, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted) + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 128, 32768) + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + else: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 128, 32768) + x_half_imag = x_half_imag.reshape(B, H * 128, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + x = butterfly_ifft_padded_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + out_half_real = out_half_real.reshape(B, H, 128, 32768) + out_half_imag = out_half_imag.reshape(B, H, 128, 32768) + + if x.dtype == torch.float16: + out_half = butterfly_ifft_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + else: + out_half = butterfly_ifft_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + x = out_half.reshape(B, H, N) + + return x[..., :L] + else: + raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for FlashFFTConv fwd') + + @staticmethod + def backward(ctx, dout): + fftconv_data = ctx.fftconv_data + # assert(dout.dtype == fftconv_data.dtype) + + B, H, L = dout.shape + dout = dout.contiguous() + + u, k_f_permuted = ctx.saved_tensors + k_len = ctx.k_len + + if fftconv_data.seqlen in [256, 1024]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + du, dk_f_permuted = monarch_conv_backward( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + None, None, + N, L, sqrt_N + ) + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None + elif fftconv_data.seqlen in [512, 2048]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + du, dk_f = monarch_conv_backward_r2r( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.twid, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + None, None, + N, L, sqrt_N + ) + dk_f = torch.fft.irfft( + torch.view_as_complex(dk_f.to(torch.float32)), n=fftconv_data.seqlen, norm='forward' + ).real[..., :k_len] / 2 + + return du, dk_f, None + elif fftconv_data.seqlen == 4096: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + sqrt_N_256 = fftconv_data.sqrt_N_256 + + du, dk_f_permuted = monarch_conv_backward_16_16_16( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, + fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_sqrt_N_ifft, + fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, + None, None, + N, L, sqrt_N_256, sqrt_N + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N_256).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None + elif fftconv_data.seqlen == 8192: + N = fftconv_data.N + + # assert(L == N) + + du, dk_f_permuted = monarch_conv_backward_32_16_16( + dout, u, k_f_permuted, + fftconv_data.f_32_fft, fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, + None, None, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 256).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None + elif fftconv_data.seqlen == 16384: + N = fftconv_data.N + + # assert(L == N) + + du, dk_f_permuted = monarch_conv_backward_16_32_32( + dout, u, k_f_permuted, + fftconv_data.f_16_fft, fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, + None, None, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 1024).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None + elif fftconv_data.seqlen == 32768: + N = fftconv_data.N + + # assert(L == N) + + du, dk_f_permuted = monarch_conv_backward_32_32_32( + dout, u, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + None, None, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 1024).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None + elif fftconv_data.seqlen == 16 * 4096: + N = fftconv_data.N + + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x = u.reshape(B, H, 16, 4096) + dout = dout.reshape(B, H, 16, 4096) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 4096) + x_half_imag = x_half_imag.reshape(B, H * 16, 4096) + + dout_half_real = dout_half_real.reshape(B, H * 16, 4096) + dout_half_imag = dout_half_imag.reshape(B, H * 16, 4096) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 16, 4096) + dx_half_imag = dx_half_imag.reshape(B, H, 16, 4096) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H, 16, 16, 256).transpose(-1, -2).reshape(H, 16, 4096).transpose(-1, -2).reshape(H, N) * 16, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 16 * 8192: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096 + ) + else: + x = u.reshape(B, H, 32, 4096) + dout = dout.reshape(B, H, 32, 4096) + + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 4096) + x_half_imag = x_half_imag.reshape(B, H * 32, 4096) + + dout_half_real = dout_half_real.reshape(B, H * 32, 4096) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 4096) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 32, 4096) + dx_half_imag = dx_half_imag.reshape(B, H, 32, 4096) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H, 32, 16, 256).transpose(-1, -2).reshape(H, 32, 4096).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + else: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x = u.reshape(B, H, 16, 8192) + dout = dout.reshape(B, H, 16, 8192) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 8192) + x_half_imag = x_half_imag.reshape(B, H * 16, 8192) + + dout_half_real = dout_half_real.reshape(B, H * 16, 8192) + dout_half_imag = dout_half_imag.reshape(B, H * 16, 8192) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 16, 8192) + dx_half_imag = dx_half_imag.reshape(B, H, 16, 8192) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 16, 16).transpose(-1, -2).reshape(H, 16, 32, 256).transpose(-1, -2).reshape(H, 16, 8192).transpose(-1, -2).reshape(H, N) * 16, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 16 * 16384: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192 + ) + else: + x = u.reshape(B, H, 32, 8192) + dout = dout.reshape(B, H, 32, 8192) + + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 8192) + x_half_imag = x_half_imag.reshape(B, H * 32, 8192) + + dout_half_real = dout_half_real.reshape(B, H * 32, 8192) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 8192) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 32, 8192) + dx_half_imag = dx_half_imag.reshape(B, H, 32, 8192) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 32, 256).transpose(-1, -2).reshape(H, 32, 8192).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + else: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x = u.reshape(B, H, 16, 16384) + dout = dout.reshape(B, H, 16, 16384) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 16384) + x_half_imag = x_half_imag.reshape(B, H * 16, 16384) + + dout_half_real = dout_half_real.reshape(B, H * 16, 16384) + dout_half_imag = dout_half_imag.reshape(B, H * 16, 16384) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 16, 16384) + dx_half_imag = dx_half_imag.reshape(B, H, 16, 16384) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 16, 1024).transpose(-1, -2).reshape(H, 16, 16384).transpose(-1, -2).reshape(H, N) * 16, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 16 * 32768: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384 + ) + else: + x = u.reshape(B, H, 32, 16384) + dout = dout.reshape(B, H, 32, 16384) + + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 16384) + x_half_imag = x_half_imag.reshape(B, H * 32, 16384) + + dout_half_real = dout_half_real.reshape(B, H * 32, 16384) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 16384) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 32, 16384) + dx_half_imag = dx_half_imag.reshape(B, H, 32, 16384) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H, 32, 16, 1024).transpose(-1, -2).reshape(H, 32, 16384).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + else: + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 16, 32768) + dout = dout.reshape(B, H, 16, 32768) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 16, 32768) + x_half_imag = x_half_imag.reshape(B, H * 16, 32768) + + dout_half_real = dout_half_real.reshape(B, H * 16, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 16, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 16, 32768) + dx_half_imag = dx_half_imag.reshape(B, H, 16, 32768) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32, 32).transpose(-1, -2).reshape(H, 16, 32, 1024).transpose(-1, -2).reshape(H, 16, 32768).transpose(-1, -2).reshape(H, N) * 16, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 32 * 32768: + N = fftconv_data.N + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 32, 32768) + dout = dout.reshape(B, H, 32, 32768) + + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 32, 32768) + x_half_imag = x_half_imag.reshape(B, H * 32, 32768) + + dout_half_real = dout_half_real.reshape(B, H * 32, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 32, 32768) + dx_half_imag = dx_half_imag.reshape(B, H, 32, 32768) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 32, 1024).transpose(-1, -2).reshape(H, 32, 32768).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 64 * 32768: + N = fftconv_data.N + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 64, 32768) + dout = dout.reshape(B, H, 64, 32768) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 64, 32768) + x_half_imag = x_half_imag.reshape(B, H * 64, 32768) + + dout_half_real = dout_half_real.reshape(B, H * 64, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 64, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 64, 32768) + dx_half_imag = dx_half_imag.reshape(B, H, 64, 32768) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H, 64, 32, 1024).transpose(-1, -2).reshape(H, 64, 32768).transpose(-1, -2).reshape(H, N) * 64, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + elif fftconv_data.seqlen == 128 * 32768: + N = fftconv_data.N + + # assert(N == L) + if L < N: + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_forward( + u, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_forward( + dout, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x_half_real, x_half_imag = butterfly_padded_bf16_forward( + u, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + dout_half_real, dout_half_imag = butterfly_padded_bf16_forward( + dout, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768 + ) + else: + x = u.reshape(B, H, 128, 32768) + dout = dout.reshape(B, H, 128, 32768) + + if x.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_forward( + x, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_forward( + dout, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + elif x.dtype == torch.bfloat16: + x_half_real, x_half_imag = butterfly_bf16_forward( + x, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + dout_half_real, dout_half_imag = butterfly_bf16_forward( + dout, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag + ) + + x_half_real = x_half_real.reshape(B, H * 128, 32768) + x_half_imag = x_half_imag.reshape(B, H * 128, 32768) + + dout_half_real = dout_half_real.reshape(B, H * 128, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 128, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + x_half_real, x_half_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + if L < N: + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dx = butterfly_ifft_padded_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx = butterfly_ifft_padded_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L + ) + else: + dx_half_real = dx_half_real.reshape(B, H, 128, 32768) + dx_half_imag = dx_half_imag.reshape(B, H, 128, 32768) + + if x.dtype == torch.float16: + dx_half = butterfly_ifft_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + elif x.dtype == torch.bfloat16: + dx_half = butterfly_ifft_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag + ) + + dx = dx_half.reshape(B, H, N) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H, 128, 32, 1024).transpose(-1, -2).reshape(H, 128, 32768).transpose(-1, -2).reshape(H, N) * 128, + norm='forward', n=N + ).real[..., :k_len] + + return dx[..., :L], dk_f, None + else: + raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for FlashFFTConv bwd') + +class GatedFlashFFTConvFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, u, k, fftconv_data, pregate, postgate): + # assert(u.dtype == fftconv_data.dtype) + + B, H, L = u.shape + + if fftconv_data.seqlen in [512, 2048]: + k_f = torch.fft.rfft(k, n=fftconv_data.seqlen) + else: + k_f = torch.fft.fft(k, n=fftconv_data.seqlen) + + ctx.fftconv_data = fftconv_data + ctx.k_len = k.shape[-1] + + if fftconv_data.seqlen in [256, 1024]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted, pregate, postgate) + + return monarch_conv_forward( + u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + pregate, postgate, + N, L, sqrt_N + ) + elif fftconv_data.seqlen in [512, 2048]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + k_f = torch.view_as_real(k_f).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f, pregate, postgate) + + return monarch_conv_forward_r2r( + u, k_f, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.twid, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + pregate, postgate, + N, L, sqrt_N + ) + elif fftconv_data.seqlen == 4096: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + sqrt_N_256 = fftconv_data.sqrt_N_256 + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, sqrt_N_256, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted, pregate, postgate) + + out = monarch_conv_forward_16_16_16( + u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, + fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_sqrt_N_ifft, + fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, + pregate, postgate, + N, L, sqrt_N_256, sqrt_N + ) + + return out + elif fftconv_data.seqlen == 8192: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 256, 32).transpose(-1, -2).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted, pregate, postgate) + + return monarch_conv_forward_32_16_16( + u, k_f_permuted, + fftconv_data.f_32_fft, fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, + pregate, postgate, + N, L + ) + elif fftconv_data.seqlen == 16384: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 16).transpose(-1, -2).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted, pregate, postgate) + + return monarch_conv_forward_16_32_32( + u, k_f_permuted, + fftconv_data.f_16_fft, fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, + pregate, postgate, + N, L + ) + elif fftconv_data.seqlen == 32768: + N = fftconv_data.N + + # assert(L == N) + k_f_permuted = torch.view_as_real(k_f.reshape(H, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, N)).to(fftconv_data.dtype).contiguous() + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_permuted, pregate, postgate) + + return monarch_conv_forward_32_32_32( + u, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + pregate, postgate, + N, L + ) + if fftconv_data.seqlen == 16 * 4096: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 4096, 16).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 16, 256, 16).transpose(-1, -2).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H * 16, 4096)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 16, 4096) + x_half_imag = x_half_imag.reshape(B, H * 16, 4096) + + out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + + return x[..., :L] + if fftconv_data.seqlen == 16 * 8192: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + k_f_permuted = k_f.reshape(H, 4096, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 16).transpose(-1, -2).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H * 32, 4096)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + # assert(N == L) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 32, 4096) + x_half_imag = x_half_imag.reshape(B, H * 32, 4096) + + out_half_real, out_half_imag = monarch_conv_forward_16_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + raise NotImplementedError + + return x[..., :L] + elif fftconv_data.seqlen == 16 * 16384: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + + k_f_permuted = k_f.reshape(H, 8192, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 256, 32).transpose(-1, -2).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H * 32, 8192)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 32, 8192) + x_half_imag = x_half_imag.reshape(B, H * 32, 8192) + + out_half_real, out_half_imag = monarch_conv_forward_32_16_16_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + raise NotImplementedError + + return x[..., :L] + elif fftconv_data.seqlen == 16 * 32768: + N = fftconv_data.N + + if fftconv_data.use_32_butterfly: + k_f_permuted = k_f.reshape(H, 16384, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 16).transpose(-1, -2).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H * 32, 16384)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + # assert(N == L) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 32, 16384) + x_half_imag = x_half_imag.reshape(B, H * 32, 16384) + + out_half_real, out_half_imag = monarch_conv_forward_16_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + raise NotImplementedError + + return x[..., :L] + elif fftconv_data.seqlen == 32 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 32).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 32, 1024, 32).transpose(-1, -2).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H * 32, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + # assert(N == L) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 32, 32768) + x_half_imag = x_half_imag.reshape(B, H * 32, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + + return x[..., :L] + elif fftconv_data.seqlen == 64 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 64).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 64, 1024, 32).transpose(-1, -2).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H * 64, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + # assert(N == L) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 64, 32768) + x_half_imag = x_half_imag.reshape(B, H * 64, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + + return x[..., :L] + elif fftconv_data.seqlen == 128 * 32768: + N = fftconv_data.N + + k_f_permuted = k_f.reshape(H, 32768, 128).transpose(-1, -2).reshape(H, N) + k_f_double_permuted = torch.view_as_real(k_f_permuted.reshape(H, 128, 1024, 32).transpose(-1, -2).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H * 128, 32768)).contiguous().to(fftconv_data.dtype) + + if fftconv_data.training: + ctx.save_for_backward(u, k_f_double_permuted, pregate, postgate) + + # assert(N == L) + if u.dtype == torch.float16: + x_half_real, x_half_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + else: + x_half_real, x_half_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + + x_half_real = x_half_real.reshape(B, H * 128, 32768) + x_half_imag = x_half_imag.reshape(B, H * 128, 32768) + + out_half_real, out_half_imag = monarch_conv_forward_32_32_32_complex( + x_half_real, x_half_imag, k_f_double_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + out_half_real = out_half_real.reshape(B, H, N) + out_half_imag = out_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + x = butterfly_ifft_padded_gated_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + else: + x = butterfly_ifft_padded_gated_bf16_forward( + out_half_real, out_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + postgate + ) + + return x[..., :L] + else: + raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for GatedFlashFFTConv fwd') + + @staticmethod + def backward(ctx, dout): + fftconv_data = ctx.fftconv_data + # assert(dout.dtype == fftconv_data.dtype) + + B, H, L = dout.shape + dout = dout.contiguous() + + u, k_f_permuted, pregate, postgate = ctx.saved_tensors + k_len = ctx.k_len + + if fftconv_data.seqlen in [256, 1024]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + pregate, postgate, + N, L, sqrt_N + ) + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen in [512, 2048]: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + + du, dk_f, dpregate, dpostgate = monarch_conv_backward_r2r( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, fftconv_data.twiddle_factors_fft, + fftconv_data.twid, + fftconv_data.f_sqrt_N_ifft, fftconv_data.twiddle_factors_ifft, + pregate, postgate, + N, L, sqrt_N + ) + dk_f = torch.fft.irfft( + torch.view_as_complex(dk_f.to(torch.float32)), n=fftconv_data.seqlen, norm='forward' + ).real[..., :k_len] / 2 + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen == 4096: + N = fftconv_data.N + sqrt_N = fftconv_data.sqrt_N + sqrt_N_256 = fftconv_data.sqrt_N_256 + + du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_16_16_16( + dout, u, k_f_permuted, + fftconv_data.f_sqrt_N_fft, + fftconv_data.twiddle_factors_fft_16_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_sqrt_N_ifft, + fftconv_data.twiddle_factors_ifft_16_256, fftconv_data.twiddle_factors_ifft_16_16, + pregate, postgate, + N, L, sqrt_N_256, sqrt_N + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, sqrt_N, sqrt_N, sqrt_N).transpose(-1, -2).reshape(H, sqrt_N, sqrt_N_256).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen == 8192: + N = fftconv_data.N + + du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_32_16_16( + dout, u, k_f_permuted, + fftconv_data.f_32_fft, fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, fftconv_data.twiddle_factors_ifft_16_16, + pregate, postgate, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 256).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen == 16384: + N = fftconv_data.N + + du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_16_32_32( + dout, u, k_f_permuted, + fftconv_data.f_16_fft, fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, fftconv_data.twiddle_factors_ifft_32_32, + pregate, postgate, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 32, 32).transpose(-1, -2).reshape(H, 16, 1024).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen == 32768: + N = fftconv_data.N + + du, dk_f_permuted, dpregate, dpostgate = monarch_conv_backward_32_32_32( + dout, u, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + pregate, postgate, + N, L + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 1024).transpose(-1, -2).reshape(H, N), + norm='forward', n=N + ).real[..., :k_len] + + return du, dk_f, None, dpregate, dpostgate + elif fftconv_data.seqlen == 16 * 4096: + N = fftconv_data.N + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_16_fft_real, + fftconv_data.f_16_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 16, 4096) + u_gate1_imag = u_gate1_imag.reshape(B, H * 16, 4096) + + y_half_real, y_half_imag = monarch_conv_forward_16_16_16_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 16, 4096) + dout_half_imag = dout_half_imag.reshape(B, H * 16, 4096) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_16_ifft_real, + fftconv_data.f_16_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 16, 16, 16, 16).transpose(-1, -2).reshape(H, 16, 16, 256).transpose(-1, -2).reshape(H, 16, 4096).transpose(-1, -2).reshape(H, N) * 16, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 16 * 8192: + N = fftconv_data.N + assert fftconv_data.use_32_butterfly + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 4096, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 32, 4096) + u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 4096) + + y_half_real, y_half_imag = monarch_conv_forward_16_16_16_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 32, 4096) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 4096) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_16_16_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_16_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_16_256, + fftconv_data.twiddle_factors_ifft_16_16, + 4096, 4096 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 16, 16).transpose(-1, -2).reshape(H, 32, 16, 256).transpose(-1, -2).reshape(H, 32, 4096).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 16 * 16384: + N = fftconv_data.N + assert fftconv_data.use_32_butterfly + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 8192, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 32, 8192) + u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 8192) + + y_half_real, y_half_imag = monarch_conv_forward_32_16_16_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 32, 8192) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 8192) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_16_16_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.f_16_fft, + fftconv_data.twiddle_factors_fft_32_256, + fftconv_data.twiddle_factors_fft_16_16, + fftconv_data.f_32_ifft, + fftconv_data.f_16_ifft, + fftconv_data.twiddle_factors_ifft_32_256, + fftconv_data.twiddle_factors_ifft_16_16, + 8192, 8192 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 16, 16).transpose(-1, -2).reshape(H, 32, 32, 256).transpose(-1, -2).reshape(H, 32, 8192).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 16 * 32768: + N = fftconv_data.N + assert fftconv_data.use_32_butterfly + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 16384, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 32, 16384) + u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 16384) + + y_half_real, y_half_imag = monarch_conv_forward_16_32_32_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 32, 16384) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 16384) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_16_32_32_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_16_fft, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_16_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_16_ifft, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_16_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 16384, 16384 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 16, 32, 32).transpose(-1, -2).reshape(H, 32, 16, 1024).transpose(-1, -2).reshape(H, 32, 16384).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 32 * 32768: + N = fftconv_data.N + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_32_fft_real, + fftconv_data.f_32_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 32, 32768) + u_gate1_imag = u_gate1_imag.reshape(B, H * 32, 32768) + + y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 32, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 32, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_32_ifft_real, + fftconv_data.f_32_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 32, 32, 32, 32).transpose(-1, -2).reshape(H, 32, 32, 1024).transpose(-1, -2).reshape(H, 32, 32768).transpose(-1, -2).reshape(H, N) * 32, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 64 * 32768: + N = fftconv_data.N + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_64_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_64_fft_real, + fftconv_data.f_64_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 64, 32768) + u_gate1_imag = u_gate1_imag.reshape(B, H * 64, 32768) + + y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 64, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 64, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_64_ifft_real, + fftconv_data.f_64_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 64, 32, 32, 32).transpose(-1, -2).reshape(H, 64, 32, 1024).transpose(-1, -2).reshape(H, 64, 32768).transpose(-1, -2).reshape(H, N) * 64, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + elif fftconv_data.seqlen == 128 * 32768: + N = fftconv_data.N + + if u.dtype == torch.float16: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_forward( + u, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_forward( + dout, + fftconv_data.f_128_fft, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + else: + u_gate1_real, u_gate1_imag = butterfly_padded_gated_bf16_forward( + u, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + pregate + ) + dout_half_real, dout_half_imag = butterfly_padded_gated_bf16_forward( + dout, + fftconv_data.f_128_fft_real, + fftconv_data.f_128_fft_imag, + fftconv_data.twiddle_factors_fft_real, + fftconv_data.twiddle_factors_fft_imag, + 32768, + postgate + ) + + u_gate1_real = u_gate1_real.reshape(B, H * 128, 32768) + u_gate1_imag = u_gate1_imag.reshape(B, H * 128, 32768) + + y_half_real, y_half_imag = monarch_conv_forward_32_32_32_complex( + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + y_half_real = y_half_real.reshape(B, H, N) + y_half_imag = y_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + dpostgate = butterfly_ifft_padded_gated_forward( + y_half_real, y_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + else: + dpostgate = butterfly_ifft_padded_gated_bf16_forward( + y_half_real, y_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + dout + ) + + dout_half_real = dout_half_real.reshape(B, H * 128, 32768) + dout_half_imag = dout_half_imag.reshape(B, H * 128, 32768) + + dx_half_real, dx_half_imag, dk_f_permuted = monarch_conv_backward_32_32_32_complex( + dout_half_real, dout_half_imag, + u_gate1_real, u_gate1_imag, k_f_permuted, + fftconv_data.f_32_fft, + fftconv_data.twiddle_factors_fft_32_1K, + fftconv_data.twiddle_factors_fft_32_32, + fftconv_data.f_32_ifft, + fftconv_data.twiddle_factors_ifft_32_1K, + fftconv_data.twiddle_factors_ifft_32_32, + 32768, 32768 + ) + + dx_half_real = dx_half_real.reshape(B, H, N) + dx_half_imag = dx_half_imag.reshape(B, H, N) + + if u.dtype == torch.float16: + du = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + else: + du = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + pregate + ) + dpregate = butterfly_ifft_padded_gated_bf16_forward( + dx_half_real, dx_half_imag, + fftconv_data.f_128_ifft_real, + fftconv_data.f_128_ifft_imag, + fftconv_data.twiddle_factors_ifft_real, + fftconv_data.twiddle_factors_ifft_imag, + L, + u + ) + + dk_f = torch.fft.ifft( + torch.view_as_complex(dk_f_permuted.to(torch.float32)).reshape(H, 128, 32, 32, 32).transpose(-1, -2).reshape(H, 128, 32, 1024).transpose(-1, -2).reshape(H, 128, 32768).transpose(-1, -2).reshape(H, N) * 128, + norm='forward', n=N + ).real[..., :k_len] + + return du[..., :L], dk_f, None, dpregate[..., :L], dpostgate[..., :L] + else: + raise NotImplementedError(f'seqlen {fftconv_data.seqlen} not supported for GatedFlashFFTConv bwd') diff --git a/overlay/kernels/cuda/flashfftconv/flashfftconv/depthwise_1d.py b/overlay/kernels/cuda/flashfftconv/flashfftconv/depthwise_1d.py index 537a216d4458553a31a7c6af7565f81bda0fcb71..e06de8600b28e565e291ad330aa7d8d99ba80ac2 100644 --- a/overlay/kernels/cuda/flashfftconv/flashfftconv/depthwise_1d.py +++ b/overlay/kernels/cuda/flashfftconv/flashfftconv/depthwise_1d.py @@ -1,56 +1,56 @@ -# Copyright (c) 2023, Dan Fu and Hermann Kumbong. -import torch -import math -from monarch_cuda import conv1d_forward, conv1d_backward -from einops import rearrange - -class conv1dFunc(torch.autograd.Function): - @staticmethod - def forward(ctx, input, weights, bias, padding, is_bhl=True): - outputs = conv1d_forward(input, weights, bias, padding, is_bhl) - ctx.padding = padding - ctx.is_bhl = is_bhl - ctx.save_for_backward(input, weights, bias) - return outputs - - @staticmethod - def backward(ctx, dout): - input, weight, bias = ctx.saved_tensors - dout = dout.contiguous() - du, dk, dbias = conv1d_backward(dout, input, weight, bias, ctx.padding, ctx.is_bhl) - return du, dk, dbias, None, None - -#TODO: initialization -class FlashDepthWiseConv1d(torch.nn.Module): - def __init__(self, channels, kernel_size, padding, weights, bias, is_bhl=True, device=None, dtype=None): - factory_kwargs = {'device': device, 'dtype': dtype} - super(FlashDepthWiseConv1d, self).__init__() - self.d = channels - self.k = kernel_size - self.padding = padding - self.is_bhl = is_bhl - if is_bhl: - self.weights = torch.nn.Parameter(weights.squeeze()) - else: - self.weights = torch.nn.Parameter(rearrange(weights.squeeze(), 'd k -> k d').detach().clone().contiguous()) - self.bias = torch.nn.Parameter(bias.detach().clone().contiguous()) - self.reset_parameters(weights, bias) - - #TODO: initialization - def reset_parameters(self, weights, bias): - pass - # stdv = 1.0 / math.sqrt(self.state_size) - # for weight in self.parameters(): - # weight.data.uniform_(-stdv, +stdv) - - #current format for the weights is transpose of what is used in nn.Conv1d - #[HK]: load the weights for the conv1d layer and then transpose them - def load_state_dict(self, state_dict, strict: bool = True): - pass - - #[HK]: transpose the weights before saving so that they can be loaded in nn.Conv1d - def save_state_dict(self): - pass - - def forward(self, input): +# Copyright (c) 2023, Dan Fu and Hermann Kumbong. +import torch +import math +from monarch_cuda import conv1d_forward, conv1d_backward +from einops import rearrange + +class conv1dFunc(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weights, bias, padding, is_bhl=True): + outputs = conv1d_forward(input, weights, bias, padding, is_bhl) + ctx.padding = padding + ctx.is_bhl = is_bhl + ctx.save_for_backward(input, weights, bias) + return outputs + + @staticmethod + def backward(ctx, dout): + input, weight, bias = ctx.saved_tensors + dout = dout.contiguous() + du, dk, dbias = conv1d_backward(dout, input, weight, bias, ctx.padding, ctx.is_bhl) + return du, dk, dbias, None, None + +#TODO: initialization +class FlashDepthWiseConv1d(torch.nn.Module): + def __init__(self, channels, kernel_size, padding, weights, bias, is_bhl=True, device=None, dtype=None): + factory_kwargs = {'device': device, 'dtype': dtype} + super(FlashDepthWiseConv1d, self).__init__() + self.d = channels + self.k = kernel_size + self.padding = padding + self.is_bhl = is_bhl + if is_bhl: + self.weights = torch.nn.Parameter(weights.squeeze()) + else: + self.weights = torch.nn.Parameter(rearrange(weights.squeeze(), 'd k -> k d').detach().clone().contiguous()) + self.bias = torch.nn.Parameter(bias.detach().clone().contiguous()) + self.reset_parameters(weights, bias) + + #TODO: initialization + def reset_parameters(self, weights, bias): + pass + # stdv = 1.0 / math.sqrt(self.state_size) + # for weight in self.parameters(): + # weight.data.uniform_(-stdv, +stdv) + + #current format for the weights is transpose of what is used in nn.Conv1d + #[HK]: load the weights for the conv1d layer and then transpose them + def load_state_dict(self, state_dict, strict: bool = True): + pass + + #[HK]: transpose the weights before saving so that they can be loaded in nn.Conv1d + def save_state_dict(self): + pass + + def forward(self, input): return conv1dFunc.apply(input, self.weights, self.bias, self.padding, self.is_bhl) \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/flashfftconv/sparse_conv.py b/overlay/kernels/cuda/flashfftconv/flashfftconv/sparse_conv.py index 65bfda6befe3ae84ce31258e850b904e1b90889f..c5d0ae4397f264c324312dd61829fe63da1e158f 100644 --- a/overlay/kernels/cuda/flashfftconv/flashfftconv/sparse_conv.py +++ b/overlay/kernels/cuda/flashfftconv/flashfftconv/sparse_conv.py @@ -1,39 +1,39 @@ -# Copyright (c) 2023, Dan Fu and Hermann Kumbong. -import torch -''' -Example implementations of partial and frequency-sparse convolutions. -These are just PyTorch examples, not optimized versions. -''' - -class PartialFFTConv(torch.nn.Module): - def __init__(self, N_partial): - super().__init__() - self.N_partial = N_partial - - def forward(self, x, k): - L = x.shape[-1] - N = 2 * L - x_dtype = x.dtype - x_f = torch.fft.rfft(x.float(), n = N) - k_f = torch.fft.rfft(k[..., :self.N_partial], n = N) - y_f = x_f * k_f - y = torch.fft.irfft(y_f, n = N)[..., :L].to(x_dtype) - - return y - -class FrequencySparseFFTConv(torch.nn.Module): - def __init__(self, N_partial): - super().__init__() - self.N_partial = N_partial - - def forward(self, x, k): - L = x.shape[-1] - N = 2 * L - x_dtype = x.dtype - x_f = torch.fft.rfft(x.float(), n = N) - k_f = torch.fft.rfft(k, n = N) - k_f[..., self.N_partial // 2:] = 0 - y_f = x_f * k_f - y = torch.fft.irfft(y_f, n = N)[..., :L].to(x_dtype) - +# Copyright (c) 2023, Dan Fu and Hermann Kumbong. +import torch +''' +Example implementations of partial and frequency-sparse convolutions. +These are just PyTorch examples, not optimized versions. +''' + +class PartialFFTConv(torch.nn.Module): + def __init__(self, N_partial): + super().__init__() + self.N_partial = N_partial + + def forward(self, x, k): + L = x.shape[-1] + N = 2 * L + x_dtype = x.dtype + x_f = torch.fft.rfft(x.float(), n = N) + k_f = torch.fft.rfft(k[..., :self.N_partial], n = N) + y_f = x_f * k_f + y = torch.fft.irfft(y_f, n = N)[..., :L].to(x_dtype) + + return y + +class FrequencySparseFFTConv(torch.nn.Module): + def __init__(self, N_partial): + super().__init__() + self.N_partial = N_partial + + def forward(self, x, k): + L = x.shape[-1] + N = 2 * L + x_dtype = x.dtype + x_f = torch.fft.rfft(x.float(), n = N) + k_f = torch.fft.rfft(k, n = N) + k_f[..., self.N_partial // 2:] = 0 + y_f = x_f * k_f + y = torch.fft.irfft(y_f, n = N)[..., :L].to(x_dtype) + return y \ No newline at end of file diff --git a/overlay/kernels/cuda/flashfftconv/setup.py b/overlay/kernels/cuda/flashfftconv/setup.py index e76b1d478fe3e0110b28b324df97368221bd3444..e79b3ffece79424ba295d489e1bc9eb79f28bcdf 100644 --- a/overlay/kernels/cuda/flashfftconv/setup.py +++ b/overlay/kernels/cuda/flashfftconv/setup.py @@ -1,22 +1,22 @@ -"""Python-wrapper setup for the vendored flashfftconv package. - -This installs only the pure-Python wrappers in `flashfftconv/`. The actual -CUDA extension (`monarch_cuda`) must be built separately via `csrc/setup.py` -— see README.md. - -License: Apache 2.0 (vendored from HazyResearch/flash-fft-conv). -""" - -from setuptools import setup - -if __name__ == "__main__": - setup( - name="flashfftconv", - version="0.0.0+hydra-vendored", - description="HazyResearch flash-fft-conv, vendored for HYDRA use", - url="https://github.com/HazyResearch/flash-fft-conv", - author="Dan Fu, Hermann Kumbong (upstream); vendored into HYDRA", - license="Apache 2.0", - packages=["flashfftconv"], - package_dir={"flashfftconv": "flashfftconv"}, - ) +"""Python-wrapper setup for the vendored flashfftconv package. + +This installs only the pure-Python wrappers in `flashfftconv/`. The actual +CUDA extension (`monarch_cuda`) must be built separately via `csrc/setup.py` +— see README.md. + +License: Apache 2.0 (vendored from HazyResearch/flash-fft-conv). +""" + +from setuptools import setup + +if __name__ == "__main__": + setup( + name="flashfftconv", + version="0.0.0+hydra-vendored", + description="HazyResearch flash-fft-conv, vendored for HYDRA use", + url="https://github.com/HazyResearch/flash-fft-conv", + author="Dan Fu, Hermann Kumbong (upstream); vendored into HYDRA", + license="Apache 2.0", + packages=["flashfftconv"], + package_dir={"flashfftconv": "flashfftconv"}, + ) diff --git a/overlay/kernels/cuda/hash_kernel.cu b/overlay/kernels/cuda/hash_kernel.cu index 164b4ed2dd35dfadbbecde809a870a5e04b7fcd1..3f36051a4cf8b1dc6a185ac84501947cab2d947d 100644 --- a/overlay/kernels/cuda/hash_kernel.cu +++ b/overlay/kernels/cuda/hash_kernel.cu @@ -1,12 +1,12 @@ -/* - * Engram CUDA hash kernel for O(1) N-gram context lookup. - * - * Phase 2: Custom CUDA kernel for batched hash computation. - * Phase 1: Uses Python-level hashing in EngramModule._hash_context(). - * - * Hash function: h = token[t] ^ (token[t-1] * prime_1) ^ (token[t-2] * prime_2) - * Output: h % n_columns (table index) - * - * This kernel parallelizes over (batch, sequence) dimensions. - */ -// Stub: Phase 2 implementation +/* + * Engram CUDA hash kernel for O(1) N-gram context lookup. + * + * Phase 2: Custom CUDA kernel for batched hash computation. + * Phase 1: Uses Python-level hashing in EngramModule._hash_context(). + * + * Hash function: h = token[t] ^ (token[t-1] * prime_1) ^ (token[t-2] * prime_2) + * Output: h % n_columns (table index) + * + * This kernel parallelizes over (batch, sequence) dimensions. + */ +// Stub: Phase 2 implementation diff --git a/overlay/kernels/tilelang/mhc_kernels.py b/overlay/kernels/tilelang/mhc_kernels.py index a92c89ba3d57ae5b8b1ee8d64678323693cac3fc..28a7f32f46dbc021ecfe29d754f3266cee610bc9 100644 --- a/overlay/kernels/tilelang/mhc_kernels.py +++ b/overlay/kernels/tilelang/mhc_kernels.py @@ -1,359 +1,359 @@ -"""5 fused mHC kernels for ManifoldHyperConnection operations. - -Phase 2: Triton kernels for stream routing operations. -(TileLang available but Triton preferred for sm_86 RTX 3060 compatibility.) - -Phase 1: Uses torch.einsum and standard ops in ManifoldHyperConnection - (subsystems/mhc_mini.py). - -Kernels (fused for n_streams=2): -1. stream_init: Replicate embedding across n_streams (torch broadcast) -2. stream_mix: Doubly-stochastic M @ streams (fused) -3. stream_inject: Additive injection of block output (fused) -4. stream_extract: Extract primary stream for block input (fused) -5. stream_merge: Weighted merge of streams (fused) - -For n_streams=2 (the only config used in HYDRA), the full forward pass -(mix -> extract -> inject) reduces to 2-3 scalar multiplies + adds per -element, fused into a single Triton kernel launch. - -DSL: Triton (@triton.jit) -Target: RTX 3060 (sm_86), bf16 compute, fp32 accumulation -""" - -from __future__ import annotations - -import torch -import triton -import triton.language as tl - - -# ============================================================================ -# Triton kernel: fused mix + extract + block_fn + inject for n_streams=2 -# ============================================================================ -# -# Given streams (2, B, T, d) and doubly-stochastic M (2x2): -# mixed = M[0,0]*s0 + M[0,1]*s1 (stream_mix row 0) -# primary_input = layernorm(mixed) (done outside kernel) -# block_output = block_fn(primary_input) (done outside kernel) -# out0 = s0 + M[0,0]*block_output (stream_inject) -# out1 = s1 + M[0,1]*block_output (stream_inject) -# -# We fuse the mix and inject into two kernels: mix_extract and inject. -# The block_fn call is opaque Python so it must happen between them. - -@triton.jit -def _mhc_mix_extract_kernel( - S0_ptr, # streams[0] (B*T*d) - S1_ptr, # streams[1] (B*T*d) - OUT_ptr, # mixed output (B*T*d) - M00, # scalar M[0,0] - M01, # scalar M[0,1] - N: tl.constexpr, # total elements = B*T*d - BLOCK: tl.constexpr, -): - """Fused stream_mix + stream_extract: mixed = M[0,0]*s0 + M[0,1]*s1.""" - pid = tl.program_id(0) - offs = pid * BLOCK + tl.arange(0, BLOCK) - mask = offs < N - - s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) - s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) - mixed = M00 * s0 + M01 * s1 - tl.store(OUT_ptr + offs, mixed.to(tl.bfloat16), mask=mask) - - -@triton.jit -def _mhc_inject_kernel( - S0_ptr, # streams[0] input/output (B*T*d) - S1_ptr, # streams[1] input/output (B*T*d) - BLOCK_OUT_ptr, # block_output (B*T*d) - OUT0_ptr, # output streams[0] (B*T*d) - OUT1_ptr, # output streams[1] (B*T*d) - M00, # scalar M[0,0] - M01, # scalar M[0,1] - N: tl.constexpr, - BLOCK: tl.constexpr, -): - """Fused stream_inject: out_i = s_i + M[0,i] * block_output.""" - pid = tl.program_id(0) - offs = pid * BLOCK + tl.arange(0, BLOCK) - mask = offs < N - - s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) - s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) - bo = tl.load(BLOCK_OUT_ptr + offs, mask=mask).to(tl.float32) - - out0 = s0 + M00 * bo - out1 = s1 + M01 * bo - - tl.store(OUT0_ptr + offs, out0.to(tl.bfloat16), mask=mask) - tl.store(OUT1_ptr + offs, out1.to(tl.bfloat16), mask=mask) - - -@triton.jit -def _mhc_merge_kernel( - S0_ptr, - S1_ptr, - OUT_ptr, - N: tl.constexpr, - BLOCK: tl.constexpr, -): - """Fused stream_merge: out = 0.5 * (s0 + s1).""" - pid = tl.program_id(0) - offs = pid * BLOCK + tl.arange(0, BLOCK) - mask = offs < N - - s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) - s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) - out = (s0 + s1) * 0.5 - tl.store(OUT_ptr + offs, out.to(tl.bfloat16), mask=mask) - - -# ============================================================================ -# Python wrappers -# ============================================================================ - -def _triton_grid(N: int, BLOCK: int): - return ((N + BLOCK - 1) // BLOCK,) - - -class MHCFusedOps: - """Fused mHC stream operations using Triton kernels. - - For n_streams=2 (the only HYDRA config), all 5 mHC operations are - covered by 3 kernel launches (mix+extract, inject, merge) instead of - 5 separate torch ops + temporaries. - - For n_streams != 2, falls back to equivalent torch operations. - """ - - BLOCK_SIZE = 1024 - - @staticmethod - def stream_init(x: torch.Tensor, n_streams: int) -> torch.Tensor: - """Replicate (B,T,d) -> (n_streams,B,T,d) via broadcast copy.""" - return x.unsqueeze(0).expand(n_streams, *x.shape).contiguous() - - @staticmethod - def stream_mix_extract( - streams: torch.Tensor, - M: torch.Tensor, - ) -> torch.Tensor: - """Fused mix + extract: returns mixed primary stream for block input. - - Args: - streams: (2, B, T, d) bf16 - M: (2, 2) fp32 doubly-stochastic matrix - - Returns: - mixed: (B, T, d) bf16 -- the primary stream after mixing - """ - n = streams.shape[0] - if n == 2: - s0 = streams[0].contiguous() - s1 = streams[1].contiguous() - N = s0.numel() - out = torch.empty_like(s0) - m00 = M[0, 0].item() - m01 = M[0, 1].item() - grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) - _mhc_mix_extract_kernel[grid]( - s0, s1, out, m00, m01, - N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, - ) - return out - # General fallback (promote to fp32 for einsum, cast back) - orig_dtype = streams.dtype - return torch.einsum("ij,jbtd->ibtd", M.float(), streams.float())[0].to(orig_dtype) - - @staticmethod - def stream_inject( - streams: torch.Tensor, - block_output: torch.Tensor, - M: torch.Tensor, - ) -> torch.Tensor: - """Fused inject: out_i = streams_i + M[0,i] * block_output. - - Args: - streams: (2, B, T, d) bf16 - block_output: (B, T, d) bf16 - M: (2, 2) fp32 doubly-stochastic matrix - - Returns: - new_streams: (2, B, T, d) bf16 - """ - n = streams.shape[0] - if n == 2: - s0 = streams[0].contiguous() - s1 = streams[1].contiguous() - bo = block_output.contiguous() - N = s0.numel() - out0 = torch.empty_like(s0) - out1 = torch.empty_like(s1) - m00 = M[0, 0].item() - m01 = M[0, 1].item() - grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) - _mhc_inject_kernel[grid]( - s0, s1, bo, out0, out1, m00, m01, - N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, - ) - return torch.stack([out0, out1], dim=0) - # General fallback (promote to fp32 for einsum, cast back) - orig_dtype = streams.dtype - update = torch.zeros_like(streams, dtype=torch.float32) - update[0] = block_output.float() - result = streams.float() + torch.einsum("ij,jbtd->ibtd", M.t().float(), update) - return result.to(orig_dtype) - - @staticmethod - def stream_merge(streams: torch.Tensor) -> torch.Tensor: - """Weighted merge: mean across streams -> (B, T, d). - - Args: - streams: (n_streams, B, T, d) bf16 - - Returns: - merged: (B, T, d) bf16 - """ - n = streams.shape[0] - if n == 2: - s0 = streams[0].contiguous() - s1 = streams[1].contiguous() - N = s0.numel() - out = torch.empty_like(s0) - grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) - _mhc_merge_kernel[grid]( - s0, s1, out, - N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, - ) - return out - return streams.mean(dim=0) - - -def mhc_fused_forward( - streams: torch.Tensor, - M: torch.Tensor, - block_fn, - stream_norm, -) -> torch.Tensor: - """Full fused mHC forward pass (excluding init). - - Equivalent to ManifoldHyperConnection.forward() from mhc_mini.py. - - Args: - streams: (n_streams, B, T, d) bf16 - M: (n_streams, n_streams) fp32 doubly-stochastic matrix - block_fn: callable (B,T,d) -> (B,T,d) - stream_norm: nn.LayerNorm(d) - - Returns: - new_streams: (n_streams, B, T, d) bf16 - """ - mixed = MHCFusedOps.stream_mix_extract(streams, M) - primary_input = stream_norm(mixed) - block_output = block_fn(primary_input) - return MHCFusedOps.stream_inject(streams, block_output, M) - - -# ============================================================================ -# Smoke test: compare fused ops vs mhc_mini reference -# ============================================================================ - -if __name__ == "__main__": - import sys - import os - - # Add project root to path for imports - project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - sys.path.insert(0, project_root) - - from subsystems.mhc_mini import ManifoldHyperConnection - - torch.manual_seed(42) - device = "cuda" - dtype = torch.bfloat16 - - B, T, d = 2, 128, 96 - n_streams = 2 - - # Reference module (bf16 weights to match bf16 data) - ref = ManifoldHyperConnection(d_model=d, n_streams=n_streams, sinkhorn_iters=5).to(device=device, dtype=dtype) - - # Input - x = torch.randn(B, T, d, device=device, dtype=dtype) - - # Init streams (both paths) - streams_ref = ref.init_streams(x) - streams_fused = MHCFusedOps.stream_init(x, n_streams) - assert torch.allclose(streams_ref, streams_fused, atol=0.0), "stream_init mismatch" - print("[PASS] stream_init") - - # Compute doubly-stochastic matrix - M = ref._sinkhorn(ref.log_alpha) - - # Test mix+extract - mixed_fused = MHCFusedOps.stream_mix_extract(streams_ref, M) - # Reference: M[0,0]*s0 + M[0,1]*s1 - mixed_ref = M[0, 0] * streams_ref[0] + M[0, 1] * streams_ref[1] - max_err = (mixed_fused.float() - mixed_ref.float()).abs().max().item() - print(f"[PASS] stream_mix_extract (max_err={max_err:.2e})") - assert max_err < 1e-2, f"mix_extract error too large: {max_err}" - - # Test inject - block_output = torch.randn(B, T, d, device=device, dtype=dtype) - injected_fused = MHCFusedOps.stream_inject(streams_ref, block_output, M) - out0_ref = streams_ref[0] + M[0, 0] * block_output - out1_ref = streams_ref[1] + M[0, 1] * block_output - injected_ref = torch.stack([out0_ref, out1_ref], dim=0) - max_err = (injected_fused.float() - injected_ref.float()).abs().max().item() - print(f"[PASS] stream_inject (max_err={max_err:.2e})") - assert max_err < 1e-2, f"inject error too large: {max_err}" - - # Test merge - merged_fused = MHCFusedOps.stream_merge(streams_ref) - merged_ref = ref.merge_streams(streams_ref) - max_err = (merged_fused.float() - merged_ref.float()).abs().max().item() - print(f"[PASS] stream_merge (max_err={max_err:.2e})") - assert max_err < 1e-2, f"merge error too large: {max_err}" - - # Full forward comparison - def dummy_block(x): - return x * 0.5 + 0.1 - - streams_for_ref = ref.init_streams(x) - streams_for_fused = MHCFusedOps.stream_init(x, n_streams) - - # Reference forward -- cast streams to float to match M dtype (fp32) - # then cast back, mirroring what actually happens in train.py where - # streams are bf16 and M is computed in fp32. - # The reference mhc_mini.py has a latent type promotion issue: M is fp32, - # streams are bf16, so mixed becomes fp32. LayerNorm then fails on fp32 - # when weights are bf16. We test the fused path directly instead. - out_fused = mhc_fused_forward( - streams_for_fused, M, dummy_block, ref.stream_norms[0], - ) - - # Manual reference: reproduce the n_streams=2 path from mhc_mini - M_ref = ref._sinkhorn(ref.log_alpha) - mixed_ref = (M_ref[0, 0] * streams_for_ref[0].float() + M_ref[0, 1] * streams_for_ref[1].float()).to(dtype) - primary_ref = ref.stream_norms[0](mixed_ref) - block_out_ref = dummy_block(primary_ref) - out0_ref = streams_for_ref[0].float() + M_ref[0, 0] * block_out_ref.float() - out1_ref = streams_for_ref[1].float() + M_ref[0, 1] * block_out_ref.float() - out_ref = torch.stack([out0_ref.to(dtype), out1_ref.to(dtype)], dim=0) - - max_err = (out_fused.float() - out_ref.float()).abs().max().item() - print(f"[PASS] full forward (max_err={max_err:.2e})") - assert max_err < 5e-2, f"full forward error too large: {max_err}" - - # Verify n_streams != 2 fallback works - ref4 = ManifoldHyperConnection(d_model=d, n_streams=4, sinkhorn_iters=5).to(device) - x4 = torch.randn(B, T, d, device=device, dtype=dtype) - s4 = MHCFusedOps.stream_init(x4, 4) - M4 = ref4._sinkhorn(ref4.log_alpha) - mixed4 = MHCFusedOps.stream_mix_extract(s4, M4) - merged4 = MHCFusedOps.stream_merge(s4) - print("[PASS] n_streams=4 fallback (torch ops)") - - print("\n=== All mHC kernel smoke tests PASSED ===") +"""5 fused mHC kernels for ManifoldHyperConnection operations. + +Phase 2: Triton kernels for stream routing operations. +(TileLang available but Triton preferred for sm_86 RTX 3060 compatibility.) + +Phase 1: Uses torch.einsum and standard ops in ManifoldHyperConnection + (subsystems/mhc_mini.py). + +Kernels (fused for n_streams=2): +1. stream_init: Replicate embedding across n_streams (torch broadcast) +2. stream_mix: Doubly-stochastic M @ streams (fused) +3. stream_inject: Additive injection of block output (fused) +4. stream_extract: Extract primary stream for block input (fused) +5. stream_merge: Weighted merge of streams (fused) + +For n_streams=2 (the only config used in HYDRA), the full forward pass +(mix -> extract -> inject) reduces to 2-3 scalar multiplies + adds per +element, fused into a single Triton kernel launch. + +DSL: Triton (@triton.jit) +Target: RTX 3060 (sm_86), bf16 compute, fp32 accumulation +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +# ============================================================================ +# Triton kernel: fused mix + extract + block_fn + inject for n_streams=2 +# ============================================================================ +# +# Given streams (2, B, T, d) and doubly-stochastic M (2x2): +# mixed = M[0,0]*s0 + M[0,1]*s1 (stream_mix row 0) +# primary_input = layernorm(mixed) (done outside kernel) +# block_output = block_fn(primary_input) (done outside kernel) +# out0 = s0 + M[0,0]*block_output (stream_inject) +# out1 = s1 + M[0,1]*block_output (stream_inject) +# +# We fuse the mix and inject into two kernels: mix_extract and inject. +# The block_fn call is opaque Python so it must happen between them. + +@triton.jit +def _mhc_mix_extract_kernel( + S0_ptr, # streams[0] (B*T*d) + S1_ptr, # streams[1] (B*T*d) + OUT_ptr, # mixed output (B*T*d) + M00, # scalar M[0,0] + M01, # scalar M[0,1] + N: tl.constexpr, # total elements = B*T*d + BLOCK: tl.constexpr, +): + """Fused stream_mix + stream_extract: mixed = M[0,0]*s0 + M[0,1]*s1.""" + pid = tl.program_id(0) + offs = pid * BLOCK + tl.arange(0, BLOCK) + mask = offs < N + + s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) + s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) + mixed = M00 * s0 + M01 * s1 + tl.store(OUT_ptr + offs, mixed.to(tl.bfloat16), mask=mask) + + +@triton.jit +def _mhc_inject_kernel( + S0_ptr, # streams[0] input/output (B*T*d) + S1_ptr, # streams[1] input/output (B*T*d) + BLOCK_OUT_ptr, # block_output (B*T*d) + OUT0_ptr, # output streams[0] (B*T*d) + OUT1_ptr, # output streams[1] (B*T*d) + M00, # scalar M[0,0] + M01, # scalar M[0,1] + N: tl.constexpr, + BLOCK: tl.constexpr, +): + """Fused stream_inject: out_i = s_i + M[0,i] * block_output.""" + pid = tl.program_id(0) + offs = pid * BLOCK + tl.arange(0, BLOCK) + mask = offs < N + + s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) + s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) + bo = tl.load(BLOCK_OUT_ptr + offs, mask=mask).to(tl.float32) + + out0 = s0 + M00 * bo + out1 = s1 + M01 * bo + + tl.store(OUT0_ptr + offs, out0.to(tl.bfloat16), mask=mask) + tl.store(OUT1_ptr + offs, out1.to(tl.bfloat16), mask=mask) + + +@triton.jit +def _mhc_merge_kernel( + S0_ptr, + S1_ptr, + OUT_ptr, + N: tl.constexpr, + BLOCK: tl.constexpr, +): + """Fused stream_merge: out = 0.5 * (s0 + s1).""" + pid = tl.program_id(0) + offs = pid * BLOCK + tl.arange(0, BLOCK) + mask = offs < N + + s0 = tl.load(S0_ptr + offs, mask=mask).to(tl.float32) + s1 = tl.load(S1_ptr + offs, mask=mask).to(tl.float32) + out = (s0 + s1) * 0.5 + tl.store(OUT_ptr + offs, out.to(tl.bfloat16), mask=mask) + + +# ============================================================================ +# Python wrappers +# ============================================================================ + +def _triton_grid(N: int, BLOCK: int): + return ((N + BLOCK - 1) // BLOCK,) + + +class MHCFusedOps: + """Fused mHC stream operations using Triton kernels. + + For n_streams=2 (the only HYDRA config), all 5 mHC operations are + covered by 3 kernel launches (mix+extract, inject, merge) instead of + 5 separate torch ops + temporaries. + + For n_streams != 2, falls back to equivalent torch operations. + """ + + BLOCK_SIZE = 1024 + + @staticmethod + def stream_init(x: torch.Tensor, n_streams: int) -> torch.Tensor: + """Replicate (B,T,d) -> (n_streams,B,T,d) via broadcast copy.""" + return x.unsqueeze(0).expand(n_streams, *x.shape).contiguous() + + @staticmethod + def stream_mix_extract( + streams: torch.Tensor, + M: torch.Tensor, + ) -> torch.Tensor: + """Fused mix + extract: returns mixed primary stream for block input. + + Args: + streams: (2, B, T, d) bf16 + M: (2, 2) fp32 doubly-stochastic matrix + + Returns: + mixed: (B, T, d) bf16 -- the primary stream after mixing + """ + n = streams.shape[0] + if n == 2: + s0 = streams[0].contiguous() + s1 = streams[1].contiguous() + N = s0.numel() + out = torch.empty_like(s0) + m00 = M[0, 0].item() + m01 = M[0, 1].item() + grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) + _mhc_mix_extract_kernel[grid]( + s0, s1, out, m00, m01, + N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, + ) + return out + # General fallback (promote to fp32 for einsum, cast back) + orig_dtype = streams.dtype + return torch.einsum("ij,jbtd->ibtd", M.float(), streams.float())[0].to(orig_dtype) + + @staticmethod + def stream_inject( + streams: torch.Tensor, + block_output: torch.Tensor, + M: torch.Tensor, + ) -> torch.Tensor: + """Fused inject: out_i = streams_i + M[0,i] * block_output. + + Args: + streams: (2, B, T, d) bf16 + block_output: (B, T, d) bf16 + M: (2, 2) fp32 doubly-stochastic matrix + + Returns: + new_streams: (2, B, T, d) bf16 + """ + n = streams.shape[0] + if n == 2: + s0 = streams[0].contiguous() + s1 = streams[1].contiguous() + bo = block_output.contiguous() + N = s0.numel() + out0 = torch.empty_like(s0) + out1 = torch.empty_like(s1) + m00 = M[0, 0].item() + m01 = M[0, 1].item() + grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) + _mhc_inject_kernel[grid]( + s0, s1, bo, out0, out1, m00, m01, + N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, + ) + return torch.stack([out0, out1], dim=0) + # General fallback (promote to fp32 for einsum, cast back) + orig_dtype = streams.dtype + update = torch.zeros_like(streams, dtype=torch.float32) + update[0] = block_output.float() + result = streams.float() + torch.einsum("ij,jbtd->ibtd", M.t().float(), update) + return result.to(orig_dtype) + + @staticmethod + def stream_merge(streams: torch.Tensor) -> torch.Tensor: + """Weighted merge: mean across streams -> (B, T, d). + + Args: + streams: (n_streams, B, T, d) bf16 + + Returns: + merged: (B, T, d) bf16 + """ + n = streams.shape[0] + if n == 2: + s0 = streams[0].contiguous() + s1 = streams[1].contiguous() + N = s0.numel() + out = torch.empty_like(s0) + grid = _triton_grid(N, MHCFusedOps.BLOCK_SIZE) + _mhc_merge_kernel[grid]( + s0, s1, out, + N=N, BLOCK=MHCFusedOps.BLOCK_SIZE, + ) + return out + return streams.mean(dim=0) + + +def mhc_fused_forward( + streams: torch.Tensor, + M: torch.Tensor, + block_fn, + stream_norm, +) -> torch.Tensor: + """Full fused mHC forward pass (excluding init). + + Equivalent to ManifoldHyperConnection.forward() from mhc_mini.py. + + Args: + streams: (n_streams, B, T, d) bf16 + M: (n_streams, n_streams) fp32 doubly-stochastic matrix + block_fn: callable (B,T,d) -> (B,T,d) + stream_norm: nn.LayerNorm(d) + + Returns: + new_streams: (n_streams, B, T, d) bf16 + """ + mixed = MHCFusedOps.stream_mix_extract(streams, M) + primary_input = stream_norm(mixed) + block_output = block_fn(primary_input) + return MHCFusedOps.stream_inject(streams, block_output, M) + + +# ============================================================================ +# Smoke test: compare fused ops vs mhc_mini reference +# ============================================================================ + +if __name__ == "__main__": + import sys + import os + + # Add project root to path for imports + project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + sys.path.insert(0, project_root) + + from subsystems.mhc_mini import ManifoldHyperConnection + + torch.manual_seed(42) + device = "cuda" + dtype = torch.bfloat16 + + B, T, d = 2, 128, 96 + n_streams = 2 + + # Reference module (bf16 weights to match bf16 data) + ref = ManifoldHyperConnection(d_model=d, n_streams=n_streams, sinkhorn_iters=5).to(device=device, dtype=dtype) + + # Input + x = torch.randn(B, T, d, device=device, dtype=dtype) + + # Init streams (both paths) + streams_ref = ref.init_streams(x) + streams_fused = MHCFusedOps.stream_init(x, n_streams) + assert torch.allclose(streams_ref, streams_fused, atol=0.0), "stream_init mismatch" + print("[PASS] stream_init") + + # Compute doubly-stochastic matrix + M = ref._sinkhorn(ref.log_alpha) + + # Test mix+extract + mixed_fused = MHCFusedOps.stream_mix_extract(streams_ref, M) + # Reference: M[0,0]*s0 + M[0,1]*s1 + mixed_ref = M[0, 0] * streams_ref[0] + M[0, 1] * streams_ref[1] + max_err = (mixed_fused.float() - mixed_ref.float()).abs().max().item() + print(f"[PASS] stream_mix_extract (max_err={max_err:.2e})") + assert max_err < 1e-2, f"mix_extract error too large: {max_err}" + + # Test inject + block_output = torch.randn(B, T, d, device=device, dtype=dtype) + injected_fused = MHCFusedOps.stream_inject(streams_ref, block_output, M) + out0_ref = streams_ref[0] + M[0, 0] * block_output + out1_ref = streams_ref[1] + M[0, 1] * block_output + injected_ref = torch.stack([out0_ref, out1_ref], dim=0) + max_err = (injected_fused.float() - injected_ref.float()).abs().max().item() + print(f"[PASS] stream_inject (max_err={max_err:.2e})") + assert max_err < 1e-2, f"inject error too large: {max_err}" + + # Test merge + merged_fused = MHCFusedOps.stream_merge(streams_ref) + merged_ref = ref.merge_streams(streams_ref) + max_err = (merged_fused.float() - merged_ref.float()).abs().max().item() + print(f"[PASS] stream_merge (max_err={max_err:.2e})") + assert max_err < 1e-2, f"merge error too large: {max_err}" + + # Full forward comparison + def dummy_block(x): + return x * 0.5 + 0.1 + + streams_for_ref = ref.init_streams(x) + streams_for_fused = MHCFusedOps.stream_init(x, n_streams) + + # Reference forward -- cast streams to float to match M dtype (fp32) + # then cast back, mirroring what actually happens in train.py where + # streams are bf16 and M is computed in fp32. + # The reference mhc_mini.py has a latent type promotion issue: M is fp32, + # streams are bf16, so mixed becomes fp32. LayerNorm then fails on fp32 + # when weights are bf16. We test the fused path directly instead. + out_fused = mhc_fused_forward( + streams_for_fused, M, dummy_block, ref.stream_norms[0], + ) + + # Manual reference: reproduce the n_streams=2 path from mhc_mini + M_ref = ref._sinkhorn(ref.log_alpha) + mixed_ref = (M_ref[0, 0] * streams_for_ref[0].float() + M_ref[0, 1] * streams_for_ref[1].float()).to(dtype) + primary_ref = ref.stream_norms[0](mixed_ref) + block_out_ref = dummy_block(primary_ref) + out0_ref = streams_for_ref[0].float() + M_ref[0, 0] * block_out_ref.float() + out1_ref = streams_for_ref[1].float() + M_ref[0, 1] * block_out_ref.float() + out_ref = torch.stack([out0_ref.to(dtype), out1_ref.to(dtype)], dim=0) + + max_err = (out_fused.float() - out_ref.float()).abs().max().item() + print(f"[PASS] full forward (max_err={max_err:.2e})") + assert max_err < 5e-2, f"full forward error too large: {max_err}" + + # Verify n_streams != 2 fallback works + ref4 = ManifoldHyperConnection(d_model=d, n_streams=4, sinkhorn_iters=5).to(device) + x4 = torch.randn(B, T, d, device=device, dtype=dtype) + s4 = MHCFusedOps.stream_init(x4, 4) + M4 = ref4._sinkhorn(ref4.log_alpha) + mixed4 = MHCFusedOps.stream_mix_extract(s4, M4) + merged4 = MHCFusedOps.stream_merge(s4) + print("[PASS] n_streams=4 fallback (torch ops)") + + print("\n=== All mHC kernel smoke tests PASSED ===") diff --git a/overlay/kernels/tilelang/ssd_mimo_prefill.py b/overlay/kernels/tilelang/ssd_mimo_prefill.py index 51ba813e4f9ea61ec2eedbce1ba4183860043094..afdde23ce3ec074b4130420ada2721b343256787 100644 --- a/overlay/kernels/tilelang/ssd_mimo_prefill.py +++ b/overlay/kernels/tilelang/ssd_mimo_prefill.py @@ -1,452 +1,452 @@ -"""MIMO prefill kernel for Mamba-3 multi-input multi-output mode. - -Phase 2 kernel -- implemented and smoke-tested but not wired. Requires -MIMO mode activation in Mamba3Block (currently SISO-only). Wire when -config.mimo_rank > 1 is supported. - -Phase 2: Triton kernel for MIMO parallel scan with multi-input -multi-output state transitions. -(TileLang available but Triton preferred for sm_86 RTX 3060 compatibility.) - -Phase 1: MIMO is disabled (SISO mode only in train.py). - -STATUS: Mathematical kernel implemented, NOT YET WIRED into training loop. -The upstream mamba_ssm package provides TileLang-based MIMO kernels -(mamba_ssm.ops.tilelang.mamba3.mamba3_mimo) for production use. This -module implements an equivalent Triton parallel scan for reference and -potential future use when MIMO is activated. - -MIMO extends SISO by sharing input projections across mimo_rank groups, -enabling richer state dynamics without proportional parameter increase. -Requires the SSD (State Space Duality) kernel for efficient chunked scan. - -The core operation is a parallel prefix scan over state transitions: - h_t = A_t * h_{t-1} + B_t * x_t (SISO: A,B,x are per-head) - H_t = A_t * H_{t-1} + B_t @ X_t (MIMO: B is (N,R), X is (R,P)) - -For MIMO rank R, each time step has: - - A_t: (H,) scalar decay per head (shared across N,P dims) - - B_t: (H, N, R) input projection -- R input channels to N state dims - - X_t: (H, R, P) input values -- R channels, P features - - H_t: (H, N, P) hidden state - -The parallel scan uses the associative operator: - (a1, b1) o (a2, b2) = (a2 * a1, a2 * b1 + b2) - -DSL: Triton (@triton.jit) -Target: RTX 3060 (sm_86), bf16 compute, fp32 accumulation -""" - -from __future__ import annotations - -import torch -import triton -import triton.language as tl - - -# ============================================================================ -# Triton kernel: MIMO parallel prefix scan (forward only) -# ============================================================================ -# -# For each head h, the recurrence is: -# state[t] = decay[t] * state[t-1] + K[t] @ V[t] -# where: -# decay[t] is a scalar (exp(A*dt) in Mamba-3) -# K[t] is (N, R) -- projects R input channels into N state dims -# V[t] is (R, P) -- the R-channel input with P features -# state[t] is (N, P) -- the hidden state -# -# The parallel scan operates over the time dimension within chunks. -# Inter-chunk state is accumulated sequentially across chunks. - -@triton.jit -def _mimo_scan_chunk_kernel( - # Inputs - DECAY_ptr, # (B, H, T) fp32 -- exp(A*dt) cumulative within chunk - K_ptr, # (B, T, H, N) bf16 -- after MIMO projection: K * mimo_v - V_ptr, # (B, T, H, P) bf16 -- value features - # Outputs - STATE_ptr, # (B, H, n_chunks, N, P) fp32 -- chunk boundary states - OUT_ptr, # (B, T, H, P) bf16 -- scan output at each step - # Dimensions - B: tl.constexpr, - T: tl.constexpr, - H: tl.constexpr, - N: tl.constexpr, - P: tl.constexpr, - CHUNK_SIZE: tl.constexpr, -): - """Intra-chunk sequential scan with state output at chunk boundaries. - - This implements the inner loop of a chunked parallel scan: - 1. Within each chunk: sequential scan (CHUNK_SIZE steps) - 2. Chunk boundary states are written to STATE for inter-chunk pass - 3. Full output is written to OUT - - For MIMO, the "BX" contribution at each step is: - contribution[n,p] = sum_r(K[t,h,n,r] * V[t,h,r,p]) - But since we store K after MIMO projection (K already multiplied by - mimo_v), K is (B,T,H,N) and V is (B,T,H,P), the rank-R contraction - reduces to an outer product K[n] * V[p] (effectively R=1 after - projection). For true MIMO rank>1, K and V would have an extra R dim - and we'd need an inner reduction. This kernel handles the projected - (post-contraction) form. - """ - # Grid: (B*H, n_chunks) - pid_bh = tl.program_id(0) - pid_chunk = tl.program_id(1) - - b = pid_bh // H - h = pid_bh % H - - n_chunks = (T + CHUNK_SIZE - 1) // CHUNK_SIZE - chunk_start = pid_chunk * CHUNK_SIZE - chunk_end = tl.minimum(chunk_start + CHUNK_SIZE, T) - - # State accumulator: (N, P) in fp32 - # For the parallel scan, each chunk starts from zero state. - # The inter-chunk correction is applied in a separate pass. - offs_n = tl.arange(0, N) - offs_p = tl.arange(0, P) - - # Initialize state to zero - # We use a flat representation: state[n*P + p] - state = tl.zeros([N * P], dtype=tl.float32) - - # Sequential scan within chunk - for t in range(CHUNK_SIZE): - actual_t = chunk_start + t - if actual_t < chunk_end: - # Load decay for this timestep - decay_offset = b * H * T + h * T + actual_t - decay = tl.load(DECAY_ptr + decay_offset) - - # Decay existing state - state = state * decay - - # Load K[b, actual_t, h, :N] and V[b, actual_t, h, :P] - k_base = b * T * H * N + actual_t * H * N + h * N - v_base = b * T * H * P + actual_t * H * P + h * P - - k_vals = tl.load(K_ptr + k_base + offs_n, mask=offs_n < N).to(tl.float32) - v_vals = tl.load(V_ptr + v_base + offs_p, mask=offs_p < P).to(tl.float32) - - # Outer product: state += k[:, None] * v[None, :] - # Flattened: state[n*P + p] += k[n] * v[p] - for ni in range(N): - k_n = tl.load(K_ptr + k_base + ni).to(tl.float32) - contrib = k_n * v_vals # (P,) vector - state_slice = tl.load( - STATE_ptr + 0, # dummy, we use state variable - mask=False, - ) - # Update state slice for this n - for pi in range(P): - idx = ni * P + pi - old = tl.load(STATE_ptr + 0, mask=False) # dummy - # Can't index into state directly in a loop, - # so we accumulate via atomic-like pattern - pass - - # NOTE: The above loop structure shows the mathematical intent but - # hits Triton limitations for dynamic N*P indexing. The practical - # implementation below uses a simpler approach for small N, P. - - -# ============================================================================ -# Practical implementation: torch-based chunked MIMO scan -# ============================================================================ -# For correctness and flexibility, we implement the MIMO scan using -# PyTorch ops with the same chunking strategy. This is the reference -# that a future fully-fused Triton kernel should match. - -def mimo_parallel_scan( - decay: torch.Tensor, # (B, H, T) fp32 -- per-step scalar decay - K: torch.Tensor, # (B, T, R, H, N) bf16 -- projected keys - V: torch.Tensor, # (B, T, H, P) bf16 -- values - chunk_size: int = 64, - initial_state: torch.Tensor | None = None, # (B, H, N, P) fp32 -) -> tuple[torch.Tensor, torch.Tensor]: - """MIMO chunked parallel scan. - - Implements the recurrence: - state[t] = decay[t] * state[t-1] + sum_r(K[t,:,r,:,:] * V[t]) - - For MIMO rank R, K has shape (B,T,R,H,N) and the rank-R contribution - is contracted: BX[t,h,n,p] = sum_r K[t,r,h,n] * V[t,h,p] - - Uses a two-pass chunked approach: - 1. Intra-chunk: sequential scan within each chunk (cheap, O(chunk_size)) - 2. Inter-chunk: parallel scan of chunk boundary states - - Args: - decay: (B, H, T) fp32 scalar decay factors per step - K: (B, T, R, H, N) bf16 input projections - V: (B, T, H, P) bf16 value features - chunk_size: chunk size for parallel scan (default 64) - initial_state: optional (B, H, N, P) fp32 starting state - - Returns: - output: (B, T, H, P) bf16 scan output (state @ C, where C=I for now) - final_state: (B, H, N, P) fp32 final hidden state - """ - B, T, R, H, N = K.shape - P = V.shape[-1] - device = K.device - - n_chunks = (T + chunk_size - 1) // chunk_size - - # Accumulate chunk-level decay products for inter-chunk propagation - # chunk_decay[b, h, c] = prod(decay[b, h, t] for t in chunk c) - chunk_decays = torch.zeros(B, H, n_chunks, device=device, dtype=torch.float32) - - # Intra-chunk states: the state at the END of each chunk (computed - # from zero initial state within each chunk) - chunk_states = torch.zeros(B, H, n_chunks, N, P, device=device, dtype=torch.float32) - - # Full output buffer - output = torch.empty(B, T, H, P, device=device, dtype=V.dtype) - - # ---- Pass 1: Intra-chunk sequential scan ---- - for c in range(n_chunks): - t_start = c * chunk_size - t_end = min(t_start + chunk_size, T) - chunk_len = t_end - t_start - - # State within this chunk (starts from zero) - state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) - cum_decay = torch.ones(B, H, device=device, dtype=torch.float32) - - for t_offset in range(chunk_len): - t = t_start + t_offset - - # decay_t: (B, H) - decay_t = decay[:, :, t] - - # Decay state - state = state * decay_t[:, :, None, None] - cum_decay = cum_decay * decay_t - - # BX contribution: sum_r K[b,t,r,h,n] * V[b,t,h,p] - # K: (B, T, R, H, N), V: (B, T, H, P) - # BX[b,h,n,p] = sum_r K[b,t,r,h,n] * V[b,t,h,p] - k_t = K[:, t, :, :, :].float() # (B, R, H, N) - v_t = V[:, t, :, :].float() # (B, H, P) - - # Contract over R: (B,R,H,N) -> sum_r -> (B,H,N) - k_sum = k_t.sum(dim=1) # (B, H, N) - - # Outer product with V: (B,H,N,1) * (B,H,1,P) -> (B,H,N,P) - bx = k_sum.unsqueeze(-1) * v_t.unsqueeze(-2) - - state = state + bx - - # Output: project state back (using identity for now) - # In full MIMO, this would involve mimo_out projection - output[:, t, :, :] = state.mean(dim=-2).to(V.dtype) - - chunk_states[:, :, c, :, :] = state - chunk_decays[:, :, c] = cum_decay - - # ---- Pass 2: Inter-chunk parallel scan (sequential for simplicity) ---- - # Propagate accumulated state across chunk boundaries - if initial_state is not None: - running_state = initial_state.clone() - else: - running_state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) - - for c in range(n_chunks): - t_start = c * chunk_size - t_end = min(t_start + chunk_size, T) - chunk_len = t_end - t_start - - if c > 0 or initial_state is not None: - # The correction for this chunk is: - # corrected_state[t] = intra_state[t] + decay_from_chunk_start_to_t * running_state - # For the output, we need to add the correction at each t - cum_d = torch.ones(B, H, device=device, dtype=torch.float32) - for t_offset in range(chunk_len): - t = t_start + t_offset - decay_t = decay[:, :, t] - cum_d = cum_d * decay_t - - # Correction: cum_d * running_state projected to output - correction = (cum_d[:, :, None, None] * running_state).mean(dim=-2) - output[:, t, :, :] = output[:, t, :, :].float() + correction - output[:, t, :, :] = output[:, t, :, :].to(V.dtype) - - # Update running state for next chunk - running_state = chunk_decays[:, :, c, None, None] * running_state + chunk_states[:, :, c, :, :] - - final_state = running_state - return output, final_state - - -# ============================================================================ -# Triton kernel: simple SISO-to-MIMO bridge scan -# ============================================================================ -# For the case where MIMO rank=1 (effectively SISO), we can use a -# vectorized Triton scan. This is the building block for rank>1. - -@triton.jit -def _siso_scan_kernel( - DECAY_ptr, # (B*H, T) fp32 - BX_ptr, # (B*H, T, NP) fp32 -- flattened N*P outer product - OUT_ptr, # (B*H, T, NP) fp32 -- scan output - T_val: tl.constexpr, - NP: tl.constexpr, - BLOCK_NP: tl.constexpr, -): - """Vectorized parallel scan for a single (B,H) slice. - - Computes: state[t] = decay[t] * state[t-1] + BX[t] - for each of the NP state dimensions independently. - - This is sequential in T but parallel across NP dimensions. - For short T (within a chunk), this is efficient. - """ - pid = tl.program_id(0) # indexes into B*H - offs_np = tl.arange(0, BLOCK_NP) - mask_np = offs_np < NP - - # Running state - state = tl.zeros([BLOCK_NP], dtype=tl.float32) - - for t in range(T_val): - # Load decay - decay = tl.load(DECAY_ptr + pid * T_val + t) - state = state * decay - - # Load BX[pid, t, :NP] - bx_base = pid * T_val * NP + t * NP - bx = tl.load(BX_ptr + bx_base + offs_np, mask=mask_np, other=0.0) - state = state + bx - - # Store output - out_base = pid * T_val * NP + t * NP - tl.store(OUT_ptr + out_base + offs_np, state, mask=mask_np) - - -def siso_scan_triton( - decay: torch.Tensor, # (B, H, T) fp32 - BX: torch.Tensor, # (B, H, T, N, P) fp32 -- outer product per step -) -> torch.Tensor: - """Triton-accelerated sequential scan (vectorized over N*P). - - This is the intra-chunk scan kernel. For short chunk sizes (16-64), - sequential scan is faster than work-inefficient parallel prefix. - - Args: - decay: (B, H, T) fp32 per-step decay - BX: (B, H, T, N, P) fp32 state update per step - - Returns: - states: (B, H, T, N, P) fp32 state at each step - """ - B, H, T_len, N, P = BX.shape - NP = N * P - - # Flatten for kernel - decay_flat = decay.reshape(B * H, T_len).contiguous() - bx_flat = BX.reshape(B * H, T_len, NP).contiguous() - out_flat = torch.empty_like(bx_flat) - - BLOCK_NP = triton.next_power_of_2(NP) - - grid = (B * H,) - _siso_scan_kernel[grid]( - decay_flat, bx_flat, out_flat, - T_val=T_len, NP=NP, BLOCK_NP=BLOCK_NP, - ) - - return out_flat.reshape(B, H, T_len, N, P) - - -# ============================================================================ -# Smoke test -# ============================================================================ - -if __name__ == "__main__": - torch.manual_seed(42) - device = "cuda" - - print("=== MIMO Parallel Scan Smoke Tests ===\n") - - # ---- Test 1: SISO scan (R=1) via Triton kernel ---- - B, H, T, N, P = 2, 4, 32, 8, 16 - decay = torch.rand(B, H, T, device=device, dtype=torch.float32) * 0.5 + 0.5 - BX = torch.randn(B, H, T, N, P, device=device, dtype=torch.float32) * 0.1 - - # Triton scan - states_triton = siso_scan_triton(decay, BX) - - # Reference sequential scan - states_ref = torch.zeros(B, H, T, N, P, device=device, dtype=torch.float32) - state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) - for t in range(T): - state = decay[:, :, t, None, None] * state + BX[:, :, t, :, :] - states_ref[:, :, t, :, :] = state - - max_err = (states_triton - states_ref).abs().max().item() - print(f"[PASS] SISO Triton scan (max_err={max_err:.2e})") - assert max_err < 1e-4, f"SISO scan error too large: {max_err}" - - # ---- Test 2: MIMO chunked scan (R=2) ---- - B, T, R, H, N, P = 2, 64, 2, 4, 8, 16 - decay = torch.rand(B, H, T, device=device, dtype=torch.float32) * 0.5 + 0.5 - K = torch.randn(B, T, R, H, N, device=device, dtype=torch.bfloat16) * 0.1 - V = torch.randn(B, T, H, P, device=device, dtype=torch.bfloat16) * 0.1 - - output, final_state = mimo_parallel_scan(decay, K, V, chunk_size=16) - - # Reference: sequential scan (no chunking) - state_ref = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) - output_ref = torch.empty(B, T, H, P, device=device, dtype=torch.bfloat16) - for t in range(T): - state_ref = decay[:, :, t, None, None] * state_ref - k_t = K[:, t, :, :, :].float().sum(dim=1) # (B, H, N) - v_t = V[:, t, :, :].float() # (B, H, P) - bx = k_t.unsqueeze(-1) * v_t.unsqueeze(-2) # (B, H, N, P) - state_ref = state_ref + bx - output_ref[:, t, :, :] = state_ref.mean(dim=-2).to(torch.bfloat16) - - max_err_out = (output.float() - output_ref.float()).abs().max().item() - max_err_state = (final_state - state_ref).abs().max().item() - print(f"[PASS] MIMO chunked scan output (max_err={max_err_out:.2e})") - print(f"[PASS] MIMO chunked scan final_state (max_err={max_err_state:.2e})") - assert max_err_out < 5e-2, f"MIMO output error too large: {max_err_out}" - assert max_err_state < 1e-3, f"MIMO state error too large: {max_err_state}" - - # ---- Test 3: MIMO with initial state ---- - init_state = torch.randn(B, H, N, P, device=device, dtype=torch.float32) * 0.01 - output_init, final_init = mimo_parallel_scan( - decay, K, V, chunk_size=16, initial_state=init_state, - ) - - state_ref2 = init_state.clone() - for t in range(T): - state_ref2 = decay[:, :, t, None, None] * state_ref2 - k_t = K[:, t, :, :, :].float().sum(dim=1) - v_t = V[:, t, :, :].float() - bx = k_t.unsqueeze(-1) * v_t.unsqueeze(-2) - state_ref2 = state_ref2 + bx - - max_err_init = (final_init - state_ref2).abs().max().item() - print(f"[PASS] MIMO with initial_state (max_err={max_err_init:.2e})") - assert max_err_init < 1e-3, f"MIMO init state error too large: {max_err_init}" - - # ---- Test 4: SISO scan with chunk_size=T (single chunk, no inter-chunk) ---- - output_1chunk, _ = mimo_parallel_scan(decay, K, V, chunk_size=T) - max_err_1c = (output_1chunk.float() - output_ref.float()).abs().max().item() - print(f"[PASS] MIMO single-chunk (max_err={max_err_1c:.2e})") - assert max_err_1c < 5e-2, f"Single chunk error too large: {max_err_1c}" - - # ---- Test 5: Shape validation ---- - assert output.shape == (B, T, H, P), f"Output shape mismatch: {output.shape}" - assert final_state.shape == (B, H, N, P), f"State shape mismatch: {final_state.shape}" - print("[PASS] Shape validation") - - print(f"\n=== All MIMO scan smoke tests PASSED ===") - print(f"NOTE: This kernel is NOT wired into the training loop.") - print(f" MIMO is a Phase 2 feature (Phase 1 uses SISO only).") - print(f" See mamba_ssm.ops.tilelang.mamba3 for production MIMO kernels.") +"""MIMO prefill kernel for Mamba-3 multi-input multi-output mode. + +Phase 2 kernel -- implemented and smoke-tested but not wired. Requires +MIMO mode activation in Mamba3Block (currently SISO-only). Wire when +config.mimo_rank > 1 is supported. + +Phase 2: Triton kernel for MIMO parallel scan with multi-input +multi-output state transitions. +(TileLang available but Triton preferred for sm_86 RTX 3060 compatibility.) + +Phase 1: MIMO is disabled (SISO mode only in train.py). + +STATUS: Mathematical kernel implemented, NOT YET WIRED into training loop. +The upstream mamba_ssm package provides TileLang-based MIMO kernels +(mamba_ssm.ops.tilelang.mamba3.mamba3_mimo) for production use. This +module implements an equivalent Triton parallel scan for reference and +potential future use when MIMO is activated. + +MIMO extends SISO by sharing input projections across mimo_rank groups, +enabling richer state dynamics without proportional parameter increase. +Requires the SSD (State Space Duality) kernel for efficient chunked scan. + +The core operation is a parallel prefix scan over state transitions: + h_t = A_t * h_{t-1} + B_t * x_t (SISO: A,B,x are per-head) + H_t = A_t * H_{t-1} + B_t @ X_t (MIMO: B is (N,R), X is (R,P)) + +For MIMO rank R, each time step has: + - A_t: (H,) scalar decay per head (shared across N,P dims) + - B_t: (H, N, R) input projection -- R input channels to N state dims + - X_t: (H, R, P) input values -- R channels, P features + - H_t: (H, N, P) hidden state + +The parallel scan uses the associative operator: + (a1, b1) o (a2, b2) = (a2 * a1, a2 * b1 + b2) + +DSL: Triton (@triton.jit) +Target: RTX 3060 (sm_86), bf16 compute, fp32 accumulation +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +# ============================================================================ +# Triton kernel: MIMO parallel prefix scan (forward only) +# ============================================================================ +# +# For each head h, the recurrence is: +# state[t] = decay[t] * state[t-1] + K[t] @ V[t] +# where: +# decay[t] is a scalar (exp(A*dt) in Mamba-3) +# K[t] is (N, R) -- projects R input channels into N state dims +# V[t] is (R, P) -- the R-channel input with P features +# state[t] is (N, P) -- the hidden state +# +# The parallel scan operates over the time dimension within chunks. +# Inter-chunk state is accumulated sequentially across chunks. + +@triton.jit +def _mimo_scan_chunk_kernel( + # Inputs + DECAY_ptr, # (B, H, T) fp32 -- exp(A*dt) cumulative within chunk + K_ptr, # (B, T, H, N) bf16 -- after MIMO projection: K * mimo_v + V_ptr, # (B, T, H, P) bf16 -- value features + # Outputs + STATE_ptr, # (B, H, n_chunks, N, P) fp32 -- chunk boundary states + OUT_ptr, # (B, T, H, P) bf16 -- scan output at each step + # Dimensions + B: tl.constexpr, + T: tl.constexpr, + H: tl.constexpr, + N: tl.constexpr, + P: tl.constexpr, + CHUNK_SIZE: tl.constexpr, +): + """Intra-chunk sequential scan with state output at chunk boundaries. + + This implements the inner loop of a chunked parallel scan: + 1. Within each chunk: sequential scan (CHUNK_SIZE steps) + 2. Chunk boundary states are written to STATE for inter-chunk pass + 3. Full output is written to OUT + + For MIMO, the "BX" contribution at each step is: + contribution[n,p] = sum_r(K[t,h,n,r] * V[t,h,r,p]) + But since we store K after MIMO projection (K already multiplied by + mimo_v), K is (B,T,H,N) and V is (B,T,H,P), the rank-R contraction + reduces to an outer product K[n] * V[p] (effectively R=1 after + projection). For true MIMO rank>1, K and V would have an extra R dim + and we'd need an inner reduction. This kernel handles the projected + (post-contraction) form. + """ + # Grid: (B*H, n_chunks) + pid_bh = tl.program_id(0) + pid_chunk = tl.program_id(1) + + b = pid_bh // H + h = pid_bh % H + + n_chunks = (T + CHUNK_SIZE - 1) // CHUNK_SIZE + chunk_start = pid_chunk * CHUNK_SIZE + chunk_end = tl.minimum(chunk_start + CHUNK_SIZE, T) + + # State accumulator: (N, P) in fp32 + # For the parallel scan, each chunk starts from zero state. + # The inter-chunk correction is applied in a separate pass. + offs_n = tl.arange(0, N) + offs_p = tl.arange(0, P) + + # Initialize state to zero + # We use a flat representation: state[n*P + p] + state = tl.zeros([N * P], dtype=tl.float32) + + # Sequential scan within chunk + for t in range(CHUNK_SIZE): + actual_t = chunk_start + t + if actual_t < chunk_end: + # Load decay for this timestep + decay_offset = b * H * T + h * T + actual_t + decay = tl.load(DECAY_ptr + decay_offset) + + # Decay existing state + state = state * decay + + # Load K[b, actual_t, h, :N] and V[b, actual_t, h, :P] + k_base = b * T * H * N + actual_t * H * N + h * N + v_base = b * T * H * P + actual_t * H * P + h * P + + k_vals = tl.load(K_ptr + k_base + offs_n, mask=offs_n < N).to(tl.float32) + v_vals = tl.load(V_ptr + v_base + offs_p, mask=offs_p < P).to(tl.float32) + + # Outer product: state += k[:, None] * v[None, :] + # Flattened: state[n*P + p] += k[n] * v[p] + for ni in range(N): + k_n = tl.load(K_ptr + k_base + ni).to(tl.float32) + contrib = k_n * v_vals # (P,) vector + state_slice = tl.load( + STATE_ptr + 0, # dummy, we use state variable + mask=False, + ) + # Update state slice for this n + for pi in range(P): + idx = ni * P + pi + old = tl.load(STATE_ptr + 0, mask=False) # dummy + # Can't index into state directly in a loop, + # so we accumulate via atomic-like pattern + pass + + # NOTE: The above loop structure shows the mathematical intent but + # hits Triton limitations for dynamic N*P indexing. The practical + # implementation below uses a simpler approach for small N, P. + + +# ============================================================================ +# Practical implementation: torch-based chunked MIMO scan +# ============================================================================ +# For correctness and flexibility, we implement the MIMO scan using +# PyTorch ops with the same chunking strategy. This is the reference +# that a future fully-fused Triton kernel should match. + +def mimo_parallel_scan( + decay: torch.Tensor, # (B, H, T) fp32 -- per-step scalar decay + K: torch.Tensor, # (B, T, R, H, N) bf16 -- projected keys + V: torch.Tensor, # (B, T, H, P) bf16 -- values + chunk_size: int = 64, + initial_state: torch.Tensor | None = None, # (B, H, N, P) fp32 +) -> tuple[torch.Tensor, torch.Tensor]: + """MIMO chunked parallel scan. + + Implements the recurrence: + state[t] = decay[t] * state[t-1] + sum_r(K[t,:,r,:,:] * V[t]) + + For MIMO rank R, K has shape (B,T,R,H,N) and the rank-R contribution + is contracted: BX[t,h,n,p] = sum_r K[t,r,h,n] * V[t,h,p] + + Uses a two-pass chunked approach: + 1. Intra-chunk: sequential scan within each chunk (cheap, O(chunk_size)) + 2. Inter-chunk: parallel scan of chunk boundary states + + Args: + decay: (B, H, T) fp32 scalar decay factors per step + K: (B, T, R, H, N) bf16 input projections + V: (B, T, H, P) bf16 value features + chunk_size: chunk size for parallel scan (default 64) + initial_state: optional (B, H, N, P) fp32 starting state + + Returns: + output: (B, T, H, P) bf16 scan output (state @ C, where C=I for now) + final_state: (B, H, N, P) fp32 final hidden state + """ + B, T, R, H, N = K.shape + P = V.shape[-1] + device = K.device + + n_chunks = (T + chunk_size - 1) // chunk_size + + # Accumulate chunk-level decay products for inter-chunk propagation + # chunk_decay[b, h, c] = prod(decay[b, h, t] for t in chunk c) + chunk_decays = torch.zeros(B, H, n_chunks, device=device, dtype=torch.float32) + + # Intra-chunk states: the state at the END of each chunk (computed + # from zero initial state within each chunk) + chunk_states = torch.zeros(B, H, n_chunks, N, P, device=device, dtype=torch.float32) + + # Full output buffer + output = torch.empty(B, T, H, P, device=device, dtype=V.dtype) + + # ---- Pass 1: Intra-chunk sequential scan ---- + for c in range(n_chunks): + t_start = c * chunk_size + t_end = min(t_start + chunk_size, T) + chunk_len = t_end - t_start + + # State within this chunk (starts from zero) + state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) + cum_decay = torch.ones(B, H, device=device, dtype=torch.float32) + + for t_offset in range(chunk_len): + t = t_start + t_offset + + # decay_t: (B, H) + decay_t = decay[:, :, t] + + # Decay state + state = state * decay_t[:, :, None, None] + cum_decay = cum_decay * decay_t + + # BX contribution: sum_r K[b,t,r,h,n] * V[b,t,h,p] + # K: (B, T, R, H, N), V: (B, T, H, P) + # BX[b,h,n,p] = sum_r K[b,t,r,h,n] * V[b,t,h,p] + k_t = K[:, t, :, :, :].float() # (B, R, H, N) + v_t = V[:, t, :, :].float() # (B, H, P) + + # Contract over R: (B,R,H,N) -> sum_r -> (B,H,N) + k_sum = k_t.sum(dim=1) # (B, H, N) + + # Outer product with V: (B,H,N,1) * (B,H,1,P) -> (B,H,N,P) + bx = k_sum.unsqueeze(-1) * v_t.unsqueeze(-2) + + state = state + bx + + # Output: project state back (using identity for now) + # In full MIMO, this would involve mimo_out projection + output[:, t, :, :] = state.mean(dim=-2).to(V.dtype) + + chunk_states[:, :, c, :, :] = state + chunk_decays[:, :, c] = cum_decay + + # ---- Pass 2: Inter-chunk parallel scan (sequential for simplicity) ---- + # Propagate accumulated state across chunk boundaries + if initial_state is not None: + running_state = initial_state.clone() + else: + running_state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) + + for c in range(n_chunks): + t_start = c * chunk_size + t_end = min(t_start + chunk_size, T) + chunk_len = t_end - t_start + + if c > 0 or initial_state is not None: + # The correction for this chunk is: + # corrected_state[t] = intra_state[t] + decay_from_chunk_start_to_t * running_state + # For the output, we need to add the correction at each t + cum_d = torch.ones(B, H, device=device, dtype=torch.float32) + for t_offset in range(chunk_len): + t = t_start + t_offset + decay_t = decay[:, :, t] + cum_d = cum_d * decay_t + + # Correction: cum_d * running_state projected to output + correction = (cum_d[:, :, None, None] * running_state).mean(dim=-2) + output[:, t, :, :] = output[:, t, :, :].float() + correction + output[:, t, :, :] = output[:, t, :, :].to(V.dtype) + + # Update running state for next chunk + running_state = chunk_decays[:, :, c, None, None] * running_state + chunk_states[:, :, c, :, :] + + final_state = running_state + return output, final_state + + +# ============================================================================ +# Triton kernel: simple SISO-to-MIMO bridge scan +# ============================================================================ +# For the case where MIMO rank=1 (effectively SISO), we can use a +# vectorized Triton scan. This is the building block for rank>1. + +@triton.jit +def _siso_scan_kernel( + DECAY_ptr, # (B*H, T) fp32 + BX_ptr, # (B*H, T, NP) fp32 -- flattened N*P outer product + OUT_ptr, # (B*H, T, NP) fp32 -- scan output + T_val: tl.constexpr, + NP: tl.constexpr, + BLOCK_NP: tl.constexpr, +): + """Vectorized parallel scan for a single (B,H) slice. + + Computes: state[t] = decay[t] * state[t-1] + BX[t] + for each of the NP state dimensions independently. + + This is sequential in T but parallel across NP dimensions. + For short T (within a chunk), this is efficient. + """ + pid = tl.program_id(0) # indexes into B*H + offs_np = tl.arange(0, BLOCK_NP) + mask_np = offs_np < NP + + # Running state + state = tl.zeros([BLOCK_NP], dtype=tl.float32) + + for t in range(T_val): + # Load decay + decay = tl.load(DECAY_ptr + pid * T_val + t) + state = state * decay + + # Load BX[pid, t, :NP] + bx_base = pid * T_val * NP + t * NP + bx = tl.load(BX_ptr + bx_base + offs_np, mask=mask_np, other=0.0) + state = state + bx + + # Store output + out_base = pid * T_val * NP + t * NP + tl.store(OUT_ptr + out_base + offs_np, state, mask=mask_np) + + +def siso_scan_triton( + decay: torch.Tensor, # (B, H, T) fp32 + BX: torch.Tensor, # (B, H, T, N, P) fp32 -- outer product per step +) -> torch.Tensor: + """Triton-accelerated sequential scan (vectorized over N*P). + + This is the intra-chunk scan kernel. For short chunk sizes (16-64), + sequential scan is faster than work-inefficient parallel prefix. + + Args: + decay: (B, H, T) fp32 per-step decay + BX: (B, H, T, N, P) fp32 state update per step + + Returns: + states: (B, H, T, N, P) fp32 state at each step + """ + B, H, T_len, N, P = BX.shape + NP = N * P + + # Flatten for kernel + decay_flat = decay.reshape(B * H, T_len).contiguous() + bx_flat = BX.reshape(B * H, T_len, NP).contiguous() + out_flat = torch.empty_like(bx_flat) + + BLOCK_NP = triton.next_power_of_2(NP) + + grid = (B * H,) + _siso_scan_kernel[grid]( + decay_flat, bx_flat, out_flat, + T_val=T_len, NP=NP, BLOCK_NP=BLOCK_NP, + ) + + return out_flat.reshape(B, H, T_len, N, P) + + +# ============================================================================ +# Smoke test +# ============================================================================ + +if __name__ == "__main__": + torch.manual_seed(42) + device = "cuda" + + print("=== MIMO Parallel Scan Smoke Tests ===\n") + + # ---- Test 1: SISO scan (R=1) via Triton kernel ---- + B, H, T, N, P = 2, 4, 32, 8, 16 + decay = torch.rand(B, H, T, device=device, dtype=torch.float32) * 0.5 + 0.5 + BX = torch.randn(B, H, T, N, P, device=device, dtype=torch.float32) * 0.1 + + # Triton scan + states_triton = siso_scan_triton(decay, BX) + + # Reference sequential scan + states_ref = torch.zeros(B, H, T, N, P, device=device, dtype=torch.float32) + state = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) + for t in range(T): + state = decay[:, :, t, None, None] * state + BX[:, :, t, :, :] + states_ref[:, :, t, :, :] = state + + max_err = (states_triton - states_ref).abs().max().item() + print(f"[PASS] SISO Triton scan (max_err={max_err:.2e})") + assert max_err < 1e-4, f"SISO scan error too large: {max_err}" + + # ---- Test 2: MIMO chunked scan (R=2) ---- + B, T, R, H, N, P = 2, 64, 2, 4, 8, 16 + decay = torch.rand(B, H, T, device=device, dtype=torch.float32) * 0.5 + 0.5 + K = torch.randn(B, T, R, H, N, device=device, dtype=torch.bfloat16) * 0.1 + V = torch.randn(B, T, H, P, device=device, dtype=torch.bfloat16) * 0.1 + + output, final_state = mimo_parallel_scan(decay, K, V, chunk_size=16) + + # Reference: sequential scan (no chunking) + state_ref = torch.zeros(B, H, N, P, device=device, dtype=torch.float32) + output_ref = torch.empty(B, T, H, P, device=device, dtype=torch.bfloat16) + for t in range(T): + state_ref = decay[:, :, t, None, None] * state_ref + k_t = K[:, t, :, :, :].float().sum(dim=1) # (B, H, N) + v_t = V[:, t, :, :].float() # (B, H, P) + bx = k_t.unsqueeze(-1) * v_t.unsqueeze(-2) # (B, H, N, P) + state_ref = state_ref + bx + output_ref[:, t, :, :] = state_ref.mean(dim=-2).to(torch.bfloat16) + + max_err_out = (output.float() - output_ref.float()).abs().max().item() + max_err_state = (final_state - state_ref).abs().max().item() + print(f"[PASS] MIMO chunked scan output (max_err={max_err_out:.2e})") + print(f"[PASS] MIMO chunked scan final_state (max_err={max_err_state:.2e})") + assert max_err_out < 5e-2, f"MIMO output error too large: {max_err_out}" + assert max_err_state < 1e-3, f"MIMO state error too large: {max_err_state}" + + # ---- Test 3: MIMO with initial state ---- + init_state = torch.randn(B, H, N, P, device=device, dtype=torch.float32) * 0.01 + output_init, final_init = mimo_parallel_scan( + decay, K, V, chunk_size=16, initial_state=init_state, + ) + + state_ref2 = init_state.clone() + for t in range(T): + state_ref2 = decay[:, :, t, None, None] * state_ref2 + k_t = K[:, t, :, :, :].float().sum(dim=1) + v_t = V[:, t, :, :].float() + bx = k_t.unsqueeze(-1) * v_t.unsqueeze(-2) + state_ref2 = state_ref2 + bx + + max_err_init = (final_init - state_ref2).abs().max().item() + print(f"[PASS] MIMO with initial_state (max_err={max_err_init:.2e})") + assert max_err_init < 1e-3, f"MIMO init state error too large: {max_err_init}" + + # ---- Test 4: SISO scan with chunk_size=T (single chunk, no inter-chunk) ---- + output_1chunk, _ = mimo_parallel_scan(decay, K, V, chunk_size=T) + max_err_1c = (output_1chunk.float() - output_ref.float()).abs().max().item() + print(f"[PASS] MIMO single-chunk (max_err={max_err_1c:.2e})") + assert max_err_1c < 5e-2, f"Single chunk error too large: {max_err_1c}" + + # ---- Test 5: Shape validation ---- + assert output.shape == (B, T, H, P), f"Output shape mismatch: {output.shape}" + assert final_state.shape == (B, H, N, P), f"State shape mismatch: {final_state.shape}" + print("[PASS] Shape validation") + + print(f"\n=== All MIMO scan smoke tests PASSED ===") + print(f"NOTE: This kernel is NOT wired into the training loop.") + print(f" MIMO is a Phase 2 feature (Phase 1 uses SISO only).") + print(f" See mamba_ssm.ops.tilelang.mamba3 for production MIMO kernels.") diff --git a/overlay/kernels/triton/bcnorm_fused.py b/overlay/kernels/triton/bcnorm_fused.py index 0f71a71e48c6fd7b85cad7c41807f11ae9ab4fb5..7967f82807bd228eead4513b60ecfa001994e97b 100644 --- a/overlay/kernels/triton/bcnorm_fused.py +++ b/overlay/kernels/triton/bcnorm_fused.py @@ -1,258 +1,258 @@ -"""Fused BCNorm + RoPE kernel for Mamba-3 B/C projections. - -Phase 2: Triton kernel fusing LayerNorm (with weight+bias) + rotary embedding. -Phase 1: Uses separate BCNorm.forward() and apply_rope_ssm() calls. - -Fuses three operations on (B, T, d_state) tensors: -1. LayerNorm per last dim (with learnable weight and bias) -2. Rotary position embedding (split-half rotation) - -Strategy: Two kernels launched together. -- Kernel 1: LayerNorm with weight+bias -> store to output. -- Kernel 2: In-place RoPE on the output. -Alternatively, a single kernel that does norm on the full D vector, -then writes out two halves with RoPE applied using separate store ops. - -We use the single-kernel approach: load full D, normalize, then write -first half and second half separately with RoPE rotation applied. -This avoids the store-reload roundtrip. -""" - -from __future__ import annotations - -import torch -import triton -import triton.language as tl - - -@triton.jit -def _bcnorm_rope_fused_kernel( - # Pointers - X_ptr, # input: (B*T, D) - OUT_ptr, # output: (B*T, D) - W_ptr, # weight: (D,) - BIAS_ptr, # bias: (D,) - COS_ptr, # cos: (T, HALF_D) - SIN_ptr, # sin: (T, HALF_D) - # Strides - stride_x_row: tl.constexpr, - stride_cos_row: tl.constexpr, - # Dimensions - D: tl.constexpr, - HALF_D: tl.constexpr, - T_total: tl.constexpr, - APPLY_ROPE: tl.constexpr, - # Block sizes - BLOCK_HALF: tl.constexpr, # next_power_of_2(HALF_D) -): - """Fused LayerNorm(weight, bias) + RoPE for a single (b, t) row of d_state. - - Approach: Load the two halves separately, compute full-vector norm stats - via two partial sums, then write out with RoPE applied. - """ - row_id = tl.program_id(0) - t_id = row_id % T_total - - half_offs = tl.arange(0, BLOCK_HALF) - mask1 = half_offs < HALF_D - - # Load first half x1 and second half x2 separately - base = X_ptr + row_id * stride_x_row - x1 = tl.load(base + half_offs, mask=mask1, other=0.0).to(tl.float32) - x2 = tl.load(base + HALF_D + half_offs, mask=mask1, other=0.0).to(tl.float32) - - # --- LayerNorm stats over full D vector --- - sum1 = tl.sum(x1, axis=0) - sum2 = tl.sum(x2, axis=0) - mean = (sum1 + sum2) / D - - x1c = x1 - mean - x2c = x2 - mean - - var1 = tl.sum(x1c * x1c, axis=0) - var2 = tl.sum(x2c * x2c, axis=0) - var = (var1 + var2) / D - inv_std = 1.0 / tl.sqrt(var + 1e-5) - - x1n = x1c * inv_std - x2n = x2c * inv_std - - # Apply weight and bias (first half and second half separately) - w1 = tl.load(W_ptr + half_offs, mask=mask1, other=1.0).to(tl.float32) - w2 = tl.load(W_ptr + HALF_D + half_offs, mask=mask1, other=1.0).to(tl.float32) - b1 = tl.load(BIAS_ptr + half_offs, mask=mask1, other=0.0).to(tl.float32) - b2 = tl.load(BIAS_ptr + HALF_D + half_offs, mask=mask1, other=0.0).to(tl.float32) - - x1n = x1n * w1 + b1 - x2n = x2n * w2 + b2 - - out_base = OUT_ptr + row_id * stride_x_row - - if APPLY_ROPE == 1: - # Load cos/sin for this timestep - cos_base = COS_ptr + t_id * stride_cos_row - sin_base = SIN_ptr + t_id * stride_cos_row - cos_val = tl.load(cos_base + half_offs, mask=mask1, other=1.0).to(tl.float32) - sin_val = tl.load(sin_base + half_offs, mask=mask1, other=0.0).to(tl.float32) - - # RoPE rotation: - # y1 = x1 * cos + x2 * sin - # y2 = x1 * (-sin) + x2 * cos - y1 = x1n * cos_val + x2n * sin_val - y2 = x1n * (-sin_val) + x2n * cos_val - - tl.store(out_base + half_offs, y1.to(tl.bfloat16), mask=mask1) - tl.store(out_base + HALF_D + half_offs, y2.to(tl.bfloat16), mask=mask1) - else: - tl.store(out_base + half_offs, x1n.to(tl.bfloat16), mask=mask1) - tl.store(out_base + HALF_D + half_offs, x2n.to(tl.bfloat16), mask=mask1) - - -def bcnorm_fused_triton( - x: torch.Tensor, - weight: torch.Tensor, - bias: torch.Tensor, - cos: torch.Tensor | None = None, - sin: torch.Tensor | None = None, -) -> torch.Tensor: - """Fused BCNorm + RoPE. - - Args: - x: (B, T, d_state) bf16 input tensor. d_state must be even. - weight: (d_state,) learnable scale. - bias: (d_state,) learnable bias. - cos: (T, d_state//2) or None. If None, RoPE is skipped. - sin: (T, d_state//2) or None. - - Returns: - (B, T, d_state) bf16 output. - """ - assert x.is_contiguous(), "Input must be contiguous" - B, T, D = x.shape - assert D % 2 == 0, f"d_state must be even, got {D}" - HALF_D = D // 2 - apply_rope = cos is not None and sin is not None - - out = torch.empty_like(x) - - x_flat = x.reshape(B * T, D) - out_flat = out.reshape(B * T, D) - - BLOCK_HALF = triton.next_power_of_2(HALF_D) - - if not apply_rope: - cos_dummy = torch.zeros(1, 1, device=x.device, dtype=x.dtype) - sin_dummy = torch.zeros(1, 1, device=x.device, dtype=x.dtype) - cos_ptr = cos_dummy - sin_ptr = sin_dummy - stride_cos_row = 1 - else: - cos_ptr = cos - sin_ptr = sin - stride_cos_row = cos.stride(0) - - grid = (B * T,) - _bcnorm_rope_fused_kernel[grid]( - x_flat, out_flat, - weight, bias, - cos_ptr, sin_ptr, - stride_x_row=D, - stride_cos_row=stride_cos_row, - D=D, - HALF_D=HALF_D, - T_total=T, - APPLY_ROPE=1 if apply_rope else 0, - BLOCK_HALF=BLOCK_HALF, - ) - - return out - - -# --------------------------------------------------------------------------- -# Phase 1 reference implementation (for smoke test comparison) -# --------------------------------------------------------------------------- - -def _bcnorm_rope_reference( - x: torch.Tensor, - weight: torch.Tensor, - bias: torch.Tensor, - cos: torch.Tensor | None = None, - sin: torch.Tensor | None = None, -) -> torch.Tensor: - """Phase 1 PyTorch reference: LayerNorm + RoPE.""" - import torch.nn.functional as F - - out = F.layer_norm(x.float(), (x.size(-1),), weight.float(), bias.float()) - - if cos is not None and sin is not None: - d = out.shape[-1] // 2 - x1, x2 = out[..., :d], out[..., d:] - c = cos[:out.shape[-2]].float() - s = sin[:out.shape[-2]].float() - y1 = x1 * c + x2 * s - y2 = x1 * (-s) + x2 * c - out = torch.cat([y1, y2], dim=-1) - - return out.bfloat16() - - -# --------------------------------------------------------------------------- -# Smoke test -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - torch.manual_seed(42) - device = torch.device("cuda") - - B, T, D = 2, 128, 64 - HALF_D = D // 2 - - x = torch.randn(B, T, D, device=device, dtype=torch.bfloat16) - weight = torch.randn(D, device=device, dtype=torch.bfloat16) - bias = torch.randn(D, device=device, dtype=torch.bfloat16) - - base = 10000.0 - freqs = 1.0 / (base ** (torch.arange(0, HALF_D, dtype=torch.float32, device=device) / HALF_D)) - t_pos = torch.arange(T, dtype=torch.float32, device=device) - angles = torch.outer(t_pos, freqs) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - - # --- Test 1: BCNorm + RoPE --- - out_triton = bcnorm_fused_triton(x, weight, bias, cos, sin) - out_ref = _bcnorm_rope_reference(x, weight, bias, cos, sin) - - max_diff = (out_triton.float() - out_ref.float()).abs().max().item() - assert out_triton.shape == out_ref.shape == (B, T, D) - close = torch.allclose(out_triton.float(), out_ref.float(), atol=1e-2, rtol=1e-2) - print(f"[bcnorm_fused] BCNorm+RoPE: shape={out_triton.shape}, max_diff={max_diff:.6f}, allclose={close}") - assert close, f"BCNorm+RoPE mismatch: max_diff={max_diff}" - - # --- Test 2: BCNorm only (no RoPE) --- - out_triton_no_rope = bcnorm_fused_triton(x, weight, bias) - out_ref_no_rope = _bcnorm_rope_reference(x, weight, bias) - - max_diff2 = (out_triton_no_rope.float() - out_ref_no_rope.float()).abs().max().item() - close2 = torch.allclose(out_triton_no_rope.float(), out_ref_no_rope.float(), atol=1e-2, rtol=1e-2) - print(f"[bcnorm_fused] BCNorm only: shape={out_triton_no_rope.shape}, max_diff={max_diff2:.6f}, allclose={close2}") - assert close2, f"BCNorm-only mismatch: max_diff={max_diff2}" - - # --- Test 3: Different d_state sizes --- - for ds in [16, 32, 128]: - hd = ds // 2 - x_s = torch.randn(1, 32, ds, device=device, dtype=torch.bfloat16) - w_s = torch.randn(ds, device=device, dtype=torch.bfloat16) - b_s = torch.randn(ds, device=device, dtype=torch.bfloat16) - freqs_s = 1.0 / (base ** (torch.arange(0, hd, dtype=torch.float32, device=device) / hd)) - t_s = torch.arange(32, dtype=torch.float32, device=device) - cos_s = torch.outer(t_s, freqs_s).cos().bfloat16() - sin_s = torch.outer(t_s, freqs_s).sin().bfloat16() - - out_t = bcnorm_fused_triton(x_s, w_s, b_s, cos_s, sin_s) - out_r = _bcnorm_rope_reference(x_s, w_s, b_s, cos_s, sin_s) - md = (out_t.float() - out_r.float()).abs().max().item() - ok = torch.allclose(out_t.float(), out_r.float(), atol=1e-2, rtol=1e-2) - print(f"[bcnorm_fused] d_state={ds}: max_diff={md:.6f}, allclose={ok}") - assert ok, f"d_state={ds} mismatch: max_diff={md}" - - print("[bcnorm_fused] ALL TESTS PASSED") +"""Fused BCNorm + RoPE kernel for Mamba-3 B/C projections. + +Phase 2: Triton kernel fusing LayerNorm (with weight+bias) + rotary embedding. +Phase 1: Uses separate BCNorm.forward() and apply_rope_ssm() calls. + +Fuses three operations on (B, T, d_state) tensors: +1. LayerNorm per last dim (with learnable weight and bias) +2. Rotary position embedding (split-half rotation) + +Strategy: Two kernels launched together. +- Kernel 1: LayerNorm with weight+bias -> store to output. +- Kernel 2: In-place RoPE on the output. +Alternatively, a single kernel that does norm on the full D vector, +then writes out two halves with RoPE applied using separate store ops. + +We use the single-kernel approach: load full D, normalize, then write +first half and second half separately with RoPE rotation applied. +This avoids the store-reload roundtrip. +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _bcnorm_rope_fused_kernel( + # Pointers + X_ptr, # input: (B*T, D) + OUT_ptr, # output: (B*T, D) + W_ptr, # weight: (D,) + BIAS_ptr, # bias: (D,) + COS_ptr, # cos: (T, HALF_D) + SIN_ptr, # sin: (T, HALF_D) + # Strides + stride_x_row: tl.constexpr, + stride_cos_row: tl.constexpr, + # Dimensions + D: tl.constexpr, + HALF_D: tl.constexpr, + T_total: tl.constexpr, + APPLY_ROPE: tl.constexpr, + # Block sizes + BLOCK_HALF: tl.constexpr, # next_power_of_2(HALF_D) +): + """Fused LayerNorm(weight, bias) + RoPE for a single (b, t) row of d_state. + + Approach: Load the two halves separately, compute full-vector norm stats + via two partial sums, then write out with RoPE applied. + """ + row_id = tl.program_id(0) + t_id = row_id % T_total + + half_offs = tl.arange(0, BLOCK_HALF) + mask1 = half_offs < HALF_D + + # Load first half x1 and second half x2 separately + base = X_ptr + row_id * stride_x_row + x1 = tl.load(base + half_offs, mask=mask1, other=0.0).to(tl.float32) + x2 = tl.load(base + HALF_D + half_offs, mask=mask1, other=0.0).to(tl.float32) + + # --- LayerNorm stats over full D vector --- + sum1 = tl.sum(x1, axis=0) + sum2 = tl.sum(x2, axis=0) + mean = (sum1 + sum2) / D + + x1c = x1 - mean + x2c = x2 - mean + + var1 = tl.sum(x1c * x1c, axis=0) + var2 = tl.sum(x2c * x2c, axis=0) + var = (var1 + var2) / D + inv_std = 1.0 / tl.sqrt(var + 1e-5) + + x1n = x1c * inv_std + x2n = x2c * inv_std + + # Apply weight and bias (first half and second half separately) + w1 = tl.load(W_ptr + half_offs, mask=mask1, other=1.0).to(tl.float32) + w2 = tl.load(W_ptr + HALF_D + half_offs, mask=mask1, other=1.0).to(tl.float32) + b1 = tl.load(BIAS_ptr + half_offs, mask=mask1, other=0.0).to(tl.float32) + b2 = tl.load(BIAS_ptr + HALF_D + half_offs, mask=mask1, other=0.0).to(tl.float32) + + x1n = x1n * w1 + b1 + x2n = x2n * w2 + b2 + + out_base = OUT_ptr + row_id * stride_x_row + + if APPLY_ROPE == 1: + # Load cos/sin for this timestep + cos_base = COS_ptr + t_id * stride_cos_row + sin_base = SIN_ptr + t_id * stride_cos_row + cos_val = tl.load(cos_base + half_offs, mask=mask1, other=1.0).to(tl.float32) + sin_val = tl.load(sin_base + half_offs, mask=mask1, other=0.0).to(tl.float32) + + # RoPE rotation: + # y1 = x1 * cos + x2 * sin + # y2 = x1 * (-sin) + x2 * cos + y1 = x1n * cos_val + x2n * sin_val + y2 = x1n * (-sin_val) + x2n * cos_val + + tl.store(out_base + half_offs, y1.to(tl.bfloat16), mask=mask1) + tl.store(out_base + HALF_D + half_offs, y2.to(tl.bfloat16), mask=mask1) + else: + tl.store(out_base + half_offs, x1n.to(tl.bfloat16), mask=mask1) + tl.store(out_base + HALF_D + half_offs, x2n.to(tl.bfloat16), mask=mask1) + + +def bcnorm_fused_triton( + x: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor, + cos: torch.Tensor | None = None, + sin: torch.Tensor | None = None, +) -> torch.Tensor: + """Fused BCNorm + RoPE. + + Args: + x: (B, T, d_state) bf16 input tensor. d_state must be even. + weight: (d_state,) learnable scale. + bias: (d_state,) learnable bias. + cos: (T, d_state//2) or None. If None, RoPE is skipped. + sin: (T, d_state//2) or None. + + Returns: + (B, T, d_state) bf16 output. + """ + assert x.is_contiguous(), "Input must be contiguous" + B, T, D = x.shape + assert D % 2 == 0, f"d_state must be even, got {D}" + HALF_D = D // 2 + apply_rope = cos is not None and sin is not None + + out = torch.empty_like(x) + + x_flat = x.reshape(B * T, D) + out_flat = out.reshape(B * T, D) + + BLOCK_HALF = triton.next_power_of_2(HALF_D) + + if not apply_rope: + cos_dummy = torch.zeros(1, 1, device=x.device, dtype=x.dtype) + sin_dummy = torch.zeros(1, 1, device=x.device, dtype=x.dtype) + cos_ptr = cos_dummy + sin_ptr = sin_dummy + stride_cos_row = 1 + else: + cos_ptr = cos + sin_ptr = sin + stride_cos_row = cos.stride(0) + + grid = (B * T,) + _bcnorm_rope_fused_kernel[grid]( + x_flat, out_flat, + weight, bias, + cos_ptr, sin_ptr, + stride_x_row=D, + stride_cos_row=stride_cos_row, + D=D, + HALF_D=HALF_D, + T_total=T, + APPLY_ROPE=1 if apply_rope else 0, + BLOCK_HALF=BLOCK_HALF, + ) + + return out + + +# --------------------------------------------------------------------------- +# Phase 1 reference implementation (for smoke test comparison) +# --------------------------------------------------------------------------- + +def _bcnorm_rope_reference( + x: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor, + cos: torch.Tensor | None = None, + sin: torch.Tensor | None = None, +) -> torch.Tensor: + """Phase 1 PyTorch reference: LayerNorm + RoPE.""" + import torch.nn.functional as F + + out = F.layer_norm(x.float(), (x.size(-1),), weight.float(), bias.float()) + + if cos is not None and sin is not None: + d = out.shape[-1] // 2 + x1, x2 = out[..., :d], out[..., d:] + c = cos[:out.shape[-2]].float() + s = sin[:out.shape[-2]].float() + y1 = x1 * c + x2 * s + y2 = x1 * (-s) + x2 * c + out = torch.cat([y1, y2], dim=-1) + + return out.bfloat16() + + +# --------------------------------------------------------------------------- +# Smoke test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + torch.manual_seed(42) + device = torch.device("cuda") + + B, T, D = 2, 128, 64 + HALF_D = D // 2 + + x = torch.randn(B, T, D, device=device, dtype=torch.bfloat16) + weight = torch.randn(D, device=device, dtype=torch.bfloat16) + bias = torch.randn(D, device=device, dtype=torch.bfloat16) + + base = 10000.0 + freqs = 1.0 / (base ** (torch.arange(0, HALF_D, dtype=torch.float32, device=device) / HALF_D)) + t_pos = torch.arange(T, dtype=torch.float32, device=device) + angles = torch.outer(t_pos, freqs) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + + # --- Test 1: BCNorm + RoPE --- + out_triton = bcnorm_fused_triton(x, weight, bias, cos, sin) + out_ref = _bcnorm_rope_reference(x, weight, bias, cos, sin) + + max_diff = (out_triton.float() - out_ref.float()).abs().max().item() + assert out_triton.shape == out_ref.shape == (B, T, D) + close = torch.allclose(out_triton.float(), out_ref.float(), atol=1e-2, rtol=1e-2) + print(f"[bcnorm_fused] BCNorm+RoPE: shape={out_triton.shape}, max_diff={max_diff:.6f}, allclose={close}") + assert close, f"BCNorm+RoPE mismatch: max_diff={max_diff}" + + # --- Test 2: BCNorm only (no RoPE) --- + out_triton_no_rope = bcnorm_fused_triton(x, weight, bias) + out_ref_no_rope = _bcnorm_rope_reference(x, weight, bias) + + max_diff2 = (out_triton_no_rope.float() - out_ref_no_rope.float()).abs().max().item() + close2 = torch.allclose(out_triton_no_rope.float(), out_ref_no_rope.float(), atol=1e-2, rtol=1e-2) + print(f"[bcnorm_fused] BCNorm only: shape={out_triton_no_rope.shape}, max_diff={max_diff2:.6f}, allclose={close2}") + assert close2, f"BCNorm-only mismatch: max_diff={max_diff2}" + + # --- Test 3: Different d_state sizes --- + for ds in [16, 32, 128]: + hd = ds // 2 + x_s = torch.randn(1, 32, ds, device=device, dtype=torch.bfloat16) + w_s = torch.randn(ds, device=device, dtype=torch.bfloat16) + b_s = torch.randn(ds, device=device, dtype=torch.bfloat16) + freqs_s = 1.0 / (base ** (torch.arange(0, hd, dtype=torch.float32, device=device) / hd)) + t_s = torch.arange(32, dtype=torch.float32, device=device) + cos_s = torch.outer(t_s, freqs_s).cos().bfloat16() + sin_s = torch.outer(t_s, freqs_s).sin().bfloat16() + + out_t = bcnorm_fused_triton(x_s, w_s, b_s, cos_s, sin_s) + out_r = _bcnorm_rope_reference(x_s, w_s, b_s, cos_s, sin_s) + md = (out_t.float() - out_r.float()).abs().max().item() + ok = torch.allclose(out_t.float(), out_r.float(), atol=1e-2, rtol=1e-2) + print(f"[bcnorm_fused] d_state={ds}: max_diff={md:.6f}, allclose={ok}") + assert ok, f"d_state={ds} mismatch: max_diff={md}" + + print("[bcnorm_fused] ALL TESTS PASSED") diff --git a/overlay/kernels/triton/oja_update.py b/overlay/kernels/triton/oja_update.py index 5e4330002b1f601157ed7f422794ef273e2a59e5..1979ddbe5b24bac063c7021c5a07b11ebf6e654f 100644 --- a/overlay/kernels/triton/oja_update.py +++ b/overlay/kernels/triton/oja_update.py @@ -1,299 +1,299 @@ -"""Oja's rule online PCA update kernel. - -Phase 2: Triton kernel for batched rank-1 updates. - -Update rule: w <- w + eta * (x * (x^T w) - w * (x^T w)^2) -Equivalent to: w <- w + eta * y * (x - y * w) where y = x^T w - -This maintains a weight vector that converges to the first principal -component of the input distribution. Used by StochasticResonanceSDR -for variance tracking. - -Phase 1 reference (train_sdr.py StochasticResonanceSDR._oja_update): - sample = x_flat[0] - y = (sample * self.oja_w).sum() - self.oja_w = F.normalize( - self.oja_w + self.oja_lr * y * (sample - y * self.oja_w), dim=0 - ) - -Phase 2 extends this to a batched kernel: update multiple weight vectors -in parallel, each with its own input vector. Each Triton program handles -one (weight, input) pair across the d_model dimension. -""" - -from __future__ import annotations - -import torch -import torch.nn.functional as F -import triton -import triton.language as tl - - -# --------------------------------------------------------------------------- -# Triton kernel: batched Oja update -# --------------------------------------------------------------------------- - -@triton.jit -def _oja_update_kernel( - x_ptr, # input vectors: (B, D) row-major, bf16 or fp32 - w_ptr, # weight vectors: (B, D) row-major, fp32 (in-place update) - eta, # learning rate, fp32 scalar - D: tl.constexpr, # feature dimension - BLOCK_D: tl.constexpr, # tile size along D (power of 2 >= D) - NORMALIZE: tl.constexpr, # whether to L2-normalize w after update -): - """Batched Oja update: one program per batch element. - - Each program: - 1. Loads x[b, :] and w[b, :] (with fp32 accumulation) - 2. Computes y = dot(x, w) - 3. Updates w <- w + eta * y * (x - y * w) - 4. Optionally L2-normalizes w - 5. Stores updated w[b, :] - """ - bid = tl.program_id(0) # batch index - offs = tl.arange(0, BLOCK_D) - mask = offs < D - - # Load x and w for this batch element (accumulate in fp32) - base_x = bid * D - base_w = bid * D - - x = tl.load(x_ptr + base_x + offs, mask=mask, other=0.0).to(tl.float32) - w = tl.load(w_ptr + base_w + offs, mask=mask, other=0.0).to(tl.float32) - - # Compute projection y = x^T w - y = tl.sum(x * w, axis=0) - - # Oja update: w <- w + eta * y * (x - y * w) - delta = y * (x - y * w) - w_new = w + eta * delta - - # Optional L2 normalization (matching Phase 1 behavior) - if NORMALIZE: - norm_sq = tl.sum(w_new * w_new, axis=0) - inv_norm = tl.rsqrt(norm_sq + 1e-12) - w_new = w_new * inv_norm - - tl.store(w_ptr + base_w + offs, w_new, mask=mask) - - -# --------------------------------------------------------------------------- -# Python wrapper -# --------------------------------------------------------------------------- - -def oja_update( - x: torch.Tensor, - w: torch.Tensor, - eta: float = 0.01, - normalize: bool = True, -) -> torch.Tensor: - """Batched Oja's rule update using Triton. - - Args: - x: (B, D) input vectors (bf16 or fp32). - w: (B, D) weight vectors (fp32, updated in-place). - eta: learning rate. - normalize: if True, L2-normalize w after each update. - - Returns: - Updated w tensor (same storage, modified in-place; also returned - for convenience). - """ - assert x.ndim == 2 and w.ndim == 2, f"Expected 2D tensors, got x={x.ndim}D, w={w.ndim}D" - B, D = x.shape - assert w.shape == (B, D), f"Shape mismatch: x={x.shape}, w={w.shape}" - assert w.dtype == torch.float32, f"w must be float32 for accumulation, got {w.dtype}" - assert x.is_cuda and w.is_cuda, "Tensors must be on CUDA" - - # Ensure contiguous - x = x.contiguous() - w = w.contiguous() - - # BLOCK_D must be power of 2 >= D - BLOCK_D = triton.next_power_of_2(D) - - _oja_update_kernel[(B,)]( - x, - w, - eta, - D=D, - BLOCK_D=BLOCK_D, - NORMALIZE=normalize, - ) - return w - - -# --------------------------------------------------------------------------- -# Single-vector wrapper (matches Phase 1 API) -# --------------------------------------------------------------------------- - -def oja_update_single( - x: torch.Tensor, - w: torch.Tensor, - eta: float = 0.01, - normalize: bool = True, -) -> torch.Tensor: - """Single-vector Oja update (Phase 1 compatible API). - - Args: - x: (D,) input vector. - w: (D,) weight vector (fp32). - eta: learning rate. - normalize: if True, L2-normalize after update. - - Returns: - Updated (D,) weight vector (new tensor). - """ - w_batch = w.unsqueeze(0).clone() # (1, D) — clone so original not mutated - x_batch = x.unsqueeze(0) # (1, D) - oja_update(x_batch, w_batch, eta=eta, normalize=normalize) - return w_batch.squeeze(0) - - -# --------------------------------------------------------------------------- -# Reference implementation (pure PyTorch, matches Phase 1) -# --------------------------------------------------------------------------- - -def _oja_reference( - x: torch.Tensor, - w: torch.Tensor, - eta: float = 0.01, - normalize: bool = True, -) -> torch.Tensor: - """Reference single-vector Oja update matching train_sdr.py.""" - x_f32 = x.to(torch.float32) - w_f32 = w.to(torch.float32) - y = (x_f32 * w_f32).sum() - w_new = w_f32 + eta * y * (x_f32 - y * w_f32) - if normalize: - w_new = F.normalize(w_new, dim=0) - return w_new - - -def _oja_reference_batched( - x: torch.Tensor, - w: torch.Tensor, - eta: float = 0.01, - normalize: bool = True, -) -> torch.Tensor: - """Reference batched Oja update (loop over batch).""" - B, D = x.shape - w_out = w.clone() - for b in range(B): - w_out[b] = _oja_reference(x[b], w[b], eta=eta, normalize=normalize) - return w_out - - -# --------------------------------------------------------------------------- -# Smoke test -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - print("=" * 60) - print("Oja Update Kernel — Smoke Test") - print("=" * 60) - - device = "cuda" if torch.cuda.is_available() else "cpu" - torch.manual_seed(42) - - D = 128 # typical d_model for SDR - - # --- Test 1: Single vector update (Phase 1 compatibility) --- - print("\n[Test 1] Single-vector Oja update vs reference") - x1 = torch.randn(D, device=device, dtype=torch.float32) - w1 = F.normalize(torch.randn(D, device=device, dtype=torch.float32), dim=0) - - ref_w1 = _oja_reference(x1, w1, eta=0.01, normalize=True) - triton_w1 = oja_update_single(x1, w1.clone(), eta=0.01, normalize=True) - - err_1 = (triton_w1 - ref_w1).abs().max().item() - norm_1 = triton_w1.norm().item() - print(f" Max abs error: {err_1:.6e}") - print(f" Output norm: {norm_1:.6f} (should be ~1.0)") - assert err_1 < 1e-5, f"Single-vector error too large: {err_1}" - assert abs(norm_1 - 1.0) < 1e-5, f"Not normalized: {norm_1}" - print(" PASSED") - - # --- Test 2: Batched update --- - print("\n[Test 2] Batched Oja update (B=32, D=128)") - B = 32 - x2 = torch.randn(B, D, device=device, dtype=torch.float32) - w2 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) - - ref_w2 = _oja_reference_batched(x2, w2, eta=0.01, normalize=True) - triton_w2 = w2.clone() - oja_update(x2, triton_w2, eta=0.01, normalize=True) - - err_2 = (triton_w2 - ref_w2).abs().max().item() - norms_2 = triton_w2.norm(dim=1) - print(f" Max abs error: {err_2:.6e}") - print(f" Norm range: [{norms_2.min():.6f}, {norms_2.max():.6f}]") - assert err_2 < 1e-5, f"Batched error too large: {err_2}" - assert (norms_2 - 1.0).abs().max() < 1e-5, "Not all normalized" - print(" PASSED") - - # --- Test 3: bf16 input (fp32 accumulation) --- - print("\n[Test 3] bf16 input vectors with fp32 weights") - x3 = torch.randn(B, D, device=device, dtype=torch.bfloat16) - w3 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) - - ref_w3 = _oja_reference_batched(x3.float(), w3, eta=0.01, normalize=True) - triton_w3 = w3.clone() - oja_update(x3, triton_w3, eta=0.01, normalize=True) - - err_3 = (triton_w3 - ref_w3).abs().max().item() - print(f" Max abs error: {err_3:.6e}") - # bf16 input introduces some quantization error - assert err_3 < 5e-4, f"bf16 error too large: {err_3}" - print(" PASSED") - - # --- Test 4: Without normalization --- - print("\n[Test 4] Oja update without normalization") - x4 = torch.randn(B, D, device=device, dtype=torch.float32) - w4 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) - - ref_w4 = _oja_reference_batched(x4, w4, eta=0.01, normalize=False) - triton_w4 = w4.clone() - oja_update(x4, triton_w4, eta=0.01, normalize=False) - - err_4 = (triton_w4 - ref_w4).abs().max().item() - print(f" Max abs error: {err_4:.6e}") - assert err_4 < 1e-5, f"No-norm error too large: {err_4}" - print(" PASSED") - - # --- Test 5: Large D (d_model=512) --- - print("\n[Test 5] Large dimension (B=8, D=512)") - D_large = 512 - x5 = torch.randn(8, D_large, device=device, dtype=torch.float32) - w5 = F.normalize(torch.randn(8, D_large, device=device, dtype=torch.float32), dim=1) - - ref_w5 = _oja_reference_batched(x5, w5, eta=0.01, normalize=True) - triton_w5 = w5.clone() - oja_update(x5, triton_w5, eta=0.01, normalize=True) - - err_5 = (triton_w5 - ref_w5).abs().max().item() - print(f" Max abs error: {err_5:.6e}") - assert err_5 < 1e-5, f"Large-D error too large: {err_5}" - print(" PASSED") - - # --- Test 6: Convergence to principal component --- - print("\n[Test 6] Convergence to PC1 (500 steps, rank-1 data)") - D_conv = 64 - # Create rank-1 data: all samples lie along a random direction - true_pc = F.normalize(torch.randn(D_conv, device=device), dim=0) - # Use higher SNR: scale along true_pc >> noise - data = torch.randn(500, 1, device=device) * true_pc.unsqueeze(0) # (500, D) - - w_conv = F.normalize(torch.randn(1, D_conv, device=device, dtype=torch.float32), dim=1) - for i in range(500): - oja_update(data[i:i+1], w_conv, eta=0.05, normalize=True) - - cosine = F.cosine_similarity(w_conv.squeeze(0).unsqueeze(0), true_pc.unsqueeze(0)).abs().item() - print(f" Cosine similarity to true PC1: {cosine:.4f}") - assert cosine > 0.90, f"Did not converge to PC1: cosine={cosine}" - print(" PASSED") - - print("\n" + "=" * 60) - print("ALL OJA TESTS PASSED") - print("=" * 60) +"""Oja's rule online PCA update kernel. + +Phase 2: Triton kernel for batched rank-1 updates. + +Update rule: w <- w + eta * (x * (x^T w) - w * (x^T w)^2) +Equivalent to: w <- w + eta * y * (x - y * w) where y = x^T w + +This maintains a weight vector that converges to the first principal +component of the input distribution. Used by StochasticResonanceSDR +for variance tracking. + +Phase 1 reference (train_sdr.py StochasticResonanceSDR._oja_update): + sample = x_flat[0] + y = (sample * self.oja_w).sum() + self.oja_w = F.normalize( + self.oja_w + self.oja_lr * y * (sample - y * self.oja_w), dim=0 + ) + +Phase 2 extends this to a batched kernel: update multiple weight vectors +in parallel, each with its own input vector. Each Triton program handles +one (weight, input) pair across the d_model dimension. +""" + +from __future__ import annotations + +import torch +import torch.nn.functional as F +import triton +import triton.language as tl + + +# --------------------------------------------------------------------------- +# Triton kernel: batched Oja update +# --------------------------------------------------------------------------- + +@triton.jit +def _oja_update_kernel( + x_ptr, # input vectors: (B, D) row-major, bf16 or fp32 + w_ptr, # weight vectors: (B, D) row-major, fp32 (in-place update) + eta, # learning rate, fp32 scalar + D: tl.constexpr, # feature dimension + BLOCK_D: tl.constexpr, # tile size along D (power of 2 >= D) + NORMALIZE: tl.constexpr, # whether to L2-normalize w after update +): + """Batched Oja update: one program per batch element. + + Each program: + 1. Loads x[b, :] and w[b, :] (with fp32 accumulation) + 2. Computes y = dot(x, w) + 3. Updates w <- w + eta * y * (x - y * w) + 4. Optionally L2-normalizes w + 5. Stores updated w[b, :] + """ + bid = tl.program_id(0) # batch index + offs = tl.arange(0, BLOCK_D) + mask = offs < D + + # Load x and w for this batch element (accumulate in fp32) + base_x = bid * D + base_w = bid * D + + x = tl.load(x_ptr + base_x + offs, mask=mask, other=0.0).to(tl.float32) + w = tl.load(w_ptr + base_w + offs, mask=mask, other=0.0).to(tl.float32) + + # Compute projection y = x^T w + y = tl.sum(x * w, axis=0) + + # Oja update: w <- w + eta * y * (x - y * w) + delta = y * (x - y * w) + w_new = w + eta * delta + + # Optional L2 normalization (matching Phase 1 behavior) + if NORMALIZE: + norm_sq = tl.sum(w_new * w_new, axis=0) + inv_norm = tl.rsqrt(norm_sq + 1e-12) + w_new = w_new * inv_norm + + tl.store(w_ptr + base_w + offs, w_new, mask=mask) + + +# --------------------------------------------------------------------------- +# Python wrapper +# --------------------------------------------------------------------------- + +def oja_update( + x: torch.Tensor, + w: torch.Tensor, + eta: float = 0.01, + normalize: bool = True, +) -> torch.Tensor: + """Batched Oja's rule update using Triton. + + Args: + x: (B, D) input vectors (bf16 or fp32). + w: (B, D) weight vectors (fp32, updated in-place). + eta: learning rate. + normalize: if True, L2-normalize w after each update. + + Returns: + Updated w tensor (same storage, modified in-place; also returned + for convenience). + """ + assert x.ndim == 2 and w.ndim == 2, f"Expected 2D tensors, got x={x.ndim}D, w={w.ndim}D" + B, D = x.shape + assert w.shape == (B, D), f"Shape mismatch: x={x.shape}, w={w.shape}" + assert w.dtype == torch.float32, f"w must be float32 for accumulation, got {w.dtype}" + assert x.is_cuda and w.is_cuda, "Tensors must be on CUDA" + + # Ensure contiguous + x = x.contiguous() + w = w.contiguous() + + # BLOCK_D must be power of 2 >= D + BLOCK_D = triton.next_power_of_2(D) + + _oja_update_kernel[(B,)]( + x, + w, + eta, + D=D, + BLOCK_D=BLOCK_D, + NORMALIZE=normalize, + ) + return w + + +# --------------------------------------------------------------------------- +# Single-vector wrapper (matches Phase 1 API) +# --------------------------------------------------------------------------- + +def oja_update_single( + x: torch.Tensor, + w: torch.Tensor, + eta: float = 0.01, + normalize: bool = True, +) -> torch.Tensor: + """Single-vector Oja update (Phase 1 compatible API). + + Args: + x: (D,) input vector. + w: (D,) weight vector (fp32). + eta: learning rate. + normalize: if True, L2-normalize after update. + + Returns: + Updated (D,) weight vector (new tensor). + """ + w_batch = w.unsqueeze(0).clone() # (1, D) — clone so original not mutated + x_batch = x.unsqueeze(0) # (1, D) + oja_update(x_batch, w_batch, eta=eta, normalize=normalize) + return w_batch.squeeze(0) + + +# --------------------------------------------------------------------------- +# Reference implementation (pure PyTorch, matches Phase 1) +# --------------------------------------------------------------------------- + +def _oja_reference( + x: torch.Tensor, + w: torch.Tensor, + eta: float = 0.01, + normalize: bool = True, +) -> torch.Tensor: + """Reference single-vector Oja update matching train_sdr.py.""" + x_f32 = x.to(torch.float32) + w_f32 = w.to(torch.float32) + y = (x_f32 * w_f32).sum() + w_new = w_f32 + eta * y * (x_f32 - y * w_f32) + if normalize: + w_new = F.normalize(w_new, dim=0) + return w_new + + +def _oja_reference_batched( + x: torch.Tensor, + w: torch.Tensor, + eta: float = 0.01, + normalize: bool = True, +) -> torch.Tensor: + """Reference batched Oja update (loop over batch).""" + B, D = x.shape + w_out = w.clone() + for b in range(B): + w_out[b] = _oja_reference(x[b], w[b], eta=eta, normalize=normalize) + return w_out + + +# --------------------------------------------------------------------------- +# Smoke test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + print("=" * 60) + print("Oja Update Kernel — Smoke Test") + print("=" * 60) + + device = "cuda" if torch.cuda.is_available() else "cpu" + torch.manual_seed(42) + + D = 128 # typical d_model for SDR + + # --- Test 1: Single vector update (Phase 1 compatibility) --- + print("\n[Test 1] Single-vector Oja update vs reference") + x1 = torch.randn(D, device=device, dtype=torch.float32) + w1 = F.normalize(torch.randn(D, device=device, dtype=torch.float32), dim=0) + + ref_w1 = _oja_reference(x1, w1, eta=0.01, normalize=True) + triton_w1 = oja_update_single(x1, w1.clone(), eta=0.01, normalize=True) + + err_1 = (triton_w1 - ref_w1).abs().max().item() + norm_1 = triton_w1.norm().item() + print(f" Max abs error: {err_1:.6e}") + print(f" Output norm: {norm_1:.6f} (should be ~1.0)") + assert err_1 < 1e-5, f"Single-vector error too large: {err_1}" + assert abs(norm_1 - 1.0) < 1e-5, f"Not normalized: {norm_1}" + print(" PASSED") + + # --- Test 2: Batched update --- + print("\n[Test 2] Batched Oja update (B=32, D=128)") + B = 32 + x2 = torch.randn(B, D, device=device, dtype=torch.float32) + w2 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) + + ref_w2 = _oja_reference_batched(x2, w2, eta=0.01, normalize=True) + triton_w2 = w2.clone() + oja_update(x2, triton_w2, eta=0.01, normalize=True) + + err_2 = (triton_w2 - ref_w2).abs().max().item() + norms_2 = triton_w2.norm(dim=1) + print(f" Max abs error: {err_2:.6e}") + print(f" Norm range: [{norms_2.min():.6f}, {norms_2.max():.6f}]") + assert err_2 < 1e-5, f"Batched error too large: {err_2}" + assert (norms_2 - 1.0).abs().max() < 1e-5, "Not all normalized" + print(" PASSED") + + # --- Test 3: bf16 input (fp32 accumulation) --- + print("\n[Test 3] bf16 input vectors with fp32 weights") + x3 = torch.randn(B, D, device=device, dtype=torch.bfloat16) + w3 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) + + ref_w3 = _oja_reference_batched(x3.float(), w3, eta=0.01, normalize=True) + triton_w3 = w3.clone() + oja_update(x3, triton_w3, eta=0.01, normalize=True) + + err_3 = (triton_w3 - ref_w3).abs().max().item() + print(f" Max abs error: {err_3:.6e}") + # bf16 input introduces some quantization error + assert err_3 < 5e-4, f"bf16 error too large: {err_3}" + print(" PASSED") + + # --- Test 4: Without normalization --- + print("\n[Test 4] Oja update without normalization") + x4 = torch.randn(B, D, device=device, dtype=torch.float32) + w4 = F.normalize(torch.randn(B, D, device=device, dtype=torch.float32), dim=1) + + ref_w4 = _oja_reference_batched(x4, w4, eta=0.01, normalize=False) + triton_w4 = w4.clone() + oja_update(x4, triton_w4, eta=0.01, normalize=False) + + err_4 = (triton_w4 - ref_w4).abs().max().item() + print(f" Max abs error: {err_4:.6e}") + assert err_4 < 1e-5, f"No-norm error too large: {err_4}" + print(" PASSED") + + # --- Test 5: Large D (d_model=512) --- + print("\n[Test 5] Large dimension (B=8, D=512)") + D_large = 512 + x5 = torch.randn(8, D_large, device=device, dtype=torch.float32) + w5 = F.normalize(torch.randn(8, D_large, device=device, dtype=torch.float32), dim=1) + + ref_w5 = _oja_reference_batched(x5, w5, eta=0.01, normalize=True) + triton_w5 = w5.clone() + oja_update(x5, triton_w5, eta=0.01, normalize=True) + + err_5 = (triton_w5 - ref_w5).abs().max().item() + print(f" Max abs error: {err_5:.6e}") + assert err_5 < 1e-5, f"Large-D error too large: {err_5}" + print(" PASSED") + + # --- Test 6: Convergence to principal component --- + print("\n[Test 6] Convergence to PC1 (500 steps, rank-1 data)") + D_conv = 64 + # Create rank-1 data: all samples lie along a random direction + true_pc = F.normalize(torch.randn(D_conv, device=device), dim=0) + # Use higher SNR: scale along true_pc >> noise + data = torch.randn(500, 1, device=device) * true_pc.unsqueeze(0) # (500, D) + + w_conv = F.normalize(torch.randn(1, D_conv, device=device, dtype=torch.float32), dim=1) + for i in range(500): + oja_update(data[i:i+1], w_conv, eta=0.05, normalize=True) + + cosine = F.cosine_similarity(w_conv.squeeze(0).unsqueeze(0), true_pc.unsqueeze(0)).abs().item() + print(f" Cosine similarity to true PC1: {cosine:.4f}") + assert cosine > 0.90, f"Did not converge to PC1: cosine={cosine}" + print(" PASSED") + + print("\n" + "=" * 60) + print("ALL OJA TESTS PASSED") + print("=" * 60) diff --git a/overlay/kernels/triton/sinkhorn_fused.py b/overlay/kernels/triton/sinkhorn_fused.py index 59459c280fdff2601266b34ec10214d8d14ec3ef..ca1e3b98e47ef6534fda6ae8ddfa3966e60ae9d1 100644 --- a/overlay/kernels/triton/sinkhorn_fused.py +++ b/overlay/kernels/triton/sinkhorn_fused.py @@ -1,234 +1,234 @@ -"""Fused Sinkhorn-Knopp normalization kernel for mHC routing. - -Phase 2: Optimized implementations replacing the Python for-loop in -ManifoldHyperConnection._sinkhorn(). - -For n_streams=2: closed-form doubly-stochastic projection (no iteration). -For n_streams>2: Triton kernel fusing exp + row_norm + col_norm iterations. - -The Phase 1 reference (mhc_mini.py) does 5-20 iterations of alternating -row/column log-sum-exp normalization on a small (n_streams x n_streams) -matrix. This module provides two fast paths: - 1. n=2 closed-form: O(1) — no loop, no kernel launch overhead. - 2. n>2 Triton kernel: single kernel launch for all sinkhorn iterations. -""" - -from __future__ import annotations - -import torch -import triton -import triton.language as tl - - -# --------------------------------------------------------------------------- -# Fast path: n_streams = 2 closed-form doubly-stochastic projection -# --------------------------------------------------------------------------- - -def sinkhorn_2x2(log_alpha: torch.Tensor) -> torch.Tensor: - """Closed-form doubly-stochastic projection for 2x2 matrices. - - For a 2x2 log-space matrix, the Sinkhorn limit is: - [[a, 1-a], [1-a, a]] - where a = sigmoid(log_alpha[0,0] - log_alpha[0,1] + log_alpha[1,1] - log_alpha[1,0]) / 2 - More precisely, the unique doubly-stochastic matrix in the Sinkhorn - equivalence class is parameterized by the single degree of freedom: - a = sigmoid((log_alpha[0,0] - log_alpha[0,1] - log_alpha[1,0] + log_alpha[1,1]) / 2) - - This is exact (no iteration needed) and avoids all kernel launch overhead. - """ - # The converged Sinkhorn for 2x2 depends only on the "cross-ratio": - # delta = (log_alpha[0,0] + log_alpha[1,1]) - (log_alpha[0,1] + log_alpha[1,0]) - # and a = sigmoid(delta / 2) gives the diagonal entry. - delta = (log_alpha[0, 0] + log_alpha[1, 1]) - (log_alpha[0, 1] + log_alpha[1, 0]) - a = torch.sigmoid(delta * 0.5) - one_minus_a = 1.0 - a - # Build result without mutation: create from flat tensor - row0 = torch.stack([a, one_minus_a]) - row1 = torch.stack([one_minus_a, a]) - return torch.stack([row0, row1]) - - -# --------------------------------------------------------------------------- -# General path: Triton kernel for n_streams > 2 -# --------------------------------------------------------------------------- - -@triton.jit -def _sinkhorn_kernel( - log_alpha_ptr, # input: (N, N) in row-major, float32 - out_ptr, # output: (N, N) in row-major, float32 - N: tl.constexpr, # matrix dimension (n_streams) - ITERS: tl.constexpr, # number of sinkhorn iterations -): - """Single-program Sinkhorn on a small NxN matrix. - - One program instance processes the entire matrix. This is efficient for - N <= 16 where the entire matrix fits in registers. - """ - # Load entire NxN matrix into registers - row_idx = tl.arange(0, N) - col_idx = tl.arange(0, N) - # 2D indexing: offsets[i, j] = i * N + j - offsets = row_idx[:, None] * N + col_idx[None, :] # (N, N) - - M = tl.load(log_alpha_ptr + offsets).to(tl.float32) # (N, N) - - # Alternating row/column log-sum-exp normalization - for _ in tl.static_range(ITERS): - # Row normalization: M[i,j] -= logsumexp(M[i,:]) - row_max = tl.max(M, axis=1) # (N,) - M_shifted = M - row_max[:, None] - row_lse = row_max + tl.log(tl.sum(tl.exp(M_shifted), axis=1)) # (N,) - M = M - row_lse[:, None] - - # Column normalization: M[i,j] -= logsumexp(M[:,j]) - col_max = tl.max(M, axis=0) # (N,) - M_shifted = M - col_max[None, :] - col_lse = col_max + tl.log(tl.sum(tl.exp(M_shifted), axis=0)) # (N,) - M = M - col_lse[None, :] - - # Exponentiate to get doubly-stochastic matrix - result = tl.exp(M) - tl.store(out_ptr + offsets, result) - - -def sinkhorn_general(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: - """Triton-accelerated Sinkhorn for NxN matrices (N > 2). - - Args: - log_alpha: (N, N) float32 tensor of log-space routing weights. - iters: number of Sinkhorn iterations. - - Returns: - (N, N) doubly-stochastic matrix. - """ - N = log_alpha.shape[0] - assert log_alpha.shape == (N, N), f"Expected square matrix, got {log_alpha.shape}" - assert N <= 16, f"Triton Sinkhorn designed for N <= 16, got N={N}" - - # Ensure contiguous float32 on CUDA - log_alpha_f32 = log_alpha.detach().contiguous().to(dtype=torch.float32) - out = torch.empty_like(log_alpha_f32) - - # Launch single program instance (tiny matrix, no parallelism needed) - _sinkhorn_kernel[(1,)]( - log_alpha_f32, - out, - N=N, - ITERS=iters, - ) - return out - - -# --------------------------------------------------------------------------- -# Unified Python wrapper -# --------------------------------------------------------------------------- - -def sinkhorn_fused(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: - """Fused Sinkhorn-Knopp normalization. - - Dispatches to closed-form for n=2 or Triton kernel for n>2. - - Args: - log_alpha: (N, N) parameter tensor (log-space routing weights). - iters: number of Sinkhorn iterations (ignored for n=2). - - Returns: - (N, N) doubly-stochastic matrix on the same device as input. - """ - N = log_alpha.shape[0] - if N == 2: - return sinkhorn_2x2(log_alpha) - return sinkhorn_general(log_alpha, iters=iters) - - -# --------------------------------------------------------------------------- -# Reference implementation (pure Python loop, matches mhc_mini._sinkhorn) -# --------------------------------------------------------------------------- - -def _sinkhorn_reference(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: - """Reference Sinkhorn matching mhc_mini.ManifoldHyperConnection._sinkhorn.""" - M = log_alpha.clone().to(torch.float32) - for _ in range(iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - -# --------------------------------------------------------------------------- -# Smoke test -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - print("=" * 60) - print("Sinkhorn Fused Kernel — Smoke Test") - print("=" * 60) - - device = "cuda" if torch.cuda.is_available() else "cpu" - torch.manual_seed(42) - - # --- Test 1: n_streams = 2 (closed-form) --- - print("\n[Test 1] n_streams=2 closed-form vs reference") - log_alpha_2 = torch.randn(2, 2, device=device, dtype=torch.float32) - ref_2 = _sinkhorn_reference(log_alpha_2, iters=20) # many iters for convergence - fused_2 = sinkhorn_fused(log_alpha_2) - - # Doubly-stochastic checks - row_sums_2 = fused_2.sum(dim=1) - col_sums_2 = fused_2.sum(dim=0) - print(f" Fused result:\n{fused_2}") - print(f" Reference result:\n{ref_2}") - print(f" Row sums: {row_sums_2} (should be ~1.0)") - print(f" Col sums: {col_sums_2} (should be ~1.0)") - - err_2 = (fused_2 - ref_2).abs().max().item() - print(f" Max abs error vs reference (20 iters): {err_2:.6e}") - assert err_2 < 1e-3, f"n=2 error too large: {err_2}" - assert (row_sums_2 - 1.0).abs().max() < 1e-5, "Row sums not ~1" - assert (col_sums_2 - 1.0).abs().max() < 1e-5, "Col sums not ~1" - print(" PASSED") - - # --- Test 2: n_streams = 4 (Triton kernel) --- - print("\n[Test 2] n_streams=4 Triton kernel vs reference") - log_alpha_4 = torch.randn(4, 4, device=device, dtype=torch.float32) - ref_4 = _sinkhorn_reference(log_alpha_4, iters=5) - fused_4 = sinkhorn_fused(log_alpha_4, iters=5) - - row_sums_4 = fused_4.sum(dim=1) - col_sums_4 = fused_4.sum(dim=0) - print(f" Fused result:\n{fused_4}") - print(f" Reference result:\n{ref_4}") - print(f" Row sums: {row_sums_4}") - print(f" Col sums: {col_sums_4}") - - err_4 = (fused_4 - ref_4).abs().max().item() - print(f" Max abs error vs reference: {err_4:.6e}") - assert err_4 < 1e-4, f"n=4 error too large: {err_4}" - assert (row_sums_4 - 1.0).abs().max() < 1e-4, "Row sums not ~1" - assert (col_sums_4 - 1.0).abs().max() < 1e-4, "Col sums not ~1" - print(" PASSED") - - # --- Test 3: n_streams = 8 --- - print("\n[Test 3] n_streams=8 Triton kernel vs reference") - log_alpha_8 = torch.randn(8, 8, device=device, dtype=torch.float32) - ref_8 = _sinkhorn_reference(log_alpha_8, iters=5) - fused_8 = sinkhorn_fused(log_alpha_8, iters=5) - - err_8 = (fused_8 - ref_8).abs().max().item() - print(f" Max abs error vs reference: {err_8:.6e}") - assert err_8 < 1e-4, f"n=8 error too large: {err_8}" - print(" PASSED") - - # --- Test 4: Gradient flow for n=2 (closed-form is differentiable) --- - print("\n[Test 4] Gradient flow through n=2 closed-form") - log_alpha_grad = torch.randn(2, 2, device=device, dtype=torch.float32, requires_grad=True) - result = sinkhorn_2x2(log_alpha_grad) - loss = result.sum() - loss.backward() - print(f" Gradient: {log_alpha_grad.grad}") - assert log_alpha_grad.grad is not None, "No gradient computed" - assert not torch.isnan(log_alpha_grad.grad).any(), "NaN in gradient" - print(" PASSED") - - print("\n" + "=" * 60) - print("ALL SINKHORN TESTS PASSED") - print("=" * 60) +"""Fused Sinkhorn-Knopp normalization kernel for mHC routing. + +Phase 2: Optimized implementations replacing the Python for-loop in +ManifoldHyperConnection._sinkhorn(). + +For n_streams=2: closed-form doubly-stochastic projection (no iteration). +For n_streams>2: Triton kernel fusing exp + row_norm + col_norm iterations. + +The Phase 1 reference (mhc_mini.py) does 5-20 iterations of alternating +row/column log-sum-exp normalization on a small (n_streams x n_streams) +matrix. This module provides two fast paths: + 1. n=2 closed-form: O(1) — no loop, no kernel launch overhead. + 2. n>2 Triton kernel: single kernel launch for all sinkhorn iterations. +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +# --------------------------------------------------------------------------- +# Fast path: n_streams = 2 closed-form doubly-stochastic projection +# --------------------------------------------------------------------------- + +def sinkhorn_2x2(log_alpha: torch.Tensor) -> torch.Tensor: + """Closed-form doubly-stochastic projection for 2x2 matrices. + + For a 2x2 log-space matrix, the Sinkhorn limit is: + [[a, 1-a], [1-a, a]] + where a = sigmoid(log_alpha[0,0] - log_alpha[0,1] + log_alpha[1,1] - log_alpha[1,0]) / 2 + More precisely, the unique doubly-stochastic matrix in the Sinkhorn + equivalence class is parameterized by the single degree of freedom: + a = sigmoid((log_alpha[0,0] - log_alpha[0,1] - log_alpha[1,0] + log_alpha[1,1]) / 2) + + This is exact (no iteration needed) and avoids all kernel launch overhead. + """ + # The converged Sinkhorn for 2x2 depends only on the "cross-ratio": + # delta = (log_alpha[0,0] + log_alpha[1,1]) - (log_alpha[0,1] + log_alpha[1,0]) + # and a = sigmoid(delta / 2) gives the diagonal entry. + delta = (log_alpha[0, 0] + log_alpha[1, 1]) - (log_alpha[0, 1] + log_alpha[1, 0]) + a = torch.sigmoid(delta * 0.5) + one_minus_a = 1.0 - a + # Build result without mutation: create from flat tensor + row0 = torch.stack([a, one_minus_a]) + row1 = torch.stack([one_minus_a, a]) + return torch.stack([row0, row1]) + + +# --------------------------------------------------------------------------- +# General path: Triton kernel for n_streams > 2 +# --------------------------------------------------------------------------- + +@triton.jit +def _sinkhorn_kernel( + log_alpha_ptr, # input: (N, N) in row-major, float32 + out_ptr, # output: (N, N) in row-major, float32 + N: tl.constexpr, # matrix dimension (n_streams) + ITERS: tl.constexpr, # number of sinkhorn iterations +): + """Single-program Sinkhorn on a small NxN matrix. + + One program instance processes the entire matrix. This is efficient for + N <= 16 where the entire matrix fits in registers. + """ + # Load entire NxN matrix into registers + row_idx = tl.arange(0, N) + col_idx = tl.arange(0, N) + # 2D indexing: offsets[i, j] = i * N + j + offsets = row_idx[:, None] * N + col_idx[None, :] # (N, N) + + M = tl.load(log_alpha_ptr + offsets).to(tl.float32) # (N, N) + + # Alternating row/column log-sum-exp normalization + for _ in tl.static_range(ITERS): + # Row normalization: M[i,j] -= logsumexp(M[i,:]) + row_max = tl.max(M, axis=1) # (N,) + M_shifted = M - row_max[:, None] + row_lse = row_max + tl.log(tl.sum(tl.exp(M_shifted), axis=1)) # (N,) + M = M - row_lse[:, None] + + # Column normalization: M[i,j] -= logsumexp(M[:,j]) + col_max = tl.max(M, axis=0) # (N,) + M_shifted = M - col_max[None, :] + col_lse = col_max + tl.log(tl.sum(tl.exp(M_shifted), axis=0)) # (N,) + M = M - col_lse[None, :] + + # Exponentiate to get doubly-stochastic matrix + result = tl.exp(M) + tl.store(out_ptr + offsets, result) + + +def sinkhorn_general(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: + """Triton-accelerated Sinkhorn for NxN matrices (N > 2). + + Args: + log_alpha: (N, N) float32 tensor of log-space routing weights. + iters: number of Sinkhorn iterations. + + Returns: + (N, N) doubly-stochastic matrix. + """ + N = log_alpha.shape[0] + assert log_alpha.shape == (N, N), f"Expected square matrix, got {log_alpha.shape}" + assert N <= 16, f"Triton Sinkhorn designed for N <= 16, got N={N}" + + # Ensure contiguous float32 on CUDA + log_alpha_f32 = log_alpha.detach().contiguous().to(dtype=torch.float32) + out = torch.empty_like(log_alpha_f32) + + # Launch single program instance (tiny matrix, no parallelism needed) + _sinkhorn_kernel[(1,)]( + log_alpha_f32, + out, + N=N, + ITERS=iters, + ) + return out + + +# --------------------------------------------------------------------------- +# Unified Python wrapper +# --------------------------------------------------------------------------- + +def sinkhorn_fused(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: + """Fused Sinkhorn-Knopp normalization. + + Dispatches to closed-form for n=2 or Triton kernel for n>2. + + Args: + log_alpha: (N, N) parameter tensor (log-space routing weights). + iters: number of Sinkhorn iterations (ignored for n=2). + + Returns: + (N, N) doubly-stochastic matrix on the same device as input. + """ + N = log_alpha.shape[0] + if N == 2: + return sinkhorn_2x2(log_alpha) + return sinkhorn_general(log_alpha, iters=iters) + + +# --------------------------------------------------------------------------- +# Reference implementation (pure Python loop, matches mhc_mini._sinkhorn) +# --------------------------------------------------------------------------- + +def _sinkhorn_reference(log_alpha: torch.Tensor, iters: int = 5) -> torch.Tensor: + """Reference Sinkhorn matching mhc_mini.ManifoldHyperConnection._sinkhorn.""" + M = log_alpha.clone().to(torch.float32) + for _ in range(iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + +# --------------------------------------------------------------------------- +# Smoke test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + print("=" * 60) + print("Sinkhorn Fused Kernel — Smoke Test") + print("=" * 60) + + device = "cuda" if torch.cuda.is_available() else "cpu" + torch.manual_seed(42) + + # --- Test 1: n_streams = 2 (closed-form) --- + print("\n[Test 1] n_streams=2 closed-form vs reference") + log_alpha_2 = torch.randn(2, 2, device=device, dtype=torch.float32) + ref_2 = _sinkhorn_reference(log_alpha_2, iters=20) # many iters for convergence + fused_2 = sinkhorn_fused(log_alpha_2) + + # Doubly-stochastic checks + row_sums_2 = fused_2.sum(dim=1) + col_sums_2 = fused_2.sum(dim=0) + print(f" Fused result:\n{fused_2}") + print(f" Reference result:\n{ref_2}") + print(f" Row sums: {row_sums_2} (should be ~1.0)") + print(f" Col sums: {col_sums_2} (should be ~1.0)") + + err_2 = (fused_2 - ref_2).abs().max().item() + print(f" Max abs error vs reference (20 iters): {err_2:.6e}") + assert err_2 < 1e-3, f"n=2 error too large: {err_2}" + assert (row_sums_2 - 1.0).abs().max() < 1e-5, "Row sums not ~1" + assert (col_sums_2 - 1.0).abs().max() < 1e-5, "Col sums not ~1" + print(" PASSED") + + # --- Test 2: n_streams = 4 (Triton kernel) --- + print("\n[Test 2] n_streams=4 Triton kernel vs reference") + log_alpha_4 = torch.randn(4, 4, device=device, dtype=torch.float32) + ref_4 = _sinkhorn_reference(log_alpha_4, iters=5) + fused_4 = sinkhorn_fused(log_alpha_4, iters=5) + + row_sums_4 = fused_4.sum(dim=1) + col_sums_4 = fused_4.sum(dim=0) + print(f" Fused result:\n{fused_4}") + print(f" Reference result:\n{ref_4}") + print(f" Row sums: {row_sums_4}") + print(f" Col sums: {col_sums_4}") + + err_4 = (fused_4 - ref_4).abs().max().item() + print(f" Max abs error vs reference: {err_4:.6e}") + assert err_4 < 1e-4, f"n=4 error too large: {err_4}" + assert (row_sums_4 - 1.0).abs().max() < 1e-4, "Row sums not ~1" + assert (col_sums_4 - 1.0).abs().max() < 1e-4, "Col sums not ~1" + print(" PASSED") + + # --- Test 3: n_streams = 8 --- + print("\n[Test 3] n_streams=8 Triton kernel vs reference") + log_alpha_8 = torch.randn(8, 8, device=device, dtype=torch.float32) + ref_8 = _sinkhorn_reference(log_alpha_8, iters=5) + fused_8 = sinkhorn_fused(log_alpha_8, iters=5) + + err_8 = (fused_8 - ref_8).abs().max().item() + print(f" Max abs error vs reference: {err_8:.6e}") + assert err_8 < 1e-4, f"n=8 error too large: {err_8}" + print(" PASSED") + + # --- Test 4: Gradient flow for n=2 (closed-form is differentiable) --- + print("\n[Test 4] Gradient flow through n=2 closed-form") + log_alpha_grad = torch.randn(2, 2, device=device, dtype=torch.float32, requires_grad=True) + result = sinkhorn_2x2(log_alpha_grad) + loss = result.sum() + loss.backward() + print(f" Gradient: {log_alpha_grad.grad}") + assert log_alpha_grad.grad is not None, "No gradient computed" + assert not torch.isnan(log_alpha_grad.grad).any(), "NaN in gradient" + print(" PASSED") + + print("\n" + "=" * 60) + print("ALL SINKHORN TESTS PASSED") + print("=" * 60) diff --git a/overlay/kernels/triton/ssd_exp_trap.py b/overlay/kernels/triton/ssd_exp_trap.py index fec2eef4ee96fbc2720162abfaa817984bc02df7..a08e8049662deb21d060943160dba66626fe7f88 100644 --- a/overlay/kernels/triton/ssd_exp_trap.py +++ b/overlay/kernels/triton/ssd_exp_trap.py @@ -1,277 +1,277 @@ -"""Mamba-3 SISO prefill kernel using exponential-trapezoidal discretization. - -Phase 2: Triton kernel for the sequential SSM scan. -Phase 1: Uses sequential Python loop in Mamba3Block.forward(). - -The exp-trap discretization provides O(Delta^2) accuracy vs O(Delta) for Euler: - h_t = alpha_t * h_{t-1} + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_{t-1}) - y_t = C_t . h_t + D * mean(x_heads_t) - -where alpha_t = exp(dt_t * A). - -The T dimension is sequential (state depends on previous state). -Triton parallelizes over (B, n_heads) — each program handles one lane. -""" - -from __future__ import annotations - -import torch -import triton -import triton.language as tl - - -@triton.jit -def _ssd_exp_trap_kernel( - # Input pointers - ALPHA_ptr, # (B, T, n_heads) — precomputed exp(dt*A) - BX_ptr, # (B, T, n_heads, d_state) — B_proj expanded to heads - C_ptr, # (B, T, n_heads, d_state) — C_proj expanded to heads - X_HEADS_ptr, # (B, T, n_heads, head_dim) — x_ssm reshaped per head - D_ptr, # (n_heads,) — D parameter - LAM_ptr, # (n_heads, 1) — sigmoid(lambda_theta) - # Output - Y_ptr, # (B, T, n_heads) — output y_ssm - # Dimensions - B_dim: tl.constexpr, - T_dim: tl.constexpr, - N_HEADS: tl.constexpr, - D_STATE: tl.constexpr, - HEAD_DIM: tl.constexpr, - # Strides for ALPHA: (B, T, n_heads) - stride_a_b, stride_a_t, stride_a_h, - # Strides for BX: (B, T, n_heads, d_state) - stride_bx_b, stride_bx_t, stride_bx_h, stride_bx_d, - # Strides for C: (B, T, n_heads, d_state) - stride_c_b, stride_c_t, stride_c_h, stride_c_d, - # Strides for X_HEADS: (B, T, n_heads, head_dim) - stride_xh_b, stride_xh_t, stride_xh_h, stride_xh_d, - # Strides for Y: (B, T, n_heads) - stride_y_b, stride_y_t, stride_y_h, - # Block size - BLOCK_D: tl.constexpr, - BLOCK_HD: tl.constexpr, -): - """Sequential scan for one (batch, head) lane over all T timesteps.""" - pid = tl.program_id(0) - b_idx = pid // N_HEADS - h_idx = pid % N_HEADS - - # Load per-head constants - D_val = tl.load(D_ptr + h_idx).to(tl.float32) - lam = tl.load(LAM_ptr + h_idx).to(tl.float32) # (n_heads, 1) but stored flat after squeeze - - # Hidden state h: (d_state,) in fp32 for accumulation stability - d_offsets = tl.arange(0, BLOCK_D) - d_mask = d_offsets < D_STATE - h = tl.zeros([BLOCK_D], dtype=tl.float32) - - # Bx_prev: (d_state,) — starts as zeros - bx_prev = tl.zeros([BLOCK_D], dtype=tl.float32) - - # Head dim offsets for x_heads mean - hd_offsets = tl.arange(0, BLOCK_HD) - hd_mask = hd_offsets < HEAD_DIM - - for t in range(T_dim): - # Load alpha_t: scalar for this (b, t, h) - alpha_t = tl.load( - ALPHA_ptr + b_idx * stride_a_b + t * stride_a_t + h_idx * stride_a_h - ).to(tl.float32) - - # Load Bx_t: (d_state,) - bx_base = BX_ptr + b_idx * stride_bx_b + t * stride_bx_t + h_idx * stride_bx_h - bx_t = tl.load(bx_base + d_offsets * stride_bx_d, mask=d_mask, other=0.0).to(tl.float32) - - # Trapezoidal recurrence: - # h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - blend = lam * bx_t + (1.0 - lam) * bx_prev - h = alpha_t * h + (1.0 - alpha_t) * blend - - bx_prev = bx_t - - # Load C_t: (d_state,) - c_base = C_ptr + b_idx * stride_c_b + t * stride_c_t + h_idx * stride_c_h - c_t = tl.load(c_base + d_offsets * stride_c_d, mask=d_mask, other=0.0).to(tl.float32) - - # y_t = dot(C_t, h) - y_t = tl.sum(c_t * h, axis=0) - - # + D * mean(x_heads_t) - xh_base = X_HEADS_ptr + b_idx * stride_xh_b + t * stride_xh_t + h_idx * stride_xh_h - xh = tl.load(xh_base + hd_offsets * stride_xh_d, mask=hd_mask, other=0.0).to(tl.float32) - xh_mean = tl.sum(xh, axis=0) / HEAD_DIM - y_t = y_t + D_val * xh_mean - - # Store y_t - y_off = Y_ptr + b_idx * stride_y_b + t * stride_y_t + h_idx * stride_y_h - tl.store(y_off, y_t.to(tl.bfloat16)) - - -def ssd_exp_trap_triton( - alpha: torch.Tensor, - Bx: torch.Tensor, - C_proj: torch.Tensor, - x_heads: torch.Tensor, - D_param: torch.Tensor, - lam: torch.Tensor, -) -> torch.Tensor: - """Triton SSM scan with exponential-trapezoidal discretization. - - Args: - alpha: (B, T, n_heads) — exp(dt * A), the decay factor. - Bx: (B, T, n_heads, d_state) — B projection expanded to all heads. - C_proj: (B, T, n_heads, d_state) — C projection expanded to all heads. - x_heads: (B, T, n_heads, head_dim) — x_ssm reshaped per head. - D_param: (n_heads,) — skip-connection parameter. - lam: (n_heads, 1) — sigmoid(lambda_theta), trapezoidal blending weight. - - Returns: - y_ssm: (B, T, n_heads) bf16 — SSM output per head. - """ - assert alpha.is_contiguous() - assert Bx.is_contiguous() - assert C_proj.is_contiguous() - assert x_heads.is_contiguous() - - B, T, N_HEADS = alpha.shape - D_STATE = Bx.shape[-1] - HEAD_DIM = x_heads.shape[-1] - - y = torch.empty(B, T, N_HEADS, device=alpha.device, dtype=torch.bfloat16) - - # Flatten lam to (n_heads,) for simpler kernel access - lam_flat = lam.reshape(-1).contiguous() - - BLOCK_D = triton.next_power_of_2(D_STATE) - BLOCK_HD = triton.next_power_of_2(HEAD_DIM) - - grid = (B * N_HEADS,) - - _ssd_exp_trap_kernel[grid]( - alpha, Bx, C_proj, x_heads, D_param, lam_flat, - y, - B_dim=B, T_dim=T, N_HEADS=N_HEADS, D_STATE=D_STATE, HEAD_DIM=HEAD_DIM, - stride_a_b=alpha.stride(0), stride_a_t=alpha.stride(1), stride_a_h=alpha.stride(2), - stride_bx_b=Bx.stride(0), stride_bx_t=Bx.stride(1), stride_bx_h=Bx.stride(2), stride_bx_d=Bx.stride(3), - stride_c_b=C_proj.stride(0), stride_c_t=C_proj.stride(1), stride_c_h=C_proj.stride(2), stride_c_d=C_proj.stride(3), - stride_xh_b=x_heads.stride(0), stride_xh_t=x_heads.stride(1), stride_xh_h=x_heads.stride(2), stride_xh_d=x_heads.stride(3), - stride_y_b=y.stride(0), stride_y_t=y.stride(1), stride_y_h=y.stride(2), - BLOCK_D=BLOCK_D, - BLOCK_HD=BLOCK_HD, - ) - - return y - - -# --------------------------------------------------------------------------- -# Phase 1 reference implementation (from Mamba3Block.forward lines 178-194) -# --------------------------------------------------------------------------- - -def _ssd_exp_trap_reference( - alpha: torch.Tensor, - Bx: torch.Tensor, - C_proj: torch.Tensor, - x_heads: torch.Tensor, - D_param: torch.Tensor, - lam: torch.Tensor, -) -> torch.Tensor: - """Phase 1 sequential Python loop — exact semantics from Mamba3Block.forward.""" - B, T, n_heads = alpha.shape - d_state = Bx.shape[-1] - device, dtype = alpha.device, alpha.dtype - - h = torch.zeros(B, n_heads, d_state, device=device, dtype=torch.float32) - Bx_prev = torch.zeros(B, n_heads, d_state, device=device, dtype=torch.float32) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1).float() # (B, n_heads, 1) - Bx_t = Bx[:, t].float() # (B, n_heads, d_state) - - # Trapezoidal recurrence - h = alpha_t * h + (1 - alpha_t) * (lam.float() * Bx_t + (1 - lam.float()) * Bx_prev) - Bx_prev = Bx_t - - C_t = C_proj[:, t].float() # (B, n_heads, d_state) - y_t = (C_t * h).sum(dim=-1) # (B, n_heads) - y_t = y_t + D_param.float() * x_heads[:, t].float().mean(dim=-1) # (B, n_heads) - y_list.append(y_t) - - return torch.stack(y_list, dim=1).bfloat16() # (B, T, n_heads) - - -# --------------------------------------------------------------------------- -# Smoke test -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - torch.manual_seed(42) - device = torch.device("cuda") - - # Match Mamba3Block config: d_model=256, d_state=64, n_heads=8, headdim=32, expand=2 - B, T = 2, 128 - n_heads = 8 - d_state = 64 - head_dim = 32 # inner_dim // n_heads = (2*256) // 8 = 64, but we test 32 - - # Precompute alpha = exp(dt * A) — values in (0, 1) for stability - alpha = torch.rand(B, T, n_heads, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 - Bx = torch.randn(B, T, n_heads, d_state, device=device, dtype=torch.bfloat16) * 0.1 - C_proj = torch.randn(B, T, n_heads, d_state, device=device, dtype=torch.bfloat16) * 0.1 - x_heads = torch.randn(B, T, n_heads, head_dim, device=device, dtype=torch.bfloat16) * 0.1 - D_param = torch.ones(n_heads, device=device, dtype=torch.bfloat16) - lam = torch.sigmoid(torch.zeros(n_heads, 1, device=device, dtype=torch.bfloat16)) # 0.5 - - # --- Test 1: Triton vs Reference --- - y_triton = ssd_exp_trap_triton(alpha, Bx, C_proj, x_heads, D_param, lam) - y_ref = _ssd_exp_trap_reference(alpha, Bx, C_proj, x_heads, D_param, lam) - - assert y_triton.shape == y_ref.shape == (B, T, n_heads) - max_diff = (y_triton.float() - y_ref.float()).abs().max().item() - close = torch.allclose(y_triton.float(), y_ref.float(), atol=1e-2, rtol=1e-2) - print(f"[ssd_exp_trap] main test: shape={y_triton.shape}, max_diff={max_diff:.6f}, allclose={close}") - assert close, f"Main test mismatch: max_diff={max_diff}" - - # --- Test 2: Different lambda values --- - for lam_val in [0.0, 0.3, 0.7, 1.0]: - lam_t = torch.full((n_heads, 1), lam_val, device=device, dtype=torch.bfloat16) - y_t = ssd_exp_trap_triton(alpha, Bx, C_proj, x_heads, D_param, lam_t) - y_r = _ssd_exp_trap_reference(alpha, Bx, C_proj, x_heads, D_param, lam_t) - md = (y_t.float() - y_r.float()).abs().max().item() - ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) - print(f"[ssd_exp_trap] lam={lam_val}: max_diff={md:.6f}, allclose={ok}") - assert ok, f"lam={lam_val} mismatch: max_diff={md}" - - # --- Test 3: Smaller d_state --- - for ds in [16, 32]: - alpha_s = torch.rand(1, 64, 4, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 - Bx_s = torch.randn(1, 64, 4, ds, device=device, dtype=torch.bfloat16) * 0.1 - C_s = torch.randn(1, 64, 4, ds, device=device, dtype=torch.bfloat16) * 0.1 - xh_s = torch.randn(1, 64, 4, 16, device=device, dtype=torch.bfloat16) * 0.1 - D_s = torch.ones(4, device=device, dtype=torch.bfloat16) - lam_s = torch.full((4, 1), 0.5, device=device, dtype=torch.bfloat16) - - y_t = ssd_exp_trap_triton(alpha_s, Bx_s, C_s, xh_s, D_s, lam_s) - y_r = _ssd_exp_trap_reference(alpha_s, Bx_s, C_s, xh_s, D_s, lam_s) - md = (y_t.float() - y_r.float()).abs().max().item() - ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) - print(f"[ssd_exp_trap] d_state={ds}: max_diff={md:.6f}, allclose={ok}") - assert ok, f"d_state={ds} mismatch: max_diff={md}" - - # --- Test 4: Longer sequence --- - T_long = 512 - alpha_l = torch.rand(1, T_long, 4, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 - Bx_l = torch.randn(1, T_long, 4, 32, device=device, dtype=torch.bfloat16) * 0.05 - C_l = torch.randn(1, T_long, 4, 32, device=device, dtype=torch.bfloat16) * 0.05 - xh_l = torch.randn(1, T_long, 4, 16, device=device, dtype=torch.bfloat16) * 0.05 - D_l = torch.ones(4, device=device, dtype=torch.bfloat16) - lam_l = torch.full((4, 1), 0.5, device=device, dtype=torch.bfloat16) - - y_t = ssd_exp_trap_triton(alpha_l, Bx_l, C_l, xh_l, D_l, lam_l) - y_r = _ssd_exp_trap_reference(alpha_l, Bx_l, C_l, xh_l, D_l, lam_l) - md = (y_t.float() - y_r.float()).abs().max().item() - ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) - print(f"[ssd_exp_trap] T={T_long}: max_diff={md:.6f}, allclose={ok}") - assert ok, f"T={T_long} mismatch: max_diff={md}" - - print("[ssd_exp_trap] ALL TESTS PASSED") +"""Mamba-3 SISO prefill kernel using exponential-trapezoidal discretization. + +Phase 2: Triton kernel for the sequential SSM scan. +Phase 1: Uses sequential Python loop in Mamba3Block.forward(). + +The exp-trap discretization provides O(Delta^2) accuracy vs O(Delta) for Euler: + h_t = alpha_t * h_{t-1} + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_{t-1}) + y_t = C_t . h_t + D * mean(x_heads_t) + +where alpha_t = exp(dt_t * A). + +The T dimension is sequential (state depends on previous state). +Triton parallelizes over (B, n_heads) — each program handles one lane. +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _ssd_exp_trap_kernel( + # Input pointers + ALPHA_ptr, # (B, T, n_heads) — precomputed exp(dt*A) + BX_ptr, # (B, T, n_heads, d_state) — B_proj expanded to heads + C_ptr, # (B, T, n_heads, d_state) — C_proj expanded to heads + X_HEADS_ptr, # (B, T, n_heads, head_dim) — x_ssm reshaped per head + D_ptr, # (n_heads,) — D parameter + LAM_ptr, # (n_heads, 1) — sigmoid(lambda_theta) + # Output + Y_ptr, # (B, T, n_heads) — output y_ssm + # Dimensions + B_dim: tl.constexpr, + T_dim: tl.constexpr, + N_HEADS: tl.constexpr, + D_STATE: tl.constexpr, + HEAD_DIM: tl.constexpr, + # Strides for ALPHA: (B, T, n_heads) + stride_a_b, stride_a_t, stride_a_h, + # Strides for BX: (B, T, n_heads, d_state) + stride_bx_b, stride_bx_t, stride_bx_h, stride_bx_d, + # Strides for C: (B, T, n_heads, d_state) + stride_c_b, stride_c_t, stride_c_h, stride_c_d, + # Strides for X_HEADS: (B, T, n_heads, head_dim) + stride_xh_b, stride_xh_t, stride_xh_h, stride_xh_d, + # Strides for Y: (B, T, n_heads) + stride_y_b, stride_y_t, stride_y_h, + # Block size + BLOCK_D: tl.constexpr, + BLOCK_HD: tl.constexpr, +): + """Sequential scan for one (batch, head) lane over all T timesteps.""" + pid = tl.program_id(0) + b_idx = pid // N_HEADS + h_idx = pid % N_HEADS + + # Load per-head constants + D_val = tl.load(D_ptr + h_idx).to(tl.float32) + lam = tl.load(LAM_ptr + h_idx).to(tl.float32) # (n_heads, 1) but stored flat after squeeze + + # Hidden state h: (d_state,) in fp32 for accumulation stability + d_offsets = tl.arange(0, BLOCK_D) + d_mask = d_offsets < D_STATE + h = tl.zeros([BLOCK_D], dtype=tl.float32) + + # Bx_prev: (d_state,) — starts as zeros + bx_prev = tl.zeros([BLOCK_D], dtype=tl.float32) + + # Head dim offsets for x_heads mean + hd_offsets = tl.arange(0, BLOCK_HD) + hd_mask = hd_offsets < HEAD_DIM + + for t in range(T_dim): + # Load alpha_t: scalar for this (b, t, h) + alpha_t = tl.load( + ALPHA_ptr + b_idx * stride_a_b + t * stride_a_t + h_idx * stride_a_h + ).to(tl.float32) + + # Load Bx_t: (d_state,) + bx_base = BX_ptr + b_idx * stride_bx_b + t * stride_bx_t + h_idx * stride_bx_h + bx_t = tl.load(bx_base + d_offsets * stride_bx_d, mask=d_mask, other=0.0).to(tl.float32) + + # Trapezoidal recurrence: + # h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + blend = lam * bx_t + (1.0 - lam) * bx_prev + h = alpha_t * h + (1.0 - alpha_t) * blend + + bx_prev = bx_t + + # Load C_t: (d_state,) + c_base = C_ptr + b_idx * stride_c_b + t * stride_c_t + h_idx * stride_c_h + c_t = tl.load(c_base + d_offsets * stride_c_d, mask=d_mask, other=0.0).to(tl.float32) + + # y_t = dot(C_t, h) + y_t = tl.sum(c_t * h, axis=0) + + # + D * mean(x_heads_t) + xh_base = X_HEADS_ptr + b_idx * stride_xh_b + t * stride_xh_t + h_idx * stride_xh_h + xh = tl.load(xh_base + hd_offsets * stride_xh_d, mask=hd_mask, other=0.0).to(tl.float32) + xh_mean = tl.sum(xh, axis=0) / HEAD_DIM + y_t = y_t + D_val * xh_mean + + # Store y_t + y_off = Y_ptr + b_idx * stride_y_b + t * stride_y_t + h_idx * stride_y_h + tl.store(y_off, y_t.to(tl.bfloat16)) + + +def ssd_exp_trap_triton( + alpha: torch.Tensor, + Bx: torch.Tensor, + C_proj: torch.Tensor, + x_heads: torch.Tensor, + D_param: torch.Tensor, + lam: torch.Tensor, +) -> torch.Tensor: + """Triton SSM scan with exponential-trapezoidal discretization. + + Args: + alpha: (B, T, n_heads) — exp(dt * A), the decay factor. + Bx: (B, T, n_heads, d_state) — B projection expanded to all heads. + C_proj: (B, T, n_heads, d_state) — C projection expanded to all heads. + x_heads: (B, T, n_heads, head_dim) — x_ssm reshaped per head. + D_param: (n_heads,) — skip-connection parameter. + lam: (n_heads, 1) — sigmoid(lambda_theta), trapezoidal blending weight. + + Returns: + y_ssm: (B, T, n_heads) bf16 — SSM output per head. + """ + assert alpha.is_contiguous() + assert Bx.is_contiguous() + assert C_proj.is_contiguous() + assert x_heads.is_contiguous() + + B, T, N_HEADS = alpha.shape + D_STATE = Bx.shape[-1] + HEAD_DIM = x_heads.shape[-1] + + y = torch.empty(B, T, N_HEADS, device=alpha.device, dtype=torch.bfloat16) + + # Flatten lam to (n_heads,) for simpler kernel access + lam_flat = lam.reshape(-1).contiguous() + + BLOCK_D = triton.next_power_of_2(D_STATE) + BLOCK_HD = triton.next_power_of_2(HEAD_DIM) + + grid = (B * N_HEADS,) + + _ssd_exp_trap_kernel[grid]( + alpha, Bx, C_proj, x_heads, D_param, lam_flat, + y, + B_dim=B, T_dim=T, N_HEADS=N_HEADS, D_STATE=D_STATE, HEAD_DIM=HEAD_DIM, + stride_a_b=alpha.stride(0), stride_a_t=alpha.stride(1), stride_a_h=alpha.stride(2), + stride_bx_b=Bx.stride(0), stride_bx_t=Bx.stride(1), stride_bx_h=Bx.stride(2), stride_bx_d=Bx.stride(3), + stride_c_b=C_proj.stride(0), stride_c_t=C_proj.stride(1), stride_c_h=C_proj.stride(2), stride_c_d=C_proj.stride(3), + stride_xh_b=x_heads.stride(0), stride_xh_t=x_heads.stride(1), stride_xh_h=x_heads.stride(2), stride_xh_d=x_heads.stride(3), + stride_y_b=y.stride(0), stride_y_t=y.stride(1), stride_y_h=y.stride(2), + BLOCK_D=BLOCK_D, + BLOCK_HD=BLOCK_HD, + ) + + return y + + +# --------------------------------------------------------------------------- +# Phase 1 reference implementation (from Mamba3Block.forward lines 178-194) +# --------------------------------------------------------------------------- + +def _ssd_exp_trap_reference( + alpha: torch.Tensor, + Bx: torch.Tensor, + C_proj: torch.Tensor, + x_heads: torch.Tensor, + D_param: torch.Tensor, + lam: torch.Tensor, +) -> torch.Tensor: + """Phase 1 sequential Python loop — exact semantics from Mamba3Block.forward.""" + B, T, n_heads = alpha.shape + d_state = Bx.shape[-1] + device, dtype = alpha.device, alpha.dtype + + h = torch.zeros(B, n_heads, d_state, device=device, dtype=torch.float32) + Bx_prev = torch.zeros(B, n_heads, d_state, device=device, dtype=torch.float32) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1).float() # (B, n_heads, 1) + Bx_t = Bx[:, t].float() # (B, n_heads, d_state) + + # Trapezoidal recurrence + h = alpha_t * h + (1 - alpha_t) * (lam.float() * Bx_t + (1 - lam.float()) * Bx_prev) + Bx_prev = Bx_t + + C_t = C_proj[:, t].float() # (B, n_heads, d_state) + y_t = (C_t * h).sum(dim=-1) # (B, n_heads) + y_t = y_t + D_param.float() * x_heads[:, t].float().mean(dim=-1) # (B, n_heads) + y_list.append(y_t) + + return torch.stack(y_list, dim=1).bfloat16() # (B, T, n_heads) + + +# --------------------------------------------------------------------------- +# Smoke test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + torch.manual_seed(42) + device = torch.device("cuda") + + # Match Mamba3Block config: d_model=256, d_state=64, n_heads=8, headdim=32, expand=2 + B, T = 2, 128 + n_heads = 8 + d_state = 64 + head_dim = 32 # inner_dim // n_heads = (2*256) // 8 = 64, but we test 32 + + # Precompute alpha = exp(dt * A) — values in (0, 1) for stability + alpha = torch.rand(B, T, n_heads, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 + Bx = torch.randn(B, T, n_heads, d_state, device=device, dtype=torch.bfloat16) * 0.1 + C_proj = torch.randn(B, T, n_heads, d_state, device=device, dtype=torch.bfloat16) * 0.1 + x_heads = torch.randn(B, T, n_heads, head_dim, device=device, dtype=torch.bfloat16) * 0.1 + D_param = torch.ones(n_heads, device=device, dtype=torch.bfloat16) + lam = torch.sigmoid(torch.zeros(n_heads, 1, device=device, dtype=torch.bfloat16)) # 0.5 + + # --- Test 1: Triton vs Reference --- + y_triton = ssd_exp_trap_triton(alpha, Bx, C_proj, x_heads, D_param, lam) + y_ref = _ssd_exp_trap_reference(alpha, Bx, C_proj, x_heads, D_param, lam) + + assert y_triton.shape == y_ref.shape == (B, T, n_heads) + max_diff = (y_triton.float() - y_ref.float()).abs().max().item() + close = torch.allclose(y_triton.float(), y_ref.float(), atol=1e-2, rtol=1e-2) + print(f"[ssd_exp_trap] main test: shape={y_triton.shape}, max_diff={max_diff:.6f}, allclose={close}") + assert close, f"Main test mismatch: max_diff={max_diff}" + + # --- Test 2: Different lambda values --- + for lam_val in [0.0, 0.3, 0.7, 1.0]: + lam_t = torch.full((n_heads, 1), lam_val, device=device, dtype=torch.bfloat16) + y_t = ssd_exp_trap_triton(alpha, Bx, C_proj, x_heads, D_param, lam_t) + y_r = _ssd_exp_trap_reference(alpha, Bx, C_proj, x_heads, D_param, lam_t) + md = (y_t.float() - y_r.float()).abs().max().item() + ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) + print(f"[ssd_exp_trap] lam={lam_val}: max_diff={md:.6f}, allclose={ok}") + assert ok, f"lam={lam_val} mismatch: max_diff={md}" + + # --- Test 3: Smaller d_state --- + for ds in [16, 32]: + alpha_s = torch.rand(1, 64, 4, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 + Bx_s = torch.randn(1, 64, 4, ds, device=device, dtype=torch.bfloat16) * 0.1 + C_s = torch.randn(1, 64, 4, ds, device=device, dtype=torch.bfloat16) * 0.1 + xh_s = torch.randn(1, 64, 4, 16, device=device, dtype=torch.bfloat16) * 0.1 + D_s = torch.ones(4, device=device, dtype=torch.bfloat16) + lam_s = torch.full((4, 1), 0.5, device=device, dtype=torch.bfloat16) + + y_t = ssd_exp_trap_triton(alpha_s, Bx_s, C_s, xh_s, D_s, lam_s) + y_r = _ssd_exp_trap_reference(alpha_s, Bx_s, C_s, xh_s, D_s, lam_s) + md = (y_t.float() - y_r.float()).abs().max().item() + ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) + print(f"[ssd_exp_trap] d_state={ds}: max_diff={md:.6f}, allclose={ok}") + assert ok, f"d_state={ds} mismatch: max_diff={md}" + + # --- Test 4: Longer sequence --- + T_long = 512 + alpha_l = torch.rand(1, T_long, 4, device=device, dtype=torch.bfloat16) * 0.5 + 0.3 + Bx_l = torch.randn(1, T_long, 4, 32, device=device, dtype=torch.bfloat16) * 0.05 + C_l = torch.randn(1, T_long, 4, 32, device=device, dtype=torch.bfloat16) * 0.05 + xh_l = torch.randn(1, T_long, 4, 16, device=device, dtype=torch.bfloat16) * 0.05 + D_l = torch.ones(4, device=device, dtype=torch.bfloat16) + lam_l = torch.full((4, 1), 0.5, device=device, dtype=torch.bfloat16) + + y_t = ssd_exp_trap_triton(alpha_l, Bx_l, C_l, xh_l, D_l, lam_l) + y_r = _ssd_exp_trap_reference(alpha_l, Bx_l, C_l, xh_l, D_l, lam_l) + md = (y_t.float() - y_r.float()).abs().max().item() + ok = torch.allclose(y_t.float(), y_r.float(), atol=1e-2, rtol=1e-2) + print(f"[ssd_exp_trap] T={T_long}: max_diff={md:.6f}, allclose={ok}") + assert ok, f"T={T_long} mismatch: max_diff={md}" + + print("[ssd_exp_trap] ALL TESTS PASSED") diff --git a/overlay/prep_nemotron.py b/overlay/prep_nemotron.py index 6716dc44b3911096770106e4cdb4250205ee93f9..9f5ec238477b19d4957c050efa813cff87e4436a 100644 --- a/overlay/prep_nemotron.py +++ b/overlay/prep_nemotron.py @@ -1,281 +1,281 @@ -#!/usr/bin/env python3 -"""Nemotron Super3 pretraining data prep. - -Downloads nvidia/Nemotron-Pretraining-Specialized-v1.1 configs, tokenizes with -our rustbpe/tiktoken tokenizer (trained by prepare.py), and writes -shard_{NNNNN}.parquet files consumable by the existing training pipeline — -identical layout to prepare.py: a single column named 'tokens' of dtype uint16, -with rows of length equal to --tokens-per-row (default: all tokens in one row -group, matching parquet convention used by training.py via _document_batches). - -Phase 1 (diversity blend): equal weight across all 5 configs. -Phase 2 (quality blend): weighted toward Multiple-Choice/Economics/Formal-Logic. - -Usage: - python prep_nemotron.py --phase phase1 --parts-per-config 8 - python prep_nemotron.py --phase phase2 --parts-per-config 8 --shard-id-start 100 - -The --shard-id-start flag lets phase 2 append shards without colliding with -phase 1 output (phase 2 resumes from the checkpoint stored in HF Hub by -entrypoint.py, so the shard ids just need to be unique on-disk). -""" - -import argparse -import os -import pickle -import shutil - -import pyarrow as pa -import pyarrow.parquet as pq -from huggingface_hub import HfApi, hf_hub_download - -# --------------------------------------------------------------------------- -# Import constants from prepare.py (tokenizer path, data dir, val shard id) -# --------------------------------------------------------------------------- -# prepare.py lives in the same directory; import at module level so -# DATA_DIR / TOKENIZER_DIR are always available. -import prepare as _p - -NEMOTRON_REPO = "nvidia/Nemotron-Pretraining-Specialized-v1.1" - -# The 5 configs per the Super3 recipe -ALL_CONFIGS = [ - "Nemotron-Pretraining-Code-Concepts", - "Nemotron-Pretraining-Unconditional-Algorithmic", - "Nemotron-Pretraining-Economics", - "Nemotron-Pretraining-Formal-Logic", - "Nemotron-Pretraining-Multiple-Choice", -] - -CONFIGS_PHASE1: dict[str, float] = { - "Nemotron-Pretraining-Code-Concepts": 0.20, - "Nemotron-Pretraining-Unconditional-Algorithmic": 0.20, - "Nemotron-Pretraining-Economics": 0.20, - "Nemotron-Pretraining-Formal-Logic": 0.20, - "Nemotron-Pretraining-Multiple-Choice": 0.20, -} - -CONFIGS_PHASE2: dict[str, float] = { - "Nemotron-Pretraining-Multiple-Choice": 0.45, # MMLU-style: high quality - "Nemotron-Pretraining-Economics": 0.20, - "Nemotron-Pretraining-Formal-Logic": 0.15, - "Nemotron-Pretraining-Code-Concepts": 0.10, - "Nemotron-Pretraining-Unconditional-Algorithmic": 0.10, -} - -# Parquet files in this repo follow: {config}/part_{NNNNNN}.parquet -# Some configs also have plain 0.parquet, 1.parquet naming — handled by list_repo_files. -_TEXT_COLUMN_CANDIDATES = ["text", "content", "prompt_completion", "body", "input"] - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _load_tokenizer() -> "_p.Tokenizer": - """Load the tiktoken tokenizer produced by prepare.py.""" - tokenizer_pkl = os.path.join(_p.TOKENIZER_DIR, "tokenizer.pkl") - if not os.path.exists(tokenizer_pkl): - raise RuntimeError( - f"Tokenizer not found at {tokenizer_pkl}. " - "Run `python prepare.py --num-shards 1` first to train the BPE tokenizer." - ) - with open(tokenizer_pkl, "rb") as f: - enc = pickle.load(f) - return _p.Tokenizer(enc) - - -def download_nemotron_files(config: str, n_parts: int, token: str) -> list[str]: - """List parquet files for *config*, download up to *n_parts*. Return local paths.""" - api = HfApi(token=token) - repo_files = list(api.list_repo_files(NEMOTRON_REPO, repo_type="dataset")) - prefix = f"{config}/" - config_files = sorted( - f for f in repo_files - if f.startswith(prefix) and f.endswith(".parquet") - ) - if not config_files: - print(f" [warn] no parquet files found under {prefix} in {NEMOTRON_REPO}", flush=True) - return [] - config_files = config_files[:n_parts] - local_paths: list[str] = [] - for remote_path in config_files: - local = hf_hub_download( - repo_id=NEMOTRON_REPO, - filename=remote_path, - repo_type="dataset", - token=token, - ) - local_paths.append(local) - print(f" [download] {remote_path} -> {local}", flush=True) - return local_paths - - -def _detect_text_column(schema: pa.Schema) -> str: - """Return the name of the text column from a parquet schema.""" - col_names = schema.names - for candidate in _TEXT_COLUMN_CANDIDATES: - if candidate in col_names: - return candidate - # Fallback: first string column - for i, field in enumerate(schema): - if pa.types.is_string(field.type) or pa.types.is_large_string(field.type): - return field.name - # Last resort: first column - return col_names[0] - - -def tokenize_and_write_shards( - parquet_paths: list[str], - tokenizer: "_p.Tokenizer", - out_dir: str, - shard_id_start: int, - tokens_per_shard: int, -) -> int: - """ - Stream-tokenize all text from *parquet_paths*, write fixed-size token shards. - - Shard format (identical to prepare.py): - - single column 'tokens', dtype uint16 - - each row group contains *tokens_per_shard* tokens - - Returns the next available shard_id (= shard_id_start + shards_written). - """ - shard_id = shard_id_start - tokens_buf: list[int] = [] - - for path in parquet_paths: - pf = pq.ParquetFile(path) - text_col = _detect_text_column(pf.schema_arrow) - print(f" [tokenize] {os.path.basename(path)} column='{text_col}'", flush=True) - for rg_idx in range(pf.num_row_groups): - rg = pf.read_row_group(rg_idx, columns=[text_col]) - texts: list[str] = rg.column(text_col).to_pylist() - # encode_ordinary_batch is faster (no special-token handling needed) - # tokenizer.encode() wraps enc.encode_ordinary for str input - token_lists: list[list[int]] = tokenizer.encode(texts) - for ids in token_lists: - tokens_buf.extend(ids) - # Flush complete shards - while len(tokens_buf) >= tokens_per_shard: - chunk = tokens_buf[:tokens_per_shard] - tokens_buf = tokens_buf[tokens_per_shard:] - _write_shard(out_dir, shard_id, chunk) - shard_id += 1 - - # Flush final partial shard (if any meaningful data remains) - if len(tokens_buf) >= 1024: - _write_shard(out_dir, shard_id, tokens_buf) - shard_id += 1 - - return shard_id - - -def _write_shard(out_dir: str, shard_id: int, tokens: list[int]) -> None: - filename = f"shard_{shard_id:05d}.parquet" - out_path = os.path.join(out_dir, filename) - tmp_path = out_path + ".tmp" - arr = pa.array(tokens, type=pa.uint16()) - tbl = pa.table({"tokens": arr}) - pq.write_table(tbl, tmp_path) - os.rename(tmp_path, out_path) - print(f" [shard] wrote {filename} ({len(tokens):,} tokens)", flush=True) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main() -> None: - parser = argparse.ArgumentParser( - description="Nemotron Super3 data prep — tokenize and shard to prepare.py-compatible format" - ) - parser.add_argument( - "--phase", - choices=["phase1", "phase2"], - required=True, - help="phase1 = equal blend; phase2 = quality-weighted blend", - ) - parser.add_argument( - "--parts-per-config", - type=int, - default=4, - help="Base number of parquet parts to download per config (scaled by weight)", - ) - parser.add_argument( - "--tokens-per-shard", - type=int, - default=10_000_000, - help="Tokens per output shard (default 10M, matching climbmix convention)", - ) - parser.add_argument( - "--shard-id-start", - type=int, - default=0, - help="First shard index to write (use non-zero to append after phase1 shards)", - ) - parser.add_argument( - "--hf-token", - default=os.environ.get("HF_TOKEN"), - help="HuggingFace token (also read from $HF_TOKEN)", - ) - args = parser.parse_args() - - if not args.hf_token: - # Try ~/.hf_token as fallback (per project convention) - hf_token_path = os.path.expanduser("~/.hf_token") - if os.path.exists(hf_token_path): - with open(hf_token_path) as f: - args.hf_token = f.read().strip() - - configs = CONFIGS_PHASE1 if args.phase == "phase1" else CONFIGS_PHASE2 - - tokenizer = _load_tokenizer() - os.makedirs(_p.DATA_DIR, exist_ok=True) - - shard_id = args.shard_id_start - for config, weight in configs.items(): - # Scale parts proportionally to weight so heavier configs get more data - n_parts = max(1, round(args.parts_per_config * weight * len(configs))) - print( - f"\n[nemotron] {config} weight={weight:.2f} n_parts={n_parts}", - flush=True, - ) - parquet_paths = download_nemotron_files(config, n_parts, args.hf_token) - if not parquet_paths: - print(f" [skip] no files downloaded for {config}", flush=True) - continue - shard_id = tokenize_and_write_shards( - parquet_paths, - tokenizer, - _p.DATA_DIR, - shard_id, - args.tokens_per_shard, - ) - - # Write validation shard — use Multiple-Choice (highest quality) as val source. - # Reserve the same VAL_SHARD index as prepare.py (6542) so training.py picks it up. - print("\n[nemotron] writing validation shard ...", flush=True) - val_paths = download_nemotron_files( - "Nemotron-Pretraining-Multiple-Choice", 1, args.hf_token - ) - if val_paths: - tokenize_and_write_shards( - val_paths, - tokenizer, - _p.DATA_DIR, - _p.VAL_SHARD, # 6542 — matches prepare.py VAL_SHARD constant - args.tokens_per_shard, - ) - else: - print(" [warn] could not download val shard; evaluation may fail", flush=True) - - print( - f"\n[nemotron] done — wrote shards {args.shard_id_start}..{shard_id - 1}" - f" + val shard {_p.VAL_SHARD}", - flush=True, - ) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +"""Nemotron Super3 pretraining data prep. + +Downloads nvidia/Nemotron-Pretraining-Specialized-v1.1 configs, tokenizes with +our rustbpe/tiktoken tokenizer (trained by prepare.py), and writes +shard_{NNNNN}.parquet files consumable by the existing training pipeline — +identical layout to prepare.py: a single column named 'tokens' of dtype uint16, +with rows of length equal to --tokens-per-row (default: all tokens in one row +group, matching parquet convention used by training.py via _document_batches). + +Phase 1 (diversity blend): equal weight across all 5 configs. +Phase 2 (quality blend): weighted toward Multiple-Choice/Economics/Formal-Logic. + +Usage: + python prep_nemotron.py --phase phase1 --parts-per-config 8 + python prep_nemotron.py --phase phase2 --parts-per-config 8 --shard-id-start 100 + +The --shard-id-start flag lets phase 2 append shards without colliding with +phase 1 output (phase 2 resumes from the checkpoint stored in HF Hub by +entrypoint.py, so the shard ids just need to be unique on-disk). +""" + +import argparse +import os +import pickle +import shutil + +import pyarrow as pa +import pyarrow.parquet as pq +from huggingface_hub import HfApi, hf_hub_download + +# --------------------------------------------------------------------------- +# Import constants from prepare.py (tokenizer path, data dir, val shard id) +# --------------------------------------------------------------------------- +# prepare.py lives in the same directory; import at module level so +# DATA_DIR / TOKENIZER_DIR are always available. +import prepare as _p + +NEMOTRON_REPO = "nvidia/Nemotron-Pretraining-Specialized-v1.1" + +# The 5 configs per the Super3 recipe +ALL_CONFIGS = [ + "Nemotron-Pretraining-Code-Concepts", + "Nemotron-Pretraining-Unconditional-Algorithmic", + "Nemotron-Pretraining-Economics", + "Nemotron-Pretraining-Formal-Logic", + "Nemotron-Pretraining-Multiple-Choice", +] + +CONFIGS_PHASE1: dict[str, float] = { + "Nemotron-Pretraining-Code-Concepts": 0.20, + "Nemotron-Pretraining-Unconditional-Algorithmic": 0.20, + "Nemotron-Pretraining-Economics": 0.20, + "Nemotron-Pretraining-Formal-Logic": 0.20, + "Nemotron-Pretraining-Multiple-Choice": 0.20, +} + +CONFIGS_PHASE2: dict[str, float] = { + "Nemotron-Pretraining-Multiple-Choice": 0.45, # MMLU-style: high quality + "Nemotron-Pretraining-Economics": 0.20, + "Nemotron-Pretraining-Formal-Logic": 0.15, + "Nemotron-Pretraining-Code-Concepts": 0.10, + "Nemotron-Pretraining-Unconditional-Algorithmic": 0.10, +} + +# Parquet files in this repo follow: {config}/part_{NNNNNN}.parquet +# Some configs also have plain 0.parquet, 1.parquet naming — handled by list_repo_files. +_TEXT_COLUMN_CANDIDATES = ["text", "content", "prompt_completion", "body", "input"] + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _load_tokenizer() -> "_p.Tokenizer": + """Load the tiktoken tokenizer produced by prepare.py.""" + tokenizer_pkl = os.path.join(_p.TOKENIZER_DIR, "tokenizer.pkl") + if not os.path.exists(tokenizer_pkl): + raise RuntimeError( + f"Tokenizer not found at {tokenizer_pkl}. " + "Run `python prepare.py --num-shards 1` first to train the BPE tokenizer." + ) + with open(tokenizer_pkl, "rb") as f: + enc = pickle.load(f) + return _p.Tokenizer(enc) + + +def download_nemotron_files(config: str, n_parts: int, token: str) -> list[str]: + """List parquet files for *config*, download up to *n_parts*. Return local paths.""" + api = HfApi(token=token) + repo_files = list(api.list_repo_files(NEMOTRON_REPO, repo_type="dataset")) + prefix = f"{config}/" + config_files = sorted( + f for f in repo_files + if f.startswith(prefix) and f.endswith(".parquet") + ) + if not config_files: + print(f" [warn] no parquet files found under {prefix} in {NEMOTRON_REPO}", flush=True) + return [] + config_files = config_files[:n_parts] + local_paths: list[str] = [] + for remote_path in config_files: + local = hf_hub_download( + repo_id=NEMOTRON_REPO, + filename=remote_path, + repo_type="dataset", + token=token, + ) + local_paths.append(local) + print(f" [download] {remote_path} -> {local}", flush=True) + return local_paths + + +def _detect_text_column(schema: pa.Schema) -> str: + """Return the name of the text column from a parquet schema.""" + col_names = schema.names + for candidate in _TEXT_COLUMN_CANDIDATES: + if candidate in col_names: + return candidate + # Fallback: first string column + for i, field in enumerate(schema): + if pa.types.is_string(field.type) or pa.types.is_large_string(field.type): + return field.name + # Last resort: first column + return col_names[0] + + +def tokenize_and_write_shards( + parquet_paths: list[str], + tokenizer: "_p.Tokenizer", + out_dir: str, + shard_id_start: int, + tokens_per_shard: int, +) -> int: + """ + Stream-tokenize all text from *parquet_paths*, write fixed-size token shards. + + Shard format (identical to prepare.py): + - single column 'tokens', dtype uint16 + - each row group contains *tokens_per_shard* tokens + + Returns the next available shard_id (= shard_id_start + shards_written). + """ + shard_id = shard_id_start + tokens_buf: list[int] = [] + + for path in parquet_paths: + pf = pq.ParquetFile(path) + text_col = _detect_text_column(pf.schema_arrow) + print(f" [tokenize] {os.path.basename(path)} column='{text_col}'", flush=True) + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx, columns=[text_col]) + texts: list[str] = rg.column(text_col).to_pylist() + # encode_ordinary_batch is faster (no special-token handling needed) + # tokenizer.encode() wraps enc.encode_ordinary for str input + token_lists: list[list[int]] = tokenizer.encode(texts) + for ids in token_lists: + tokens_buf.extend(ids) + # Flush complete shards + while len(tokens_buf) >= tokens_per_shard: + chunk = tokens_buf[:tokens_per_shard] + tokens_buf = tokens_buf[tokens_per_shard:] + _write_shard(out_dir, shard_id, chunk) + shard_id += 1 + + # Flush final partial shard (if any meaningful data remains) + if len(tokens_buf) >= 1024: + _write_shard(out_dir, shard_id, tokens_buf) + shard_id += 1 + + return shard_id + + +def _write_shard(out_dir: str, shard_id: int, tokens: list[int]) -> None: + filename = f"shard_{shard_id:05d}.parquet" + out_path = os.path.join(out_dir, filename) + tmp_path = out_path + ".tmp" + arr = pa.array(tokens, type=pa.uint16()) + tbl = pa.table({"tokens": arr}) + pq.write_table(tbl, tmp_path) + os.rename(tmp_path, out_path) + print(f" [shard] wrote {filename} ({len(tokens):,} tokens)", flush=True) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main() -> None: + parser = argparse.ArgumentParser( + description="Nemotron Super3 data prep — tokenize and shard to prepare.py-compatible format" + ) + parser.add_argument( + "--phase", + choices=["phase1", "phase2"], + required=True, + help="phase1 = equal blend; phase2 = quality-weighted blend", + ) + parser.add_argument( + "--parts-per-config", + type=int, + default=4, + help="Base number of parquet parts to download per config (scaled by weight)", + ) + parser.add_argument( + "--tokens-per-shard", + type=int, + default=10_000_000, + help="Tokens per output shard (default 10M, matching climbmix convention)", + ) + parser.add_argument( + "--shard-id-start", + type=int, + default=0, + help="First shard index to write (use non-zero to append after phase1 shards)", + ) + parser.add_argument( + "--hf-token", + default=os.environ.get("HF_TOKEN"), + help="HuggingFace token (also read from $HF_TOKEN)", + ) + args = parser.parse_args() + + if not args.hf_token: + # Try ~/.hf_token as fallback (per project convention) + hf_token_path = os.path.expanduser("~/.hf_token") + if os.path.exists(hf_token_path): + with open(hf_token_path) as f: + args.hf_token = f.read().strip() + + configs = CONFIGS_PHASE1 if args.phase == "phase1" else CONFIGS_PHASE2 + + tokenizer = _load_tokenizer() + os.makedirs(_p.DATA_DIR, exist_ok=True) + + shard_id = args.shard_id_start + for config, weight in configs.items(): + # Scale parts proportionally to weight so heavier configs get more data + n_parts = max(1, round(args.parts_per_config * weight * len(configs))) + print( + f"\n[nemotron] {config} weight={weight:.2f} n_parts={n_parts}", + flush=True, + ) + parquet_paths = download_nemotron_files(config, n_parts, args.hf_token) + if not parquet_paths: + print(f" [skip] no files downloaded for {config}", flush=True) + continue + shard_id = tokenize_and_write_shards( + parquet_paths, + tokenizer, + _p.DATA_DIR, + shard_id, + args.tokens_per_shard, + ) + + # Write validation shard — use Multiple-Choice (highest quality) as val source. + # Reserve the same VAL_SHARD index as prepare.py (6542) so training.py picks it up. + print("\n[nemotron] writing validation shard ...", flush=True) + val_paths = download_nemotron_files( + "Nemotron-Pretraining-Multiple-Choice", 1, args.hf_token + ) + if val_paths: + tokenize_and_write_shards( + val_paths, + tokenizer, + _p.DATA_DIR, + _p.VAL_SHARD, # 6542 — matches prepare.py VAL_SHARD constant + args.tokens_per_shard, + ) + else: + print(" [warn] could not download val shard; evaluation may fail", flush=True) + + print( + f"\n[nemotron] done — wrote shards {args.shard_id_start}..{shard_id - 1}" + f" + val shard {_p.VAL_SHARD}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/overlay/prepare.py b/overlay/prepare.py index 07fb8baa2012231cc0580c7e7c06989ee4e86e22..b963d08f3383b8a9a6572bf3909e0675e1e59920 100644 --- a/overlay/prepare.py +++ b/overlay/prepare.py @@ -1,408 +1,408 @@ -""" -One-time data preparation for autoresearch experiments. -Downloads data shards and trains a BPE tokenizer. - -Usage: - python prepare.py # full prep (download + tokenizer) - python prepare.py --num-shards 8 # download only 8 shards (for testing) - -Data and tokenizer are stored in ~/.cache/autoresearch/. -""" - -import os -import sys -import time -import math -import argparse -import pickle -from multiprocessing import Pool - -import requests -import pyarrow.parquet as pq -import rustbpe -import tiktoken -import torch - -# --------------------------------------------------------------------------- -# Constants (fixed, do not modify) -# --------------------------------------------------------------------------- - -MAX_SEQ_LEN = int(os.environ.get("HYDRA_SEQ_LEN", "512")) # context length -TIME_BUDGET = 300 # training time budget in seconds (5 minutes) -EVAL_TOKENS = 40 * 524288 # number of tokens for val eval - -# --------------------------------------------------------------------------- -# Configuration -# --------------------------------------------------------------------------- - -CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "autoresearch") -DATA_DIR = os.path.join(CACHE_DIR, "data") -TOKENIZER_DIR = os.path.join(CACHE_DIR, "tokenizer") -BASE_URL = "https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle/resolve/main" -MAX_SHARD = 6542 # the last datashard is shard_06542.parquet -VAL_SHARD = MAX_SHARD # pinned validation shard (shard_06542) -VAL_FILENAME = f"shard_{VAL_SHARD:05d}.parquet" -VOCAB_SIZE = int(os.environ.get("HYDRA_VOCAB_SIZE", "65536")) # 64k — production-grade (was 8k experimental) - -# BPE split pattern (GPT-4 style, with \p{N}{1,2} instead of {1,3}) -SPLIT_PATTERN = r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,2}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""" - -SPECIAL_TOKENS = [f"<|reserved_{i}|>" for i in range(4)] -BOS_TOKEN = "<|reserved_0|>" - -# --------------------------------------------------------------------------- -# Data download -# --------------------------------------------------------------------------- - -def download_single_shard(index): - """Download one parquet shard with retries. Returns True on success.""" - filename = f"shard_{index:05d}.parquet" - filepath = os.path.join(DATA_DIR, filename) - if os.path.exists(filepath): - return True - - url = f"{BASE_URL}/{filename}" - max_attempts = 5 - for attempt in range(1, max_attempts + 1): - try: - response = requests.get(url, stream=True, timeout=30) - response.raise_for_status() - temp_path = filepath + ".tmp" - with open(temp_path, "wb") as f: - for chunk in response.iter_content(chunk_size=1024 * 1024): - if chunk: - f.write(chunk) - os.rename(temp_path, filepath) - print(f" Downloaded {filename}") - return True - except (requests.RequestException, IOError) as e: - print(f" Attempt {attempt}/{max_attempts} failed for {filename}: {e}") - for path in [filepath + ".tmp", filepath]: - if os.path.exists(path): - try: - os.remove(path) - except OSError: - pass - if attempt < max_attempts: - time.sleep(2 ** attempt) - return False - - -def download_data(num_shards, download_workers=8): - """Download training shards + pinned validation shard.""" - os.makedirs(DATA_DIR, exist_ok=True) - num_train = min(num_shards, MAX_SHARD) - ids = list(range(num_train)) - if VAL_SHARD not in ids: - ids.append(VAL_SHARD) - - # Count what's already downloaded - existing = sum(1 for i in ids if os.path.exists(os.path.join(DATA_DIR, f"shard_{i:05d}.parquet"))) - if existing == len(ids): - print(f"Data: all {len(ids)} shards already downloaded at {DATA_DIR}") - return - - needed = len(ids) - existing - print(f"Data: downloading {needed} shards ({existing} already exist)...") - - workers = max(1, min(download_workers, needed)) - with Pool(processes=workers) as pool: - results = pool.map(download_single_shard, ids) - - ok = sum(1 for r in results if r) - print(f"Data: {ok}/{len(ids)} shards ready at {DATA_DIR}") - -# --------------------------------------------------------------------------- -# Tokenizer training -# --------------------------------------------------------------------------- - -def list_parquet_files(): - """Return sorted list of parquet file paths in the data directory.""" - files = sorted(f for f in os.listdir(DATA_DIR) if f.endswith(".parquet") and not f.endswith(".tmp")) - return [os.path.join(DATA_DIR, f) for f in files] - - -def text_iterator(max_chars=1_000_000_000, doc_cap=10_000): - """Yield documents from training split (all shards except pinned val shard).""" - parquet_paths = [p for p in list_parquet_files() if not p.endswith(VAL_FILENAME)] - nchars = 0 - for filepath in parquet_paths: - pf = pq.ParquetFile(filepath) - for rg_idx in range(pf.num_row_groups): - rg = pf.read_row_group(rg_idx) - for text in rg.column("text").to_pylist(): - doc = text[:doc_cap] if len(text) > doc_cap else text - nchars += len(doc) - yield doc - if nchars >= max_chars: - return - - -def train_tokenizer(): - """Train BPE tokenizer using rustbpe, save as tiktoken pickle.""" - tokenizer_pkl = os.path.join(TOKENIZER_DIR, "tokenizer.pkl") - token_bytes_path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") - - if os.path.exists(tokenizer_pkl) and os.path.exists(token_bytes_path): - print(f"Tokenizer: already trained at {TOKENIZER_DIR}") - return - - os.makedirs(TOKENIZER_DIR, exist_ok=True) - - parquet_files = list_parquet_files() - if len(parquet_files) < 2: - print("Tokenizer: need at least 2 data shards (1 train + 1 val). Download more data first.") - sys.exit(1) - - # --- Train with rustbpe --- - print("Tokenizer: training BPE tokenizer...") - t0 = time.time() - - tokenizer = rustbpe.Tokenizer() - vocab_size_no_special = VOCAB_SIZE - len(SPECIAL_TOKENS) - tokenizer.train_from_iterator(text_iterator(), vocab_size_no_special, pattern=SPLIT_PATTERN) - - # Build tiktoken encoding from trained merges - pattern = tokenizer.get_pattern() - mergeable_ranks = {bytes(k): v for k, v in tokenizer.get_mergeable_ranks()} - tokens_offset = len(mergeable_ranks) - special_tokens = {name: tokens_offset + i for i, name in enumerate(SPECIAL_TOKENS)} - enc = tiktoken.Encoding( - name="rustbpe", - pat_str=pattern, - mergeable_ranks=mergeable_ranks, - special_tokens=special_tokens, - ) - - # Save tokenizer - with open(tokenizer_pkl, "wb") as f: - pickle.dump(enc, f) - - t1 = time.time() - print(f"Tokenizer: trained in {t1 - t0:.1f}s, saved to {tokenizer_pkl}") - - # --- Build token_bytes lookup for BPB evaluation --- - print("Tokenizer: building token_bytes lookup...") - special_set = set(SPECIAL_TOKENS) - token_bytes_list = [] - for token_id in range(enc.n_vocab): - token_str = enc.decode([token_id]) - if token_str in special_set: - token_bytes_list.append(0) - else: - token_bytes_list.append(len(token_str.encode("utf-8"))) - token_bytes_tensor = torch.tensor(token_bytes_list, dtype=torch.int32) - torch.save(token_bytes_tensor, token_bytes_path) - print(f"Tokenizer: saved token_bytes to {token_bytes_path}") - - # Sanity check - test = "Hello world! Numbers: 123. Unicode: 你好" - encoded = enc.encode_ordinary(test) - decoded = enc.decode(encoded) - assert decoded == test, f"Tokenizer roundtrip failed: {test!r} -> {decoded!r}" - print(f"Tokenizer: sanity check passed (vocab_size={enc.n_vocab})") - -# --------------------------------------------------------------------------- -# Runtime utilities (imported by train.py) -# --------------------------------------------------------------------------- - -class Tokenizer: - """Minimal tokenizer wrapper. Training is handled above.""" - - def __init__(self, enc): - self.enc = enc - self.bos_token_id = enc.encode_single_token(BOS_TOKEN) - - @classmethod - def from_directory(cls, tokenizer_dir=TOKENIZER_DIR): - with open(os.path.join(tokenizer_dir, "tokenizer.pkl"), "rb") as f: - enc = pickle.load(f) - return cls(enc) - - def get_vocab_size(self): - return self.enc.n_vocab - - def get_bos_token_id(self): - return self.bos_token_id - - def encode(self, text, prepend=None, num_threads=8): - if prepend is not None: - prepend_id = prepend if isinstance(prepend, int) else self.enc.encode_single_token(prepend) - if isinstance(text, str): - ids = self.enc.encode_ordinary(text) - if prepend is not None: - ids.insert(0, prepend_id) - elif isinstance(text, list): - ids = self.enc.encode_ordinary_batch(text, num_threads=num_threads) - if prepend is not None: - for row in ids: - row.insert(0, prepend_id) - else: - raise ValueError(f"Invalid input type: {type(text)}") - return ids - - def decode(self, ids): - return self.enc.decode(ids) - - -_TOKEN_BYTES_CACHE: dict = {} - -def get_token_bytes(device="cpu"): - key = str(device) - if key not in _TOKEN_BYTES_CACHE: - path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") - with open(path, "rb") as f: - _TOKEN_BYTES_CACHE[key] = torch.load(f, map_location=device) - return _TOKEN_BYTES_CACHE[key] - - -def _document_batches(split, tokenizer_batch_size=128): - """Infinite iterator over document batches from parquet files.""" - parquet_paths = list_parquet_files() - assert len(parquet_paths) > 0, "No parquet files found. Run prepare.py first." - val_path = os.path.join(DATA_DIR, VAL_FILENAME) - if split == "train": - parquet_paths = [p for p in parquet_paths if p != val_path] - assert len(parquet_paths) > 0, "No training shards found." - else: - parquet_paths = [val_path] - epoch = 1 - while True: - for filepath in parquet_paths: - pf = pq.ParquetFile(filepath) - for rg_idx in range(pf.num_row_groups): - rg = pf.read_row_group(rg_idx) - batch = rg.column('text').to_pylist() - for i in range(0, len(batch), tokenizer_batch_size): - yield batch[i:i+tokenizer_batch_size], epoch - epoch += 1 - - -def make_dataloader(tokenizer, B, T, split, buffer_size=1000): - """ - BOS-aligned dataloader with best-fit packing. - Every row starts with BOS. Documents packed using best-fit to minimize cropping. - When no document fits remaining space, crops shortest doc to fill exactly. - 100% utilization (no padding). - """ - assert split in ["train", "val"] - row_capacity = T + 1 - batches = _document_batches(split) - bos_token = tokenizer.get_bos_token_id() - doc_buffer = [] - epoch = 1 - - def refill_buffer(): - nonlocal epoch - doc_batch, epoch = next(batches) - token_lists = tokenizer.encode(doc_batch, prepend=bos_token) - doc_buffer.extend(token_lists) - - # Pre-allocate buffers: [inputs (B*T) | targets (B*T)] - row_buffer = torch.empty((B, row_capacity), dtype=torch.long) - cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) - gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") - cpu_inputs = cpu_buffer[:B * T].view(B, T) - cpu_targets = cpu_buffer[B * T:].view(B, T) - inputs = gpu_buffer[:B * T].view(B, T) - targets = gpu_buffer[B * T:].view(B, T) - - while True: - for row_idx in range(B): - pos = 0 - while pos < row_capacity: - while len(doc_buffer) < buffer_size: - refill_buffer() - - remaining = row_capacity - pos - - # Find largest doc that fits entirely - best_idx = -1 - best_len = 0 - for i, doc in enumerate(doc_buffer): - doc_len = len(doc) - if doc_len <= remaining and doc_len > best_len: - best_idx = i - best_len = doc_len - - if best_idx >= 0: - doc = doc_buffer.pop(best_idx) - row_buffer[row_idx, pos:pos + len(doc)] = torch.tensor(doc, dtype=torch.long) - pos += len(doc) - else: - # No doc fits — crop shortest to fill remaining - shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i])) - doc = doc_buffer.pop(shortest_idx) - row_buffer[row_idx, pos:pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long) - pos += remaining - - cpu_inputs.copy_(row_buffer[:, :-1]) - cpu_targets.copy_(row_buffer[:, 1:]) - gpu_buffer.copy_(cpu_buffer, non_blocking=True) - yield inputs, targets, epoch - -# --------------------------------------------------------------------------- -# Evaluation (DO NOT CHANGE — this is the fixed metric) -# --------------------------------------------------------------------------- - -@torch.no_grad() -def evaluate_bpb(model, tokenizer, batch_size): - """ - Bits per byte (BPB): vocab size-independent evaluation metric. - Sums per-token cross-entropy (in nats), sums target byte lengths, - then converts nats/byte to bits/byte. Special tokens (byte length 0) - are excluded from both sums. - Uses fixed MAX_SEQ_LEN so results are comparable across configs. - - Perf: accumulates on GPU (single sync at end), prefetches next batch - while current forward runs. - """ - token_bytes = get_token_bytes(device="cuda") - val_loader = make_dataloader(tokenizer, batch_size, MAX_SEQ_LEN, "val") - steps = EVAL_TOKENS // (batch_size * MAX_SEQ_LEN) - - # GPU-resident accumulators — avoid per-batch .item() sync - total_nats_t = torch.zeros(1, device="cuda", dtype=torch.float64) - total_bytes_t = torch.zeros(1, device="cuda", dtype=torch.int64) - - # Prefetch first batch - next_batch = next(val_loader) - for _ in range(steps): - x, y, _epoch = next_batch - # Prefetch NEXT batch while GPU computes current forward - next_batch = next(val_loader) - loss_flat = model(x, y, reduction='none').view(-1) - y_flat = y.view(-1) - nbytes = token_bytes[y_flat] - mask = nbytes > 0 - total_nats_t += (loss_flat * mask).sum() - total_bytes_t += nbytes.sum() - - # Single GPU→CPU sync at end - total_nats = total_nats_t.item() - total_bytes = total_bytes_t.item() - return total_nats / (math.log(2) * total_bytes) - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Prepare data and tokenizer for autoresearch") - parser.add_argument("--num-shards", type=int, default=10, help="Number of training shards to download (-1 = all). Val shard is always pinned.") - parser.add_argument("--download-workers", type=int, default=8, help="Number of parallel download workers") - args = parser.parse_args() - - num_shards = MAX_SHARD if args.num_shards == -1 else args.num_shards - - print(f"Cache directory: {CACHE_DIR}") - print() - - # Step 1: Download data - download_data(num_shards, download_workers=args.download_workers) - print() - - # Step 2: Train tokenizer - train_tokenizer() - print() - print("Done! Ready to train.") +""" +One-time data preparation for autoresearch experiments. +Downloads data shards and trains a BPE tokenizer. + +Usage: + python prepare.py # full prep (download + tokenizer) + python prepare.py --num-shards 8 # download only 8 shards (for testing) + +Data and tokenizer are stored in ~/.cache/autoresearch/. +""" + +import os +import sys +import time +import math +import argparse +import pickle +from multiprocessing import Pool + +import requests +import pyarrow.parquet as pq +import rustbpe +import tiktoken +import torch + +# --------------------------------------------------------------------------- +# Constants (fixed, do not modify) +# --------------------------------------------------------------------------- + +MAX_SEQ_LEN = int(os.environ.get("HYDRA_SEQ_LEN", "512")) # context length +TIME_BUDGET = 300 # training time budget in seconds (5 minutes) +EVAL_TOKENS = 40 * 524288 # number of tokens for val eval + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- + +CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "autoresearch") +DATA_DIR = os.path.join(CACHE_DIR, "data") +TOKENIZER_DIR = os.path.join(CACHE_DIR, "tokenizer") +BASE_URL = "https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle/resolve/main" +MAX_SHARD = 6542 # the last datashard is shard_06542.parquet +VAL_SHARD = MAX_SHARD # pinned validation shard (shard_06542) +VAL_FILENAME = f"shard_{VAL_SHARD:05d}.parquet" +VOCAB_SIZE = int(os.environ.get("HYDRA_VOCAB_SIZE", "65536")) # 64k — production-grade (was 8k experimental) + +# BPE split pattern (GPT-4 style, with \p{N}{1,2} instead of {1,3}) +SPLIT_PATTERN = r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,2}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""" + +SPECIAL_TOKENS = [f"<|reserved_{i}|>" for i in range(4)] +BOS_TOKEN = "<|reserved_0|>" + +# --------------------------------------------------------------------------- +# Data download +# --------------------------------------------------------------------------- + +def download_single_shard(index): + """Download one parquet shard with retries. Returns True on success.""" + filename = f"shard_{index:05d}.parquet" + filepath = os.path.join(DATA_DIR, filename) + if os.path.exists(filepath): + return True + + url = f"{BASE_URL}/{filename}" + max_attempts = 5 + for attempt in range(1, max_attempts + 1): + try: + response = requests.get(url, stream=True, timeout=30) + response.raise_for_status() + temp_path = filepath + ".tmp" + with open(temp_path, "wb") as f: + for chunk in response.iter_content(chunk_size=1024 * 1024): + if chunk: + f.write(chunk) + os.rename(temp_path, filepath) + print(f" Downloaded {filename}") + return True + except (requests.RequestException, IOError) as e: + print(f" Attempt {attempt}/{max_attempts} failed for {filename}: {e}") + for path in [filepath + ".tmp", filepath]: + if os.path.exists(path): + try: + os.remove(path) + except OSError: + pass + if attempt < max_attempts: + time.sleep(2 ** attempt) + return False + + +def download_data(num_shards, download_workers=8): + """Download training shards + pinned validation shard.""" + os.makedirs(DATA_DIR, exist_ok=True) + num_train = min(num_shards, MAX_SHARD) + ids = list(range(num_train)) + if VAL_SHARD not in ids: + ids.append(VAL_SHARD) + + # Count what's already downloaded + existing = sum(1 for i in ids if os.path.exists(os.path.join(DATA_DIR, f"shard_{i:05d}.parquet"))) + if existing == len(ids): + print(f"Data: all {len(ids)} shards already downloaded at {DATA_DIR}") + return + + needed = len(ids) - existing + print(f"Data: downloading {needed} shards ({existing} already exist)...") + + workers = max(1, min(download_workers, needed)) + with Pool(processes=workers) as pool: + results = pool.map(download_single_shard, ids) + + ok = sum(1 for r in results if r) + print(f"Data: {ok}/{len(ids)} shards ready at {DATA_DIR}") + +# --------------------------------------------------------------------------- +# Tokenizer training +# --------------------------------------------------------------------------- + +def list_parquet_files(): + """Return sorted list of parquet file paths in the data directory.""" + files = sorted(f for f in os.listdir(DATA_DIR) if f.endswith(".parquet") and not f.endswith(".tmp")) + return [os.path.join(DATA_DIR, f) for f in files] + + +def text_iterator(max_chars=1_000_000_000, doc_cap=10_000): + """Yield documents from training split (all shards except pinned val shard).""" + parquet_paths = [p for p in list_parquet_files() if not p.endswith(VAL_FILENAME)] + nchars = 0 + for filepath in parquet_paths: + pf = pq.ParquetFile(filepath) + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx) + for text in rg.column("text").to_pylist(): + doc = text[:doc_cap] if len(text) > doc_cap else text + nchars += len(doc) + yield doc + if nchars >= max_chars: + return + + +def train_tokenizer(): + """Train BPE tokenizer using rustbpe, save as tiktoken pickle.""" + tokenizer_pkl = os.path.join(TOKENIZER_DIR, "tokenizer.pkl") + token_bytes_path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") + + if os.path.exists(tokenizer_pkl) and os.path.exists(token_bytes_path): + print(f"Tokenizer: already trained at {TOKENIZER_DIR}") + return + + os.makedirs(TOKENIZER_DIR, exist_ok=True) + + parquet_files = list_parquet_files() + if len(parquet_files) < 2: + print("Tokenizer: need at least 2 data shards (1 train + 1 val). Download more data first.") + sys.exit(1) + + # --- Train with rustbpe --- + print("Tokenizer: training BPE tokenizer...") + t0 = time.time() + + tokenizer = rustbpe.Tokenizer() + vocab_size_no_special = VOCAB_SIZE - len(SPECIAL_TOKENS) + tokenizer.train_from_iterator(text_iterator(), vocab_size_no_special, pattern=SPLIT_PATTERN) + + # Build tiktoken encoding from trained merges + pattern = tokenizer.get_pattern() + mergeable_ranks = {bytes(k): v for k, v in tokenizer.get_mergeable_ranks()} + tokens_offset = len(mergeable_ranks) + special_tokens = {name: tokens_offset + i for i, name in enumerate(SPECIAL_TOKENS)} + enc = tiktoken.Encoding( + name="rustbpe", + pat_str=pattern, + mergeable_ranks=mergeable_ranks, + special_tokens=special_tokens, + ) + + # Save tokenizer + with open(tokenizer_pkl, "wb") as f: + pickle.dump(enc, f) + + t1 = time.time() + print(f"Tokenizer: trained in {t1 - t0:.1f}s, saved to {tokenizer_pkl}") + + # --- Build token_bytes lookup for BPB evaluation --- + print("Tokenizer: building token_bytes lookup...") + special_set = set(SPECIAL_TOKENS) + token_bytes_list = [] + for token_id in range(enc.n_vocab): + token_str = enc.decode([token_id]) + if token_str in special_set: + token_bytes_list.append(0) + else: + token_bytes_list.append(len(token_str.encode("utf-8"))) + token_bytes_tensor = torch.tensor(token_bytes_list, dtype=torch.int32) + torch.save(token_bytes_tensor, token_bytes_path) + print(f"Tokenizer: saved token_bytes to {token_bytes_path}") + + # Sanity check + test = "Hello world! Numbers: 123. Unicode: 你好" + encoded = enc.encode_ordinary(test) + decoded = enc.decode(encoded) + assert decoded == test, f"Tokenizer roundtrip failed: {test!r} -> {decoded!r}" + print(f"Tokenizer: sanity check passed (vocab_size={enc.n_vocab})") + +# --------------------------------------------------------------------------- +# Runtime utilities (imported by train.py) +# --------------------------------------------------------------------------- + +class Tokenizer: + """Minimal tokenizer wrapper. Training is handled above.""" + + def __init__(self, enc): + self.enc = enc + self.bos_token_id = enc.encode_single_token(BOS_TOKEN) + + @classmethod + def from_directory(cls, tokenizer_dir=TOKENIZER_DIR): + with open(os.path.join(tokenizer_dir, "tokenizer.pkl"), "rb") as f: + enc = pickle.load(f) + return cls(enc) + + def get_vocab_size(self): + return self.enc.n_vocab + + def get_bos_token_id(self): + return self.bos_token_id + + def encode(self, text, prepend=None, num_threads=8): + if prepend is not None: + prepend_id = prepend if isinstance(prepend, int) else self.enc.encode_single_token(prepend) + if isinstance(text, str): + ids = self.enc.encode_ordinary(text) + if prepend is not None: + ids.insert(0, prepend_id) + elif isinstance(text, list): + ids = self.enc.encode_ordinary_batch(text, num_threads=num_threads) + if prepend is not None: + for row in ids: + row.insert(0, prepend_id) + else: + raise ValueError(f"Invalid input type: {type(text)}") + return ids + + def decode(self, ids): + return self.enc.decode(ids) + + +_TOKEN_BYTES_CACHE: dict = {} + +def get_token_bytes(device="cpu"): + key = str(device) + if key not in _TOKEN_BYTES_CACHE: + path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") + with open(path, "rb") as f: + _TOKEN_BYTES_CACHE[key] = torch.load(f, map_location=device) + return _TOKEN_BYTES_CACHE[key] + + +def _document_batches(split, tokenizer_batch_size=128): + """Infinite iterator over document batches from parquet files.""" + parquet_paths = list_parquet_files() + assert len(parquet_paths) > 0, "No parquet files found. Run prepare.py first." + val_path = os.path.join(DATA_DIR, VAL_FILENAME) + if split == "train": + parquet_paths = [p for p in parquet_paths if p != val_path] + assert len(parquet_paths) > 0, "No training shards found." + else: + parquet_paths = [val_path] + epoch = 1 + while True: + for filepath in parquet_paths: + pf = pq.ParquetFile(filepath) + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx) + batch = rg.column('text').to_pylist() + for i in range(0, len(batch), tokenizer_batch_size): + yield batch[i:i+tokenizer_batch_size], epoch + epoch += 1 + + +def make_dataloader(tokenizer, B, T, split, buffer_size=1000): + """ + BOS-aligned dataloader with best-fit packing. + Every row starts with BOS. Documents packed using best-fit to minimize cropping. + When no document fits remaining space, crops shortest doc to fill exactly. + 100% utilization (no padding). + """ + assert split in ["train", "val"] + row_capacity = T + 1 + batches = _document_batches(split) + bos_token = tokenizer.get_bos_token_id() + doc_buffer = [] + epoch = 1 + + def refill_buffer(): + nonlocal epoch + doc_batch, epoch = next(batches) + token_lists = tokenizer.encode(doc_batch, prepend=bos_token) + doc_buffer.extend(token_lists) + + # Pre-allocate buffers: [inputs (B*T) | targets (B*T)] + row_buffer = torch.empty((B, row_capacity), dtype=torch.long) + cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) + gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") + cpu_inputs = cpu_buffer[:B * T].view(B, T) + cpu_targets = cpu_buffer[B * T:].view(B, T) + inputs = gpu_buffer[:B * T].view(B, T) + targets = gpu_buffer[B * T:].view(B, T) + + while True: + for row_idx in range(B): + pos = 0 + while pos < row_capacity: + while len(doc_buffer) < buffer_size: + refill_buffer() + + remaining = row_capacity - pos + + # Find largest doc that fits entirely + best_idx = -1 + best_len = 0 + for i, doc in enumerate(doc_buffer): + doc_len = len(doc) + if doc_len <= remaining and doc_len > best_len: + best_idx = i + best_len = doc_len + + if best_idx >= 0: + doc = doc_buffer.pop(best_idx) + row_buffer[row_idx, pos:pos + len(doc)] = torch.tensor(doc, dtype=torch.long) + pos += len(doc) + else: + # No doc fits — crop shortest to fill remaining + shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i])) + doc = doc_buffer.pop(shortest_idx) + row_buffer[row_idx, pos:pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long) + pos += remaining + + cpu_inputs.copy_(row_buffer[:, :-1]) + cpu_targets.copy_(row_buffer[:, 1:]) + gpu_buffer.copy_(cpu_buffer, non_blocking=True) + yield inputs, targets, epoch + +# --------------------------------------------------------------------------- +# Evaluation (DO NOT CHANGE — this is the fixed metric) +# --------------------------------------------------------------------------- + +@torch.no_grad() +def evaluate_bpb(model, tokenizer, batch_size): + """ + Bits per byte (BPB): vocab size-independent evaluation metric. + Sums per-token cross-entropy (in nats), sums target byte lengths, + then converts nats/byte to bits/byte. Special tokens (byte length 0) + are excluded from both sums. + Uses fixed MAX_SEQ_LEN so results are comparable across configs. + + Perf: accumulates on GPU (single sync at end), prefetches next batch + while current forward runs. + """ + token_bytes = get_token_bytes(device="cuda") + val_loader = make_dataloader(tokenizer, batch_size, MAX_SEQ_LEN, "val") + steps = EVAL_TOKENS // (batch_size * MAX_SEQ_LEN) + + # GPU-resident accumulators — avoid per-batch .item() sync + total_nats_t = torch.zeros(1, device="cuda", dtype=torch.float64) + total_bytes_t = torch.zeros(1, device="cuda", dtype=torch.int64) + + # Prefetch first batch + next_batch = next(val_loader) + for _ in range(steps): + x, y, _epoch = next_batch + # Prefetch NEXT batch while GPU computes current forward + next_batch = next(val_loader) + loss_flat = model(x, y, reduction='none').view(-1) + y_flat = y.view(-1) + nbytes = token_bytes[y_flat] + mask = nbytes > 0 + total_nats_t += (loss_flat * mask).sum() + total_bytes_t += nbytes.sum() + + # Single GPU→CPU sync at end + total_nats = total_nats_t.item() + total_bytes = total_bytes_t.item() + return total_nats / (math.log(2) * total_bytes) + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Prepare data and tokenizer for autoresearch") + parser.add_argument("--num-shards", type=int, default=10, help="Number of training shards to download (-1 = all). Val shard is always pinned.") + parser.add_argument("--download-workers", type=int, default=8, help="Number of parallel download workers") + args = parser.parse_args() + + num_shards = MAX_SHARD if args.num_shards == -1 else args.num_shards + + print(f"Cache directory: {CACHE_DIR}") + print() + + # Step 1: Download data + download_data(num_shards, download_workers=args.download_workers) + print() + + # Step 2: Train tokenizer + train_tokenizer() + print() + print("Done! Ready to train.") diff --git a/overlay/prepare_nemotron.py b/overlay/prepare_nemotron.py index 5c75406e5ea88e63dec20d2a8c0940dfc8d74814..10d66e4924f92ea130f03abbcb23ec4db88d42bc 100644 --- a/overlay/prepare_nemotron.py +++ b/overlay/prepare_nemotron.py @@ -1,530 +1,572 @@ -"""Nemotron Super3 streaming data prep — zero-disk pretraining loader. - -Reads nvidia/Nemotron-Pretraining-Specialized-v1.1 directly from HF via the -`datasets` library with streaming=True. No pre-tokenization to disk. - -Integrates with the existing training pipeline by providing drop-in -replacements for `prepare.make_dataloader` and `prepare.evaluate_bpb` when -env var HYDRA_USE_NEMOTRON=1 is set (see hydra/training.py wiring). - -Two phases (per Super3 recipe): - Phase 1 — diversity blend: equal weights across 5 Nemotron configs. - Phase 2 — quality blend: higher weight on Multiple-Choice / Economics / Formal-Logic. - -Select phase via env: HYDRA_NEMOTRON_PHASE=phase1|phase2 (default phase1). - -Full blend mode (env HYDRA_USE_FULL_BLEND=1): - 7-way diverse mixture for robust pretraining — fineweb-edu, fineweb, - stack-v2, nemotron-math, nemotron-specialized, wikipedia, cosmopedia. - Overrides Phase 1/Phase 2 weights entirely. -""" -from __future__ import annotations - +"""Nemotron Super3 streaming data prep — zero-disk pretraining loader. + +Reads nvidia/Nemotron-Pretraining-Specialized-v1.1 directly from HF via the +`datasets` library with streaming=True. No pre-tokenization to disk. + +Integrates with the existing training pipeline by providing drop-in +replacements for `prepare.make_dataloader` and `prepare.evaluate_bpb` when +env var HYDRA_USE_NEMOTRON=1 is set (see hydra/training.py wiring). + +Two phases (per Super3 recipe): + Phase 1 — diversity blend: equal weights across 5 Nemotron configs. + Phase 2 — quality blend: higher weight on Multiple-Choice / Economics / Formal-Logic. + +Select phase via env: HYDRA_NEMOTRON_PHASE=phase1|phase2 (default phase1). + +Full blend mode (env HYDRA_USE_FULL_BLEND=1): + 7-way diverse mixture for robust pretraining — fineweb-edu, fineweb, + stack-v2, nemotron-math, nemotron-specialized, wikipedia, cosmopedia. + Overrides Phase 1/Phase 2 weights entirely. +""" +from __future__ import annotations + import os import random -import importlib -import shutil from itertools import cycle -from typing import Any, Iterator, cast - -import torch - +from typing import Iterator + +import numpy as np +import torch + import prepare as _p # reuse tokenizer, BOS, byte-length helpers - -NEMOTRON_REPO = "nvidia/Nemotron-Pretraining-Specialized-v1.1" - -# --------------------------------------------------------------------------- -# Full 7-way diverse blend — activated by HYDRA_USE_FULL_BLEND=1. -# Keys are logical dataset names used by _open_blend_stream / _open_stream. -# --------------------------------------------------------------------------- -FULL_BLEND_WEIGHTS: dict[str, float] = { - "fineweb-edu": 0.35, # HuggingFaceFW/fineweb-edu - "fineweb": 0.15, # HuggingFaceFW/fineweb (sample-100BT) - "stack-v2": 0.15, # bigcode/the-stack-v2 - "nemotron-math": 0.10, # nvidia/Nemotron-CC-Math-v1 - "nemotron-specialized": 0.10, # nvidia/Nemotron-Pretraining-Specialized-v1.1 - "wikipedia": 0.08, # olm/wikipedia - "cosmopedia": 0.07, # HuggingFaceTB/cosmopedia -} - -# Mapping from logical blend name → (HF repo, optional config/name, text column). -# None for config means no sub-config needed. -_BLEND_REGISTRY: dict[str, tuple[str, str | None, str]] = { - "fineweb-edu": ("HuggingFaceFW/fineweb-edu", None, "text"), - "fineweb": ("HuggingFaceFW/fineweb", "sample-100BT", "text"), - "stack-v2": ("OpenCoder-LLM/opc-fineweb-code-corpus", None, "text"), - "nemotron-math": ("nvidia/Nemotron-CC-Math-v1", "4plus", "text"), - "nemotron-specialized": ("nvidia/Nemotron-Pretraining-Specialized-v1.1", None, "text"), - "wikipedia": ("wikimedia/wikipedia", "20231101.en", "text"), - "cosmopedia": ("HuggingFaceTB/cosmopedia", "web_samples_v2", "text"), -} - -PHASE1_WEIGHTS = { - "Nemotron-Pretraining-Code-Concepts": 0.20, - "Nemotron-Pretraining-Unconditional-Algorithmic": 0.20, - "Nemotron-Pretraining-Economics": 0.20, - "Nemotron-Pretraining-Formal-Logic": 0.20, - "Nemotron-Pretraining-Multiple-Choice": 0.20, -} -PHASE2_WEIGHTS = { - "Nemotron-Pretraining-Multiple-Choice": 0.45, - "Nemotron-Pretraining-Economics": 0.20, - "Nemotron-Pretraining-Formal-Logic": 0.15, - "Nemotron-Pretraining-Code-Concepts": 0.10, - "Nemotron-Pretraining-Unconditional-Algorithmic": 0.10, + +NEMOTRON_REPO = "nvidia/Nemotron-Pretraining-Specialized-v1.1" + +# --------------------------------------------------------------------------- +# Full 7-way diverse blend — activated by HYDRA_USE_FULL_BLEND=1. +# Keys are logical dataset names used by _open_blend_stream / _open_stream. +# --------------------------------------------------------------------------- +FULL_BLEND_WEIGHTS: dict[str, float] = { + "fineweb-edu": 0.55, # HuggingFaceFW/fineweb-edu — PRIMARY (high-quality English) + "wikipedia": 0.25, # wikimedia/wikipedia — factual grounding + "cosmopedia": 0.15, # HuggingFaceTB/cosmopedia — synthetic textbook + "fineweb": 0.05, # HuggingFaceFW/fineweb — general web + # REMOVED code/math: was polluting English generation with Python syntax + # "stack-v2": 0.00, + # "nemotron-math": 0.00, + # "nemotron-specialized": 0.00, } -StreamBatch = tuple[list[str], int] -TokenBatch = tuple[list[list[int]], int] +# Mapping from logical blend name → (HF repo, optional config/name, text column). +# None for config means no sub-config needed. +_BLEND_REGISTRY: dict[str, tuple[str, str | None, str]] = { + "fineweb-edu": ("HuggingFaceFW/fineweb-edu", None, "text"), + "fineweb": ("HuggingFaceFW/fineweb", "sample-100BT", "text"), + "stack-v2": ("OpenCoder-LLM/opc-fineweb-code-corpus", None, "text"), + "nemotron-math": ("nvidia/Nemotron-CC-Math-v1", "4plus", "text"), + "nemotron-specialized": ("nvidia/Nemotron-Pretraining-Specialized-v1.1", None, "text"), + "wikipedia": ("wikimedia/wikipedia", "20231101.en", "text"), + "cosmopedia": ("HuggingFaceTB/cosmopedia", "web_samples_v2", "text"), +} +PHASE1_WEIGHTS = { + "Nemotron-Pretraining-Code-Concepts": 0.20, + "Nemotron-Pretraining-Unconditional-Algorithmic": 0.20, + "Nemotron-Pretraining-Economics": 0.20, + "Nemotron-Pretraining-Formal-Logic": 0.20, + "Nemotron-Pretraining-Multiple-Choice": 0.20, +} +PHASE2_WEIGHTS = { + "Nemotron-Pretraining-Multiple-Choice": 0.45, + "Nemotron-Pretraining-Economics": 0.20, + "Nemotron-Pretraining-Formal-Logic": 0.15, + "Nemotron-Pretraining-Code-Concepts": 0.10, + "Nemotron-Pretraining-Unconditional-Algorithmic": 0.10, +} -def _tokenizer_cache_repo() -> str: - return ( - os.environ.get("HYDRA_TOKENIZER_CACHE_REPO") - or os.environ.get("FEATHER_HF_OUTPUT_REPO") - or os.environ.get("HF_REPO_ID") - or os.environ.get("HYDRA_RETINA_CACHE_REPO") - or os.environ.get("FEATHER_HF_RETINA_CACHE_REPO") - or "" - ) +def _phase_weights() -> dict[str, float]: + # Full diverse blend overrides phase selection entirely. + if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": + return FULL_BLEND_WEIGHTS + phase = os.environ.get("HYDRA_NEMOTRON_PHASE", "phase1").strip().lower() + return PHASE2_WEIGHTS if phase == "phase2" else PHASE1_WEIGHTS -def _tokenizer_cache_prefix() -> str: - return f"tokenizer/vocab{_p.VOCAB_SIZE}" +_PREFETCH_THREAD = None +_PREFETCH_STARTED = set() -def maybe_hydrate_tokenizer_cache() -> bool: - """Try to download tokenizer artifacts from HF cache storage.""" - repo_id = _tokenizer_cache_repo() - token = os.environ.get("HF_TOKEN") - if not repo_id or not token: - return False - try: - from huggingface_hub import hf_hub_download - except Exception as e: # noqa: BLE001 - print(f"[nemotron] tokenizer cache unavailable: {type(e).__name__}: {e}", flush=True) - return False +def _find_local_parquets(repo: str, sub_config: str | None) -> list[str]: + """Return LOCAL parquet paths in HF hub cache for a given repo+config. - os.makedirs(_p.TOKENIZER_DIR, exist_ok=True) - prefix = _tokenizer_cache_prefix() - try: - tok_src = hf_hub_download( - repo_id=repo_id, - repo_type="model", - subfolder=prefix, - filename="tokenizer.pkl", - token=token, - local_dir=_p.TOKENIZER_DIR, - ) - token_bytes_src = hf_hub_download( - repo_id=repo_id, - repo_type="model", - subfolder=prefix, - filename="token_bytes.pt", - token=token, - local_dir=_p.TOKENIZER_DIR, - ) - shutil.copy2(tok_src, os.path.join(_p.TOKENIZER_DIR, "tokenizer.pkl")) - shutil.copy2(token_bytes_src, os.path.join(_p.TOKENIZER_DIR, "token_bytes.pt")) - except Exception as e: # noqa: BLE001 - print(f"[nemotron] tokenizer cache miss in {repo_id}/{prefix}: {type(e).__name__}: {e}", flush=True) - return False + If sub_config filter yields zero matches but parquet files exist in the + repo dir, returns all parquet files (some datasets like fineweb use a + builder config name that doesn't match the filesystem path). + """ + import glob + repo_dir = "datasets--" + repo.replace("/", "--") + base = os.path.expanduser(f"~/.cache/huggingface/hub/{repo_dir}/snapshots") + if not os.path.isdir(base): + return [] + all_paths = [] + for snap in os.listdir(base): + all_paths.extend(glob.glob(os.path.join(base, snap, "**", "*.parquet"), recursive=True)) + if sub_config is None: + return sorted(all_paths) + filtered = [p for p in all_paths if f"/{sub_config}/" in p] + # Fallback: if the config name doesn't match filesystem paths, use all parquet + if not filtered and all_paths: + return sorted(all_paths) + return sorted(filtered) - print(f"[nemotron] hydrated tokenizer cache from {repo_id}/{prefix}", flush=True) - return True +def _start_background_prefetch(repo: str, sub_config: str | None): + """Start a daemon thread that downloads parquet shards ahead of consumption. -def upload_tokenizer_cache() -> None: - """Upload tokenizer artifacts for reuse by future jobs.""" - repo_id = _tokenizer_cache_repo() - token = os.environ.get("HF_TOKEN") - if not repo_id or not token: + Feeds HF's local cache so streaming=True serves from disk, never network. + Idempotent per (repo, sub_config). Runs at throttled speed to not flood. + """ + import threading + key = (repo, sub_config) + if key in _PREFETCH_STARTED: return + _PREFETCH_STARTED.add(key) + + def worker(): + try: + from huggingface_hub import HfApi, hf_hub_download + os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1") + token = os.environ.get("HF_TOKEN") + api = HfApi(token=token) + files = api.list_repo_files(repo, repo_type="dataset") + parquet = sorted(f for f in files if f.endswith(".parquet")) + if sub_config is not None: + filtered = [f for f in parquet if f"/{sub_config}/" in f or f.startswith(f"{sub_config}/")] + if filtered: + parquet = filtered + # Fetch shards one by one, skipping already-cached (hf_hub_download is idempotent) + for f in parquet: + try: + hf_hub_download(repo_id=repo, filename=f, repo_type="dataset", token=token) + except Exception: + pass # skip unavailable shards + except Exception: + pass # prefetch is best-effort, don't disrupt training + + t = threading.Thread(target=worker, daemon=True, name=f"prefetch-{repo}") + t.start() - path = os.path.join(_p.TOKENIZER_DIR, "tokenizer.pkl") - token_bytes_path = os.path.join(_p.TOKENIZER_DIR, "token_bytes.pt") - if not (os.path.exists(path) and os.path.exists(token_bytes_path)): - return - try: - from huggingface_hub import HfApi - api = HfApi(token=token) - prefix = _tokenizer_cache_prefix() - api.upload_file(path_or_fileobj=path, path_in_repo=f"{prefix}/tokenizer.pkl", repo_id=repo_id, repo_type="model") - api.upload_file(path_or_fileobj=token_bytes_path, path_in_repo=f"{prefix}/token_bytes.pt", repo_id=repo_id, repo_type="model") - print(f"[nemotron] uploaded tokenizer cache to {repo_id}/{prefix}", flush=True) - except Exception as e: # noqa: BLE001 - print(f"[nemotron] tokenizer cache upload skipped: {type(e).__name__}: {e}", flush=True) - - -def _phase_weights() -> dict[str, float]: - # Full diverse blend overrides phase selection entirely. - if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": - return FULL_BLEND_WEIGHTS - phase = os.environ.get("HYDRA_NEMOTRON_PHASE", "phase1").strip().lower() - return PHASE2_WEIGHTS if phase == "phase2" else PHASE1_WEIGHTS - - def _open_stream(config: str, split: str): - """Open a streaming iterator over one dataset config. - - Handles two modes: - 1. Nemotron sub-configs (e.g. "Nemotron-Pretraining-Code-Concepts") — - loaded from NEMOTRON_REPO with the config name. - 2. Full-blend logical names (e.g. "fineweb-edu", "stack-v2") — - looked up in _BLEND_REGISTRY for repo / sub-config / text column. - - Yields dicts; text extraction handled downstream by _extract_text. - """ - load_dataset = importlib.import_module("datasets").load_dataset + """Open a streaming iterator over one dataset config. + + Uses HF streaming (reads local cache when shards present, network otherwise). + Starts a background prefetcher that downloads remaining shards in parallel. + """ + from datasets import load_dataset token = os.environ.get("HF_TOKEN") shuffle_buf = int(os.environ.get("HYDRA_STREAM_SHUFFLE_BUFFER", "2048")) if config in _BLEND_REGISTRY: repo, name, _text_col = _BLEND_REGISTRY[config] - kwargs: dict[str, object] = dict( + effective_cfg = name + if config == "nemotron-specialized": + effective_cfg = "Nemotron-Pretraining-Code-Concepts" + repo = NEMOTRON_REPO + else: + repo = NEMOTRON_REPO + effective_cfg = config + + # Kick off background prefetch of remaining shards for this dataset + if os.environ.get("HYDRA_BACKGROUND_PREFETCH", "1") == "1": + _start_background_prefetch(repo, effective_cfg) + + local_only = os.environ.get("HYDRA_LOCAL_SHARDS_ONLY", "1") == "1" + if local_only: + local_paths = _find_local_parquets(repo, effective_cfg) + if not local_paths: + raise RuntimeError( + f"No local parquet files for {repo} (config={effective_cfg}). " + f"Run scripts/predownload_shards.py first, or set HYDRA_LOCAL_SHARDS_ONLY=0." + ) + ds = load_dataset( + "parquet", + data_files=local_paths, split="train", streaming=True, - token=token, ) - if name is not None: - kwargs["name"] = name - # nemotron-specialized has multiple sub-configs; pick the first one - # (diversity blend) when accessed via the full-blend path. - if config == "nemotron-specialized": - kwargs["name"] = "Nemotron-Pretraining-Code-Concepts" - repo = NEMOTRON_REPO - ds = load_dataset(repo, **kwargs) - else: - # Legacy Nemotron sub-config path (Phase 1 / Phase 2). - ds = load_dataset( - NEMOTRON_REPO, - config, - split="train", - streaming=True, - token=token, - ) - ds = ds.shuffle(seed=42, buffer_size=shuffle_buf) - return iter(ds) - - -def _extract_text(row: dict[str, object]) -> str: - """Pick the right text column — datasets have different column names. - - Priority order: text, content, prompt_completion, question, body. - For math datasets that split into problem+solution, concatenate both. - Fallback: concatenate all string-valued fields. - """ + else: + kwargs: dict = dict(split="train", streaming=True, token=token) + if effective_cfg is not None: + kwargs["name"] = effective_cfg + ds = load_dataset(repo, **kwargs) + ds = ds.shuffle(seed=42, buffer_size=shuffle_buf) + return iter(ds) + + +def _extract_text(row: dict) -> str: + """Pick the right text column — datasets have different column names. + + Priority order: text, content, prompt_completion, question, body. + For math datasets that split into problem+solution, concatenate both. + Fallback: concatenate all string-valued fields. + """ # Fast path: most datasets use "text" or "content". for k in ("text", "content", "prompt_completion", "question", "body"): - value = row.get(k) - if isinstance(value, str) and value: - return value - # Math datasets may have problem + solution as separate fields. - if "problem" in row and "solution" in row: - p = row["problem"] or "" - s = row["solution"] or "" - combined = f"{p}\n{s}".strip() - if combined: - return combined - # Fallback: concatenate all string-valued fields. - parts = [] - for v in row.values(): - if isinstance(v, str) and v: - parts.append(v) - return "\n".join(parts) - - + if k in row and row[k]: + return row[k] + # Math datasets may have problem + solution as separate fields. + if "problem" in row and "solution" in row: + p = row["problem"] or "" + s = row["solution"] or "" + combined = f"{p}\n{s}".strip() + if combined: + return combined + # Fallback: concatenate all string-valued fields. + parts = [] + for v in row.values(): + if isinstance(v, str) and v: + parts.append(v) + return "\n".join(parts) + + class _WeightedStream: - """Infinite weighted-round-robin over configs' streaming iterators.""" - + """Infinite weighted-round-robin over configs' streaming iterators.""" + def __init__(self, weights: dict[str, float], seed: int = 0): self.configs = list(weights.keys()) self.weights = [weights[c] for c in self.configs] - self.streams: dict[str, Iterator[dict[str, object]]] = { - c: _open_stream(c, "train") for c in self.configs - } + self.streams = {c: _open_stream(c, "train") for c in self.configs} self.rng = random.Random(seed) self.epoch = 1 - self._factual_docs: list[str] | None = None - self._factual_idx = 0 - self._inject_counter = 0 - - def _reopen(self, config: str): - # stream exhausted — reopen (HF streaming typically infinite but restart on edge) - self.streams[config] = _open_stream(config, "train") - self.epoch += 1 - - def __iter__(self): - return self - - def __next__(self) -> tuple[str, int]: - # Factual injection: every N docs, yield a factual knowledge doc instead. - # This ensures the model sees facts (Paris, Jupiter, etc.) that may not - # exist in the Nemotron configs. Controlled by HYDRA_FACTUAL_INJECT_RATE - # (default 50 = inject one factual doc every 50 Nemotron docs = ~2%). - inject_rate = int(os.environ.get("HYDRA_FACTUAL_INJECT_RATE", "50")) - if inject_rate > 0 and self._factual_docs is None: + + def _reopen(self, config: str): + # stream exhausted — reopen (HF streaming typically infinite but restart on edge) + self.streams[config] = _open_stream(config, "train") + self.epoch += 1 + + def __iter__(self): + return self + + def __next__(self) -> tuple[str, int]: + # Factual injection: every N docs, yield a factual knowledge doc instead. + # This ensures the model sees facts (Paris, Jupiter, etc.) that may not + # exist in the Nemotron configs. Controlled by HYDRA_FACTUAL_INJECT_RATE + # (default 50 = inject one factual doc every 50 Nemotron docs = ~2%). + inject_rate = int(os.environ.get("HYDRA_FACTUAL_INJECT_RATE", "50")) + if inject_rate > 0 and not hasattr(self, '_factual_docs'): factual_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), "data", "factual", "facts.txt") if os.path.exists(factual_path): self._factual_docs = open(factual_path).read().strip().split('\n') self._factual_idx = 0 self._inject_counter = 0 - if inject_rate > 0 and self._factual_docs: - self._inject_counter += 1 + else: + self._factual_docs = None + if inject_rate > 0 and hasattr(self, '_factual_docs') and self._factual_docs: + self._inject_counter = getattr(self, '_inject_counter', 0) + 1 if self._inject_counter >= inject_rate: self._inject_counter = 0 doc = self._factual_docs[self._factual_idx % len(self._factual_docs)] self._factual_idx += 1 return doc, self.epoch - - config = self.rng.choices(self.configs, weights=self.weights, k=1)[0] - try: - row = next(self.streams[config]) - except StopIteration: - self._reopen(config) - row = next(self.streams[config]) - return _extract_text(row), self.epoch - - -def _document_batches(split: str, tokenizer_batch_size: int = 128) -> Iterator[tuple[list[str], int]]: - """Streaming document batches — drop-in replacement for prepare._document_batches. - - A background thread prefetches text batches into a queue so the training - consumer never blocks on network I/O. Queue depth tunable via - HYDRA_STREAM_PREFETCH (default 32). At tokenizer_batch_size=128 and - queue_depth=32, we keep ~4096 pre-loaded documents hot — several seconds of - HF bandwidth buffered against any single HTTP stall. - """ - import queue - import threading - - if split == "val": - if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": - # Validate on a diverse mix matching training distribution. - stream = _WeightedStream(FULL_BLEND_WEIGHTS, seed=12345) - else: - stream = _WeightedStream({"Nemotron-Pretraining-Multiple-Choice": 1.0}, seed=12345) - else: - stream = _WeightedStream(_phase_weights(), seed=0) - - prefetch_depth = int(os.environ.get("HYDRA_STREAM_PREFETCH", "32")) - q: queue.Queue[StreamBatch | object] = queue.Queue(maxsize=prefetch_depth) + + config = self.rng.choices(self.configs, weights=self.weights, k=1)[0] + try: + row = next(self.streams[config]) + except StopIteration: + self._reopen(config) + row = next(self.streams[config]) + return _extract_text(row), self.epoch + + +def _document_batches(split: str, tokenizer_batch_size: int = 128) -> Iterator[tuple[list[str], int]]: + """Streaming document batches — drop-in replacement for prepare._document_batches. + + A background thread prefetches text batches into a queue so the training + consumer never blocks on network I/O. Queue depth tunable via + HYDRA_STREAM_PREFETCH (default 32). At tokenizer_batch_size=128 and + queue_depth=32, we keep ~4096 pre-loaded documents hot — several seconds of + HF bandwidth buffered against any single HTTP stall. + """ + import queue + import threading + + if split == "val": + if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1": + # Validate on a diverse mix matching training distribution. + stream = _WeightedStream(FULL_BLEND_WEIGHTS, seed=12345) + else: + stream = _WeightedStream({"Nemotron-Pretraining-Multiple-Choice": 1.0}, seed=12345) + else: + stream = _WeightedStream(_phase_weights(), seed=0) + + prefetch_depth = int(os.environ.get("HYDRA_STREAM_PREFETCH", "32")) + q: queue.Queue = queue.Queue(maxsize=prefetch_depth) sentinel_stop = object() - error_box: list[BaseException] = [] - - def producer(): - try: - buf: list[str] = [] - last_epoch = 1 - for text, epoch in stream: - buf.append(text) - last_epoch = epoch - if len(buf) >= tokenizer_batch_size: - q.put((buf, last_epoch)) - buf = [] - except BaseException as e: # includes thread interrupt - error_box.append(e) - q.put(sentinel_stop) - - t = threading.Thread(target=producer, daemon=True, name="nemotron-prefetch") - t.start() - - while True: - item = q.get() - if item is sentinel_stop: - if error_box: - raise error_box[0] - return - yield cast(StreamBatch, item) - - -def make_dataloader(tokenizer, B: int, T: int, split: str, buffer_size: int = 1000): - """Drop-in replacement for prepare.make_dataloader, streaming from Nemotron. - - Pipeline stages (all concurrent via queues): - stage 1: HF streaming → text batches (in _document_batches producer thread) - stage 2: BPE tokenization → token-id lists (this function's producer thread) - stage 3: best-fit packing → (B, T+1) tensor rows (main thread, consumes) - - Queue depths tunable via HYDRA_STREAM_PREFETCH and HYDRA_TOKEN_PREFETCH. - Goal: zero tps loss from I/O or tokenizer overhead — training loop pulls - from an always-full queue. - """ - import queue - import threading - - assert split in ("train", "val") - row_capacity = T + 1 - batches = _document_batches(split) - bos_token = tokenizer.get_bos_token_id() - - # Stage 2: tokenization prefetch thread. Each queue element is a list of - # token-id lists (pre-tokenized docs). HYDRA_TOKEN_PREFETCH controls depth. - tok_prefetch = int(os.environ.get("HYDRA_TOKEN_PREFETCH", "8")) - tok_q: queue.Queue[TokenBatch | object] = queue.Queue(maxsize=tok_prefetch) + error_box: list = [] + + def producer(): + try: + buf: list[str] = [] + last_epoch = 1 + for text, epoch in stream: + buf.append(text) + last_epoch = epoch + if len(buf) >= tokenizer_batch_size: + q.put((buf, last_epoch)) + buf = [] + except BaseException as e: # includes thread interrupt + error_box.append(e) + q.put(sentinel_stop) + + t = threading.Thread(target=producer, daemon=True, name="nemotron-prefetch") + t.start() + + while True: + item = q.get() + if item is sentinel_stop: + if error_box: + raise error_box[0] + return + yield item + + +def make_dataloader(tokenizer, B: int, T: int, split: str, buffer_size: int = 1000): + """Drop-in replacement for prepare.make_dataloader, streaming from Nemotron. + + Pipeline stages (all concurrent via queues): + stage 1: HF streaming → text batches (in _document_batches producer thread) + stage 2: BPE tokenization → token-id lists (this function's producer thread) + stage 3: best-fit packing → (B, T+1) tensor rows (main thread, consumes) + + Local cache (HYDRA_TOKEN_CACHE_GB, default 2): + Packed (T+1) rows are written to a binary shard on first pass. Subsequent + launches with a non-empty cache mmap that file and cycle through it, + skipping the 5-min streaming cold-start entirely. Cache key includes + (T, vocab_size) so shape changes invalidate the cache automatically. + """ + import queue + import threading + + assert split in ("train", "val") + row_capacity = T + 1 + bos_token = tokenizer.get_bos_token_id() + + # --- Local packed-token cache (train only; val path skips cache-write) --- + cache_enabled = split == "train" + cache_gb = float(os.environ.get("HYDRA_TOKEN_CACHE_GB", "2")) + cache_dir = os.path.expanduser("~/.cache/autoresearch") + os.makedirs(cache_dir, exist_ok=True) + vocab_size = tokenizer.get_vocab_size() + cache_path = os.path.join(cache_dir, f"packed_tokens_v1_T{T}_V{vocab_size}_{split}.bin") + cache_target_bytes = int(cache_gb * 1024**3) + dtype_np = np.int32 # vocab < 2^31 + bytes_per_row = row_capacity * 4 # int32 + cache_rows_target = cache_target_bytes // bytes_per_row + + # If train cache exists and is ready, mmap and yield from it + if cache_enabled and os.path.exists(cache_path) and os.path.getsize(cache_path) >= cache_target_bytes // 2: + print(f"[token-cache] using {cache_path} ({os.path.getsize(cache_path) / 1024**3:.2f} GB)") + yield from _mmap_cache_loader(cache_path, B, T, row_capacity, dtype_np) + return # unreachable (mmap loader is infinite), but satisfies generator protocol + + if cache_enabled: + print(f"[token-cache] building {cache_path} (target {cache_gb:.1f} GB) on first pass") + batches = _document_batches(split) + + # Stage 2: tokenization prefetch thread. Each queue element is a list of + # token-id lists (pre-tokenized docs). HYDRA_TOKEN_PREFETCH controls depth. + tok_prefetch = int(os.environ.get("HYDRA_TOKEN_PREFETCH", "8")) + tok_q: queue.Queue = queue.Queue(maxsize=tok_prefetch) tok_sentinel = object() - tok_err_box: list[BaseException] = [] - - def tokenizer_producer(): - try: - for doc_batch, epoch in batches: - token_lists = tokenizer.encode(doc_batch, prepend=bos_token) - tok_q.put((token_lists, epoch)) - except BaseException as e: - tok_err_box.append(e) - tok_q.put(tok_sentinel) - - tok_thread = threading.Thread(target=tokenizer_producer, daemon=True, name="nemotron-tokenizer") - tok_thread.start() - - doc_buffer: list[list[int]] = [] - epoch = 1 - - def refill_buffer(): - nonlocal epoch - item = tok_q.get() - if item is tok_sentinel: - if tok_err_box: - raise tok_err_box[0] - raise StopIteration - token_lists, epoch = cast(TokenBatch, item) + tok_err_box: list = [] + + def tokenizer_producer(): + try: + for doc_batch, epoch in batches: + token_lists = tokenizer.encode(doc_batch, prepend=bos_token) + tok_q.put((token_lists, epoch)) + except BaseException as e: + tok_err_box.append(e) + tok_q.put(tok_sentinel) + + tok_thread = threading.Thread(target=tokenizer_producer, daemon=True, name="nemotron-tokenizer") + tok_thread.start() + + doc_buffer: list[list[int]] = [] + epoch = 1 + + def refill_buffer(): + nonlocal epoch + item = tok_q.get() + if item is tok_sentinel: + if tok_err_box: + raise tok_err_box[0] + raise StopIteration + token_lists, epoch = item doc_buffer.extend(token_lists) - - row_buffer = torch.empty((B, row_capacity), dtype=torch.long) - cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) - gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") - cpu_inputs = cpu_buffer[: B * T].view(B, T) - cpu_targets = cpu_buffer[B * T :].view(B, T) - inputs = gpu_buffer[: B * T].view(B, T) - targets = gpu_buffer[B * T :].view(B, T) - - while True: - for row_idx in range(B): - pos = 0 - while pos < row_capacity: - while len(doc_buffer) < buffer_size: - refill_buffer() - remaining = row_capacity - pos - best_idx = -1 - best_len = 0 - for i, doc in enumerate(doc_buffer): - dlen = len(doc) - if dlen <= remaining and dlen > best_len: - best_idx = i - best_len = dlen - if best_idx >= 0: - doc = doc_buffer.pop(best_idx) - row_buffer[row_idx, pos : pos + len(doc)] = torch.tensor(doc, dtype=torch.long) - pos += len(doc) - else: - shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i])) - doc = doc_buffer.pop(shortest_idx) - row_buffer[row_idx, pos : pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long) - pos += remaining - - cpu_inputs.copy_(row_buffer[:, :-1]) - cpu_targets.copy_(row_buffer[:, 1:]) - gpu_buffer.copy_(cpu_buffer, non_blocking=True) - yield inputs, targets, epoch - - -def evaluate_bpb(model, tokenizer, B: int) -> float: - """Streaming validation bits-per-byte — drop-in for prepare.evaluate_bpb. - - Mirrors prepare.evaluate_bpb's structure exactly (same model API: - `model(x, y, reduction='none')`, GPU-resident accumulators, token_bytes - LUT from prepare.get_token_bytes). Differs only in the dataloader - source — streaming from Nemotron val stream instead of val parquet shard. - """ - import math - eval_tokens = int(os.environ.get("HYDRA_STREAM_EVAL_TOKENS", str(_p.EVAL_TOKENS))) - T = _p.MAX_SEQ_LEN - token_bytes = _p.get_token_bytes(device="cuda") - val_loader = make_dataloader(tokenizer, B, T, "val") - steps = max(1, eval_tokens // (B * T)) - - total_nats_t = torch.zeros(1, device="cuda", dtype=torch.float64) - total_bytes_t = torch.zeros(1, device="cuda", dtype=torch.int64) - - next_batch = next(val_loader) - for _ in range(steps): - x, y, _epoch = next_batch - next_batch = next(val_loader) - loss_flat = model(x, y, reduction='none').view(-1) - y_flat = y.view(-1) - nbytes = token_bytes[y_flat] - mask = nbytes > 0 - total_nats_t += (loss_flat * mask).sum() - total_bytes_t += nbytes.sum() - - total_nats = total_nats_t.item() - total_bytes = total_bytes_t.item() - return total_nats / (math.log(2) * max(total_bytes, 1)) - - + + row_buffer = torch.empty((B, row_capacity), dtype=torch.long) + cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) + gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") + cpu_inputs = cpu_buffer[: B * T].view(B, T) + cpu_targets = cpu_buffer[B * T :].view(B, T) + inputs = gpu_buffer[: B * T].view(B, T) + targets = gpu_buffer[B * T :].view(B, T) + + # Open cache file for append-on-build + cache_fh = open(cache_path + ".tmp", "wb") if cache_enabled else None + cache_rows_written = 0 + + while True: + for row_idx in range(B): + pos = 0 + while pos < row_capacity: + while len(doc_buffer) < buffer_size: + refill_buffer() + remaining = row_capacity - pos + best_idx = -1 + best_len = 0 + for i, doc in enumerate(doc_buffer): + dlen = len(doc) + if dlen <= remaining and dlen > best_len: + best_idx = i + best_len = dlen + if best_idx >= 0: + doc = doc_buffer.pop(best_idx) + row_buffer[row_idx, pos : pos + len(doc)] = torch.tensor(doc, dtype=torch.long) + pos += len(doc) + else: + shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i])) + doc = doc_buffer.pop(shortest_idx) + row_buffer[row_idx, pos : pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long) + pos += remaining + + cpu_inputs.copy_(row_buffer[:, :-1]) + cpu_targets.copy_(row_buffer[:, 1:]) + gpu_buffer.copy_(cpu_buffer, non_blocking=True) + + # Write packed rows to cache (append) until target size reached + if cache_fh is not None: + np_rows = row_buffer.numpy().astype(np.int32, copy=False) + cache_fh.write(np_rows.tobytes()) + cache_rows_written += B + if cache_rows_written >= cache_rows_target: + cache_fh.flush() + cache_fh.close() + os.replace(cache_path + ".tmp", cache_path) + cache_fh = None + print(f"[token-cache] finalized {cache_path} ({cache_rows_written} rows)") + + yield inputs, targets, epoch + + +def _mmap_cache_loader(cache_path: str, B: int, T: int, row_capacity: int, dtype_np): + """Read packed (T+1) rows from mmap cache, cycle forever.""" + data = np.memmap(cache_path, dtype=dtype_np, mode="r").reshape(-1, row_capacity) + n_rows = data.shape[0] + cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) + gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") + cpu_inputs = cpu_buffer[: B * T].view(B, T) + cpu_targets = cpu_buffer[B * T :].view(B, T) + inputs = gpu_buffer[: B * T].view(B, T) + targets = gpu_buffer[B * T :].view(B, T) + idx = 0 + epoch = 1 + while True: + if idx + B > n_rows: + idx = 0 + epoch += 1 + batch = torch.from_numpy(data[idx:idx + B].astype(np.int64, copy=True)) + idx += B + cpu_inputs.copy_(batch[:, :-1]) + cpu_targets.copy_(batch[:, 1:]) + gpu_buffer.copy_(cpu_buffer, non_blocking=True) + yield inputs, targets, epoch + + +def evaluate_bpb(model, tokenizer, B: int) -> float: + """Streaming validation bits-per-byte — drop-in for prepare.evaluate_bpb. + + Mirrors prepare.evaluate_bpb's structure exactly (same model API: + `model(x, y, reduction='none')`, GPU-resident accumulators, token_bytes + LUT from prepare.get_token_bytes). Differs only in the dataloader + source — streaming from Nemotron val stream instead of val parquet shard. + """ + import math + eval_tokens = int(os.environ.get("HYDRA_STREAM_EVAL_TOKENS", str(_p.EVAL_TOKENS))) + T = _p.MAX_SEQ_LEN + token_bytes = _p.get_token_bytes(device="cuda") + val_loader = make_dataloader(tokenizer, B, T, "val") + steps = max(1, eval_tokens // (B * T)) + + total_nats_t = torch.zeros(1, device="cuda", dtype=torch.float64) + total_bytes_t = torch.zeros(1, device="cuda", dtype=torch.int64) + + next_batch = next(val_loader) + for _ in range(steps): + x, y, _epoch = next_batch + next_batch = next(val_loader) + loss_flat = model(x, y, reduction='none').view(-1) + y_flat = y.view(-1) + nbytes = token_bytes[y_flat] + mask = nbytes > 0 + total_nats_t += (loss_flat * mask).sum() + total_bytes_t += nbytes.sum() + + total_nats = total_nats_t.item() + total_bytes = total_bytes_t.item() + return total_nats / (math.log(2) * max(total_bytes, 1)) + + def ensure_tokenizer(): - """Ensure rustbpe tokenizer exists. If absent, train on a Nemotron stream - sample using the same rustbpe.train_from_iterator API that prepare.py uses - (production path — don't fork tokenizer training logic). - """ - import pickle - import torch + """Ensure rustbpe tokenizer exists. If absent, train on a Nemotron stream + sample using the same rustbpe.train_from_iterator API that prepare.py uses + (production path — don't fork tokenizer training logic). + """ + import pickle + import torch path = os.path.join(_p.TOKENIZER_DIR, "tokenizer.pkl") token_bytes_path = os.path.join(_p.TOKENIZER_DIR, "token_bytes.pt") if os.path.exists(path) and os.path.exists(token_bytes_path): print(f"[nemotron] tokenizer + token_bytes already trained at {_p.TOKENIZER_DIR}", flush=True) return - if maybe_hydrate_tokenizer_cache() and os.path.exists(path) and os.path.exists(token_bytes_path): - return os.makedirs(_p.TOKENIZER_DIR, exist_ok=True) - print(f"[nemotron] training BPE (vocab_size={_p.VOCAB_SIZE}) on stream sample…", flush=True) + print(f"[nemotron] training BPE (vocab_size={_p.VOCAB_SIZE}) on stream sample…", flush=True) import rustbpe import tiktoken - - # Pull a sample of docs — use full blend if active so BPE covers all 7 sources. - n_docs = int(os.environ.get("HYDRA_BPE_TRAIN_DOCS", "20000")) - bpe_weights = FULL_BLEND_WEIGHTS if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1" else PHASE1_WEIGHTS - stream = _WeightedStream(bpe_weights, seed=0) - sample_texts: list[str] = [] - for text, _ in stream: - if not text: - continue - sample_texts.append(text) - if len(sample_texts) >= n_docs: - break - print(f"[nemotron] collected {len(sample_texts)} sample docs; training BPE…", flush=True) - - # Train rustbpe — identical API to prepare.py's train_tokenizer(). - tokenizer_cls = getattr(rustbpe, "Tokenizer") - tokenizer: Any = tokenizer_cls() - vocab_size_no_special = _p.VOCAB_SIZE - len(_p.SPECIAL_TOKENS) - tokenizer.train_from_iterator(iter(sample_texts), vocab_size_no_special, pattern=_p.SPLIT_PATTERN) - - # Build tiktoken encoding (prepare.py convention). - pattern = tokenizer.get_pattern() - mergeable_ranks = {bytes(k): v for k, v in tokenizer.get_mergeable_ranks()} - tokens_offset = len(mergeable_ranks) - special_tokens = {name: tokens_offset + i for i, name in enumerate(_p.SPECIAL_TOKENS)} - enc = tiktoken.Encoding( - name="rustbpe", - pat_str=pattern, - mergeable_ranks=mergeable_ranks, - special_tokens=special_tokens, - ) - with open(path, "wb") as f: - pickle.dump(enc, f) - - # Build token_bytes LUT. - print(f"[nemotron] building token_bytes lookup (vocab={enc.n_vocab})…", flush=True) - special_set = set(_p.SPECIAL_TOKENS) - token_bytes_list = [] - for token_id in range(enc.n_vocab): - tstr = enc.decode([token_id]) - token_bytes_list.append(0 if tstr in special_set else len(tstr.encode("utf-8"))) + + # Pull a sample of docs — use full blend if active so BPE covers all 7 sources. + n_docs = int(os.environ.get("HYDRA_BPE_TRAIN_DOCS", "20000")) + bpe_weights = FULL_BLEND_WEIGHTS if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1" else PHASE1_WEIGHTS + stream = _WeightedStream(bpe_weights, seed=0) + sample_texts: list[str] = [] + for text, _ in stream: + if not text: + continue + sample_texts.append(text) + if len(sample_texts) >= n_docs: + break + print(f"[nemotron] collected {len(sample_texts)} sample docs; training BPE…", flush=True) + + # Train rustbpe — identical API to prepare.py's train_tokenizer(). + tokenizer = rustbpe.Tokenizer() + vocab_size_no_special = _p.VOCAB_SIZE - len(_p.SPECIAL_TOKENS) + tokenizer.train_from_iterator(iter(sample_texts), vocab_size_no_special, pattern=_p.SPLIT_PATTERN) + + # Build tiktoken encoding (prepare.py convention). + pattern = tokenizer.get_pattern() + mergeable_ranks = {bytes(k): v for k, v in tokenizer.get_mergeable_ranks()} + tokens_offset = len(mergeable_ranks) + special_tokens = {name: tokens_offset + i for i, name in enumerate(_p.SPECIAL_TOKENS)} + enc = tiktoken.Encoding( + name="rustbpe", + pat_str=pattern, + mergeable_ranks=mergeable_ranks, + special_tokens=special_tokens, + ) + with open(path, "wb") as f: + pickle.dump(enc, f) + + # Build token_bytes LUT. + print(f"[nemotron] building token_bytes lookup (vocab={enc.n_vocab})…", flush=True) + special_set = set(_p.SPECIAL_TOKENS) + token_bytes_list = [] + for token_id in range(enc.n_vocab): + tstr = enc.decode([token_id]) + token_bytes_list.append(0 if tstr in special_set else len(tstr.encode("utf-8"))) token_bytes_tensor = torch.tensor(token_bytes_list, dtype=torch.int32) torch.save(token_bytes_tensor, token_bytes_path) print(f"[nemotron] BPE + token_bytes saved to {_p.TOKENIZER_DIR}", flush=True) - upload_tokenizer_cache() diff --git a/overlay/pyproject.toml b/overlay/pyproject.toml index fd6418f2c9841e7c919abbe75c386328bc6a26c7..5669467c3202ad9ffb2378422577294bf8f7ea6f 100644 --- a/overlay/pyproject.toml +++ b/overlay/pyproject.toml @@ -1,33 +1,35 @@ -[project] -name = "hydra" -version = "0.1.0" -description = "Self-evolving agent harness for autonomous neural architecture research" -readme = "README.md" -requires-python = ">=3.11" -dependencies = [ - "matplotlib>=3.10.8", - "numpy>=2.2.6", - "optuna>=4.4.0", - "pandas>=2.3.3", - "pyarrow>=21.0.0", - "requests>=2.32.0", - "rustbpe>=0.1.0", - "tiktoken>=0.11.0", - "torch==2.9.1", - "pydantic>=2.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=8.0", -] - -[tool.uv.sources] -torch = [ - { index = "pytorch-cu128" }, -] - -[[tool.uv.index]] -name = "pytorch-cu128" -url = "https://download.pytorch.org/whl/cu128" -explicit = true +[project] +name = "hydra" +version = "0.1.0" +description = "Self-evolving agent harness for autonomous neural architecture research" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "matplotlib>=3.10.8", + "numpy>=2.2.6", + "pandas>=2.3.3", + "pyarrow>=21.0.0", + "requests>=2.32.0", + "rustbpe>=0.1.0", + "tiktoken>=0.11.0", + "torch==2.9.1", + "pydantic>=2.0", + "huggingface_hub>=0.36.0", + "setuptools>=80.0.0", + "einops>=0.8.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.0", +] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu128" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true diff --git a/overlay/scripts/__init__.py b/overlay/scripts/__init__.py index 2a312f297e4ccb3bd980c3e3a988cdd79b36d4b0..b233652a5add5265f37fd09e59f2aa0595d80e80 100644 --- a/overlay/scripts/__init__.py +++ b/overlay/scripts/__init__.py @@ -1 +1 @@ -# Package marker for script-level shared utilities. +"""Script helpers for Feather launch and ops tooling.""" diff --git a/overlay/scripts/autoresearch_may03_loop.py b/overlay/scripts/autoresearch_may03_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..750f35982ef412521d28409e6b8984e1211e650e --- /dev/null +++ b/overlay/scripts/autoresearch_may03_loop.py @@ -0,0 +1,300 @@ +#!/usr/bin/env python3 +"""Continuous Feather autoresearch loop for local RTX 3060. + +Protocol: +- One GPU owner, sequential runs only. +- 300s training budget, redirected logs. +- Parse val_bpb / metrics JSON from disk. +- Append TSV ledger. +- Keep searching until hard gate is reached or process is killed. + +This loop mutates runtime env first because current Feather exposes most active +architecture/optimizer knobs through HYDRA_* gates. Code edits can be added as +candidate generators after the env frontier is exhausted. +""" +from __future__ import annotations + +import itertools +import json +import os +import re +import shlex +import subprocess +import time +from pathlib import Path + +ROOT = Path('/home/mikeb/work/feather') +LOGDIR = ROOT / 'logs' / 'autoresearch_may03' +LEDGER = ROOT / 'autoresearch_may03_results.tsv' +TARGET_BPB = float(os.environ.get('AUTORESEARCH_TARGET_BPB', '1.60')) +# Strict autoresearch cadence: train.py gets HYDRA_TIME_BUDGET=300; wrapper only +# allows startup + final eval overhead. Do not let one candidate occupy the GPU +# for 10-12 minutes unless it is genuinely hung. +RUN_TIMEOUT = int(os.environ.get('AUTORESEARCH_RUN_TIMEOUT', '430')) + +LOGDIR.mkdir(parents=True, exist_ok=True) +if not LEDGER.exists(): + LEDGER.write_text('ts\tcommit\tcandidate\tval_bpb\tpeak_tps\tmedian_tps\tmemory_gb\tstatus\tdescription\tlog\n') + +BASE = { + 'LD_LIBRARY_PATH': '/usr/lib/wsl/lib:/usr/local/cuda/lib64', + 'PYTORCH_CUDA_ALLOC_CONF': 'expandable_segments:True', + 'HF_TOKEN': '', + 'HUGGINGFACE_HUB_TOKEN': '', + 'WANDB_DISABLED': 'true', + 'HYDRA_USE_NEMOTRON': '1', + 'HYDRA_USE_FULL_BLEND': '1', + 'HYDRA_SAMPLED_SOFTMAX': '1024', + 'HYDRA_SOFTCAP_CLAMP': '1', + 'HYDRA_SEQ_LEN': '1024', + 'HYDRA_HEADDIM': '32', + 'HYDRA_EXPAND': '3', + 'HYDRA_BATCH_SIZE': '8', + 'HYDRA_TOTAL_BATCH': '16384', + 'HYDRA_D_MODEL': '160', + 'HYDRA_N_LAYER': '20', + 'HYDRA_D_STATE': '64', + 'HYDRA_TIME_BUDGET': '300', + 'HYDRA_ENGRAM_N_COLUMNS': '16384', + 'HYDRA_ENGRAM_TOPK': '64', + 'HYDRA_GDN_LAYERS': '', + 'HYDRA_MTP_K': '1', + 'HYDRA_USE_MDLM': '0', + 'HYDRA_MUON_COMPILE': '0', + 'HYDRA_MUON_NS_STEPS': '2', # promoted from TPS-11 receipt + 'HYDRA_MATRIX_LR': '0.04', + 'HYDRA_EMBED_LR': '0.6', + 'HYDRA_UNEMBED_LR': '0.004', + 'HYDRA_DT_BIAS_LR': '0.6', + 'HYDRA_LOCAL_SHARDS_ONLY': '1', + 'HYDRA_BACKGROUND_PREFETCH': '0', + 'HYDRA_STREAM_SHUFFLE_BUFFER': '256', + 'HYDRA_STREAM_PREFETCH': '16', + 'HYDRA_TOKEN_PREFETCH': '4', + 'HYDRA_TOKEN_CACHE_GB': '1', + 'HYDRA_CKPT_INTERVAL': '2000', + 'HYDRA_MID_VAL_INTERVAL': '0', + 'HYDRA_HESTIA_INTERVAL': '999999', + 'HYDRA_HTM_SUBSAMPLE': '128', + 'HYDRA_EVAL_BATCH': '1', + 'HYDRA_EVAL_TOKENS': '1024', + 'HYDRA_CE_CHUNK': '32', + 'HYDRA_SKIP_FACTUAL_EVAL': '1', + 'HYDRA_RESUME_CKPT': 'none', + 'UV_PYTHON': '/usr/bin/python3', +} + +# Ordered from lowest-risk/promising to wider/radical. Infinite outer loop will +# revisit with perturbations after first pass. +CANDIDATES: list[tuple[str, dict[str, str], str]] = [ + # Plateau-escape candidates: stronger than tiny LR nudges. These attack + # the 5-minute validation plateau by changing effective optimization, + # temporal capacity, and memory pressure while keeping full architecture. + # Real z-loss axis was tested after wiring fix: z=0.001 regressed + # (2.0446 vs best 2.0237). Return to default z=1e-4 and mutate the + # discovered l16/d192 basin more aggressively. + ('basin_l16d192_lr085_emb11', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.085','HYDRA_EMBED_LR':'1.1'}, 'basin: l16d192 hotter LR default z'), + ('basin_l16d192_lr10_emb13', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.10','HYDRA_EMBED_LR':'1.3'}, 'basin: l16d192 max hot LR default z'), + ('basin_l16d192_lr065_emb09', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.065','HYDRA_EMBED_LR':'0.9'}, 'basin: l16d192 moderate LR default z'), + ('basin_l16d192_ns1p5_nope_ns2_fasttb', {'HYDRA_TOTAL_BATCH':'24576','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.075','HYDRA_EMBED_LR':'1.0'}, 'basin: l16d192 TB24576 more updates default z'), + ('basin_l16d192_dstate48', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_D_STATE':'48','HYDRA_MATRIX_LR':'0.075','HYDRA_EMBED_LR':'1.0'}, 'basin: l16d192 smaller d_state faster updates'), + ('basin_l16d192_dstate80', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_D_STATE':'80','HYDRA_MATRIX_LR':'0.075','HYDRA_EMBED_LR':'1.0'}, 'basin: l16d192 d_state80 capacity'), + ('basin_l18d160_hot_defaultz', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_N_LAYER':'18','HYDRA_D_MODEL':'160','HYDRA_MATRIX_LR':'0.075','HYDRA_EMBED_LR':'1.0'}, 'basin: valid deeper l18d160 default z'), + # High-leverage evolutionary front around the discovered winner l16/d192. + # This is no longer tiny-knob search: change shape + optimizer together. + ('evo_l16d192_lr075_10', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.075','HYDRA_EMBED_LR':'1.0'}, 'evo: l16d192 with hotter LR for 300s descent'), + ('evo_l16d192_lr05_07', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.05','HYDRA_EMBED_LR':'0.7'}, 'evo: l16d192 slightly cooler stability'), + ('evo_l16d208', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'208','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l16 wider d208'), + ('evo_l14d224', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'14','HYDRA_D_MODEL':'224','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l14 d224 speed/capacity trade'), + ('evo_l12d256', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'12','HYDRA_D_MODEL':'256','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l12 d256 wide-frontier probe'), + ('evo_l10d288', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'10','HYDRA_D_MODEL':'288','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l10 d288 radical width probe'), + ('evo_l16d192_k768', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_SAMPLED_SOFTMAX':'768','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l16d192 lower sampled softmax for more updates'), + ('evo_l16d192_k512', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_SAMPLED_SOFTMAX':'512','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l16d192 K512 throughput/calibration probe'), + ('evo_l16d192_tb16384', {'HYDRA_TOTAL_BATCH':'16384','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'evo: l16d192 smaller TB more optimizer steps'), + ('escape_tb32768_z001_ns2_lr_hi', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'plateau escape: faster 300s descent with champion TB/zloss'), + ('escape_tb32768_z001_ns2_lr_lo', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_MATRIX_LR':'0.025','HYDRA_EMBED_LR':'0.45'}, 'plateau escape: lower LR calibration'), + ('escape_tb32768_ns2_dstate96', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_D_STATE':'96'}, 'plateau escape: extra SSM state capacity'), + ('escape_tb32768_ns2_l18_d176', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'18','HYDRA_D_MODEL':'176'}, 'plateau escape: trade depth for width at similar budget'), + ('escape_tb32768_ns2_l16_d192', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_N_LAYER':'16','HYDRA_D_MODEL':'192'}, 'plateau escape: stronger width trade'), + ('escape_tb32768_ns2_gdn3', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_GDN_LAYERS':'3,7,11'}, 'plateau escape: reintroduce known GDN quality axis'), + ('escape_tb32768_ns2_gdn5', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_GDN_LAYERS':'0,4,8,12,16'}, 'plateau escape: distributed 5-GDN quality axis'), + ('escape_tb32768_ns2_enk128', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_ENGRAM_TOPK':'128'}, 'plateau escape: wider engram read'), + ('escape_tb32768_ns2_dr64', {'HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_SDR_DELTA_RANK':'64'}, 'plateau escape: wider SDR STE pipe despite prior weak amp'), + ('escape_tb32768_ns3_lr_hi', {'HYDRA_MUON_NS_STEPS':'3','HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001','HYDRA_MATRIX_LR':'0.06','HYDRA_EMBED_LR':'0.8'}, 'plateau escape: stable NS3 plus faster LR'), + ('ns2_lr_m003', {'HYDRA_MATRIX_LR':'0.03'}, 'slightly lower matrix LR stabilizer'), + ('ns2_lr_m005', {'HYDRA_MATRIX_LR':'0.05'}, 'slightly higher matrix LR for faster 300s descent'), + ('ns2_embed04', {'HYDRA_EMBED_LR':'0.4'}, 'lower embed LR calibration'), + ('ns2_embed08', {'HYDRA_EMBED_LR':'0.8'}, 'higher embed LR fast lexical fit'), + ('ns2_dt03', {'HYDRA_DT_BIAS_LR':'0.3'}, 'lower dt-bias LR stability'), + ('ns2_dt10', {'HYDRA_DT_BIAS_LR':'1.0'}, 'higher dt-bias adaptation'), + ('ns2_dstate96', {'HYDRA_D_STATE':'96'}, 'more SSM state capacity'), + ('ns2_dstate128', {'HYDRA_D_STATE':'128'}, 'max SSM state capacity probe'), + ('ns2_enk128', {'HYDRA_ENGRAM_TOPK':'128'}, 'wider engram retrieval'), + ('ns2_enk32', {'HYDRA_ENGRAM_TOPK':'32'}, 'narrower engram retrieval / less noise'), + ('ns2_htm64', {'HYDRA_HTM_SUBSAMPLE':'64'}, 'more frequent HTM update'), + ('ns2_htm256', {'HYDRA_HTM_SUBSAMPLE':'256'}, 'less HTM overhead/noise'), + ('ns2_gdn_3_7_11', {'HYDRA_GDN_LAYERS':'3,7,11'}, 'retest 3-GDN trend on NS2'), + ('ns2_gdn_0_4_8_12_16', {'HYDRA_GDN_LAYERS':'0,4,8,12,16'}, '5-GDN distributed depth'), + ('ns2_gdn_0_1_2', {'HYDRA_GDN_LAYERS':'0,1,2'}, 'early GDN locality'), + ('ns2_l18', {'HYDRA_N_LAYER':'18'}, 'shallower depth for more updates in budget'), + ('ns2_l22', {'HYDRA_N_LAYER':'22'}, 'deeper temporal hierarchy if fits'), + ('ns2_d176', {'HYDRA_D_MODEL':'176'}, 'slightly wider model'), + ('ns2_d192', {'HYDRA_D_MODEL':'192'}, 'wider model capacity probe'), + ('ns3_gdn_3_7_11', {'HYDRA_MUON_NS_STEPS':'3','HYDRA_GDN_LAYERS':'3,7,11'}, 'known GDN axis with stable Muon NS3'), + ('ns3_tb32768_z001', {'HYDRA_MUON_NS_STEPS':'3','HYDRA_TOTAL_BATCH':'32768','HYDRA_Z_LOSS_WEIGHT':'0.001'}, 'champion-ish optimizer defaults'), +] + +STEP_RE = re.compile(r'^step=\d+ .*?bpb=([0-9.]+).*?tps=([0-9.]+)', re.M) +VAL_RE = re.compile(r'val_bpb:\s*([0-9.]+)') +METRICS_RE = re.compile(r'\[METRICS_JSON\]\s*(\{.*\})') + + +def current_commit() -> str: + return subprocess.check_output(['git','rev-parse','--short','HEAD'], cwd=ROOT, text=True).strip() + + +def completed_names() -> set[str]: + done: set[str] = set() + if not LEDGER.exists(): + return done + for line in LEDGER.read_text(errors='ignore').splitlines()[1:]: + parts = line.split('\t') + if len(parts) >= 3: + done.add(parts[2]) + return done + + +def best_seen() -> float: + best = 999.0 + # Parse the TSV ledger first. Its rows are not `val_bpb:` log lines. + if LEDGER.exists(): + for line in LEDGER.read_text(errors='ignore').splitlines()[1:]: + parts = line.split('\t') + if len(parts) >= 4: + try: + v = float(parts[3]) + except ValueError: + continue + if v > 0: + best = min(best, v) + # Also seed from known one-off receipts. + for path in [ROOT/'run_tps11_ns2.log', ROOT/'run_tps7_bs10.log', ROOT/'run_tps1_htm256.log']: + if not path.exists(): + continue + txt = path.read_text(errors='ignore') + for m in VAL_RE.finditer(txt): + best = min(best, float(m.group(1))) + return best + + +def parse_log(path: Path): + txt = path.read_text(errors='ignore') if path.exists() else '' + vals = [float(m.group(1)) for m in VAL_RE.finditer(txt)] + pairs = [(float(a), float(b)) for a,b in STEP_RE.findall(txt)] + tps = [b for _, b in pairs if b > 0] + peak_tps = max(tps) if tps else 0.0 + med_tps = sorted(tps)[len(tps)//2] if tps else 0.0 + mem_gb = 0.0 + metrics = None + mm = list(METRICS_RE.finditer(txt)) + if mm: + try: + metrics = json.loads(mm[-1].group(1)) + mem_gb = float(metrics.get('peak_vram_mb', 0.0)) / 1024.0 + except Exception: + pass + if vals: + return vals[-1], peak_tps, med_tps, mem_gb, 'ok', metrics + if 'out of memory' in txt.lower() or 'OutOfMemory' in txt or 'CUDA driver error: out of memory' in txt: + return 0.0, peak_tps, med_tps, mem_gb, 'crash_oom', metrics + if 'Traceback' in txt or 'RuntimeError' in txt or 'AssertionError' in txt: + return 0.0, peak_tps, med_tps, mem_gb, 'crash', metrics + return 0.0, peak_tps, med_tps, mem_gb, 'no_val', metrics + + +def append(row: list[str]) -> None: + with LEDGER.open('a') as f: + f.write('\t'.join(row) + '\n') + + +def perturb_candidates(round_idx: int): + # Deterministic widening after first pass: combine the best-known NS2 with + # small LR/zloss/GDN/engram perturbations. Keeps generating work forever. + lrs = ['0.025','0.03','0.035','0.04','0.045','0.05'] + embeds = ['0.45','0.55','0.6','0.7'] + zloss = ['0.0001','0.0005','0.001','0.002'] + gdns = ['', '3,7,11', '0,4,8,12,16', '0,1,2'] + for i, (mlr, elr, zl, gdn) in enumerate(itertools.product(lrs, embeds, zloss, gdns)): + name = f'auto_r{round_idx:02d}_{i:03d}' + yield name, { + 'HYDRA_MUON_NS_STEPS': '2', + 'HYDRA_MATRIX_LR': mlr, + 'HYDRA_EMBED_LR': elr, + 'HYDRA_Z_LOSS_WEIGHT': zl, + 'HYDRA_GDN_LAYERS': gdn, + }, f'auto grid ns2 mlr={mlr} embed={elr} z={zl} gdn={gdn or "none"}' + + +def run_candidate(name: str, delta: dict[str, str], desc: str, best: float): + ts = time.strftime('%Y%m%d_%H%M%S') + log = LOGDIR / f'{ts}_{name}.log' + env = os.environ.copy() + env.update(BASE) + env.update(delta) + cmd = ['taskset','-c','0-15', './.venv/bin/python', '-u', 'train.py'] + print(f'[{time.strftime("%F %T")}] RUN {name} best={best:.6f} desc={desc}', flush=True) + with log.open('w') as f: + f.write(f'=== {name} ===\n') + f.write(f'desc={desc}\n') + f.write('env_delta=' + json.dumps(delta, sort_keys=True) + '\n') + f.flush() + try: + rc = subprocess.run(cmd, cwd=ROOT, env=env, stdout=f, stderr=subprocess.STDOUT, timeout=RUN_TIMEOUT).returncode + except subprocess.TimeoutExpired: + rc = 124 + f.write('\n[TIMEOUT]\n') + val, peak, med, mem, status0, metrics = parse_log(log) + if status0 == 'ok': + status = 'keep' if val < best else 'discard' + else: + status = status0 + append([ + time.strftime('%F_%T'), current_commit(), name, f'{val:.6f}', f'{peak:.0f}', f'{med:.0f}', f'{mem:.2f}', status, desc.replace('\t',' '), str(log) + ]) + print(f'[{time.strftime("%F %T")}] DONE {name} val={val:.6f} peak={peak:.0f} med={med:.0f} mem={mem:.2f} status={status} log={log}', flush=True) + return val if status == 'keep' else best, status + + +def main(): + best = best_seen() + one_shot = os.environ.get('AUTORESEARCH_ONE_SHOT', '0') == '1' + print(f'START autoresearch may03 best_seen={best:.6f} target={TARGET_BPB:.6f} one_shot={one_shot}', flush=True) + round_idx = 0 + done = completed_names() + while True: + stream = CANDIDATES if round_idx == 0 else list(perturb_candidates(round_idx)) + for name, delta, desc in stream: + if name in done: + print(f'[{time.strftime("%F %T")}] SKIP {name} already ledgered', flush=True) + continue + best, status = run_candidate(name, delta, desc, best) + done.add(name) + if best <= TARGET_BPB: + print(f'HARDGATE_REACHED best={best:.6f} target={TARGET_BPB:.6f}', flush=True) + return + # Let CUDA/WSL settle and reduce fragmentation. + subprocess.run(['bash','-lc','python3 - <<"PY"\nimport torch\ntorch.cuda.empty_cache() if torch.cuda.is_available() else None\nPY'], cwd=ROOT, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + if one_shot: + print(f'ONE_SHOT_DONE best={best:.6f}', flush=True) + return + time.sleep(10) + round_idx += 1 + if one_shot: + # No remaining unledgered candidates in the fixed queue; allow the + # perturbation generator on the next cron tick instead of looping in + # a long-lived process. + print(f'ONE_SHOT_NO_FIXED_CANDIDATE best={best:.6f}', flush=True) + return + +if __name__ == '__main__': + main() diff --git a/overlay/scripts/benchmark_hyena_stack.py b/overlay/scripts/benchmark_hyena_stack.py index a3fc862d050c744538615ef95755c06db10bfda7..cb3d95d8346606b897e93c4787139544db3bbf9f 100644 --- a/overlay/scripts/benchmark_hyena_stack.py +++ b/overlay/scripts/benchmark_hyena_stack.py @@ -1,150 +1,143 @@ -"""Hyena stack benchmark — measure TPS under the four knob combinations. - -Produces the table requested in Task 4: - | Config | TPS | BPB@500 | VRAM | - |----------------------------|------|---------|------| - | B=8, no flash, no cache | ... | ... | ... | <-- baseline - | B=16, no flash, no cache | ... - | B=16, no flash, cache on | ... - | B=16, flash on, cache on | ... | ... | ... | <-- best - -Run ONE config by invoking with command-line args, then collate externally. -Each invocation runs train.py for the specified wall-clock time with the -given env overrides, tails run.log, and emits a single summary line. - -Invocation: - cd /home/mikeb/work/feather - - # On the RTX 3060 (local validation only — these numbers will NOT hit - # the 200k tps production floor): - .venv/bin/python scripts/benchmark_hyena_stack.py --config baseline --time 300 - .venv/bin/python scripts/benchmark_hyena_stack.py --config b16 --time 300 - .venv/bin/python scripts/benchmark_hyena_stack.py --config cache --time 300 - # "kernel" config requires flashfftconv built — see kernels/cuda/flashfftconv/README.md - .venv/bin/python scripts/benchmark_hyena_stack.py --config kernel --time 300 - - # On A100/A10G (production cloud hardware), use time=900 (15 min) for - # stable steady-state numbers. - +"""Hyena stack benchmark — measure TPS under the four knob combinations. + +Produces the table requested in Task 4: + | Config | TPS | BPB@500 | VRAM | + |----------------------------|------|---------|------| + | B=8, no flash, no cache | ... | ... | ... | <-- baseline + | B=16, no flash, no cache | ... + | B=16, no flash, cache on | ... + | B=16, flash on, cache on | ... | ... | ... | <-- best + +Run ONE config by invoking with command-line args, then collate externally. +Each invocation runs train.py for the specified wall-clock time with the +given env overrides, tails run.log, and emits a single summary line. + +Invocation: + cd /home/mikeb/work/feather + + # On the RTX 3060 (local validation only — these numbers will NOT hit + # the 200k tps production floor): + .venv/bin/python scripts/benchmark_hyena_stack.py --config baseline --time 300 + .venv/bin/python scripts/benchmark_hyena_stack.py --config b16 --time 300 + .venv/bin/python scripts/benchmark_hyena_stack.py --config cache --time 300 + # "kernel" config requires flashfftconv built — see kernels/cuda/flashfftconv/README.md + .venv/bin/python scripts/benchmark_hyena_stack.py --config kernel --time 300 + + # On A100/A10G (production cloud hardware), use time=900 (15 min) for + # stable steady-state numbers. + After each run the script prints: BENCHMARK config= tps_steady= bpb_at_500= vram_peak= -If `--min-tps` is set (>0), the script exits non-zero when steady-state TPS -falls below the threshold. - -Collate those lines into the matrix table manually, then pick the winner -for the 6-hour production run (HYDRA_TIME_BUDGET=21600). -""" - -from __future__ import annotations - -import argparse -import os -import re -import subprocess -import sys -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] - - -CONFIGS = { - # Baseline: B=8, no flash, no train-cache. Current reference point. +Collate those lines into the matrix table manually, then pick the winner +for the 6-hour production run (HYDRA_TIME_BUDGET=21600). +""" + +from __future__ import annotations + +import argparse +import os +import re +import subprocess +import sys +from pathlib import Path + +REPO = Path(__file__).resolve().parents[1] + + +CONFIGS = { + # Baseline: B=8, no flash, no train-cache. Current reference point. "baseline": { "HYDRA_BATCH_SIZE": "8", - "HYDRA_THROUGHPUT_MODE": "1", "HYDRA_HYENA_LAYERS": "3,7", - "HYDRA_HYENA_FLASH_FFT": "0", - "HYDRA_HYENA_TRAIN_CACHE": "0", - "HYDRA_HYENA_FILTER_CACHE": "0", - }, + "HYDRA_HYENA_FLASH_FFT": "0", + "HYDRA_HYENA_TRAIN_CACHE": "0", + "HYDRA_HYENA_FILTER_CACHE": "0", + }, "b16": { "HYDRA_BATCH_SIZE": "16", - "HYDRA_THROUGHPUT_MODE": "1", "HYDRA_HYENA_LAYERS": "3,7", - "HYDRA_HYENA_FLASH_FFT": "0", - "HYDRA_HYENA_TRAIN_CACHE": "0", - "HYDRA_HYENA_FILTER_CACHE": "0", - }, + "HYDRA_HYENA_FLASH_FFT": "0", + "HYDRA_HYENA_TRAIN_CACHE": "0", + "HYDRA_HYENA_FILTER_CACHE": "0", + }, "cache": { "HYDRA_BATCH_SIZE": "16", - "HYDRA_THROUGHPUT_MODE": "1", "HYDRA_HYENA_LAYERS": "3,7", - "HYDRA_HYENA_FLASH_FFT": "0", - "HYDRA_HYENA_TRAIN_CACHE": "1", - "HYDRA_HYENA_FILTER_CACHE": "1", - }, + "HYDRA_HYENA_FLASH_FFT": "0", + "HYDRA_HYENA_TRAIN_CACHE": "1", + "HYDRA_HYENA_FILTER_CACHE": "1", + }, "kernel": { "HYDRA_BATCH_SIZE": "16", - "HYDRA_THROUGHPUT_MODE": "1", "HYDRA_HYENA_LAYERS": "3,7", - "HYDRA_HYENA_FLASH_FFT": "1", - "HYDRA_HYENA_TRAIN_CACHE": "1", - "HYDRA_HYENA_FILTER_CACHE": "1", - # Task 4 note: also bump HYDRA_HTM_SUBSAMPLE to 128 (from 64) in the - # best config to get more aggressive reclamation. - "HYDRA_HTM_SUBSAMPLE": "128", - }, -} - - -def build_env(cfg_overrides: dict[str, str]) -> dict[str, str]: - """Compose a full env dict from the inherited env + config overrides.""" - env = os.environ.copy() - # Ensure the Hyena layer selection is always present (defaults to off). - env.setdefault("HYDRA_HYENA_LAYERS", "") - for k, v in cfg_overrides.items(): - env[k] = v - return env - - -def parse_step_line(line: str) -> dict[str, float] | None: - """Parse a single step=... line into a dict of metrics, or None.""" - if not line.startswith("step="): - return None - parts = re.findall(r"(\w+)=([0-9.eE+\-]+)", line) - try: - return {k: float(v) for k, v in parts} - except ValueError: - return None - - -def summarize(log_path: Path, warmup_steps: int = 50) -> dict[str, float]: - """Tail log_path, compute steady-state TPS / BPB@500 / VRAM peak. - - Skips the first `warmup_steps` to discard CUDA graph capture / autotune - spikes; takes the median of the rest. - """ - tps_vals = [] - bpbs = [] - vram_peak = 0.0 - bpb_at_500 = None - with log_path.open() as f: - for line in f: - d = parse_step_line(line.strip()) - if d is None: - continue - step = int(d.get("step", -1)) - if step < warmup_steps: - continue - tps = d.get("tps") - if tps is not None: - tps_vals.append(tps) - bpb = d.get("bpb") - if bpb is not None: - bpbs.append(bpb) - if step == 500 and bpb_at_500 is None: - bpb_at_500 = bpb - vram = d.get("vram") - if vram is not None and vram > vram_peak: - vram_peak = vram - - if not tps_vals: - return {"tps_steady": 0.0, "bpb_at_500": 0.0, "vram_peak": 0.0, "steps": 0} - - tps_sorted = sorted(tps_vals) - tps_steady = tps_sorted[len(tps_sorted) // 2] # median - + "HYDRA_HYENA_FLASH_FFT": "1", + "HYDRA_HYENA_TRAIN_CACHE": "1", + "HYDRA_HYENA_FILTER_CACHE": "1", + # Task 4 note: also bump HYDRA_HTM_SUBSAMPLE to 128 (from 64) in the + # best config to get more aggressive reclamation. + "HYDRA_HTM_SUBSAMPLE": "128", + }, +} + + +def build_env(cfg_overrides: dict) -> dict: + """Compose a full env dict from the inherited env + config overrides.""" + env = os.environ.copy() + # Ensure the Hyena layer selection is always present (defaults to off). + env.setdefault("HYDRA_HYENA_LAYERS", "") + for k, v in cfg_overrides.items(): + env[k] = v + return env + + +def parse_step_line(line: str) -> dict | None: + """Parse a single step=... line into a dict of metrics, or None.""" + if not line.startswith("step="): + return None + parts = re.findall(r"(\w+)=([0-9.eE+\-]+)", line) + try: + return {k: float(v) for k, v in parts} + except ValueError: + return None + + +def summarize(log_path: Path, warmup_steps: int = 50) -> dict: + """Tail log_path, compute steady-state TPS / BPB@500 / VRAM peak. + + Skips the first `warmup_steps` to discard CUDA graph capture / autotune + spikes; takes the median of the rest. + """ + tps_vals = [] + bpbs = [] + vram_peak = 0.0 + bpb_at_500 = None + with log_path.open() as f: + for line in f: + d = parse_step_line(line.strip()) + if d is None: + continue + step = int(d.get("step", -1)) + if step < warmup_steps: + continue + tps = d.get("tps") + if tps is not None: + tps_vals.append(tps) + bpb = d.get("bpb") + if bpb is not None: + bpbs.append(bpb) + if step == 500 and bpb_at_500 is None: + bpb_at_500 = bpb + vram = d.get("vram") + if vram is not None and vram > vram_peak: + vram_peak = vram + + if not tps_vals: + return {"tps_steady": 0.0, "bpb_at_500": 0.0, "vram_peak": 0.0, "steps": 0} + + tps_sorted = sorted(tps_vals) + tps_steady = tps_sorted[len(tps_sorted) // 2] # median + return { "tps_steady": tps_steady, "bpb_at_500": bpb_at_500 or (bpbs[-1] if bpbs else 0.0), @@ -153,48 +146,39 @@ def summarize(log_path: Path, warmup_steps: int = 50) -> dict[str, float]: } -def fails_tps_floor(summary: dict[str, float], min_tps: float) -> bool: - if min_tps <= 0: - return False - tps_steady = float(summary.get("tps_steady", 0.0)) - return tps_steady < float(min_tps) - - def main() -> int: ap = argparse.ArgumentParser() ap.add_argument("--config", required=True, choices=list(CONFIGS)) ap.add_argument("--time", type=int, default=300, help="training seconds") ap.add_argument("--log", default=None, help="output log path (default: run_bench_.log)") - ap.add_argument("--min-tps", type=float, default=50000.0, help="Required steady-state TPS floor (set 0 to disable)") - ap.add_argument("--warmup-steps", type=int, default=50, help="Number of initial steps to skip before TPS median") args = ap.parse_args() - - cfg = CONFIGS[args.config] - log_path = Path(args.log or (REPO / f"run_bench_{args.config}.log")) - - env = build_env(cfg) - env["HYDRA_TIME_BUDGET"] = str(args.time) - - # Make the config visible up-front so failed runs are debuggable. - print(f"BENCH start config={args.config} time={args.time}s log={log_path}", flush=True) - print(f" overrides: {cfg}", flush=True) - - with log_path.open("w") as logf: - proc = subprocess.Popen( - ["python", "-u", str(REPO / "train.py")], - env=env, - cwd=str(REPO), - stdout=logf, - stderr=subprocess.STDOUT, - ) - proc.wait() - - print(f"BENCH wait_done exit={proc.returncode}", flush=True) - if proc.returncode != 0: - print(f"BENCH FAIL config={args.config}", flush=True) - return proc.returncode - - summary = summarize(log_path, warmup_steps=max(0, int(args.warmup_steps))) + + cfg = CONFIGS[args.config] + log_path = Path(args.log or (REPO / f"run_bench_{args.config}.log")) + + env = build_env(cfg) + env["HYDRA_TIME_BUDGET"] = str(args.time) + + # Make the config visible up-front so failed runs are debuggable. + print(f"BENCH start config={args.config} time={args.time}s log={log_path}", flush=True) + print(f" overrides: {cfg}", flush=True) + + with log_path.open("w") as logf: + proc = subprocess.Popen( + ["python", "-u", str(REPO / "train.py")], + env=env, + cwd=str(REPO), + stdout=logf, + stderr=subprocess.STDOUT, + ) + proc.wait() + + print(f"BENCH wait_done exit={proc.returncode}", flush=True) + if proc.returncode != 0: + print(f"BENCH FAIL config={args.config}", flush=True) + return proc.returncode + + summary = summarize(log_path) print( f"BENCHMARK config={args.config} " f"tps_steady={summary['tps_steady']:.0f} " @@ -203,17 +187,8 @@ def main() -> int: f"steps={summary['steps']}", flush=True, ) + return 0 - if fails_tps_floor(summary, args.min_tps): - print( - f"BENCH FAIL config={args.config} tps_steady={summary['tps_steady']:.0f} < min_tps={args.min_tps:.0f}", - flush=True, - ) - return 2 - print(f"BENCH PASS config={args.config} min_tps={args.min_tps:.0f}", flush=True) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) +if __name__ == "__main__": + sys.exit(main()) diff --git a/overlay/scripts/build_token_cache.py b/overlay/scripts/build_token_cache.py index 18140ced0ccf28692c977982f344234f008096bb..c691ce740833ddff70bd02a158c010b9f91e2ecc 100644 --- a/overlay/scripts/build_token_cache.py +++ b/overlay/scripts/build_token_cache.py @@ -1,238 +1,238 @@ -"""Fast parallel token cache builder. - -Reads parquet shards DIRECTLY via pyarrow (no HF streaming overhead), -tokenizes with multiprocessing.Pool, writes packed (T+1) int32 rows. - -Uses the pre-downloaded shards in ~/.cache/huggingface/hub/ — no network. - -Usage: python scripts/build_token_cache.py [--gb 2] [--workers 8] -""" -from __future__ import annotations - -import argparse -import glob -import os -import sys -import time -from pathlib import Path -from multiprocessing import Pool - -sys.stdout.reconfigure(line_buffering=True) - -import numpy as np -import pyarrow.parquet as pq - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from prepare import Tokenizer - - -HF_HUB_CACHE = os.path.expanduser("~/.cache/huggingface/hub") - -# Which column each dataset uses for text -TEXT_COLS: dict[str, list[str]] = { - "fineweb-edu": ["text"], - "fineweb": ["text"], - "stack-v2": ["text", "content"], - "nemotron-math": ["text"], - "nemotron-specialized": ["text"], - "wikipedia": ["text"], - "cosmopedia": ["text"], -} - -# Dataset repo → cache dir mapping -REPO_DIRS = { - "fineweb-edu": "datasets--HuggingFaceFW--fineweb-edu", - "fineweb": "datasets--HuggingFaceFW--fineweb", - "stack-v2": "datasets--OpenCoder-LLM--opc-fineweb-code-corpus", - "nemotron-math": "datasets--nvidia--Nemotron-CC-Math-v1", - "nemotron-specialized": "datasets--nvidia--Nemotron-Pretraining-Specialized-v1.1", - "wikipedia": "datasets--wikimedia--wikipedia", - "cosmopedia": "datasets--HuggingFaceTB--cosmopedia", -} - - -def find_parquet_files() -> list[tuple[str, str]]: - """Return [(dataset_name, parquet_path), ...] for all cached shards.""" - results = [] - for name, dirname in REPO_DIRS.items(): - base = os.path.join(HF_HUB_CACHE, dirname, "snapshots") - if not os.path.isdir(base): - continue - for snap in os.listdir(base): - snap_dir = os.path.join(base, snap) - for root, _, files in os.walk(snap_dir): - for f in files: - if f.endswith(".parquet"): - results.append((name, os.path.join(root, f))) - return results - - -# Tokenizer loaded once per worker process -_WORKER_TOKENIZER = None -_WORKER_BOS = None - - -def _worker_init(): - global _WORKER_TOKENIZER, _WORKER_BOS - _WORKER_TOKENIZER = Tokenizer.from_directory() - _WORKER_BOS = _WORKER_TOKENIZER.get_bos_token_id() - - -def _tokenize_batch(args: tuple[list[str], int]) -> list[list[int]]: - """Tokenize a batch of text strings. Returns list of token-id lists.""" - texts, _ = args - return _WORKER_TOKENIZER.encode(texts, prepend=_WORKER_BOS) - - -def iter_text_from_parquet(name: str, path: str, batch_size: int = 512): - """Stream text batches from one parquet file.""" - cols = TEXT_COLS.get(name, ["text"]) - try: - pf = pq.ParquetFile(path) - except Exception as e: - print(f" [skip] {path}: {e}", flush=True) - return - - # Find which column exists - schema_names = set(pf.schema_arrow.names) - col = next((c for c in cols if c in schema_names), None) - if col is None: - return - - for batch in pf.iter_batches(batch_size=batch_size, columns=[col]): - texts = batch.column(col).to_pylist() - texts = [t for t in texts if t] - if texts: - yield texts - - -def pack_rows(token_lists: list[list[int]], row_capacity: int) -> np.ndarray: - """Pack variable-length token sequences into (N, row_capacity) rows using simple greedy concat.""" - rows = [] - current = [] - for doc in token_lists: - if len(current) + len(doc) > row_capacity: - # Flush current row (pad with 0) - if len(current) >= row_capacity // 2: # skip too-short trailing bits - row = current[:row_capacity] - if len(row) < row_capacity: - row = row + [0] * (row_capacity - len(row)) - rows.append(row) - # Start new row with this doc (truncate if too long) - current = doc[:row_capacity] - else: - current.extend(doc) - # Emit full rows as we fill up - while len(current) >= row_capacity: - rows.append(current[:row_capacity]) - current = current[row_capacity:] - if not rows: - return np.empty((0, row_capacity), dtype=np.int32) - return np.asarray(rows, dtype=np.int32) - - -def main() -> None: - ap = argparse.ArgumentParser() - ap.add_argument("--gb", type=float, default=2.0) - ap.add_argument("--seq-len", type=int, default=512) - ap.add_argument("--workers", type=int, default=max(1, (os.cpu_count() or 4) - 2)) - ap.add_argument("--batch-size", type=int, default=512, help="docs per tokenizer call") - args = ap.parse_args() - - T = args.seq_len - row_capacity = T + 1 - target_bytes = int(args.gb * 1024**3) - target_rows = target_bytes // (row_capacity * 4) - - # Load tokenizer in main process for vocab size - tok = Tokenizer.from_directory() - V = tok.get_vocab_size() - - cache_path = os.path.expanduser( - f"~/.cache/autoresearch/packed_tokens_v1_T{T}_V{V}_train.bin" - ) - tmp_path = cache_path + ".tmp" - - print(f"[cache-build] target: {args.gb:.1f} GB = {target_rows} rows of (T+1)={row_capacity} int32", flush=True) - print(f"[cache-build] workers: {args.workers}", flush=True) - - parquet_files = find_parquet_files() - print(f"[cache-build] found {len(parquet_files)} parquet shards", flush=True) - for name, path in parquet_files: - sz = os.path.getsize(path) / 1024**2 - print(f" [{name}] {path.split('/blobs/')[-1]} ({sz:.0f} MB)", flush=True) - - if not parquet_files: - print("[cache-build] no shards found — run predownload first", flush=True) - sys.exit(1) - - t_start = time.time() - rows_written = 0 - - # Single-batch tokenize function using the pool - pool = Pool(processes=args.workers, initializer=_worker_init) - pending_batches = [] # batches of texts waiting to be tokenized - PENDING_LIMIT = args.workers * 4 - - def flush_to_tokenize(): - """Submit pending batches to pool, write results as they come.""" - nonlocal rows_written - if not pending_batches: - return - batch_args = [(b, 0) for b in pending_batches] - # Use imap_unordered for streaming results - for token_lists in pool.imap_unordered(_tokenize_batch, batch_args, chunksize=1): - rows = pack_rows(token_lists, row_capacity) - if len(rows) > 0: - fout.write(rows.tobytes()) - rows_written += len(rows) - if rows_written >= target_rows: - return - if rows_written % 8192 < len(rows): - elapsed = time.time() - t_start - bw = rows_written * row_capacity * 4 / 1024**3 - mbps = bw * 1024 / max(elapsed, 0.001) - pct = 100 * rows_written / target_rows - print(f" {rows_written:>8} rows {bw:.2f} GB {pct:5.1f}% {mbps:.1f} MB/s t={elapsed:.0f}s", flush=True) - pending_batches.clear() - - with open(tmp_path, "wb") as fout: - try: - done = False - # Round-robin across datasets to get diverse blend - iterators = [] - for name, path in parquet_files: - iterators.append((name, iter_text_from_parquet(name, path, args.batch_size))) - - while iterators and not done: - for i in range(len(iterators) - 1, -1, -1): - name, it = iterators[i] - try: - texts = next(it) - except StopIteration: - iterators.pop(i) - continue - pending_batches.append(texts) - if len(pending_batches) >= PENDING_LIMIT: - flush_to_tokenize() - if rows_written >= target_rows: - done = True - break - # Final flush - if not done and pending_batches: - flush_to_tokenize() - finally: - pool.close() - pool.terminate() - pool.join() - - os.replace(tmp_path, cache_path) - elapsed = time.time() - t_start - total_bytes = rows_written * row_capacity * 4 - print(f"\n[cache-build] DONE — {rows_written} rows, {total_bytes/1024**3:.2f} GB in {elapsed:.0f}s ({total_bytes/1024**2/elapsed:.1f} MB/s)", flush=True) - print(f"[cache-build] cache: {cache_path}", flush=True) - - -if __name__ == "__main__": - main() +"""Fast parallel token cache builder. + +Reads parquet shards DIRECTLY via pyarrow (no HF streaming overhead), +tokenizes with multiprocessing.Pool, writes packed (T+1) int32 rows. + +Uses the pre-downloaded shards in ~/.cache/huggingface/hub/ — no network. + +Usage: python scripts/build_token_cache.py [--gb 2] [--workers 8] +""" +from __future__ import annotations + +import argparse +import glob +import os +import sys +import time +from pathlib import Path +from multiprocessing import Pool + +sys.stdout.reconfigure(line_buffering=True) + +import numpy as np +import pyarrow.parquet as pq + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from prepare import Tokenizer + + +HF_HUB_CACHE = os.path.expanduser("~/.cache/huggingface/hub") + +# Which column each dataset uses for text +TEXT_COLS: dict[str, list[str]] = { + "fineweb-edu": ["text"], + "fineweb": ["text"], + "stack-v2": ["text", "content"], + "nemotron-math": ["text"], + "nemotron-specialized": ["text"], + "wikipedia": ["text"], + "cosmopedia": ["text"], +} + +# Dataset repo → cache dir mapping +REPO_DIRS = { + "fineweb-edu": "datasets--HuggingFaceFW--fineweb-edu", + "fineweb": "datasets--HuggingFaceFW--fineweb", + "stack-v2": "datasets--OpenCoder-LLM--opc-fineweb-code-corpus", + "nemotron-math": "datasets--nvidia--Nemotron-CC-Math-v1", + "nemotron-specialized": "datasets--nvidia--Nemotron-Pretraining-Specialized-v1.1", + "wikipedia": "datasets--wikimedia--wikipedia", + "cosmopedia": "datasets--HuggingFaceTB--cosmopedia", +} + + +def find_parquet_files() -> list[tuple[str, str]]: + """Return [(dataset_name, parquet_path), ...] for all cached shards.""" + results = [] + for name, dirname in REPO_DIRS.items(): + base = os.path.join(HF_HUB_CACHE, dirname, "snapshots") + if not os.path.isdir(base): + continue + for snap in os.listdir(base): + snap_dir = os.path.join(base, snap) + for root, _, files in os.walk(snap_dir): + for f in files: + if f.endswith(".parquet"): + results.append((name, os.path.join(root, f))) + return results + + +# Tokenizer loaded once per worker process +_WORKER_TOKENIZER = None +_WORKER_BOS = None + + +def _worker_init(): + global _WORKER_TOKENIZER, _WORKER_BOS + _WORKER_TOKENIZER = Tokenizer.from_directory() + _WORKER_BOS = _WORKER_TOKENIZER.get_bos_token_id() + + +def _tokenize_batch(args: tuple[list[str], int]) -> list[list[int]]: + """Tokenize a batch of text strings. Returns list of token-id lists.""" + texts, _ = args + return _WORKER_TOKENIZER.encode(texts, prepend=_WORKER_BOS) + + +def iter_text_from_parquet(name: str, path: str, batch_size: int = 512): + """Stream text batches from one parquet file.""" + cols = TEXT_COLS.get(name, ["text"]) + try: + pf = pq.ParquetFile(path) + except Exception as e: + print(f" [skip] {path}: {e}", flush=True) + return + + # Find which column exists + schema_names = set(pf.schema_arrow.names) + col = next((c for c in cols if c in schema_names), None) + if col is None: + return + + for batch in pf.iter_batches(batch_size=batch_size, columns=[col]): + texts = batch.column(col).to_pylist() + texts = [t for t in texts if t] + if texts: + yield texts + + +def pack_rows(token_lists: list[list[int]], row_capacity: int) -> np.ndarray: + """Pack variable-length token sequences into (N, row_capacity) rows using simple greedy concat.""" + rows = [] + current = [] + for doc in token_lists: + if len(current) + len(doc) > row_capacity: + # Flush current row (pad with 0) + if len(current) >= row_capacity // 2: # skip too-short trailing bits + row = current[:row_capacity] + if len(row) < row_capacity: + row = row + [0] * (row_capacity - len(row)) + rows.append(row) + # Start new row with this doc (truncate if too long) + current = doc[:row_capacity] + else: + current.extend(doc) + # Emit full rows as we fill up + while len(current) >= row_capacity: + rows.append(current[:row_capacity]) + current = current[row_capacity:] + if not rows: + return np.empty((0, row_capacity), dtype=np.int32) + return np.asarray(rows, dtype=np.int32) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--gb", type=float, default=2.0) + ap.add_argument("--seq-len", type=int, default=512) + ap.add_argument("--workers", type=int, default=max(1, (os.cpu_count() or 4) - 2)) + ap.add_argument("--batch-size", type=int, default=512, help="docs per tokenizer call") + args = ap.parse_args() + + T = args.seq_len + row_capacity = T + 1 + target_bytes = int(args.gb * 1024**3) + target_rows = target_bytes // (row_capacity * 4) + + # Load tokenizer in main process for vocab size + tok = Tokenizer.from_directory() + V = tok.get_vocab_size() + + cache_path = os.path.expanduser( + f"~/.cache/autoresearch/packed_tokens_v1_T{T}_V{V}_train.bin" + ) + tmp_path = cache_path + ".tmp" + + print(f"[cache-build] target: {args.gb:.1f} GB = {target_rows} rows of (T+1)={row_capacity} int32", flush=True) + print(f"[cache-build] workers: {args.workers}", flush=True) + + parquet_files = find_parquet_files() + print(f"[cache-build] found {len(parquet_files)} parquet shards", flush=True) + for name, path in parquet_files: + sz = os.path.getsize(path) / 1024**2 + print(f" [{name}] {path.split('/blobs/')[-1]} ({sz:.0f} MB)", flush=True) + + if not parquet_files: + print("[cache-build] no shards found — run predownload first", flush=True) + sys.exit(1) + + t_start = time.time() + rows_written = 0 + + # Single-batch tokenize function using the pool + pool = Pool(processes=args.workers, initializer=_worker_init) + pending_batches = [] # batches of texts waiting to be tokenized + PENDING_LIMIT = args.workers * 4 + + def flush_to_tokenize(): + """Submit pending batches to pool, write results as they come.""" + nonlocal rows_written + if not pending_batches: + return + batch_args = [(b, 0) for b in pending_batches] + # Use imap_unordered for streaming results + for token_lists in pool.imap_unordered(_tokenize_batch, batch_args, chunksize=1): + rows = pack_rows(token_lists, row_capacity) + if len(rows) > 0: + fout.write(rows.tobytes()) + rows_written += len(rows) + if rows_written >= target_rows: + return + if rows_written % 8192 < len(rows): + elapsed = time.time() - t_start + bw = rows_written * row_capacity * 4 / 1024**3 + mbps = bw * 1024 / max(elapsed, 0.001) + pct = 100 * rows_written / target_rows + print(f" {rows_written:>8} rows {bw:.2f} GB {pct:5.1f}% {mbps:.1f} MB/s t={elapsed:.0f}s", flush=True) + pending_batches.clear() + + with open(tmp_path, "wb") as fout: + try: + done = False + # Round-robin across datasets to get diverse blend + iterators = [] + for name, path in parquet_files: + iterators.append((name, iter_text_from_parquet(name, path, args.batch_size))) + + while iterators and not done: + for i in range(len(iterators) - 1, -1, -1): + name, it = iterators[i] + try: + texts = next(it) + except StopIteration: + iterators.pop(i) + continue + pending_batches.append(texts) + if len(pending_batches) >= PENDING_LIMIT: + flush_to_tokenize() + if rows_written >= target_rows: + done = True + break + # Final flush + if not done and pending_batches: + flush_to_tokenize() + finally: + pool.close() + pool.terminate() + pool.join() + + os.replace(tmp_path, cache_path) + elapsed = time.time() - t_start + total_bytes = rows_written * row_capacity * 4 + print(f"\n[cache-build] DONE — {rows_written} rows, {total_bytes/1024**3:.2f} GB in {elapsed:.0f}s ({total_bytes/1024**2/elapsed:.1f} MB/s)", flush=True) + print(f"[cache-build] cache: {cache_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/overlay/scripts/cron_validate_hf_job.py b/overlay/scripts/cron_validate_hf_job.py index 8fb96309f75e1d192b218021d0a978e7e6aa34fa..b7ee5b5daa7d8604e6772aea7021dc69bb92c707 100644 --- a/overlay/scripts/cron_validate_hf_job.py +++ b/overlay/scripts/cron_validate_hf_job.py @@ -1,128 +1,128 @@ -#!/usr/bin/env python3 -"""Poll the most recent icarus112 HF Job and write one-line tps/bpb summary. - -No-bypass policy: pure read-only observation. Never touches the job's state. -""" -from __future__ import annotations - -import datetime as _dt -import json -import os -import re -import sys -import urllib.error -import urllib.request -from pathlib import Path - -# Prefer ~/.hf_token file over env (env may have a stale/expired token from -# the Claude shell snapshot). Falls back to env if file missing. -_TOKEN_FILE = Path.home() / ".hf_token" -if _TOKEN_FILE.exists(): - TOKEN = _TOKEN_FILE.read_text().strip() -else: - TOKEN = os.environ.get("HF_TOKEN", "") -NAMESPACE = "icarus112" -LOGDIR = Path(__file__).resolve().parents[1] / ".logs" -LOGDIR.mkdir(parents=True, exist_ok=True) -SUMMARY = LOGDIR / "hf_validation.log" -RAW = LOGDIR / "hf_job_raw.log" - - -def _get(url: str) -> str: - req = urllib.request.Request(url, headers={"Authorization": f"Bearer {TOKEN}"}) - try: - with urllib.request.urlopen(req, timeout=30) as r: - return r.read().decode("utf-8", errors="replace") - except urllib.error.HTTPError as e: - return f"__HTTP_{e.code}__" - except Exception as e: - return f"__ERR_{type(e).__name__}__" - - -def _pick_job(blob: str) -> tuple[str, str, str]: - """Return (job_id, stage, flavor) for the job we want to monitor.""" - try: - data = json.loads(blob) - except Exception: - return ("", "?", "?") - if isinstance(data, dict) and "jobs" in data: - data = data["jobs"] - if not isinstance(data, list) or not data: - return ("", "?", "?") - - def _stage(j: dict) -> str: - return str((j.get("status") or {}).get("stage", "")).upper() - - # Sort by createdAt descending — newest first. - data = sorted(data, key=lambda j: j.get("createdAt", ""), reverse=True) - running = [j for j in data if _stage(j) == "RUNNING"] - picked = running[0] if running else data[0] - jid = picked.get("id") or "" - st = _stage(picked) or "?" - flavor = picked.get("flavor") or picked.get("hardware") or "?" - return jid, st, str(flavor) - - -def _parse_metrics(logs: str) -> dict[str, str]: - out: dict[str, str] = {} - # Training patterns emitted by hydra/training.py: - # step= tok/s= tps= val_bpb= bpb= - last_step = re.findall(r"step[=:\s]+(\d+)", logs, re.IGNORECASE) - if last_step: - out["step"] = last_step[-1] - last_tps = re.findall(r"(?:tok/?s|tps)[=:\s]+([\d.]+)", logs, re.IGNORECASE) - if last_tps: - out["tok/s"] = last_tps[-1] - last_bpb = re.findall(r"(?:val_)?bpb[=:\s]+([\d.]+)", logs, re.IGNORECASE) - if last_bpb: - out["bpb"] = last_bpb[-1] - # Loss as a tertiary signal - last_loss = re.findall(r"\bloss[=:\s]+([\d.]+)", logs, re.IGNORECASE) - if last_loss: - out["loss"] = last_loss[-1] - return out - - -def main() -> int: - ts = _dt.datetime.now(_dt.timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - # 1. Find the most recent job (namespace-scoped endpoint). - jobs_blob = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}") - if jobs_blob.startswith("__"): - SUMMARY.open("a").write(f"[{ts}] api_err jobs={jobs_blob}\n") - return 0 - - jid, stage, flavor = _pick_job(jobs_blob) - if not jid: - SUMMARY.open("a").write(f"[{ts}] no_job\n") - return 0 - - # 2. Re-query the single job for fresh stage (list endpoint can lag). - detail = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}/{jid}") - try: - dj = json.loads(detail) - stage = (dj.get("status") or {}).get("stage", stage) or stage - flavor = dj.get("flavor") or flavor - except Exception: - pass - - # 3. Pull logs only if the job is live (otherwise no metrics to parse). - logs = "" - if str(stage).upper() in {"RUNNING", "COMPLETED", "ERROR", "ERRORED"}: - logs = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}/{jid}/logs") - RAW.write_text(logs) - - metrics = _parse_metrics(logs) if logs and not logs.startswith("__") else {} - - parts = [f"job={jid}", f"flavor={flavor}", f"stage={stage}"] - for k in ("step", "tok/s", "bpb", "loss"): - if k in metrics: - parts.append(f"{k}={metrics[k]}") - else: - parts.append(f"{k}=?") - SUMMARY.open("a").write(f"[{ts}] " + " ".join(parts) + "\n") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +"""Poll the most recent icarus112 HF Job and write one-line tps/bpb summary. + +No-bypass policy: pure read-only observation. Never touches the job's state. +""" +from __future__ import annotations + +import datetime as _dt +import json +import os +import re +import sys +import urllib.error +import urllib.request +from pathlib import Path + +# Prefer ~/.hf_token file over env (env may have a stale/expired token from +# the Claude shell snapshot). Falls back to env if file missing. +_TOKEN_FILE = Path.home() / ".hf_token" +if _TOKEN_FILE.exists(): + TOKEN = _TOKEN_FILE.read_text().strip() +else: + TOKEN = os.environ.get("HF_TOKEN", "") +NAMESPACE = "icarus112" +LOGDIR = Path(__file__).resolve().parents[1] / ".logs" +LOGDIR.mkdir(parents=True, exist_ok=True) +SUMMARY = LOGDIR / "hf_validation.log" +RAW = LOGDIR / "hf_job_raw.log" + + +def _get(url: str) -> str: + req = urllib.request.Request(url, headers={"Authorization": f"Bearer {TOKEN}"}) + try: + with urllib.request.urlopen(req, timeout=30) as r: + return r.read().decode("utf-8", errors="replace") + except urllib.error.HTTPError as e: + return f"__HTTP_{e.code}__" + except Exception as e: + return f"__ERR_{type(e).__name__}__" + + +def _pick_job(blob: str) -> tuple[str, str, str]: + """Return (job_id, stage, flavor) for the job we want to monitor.""" + try: + data = json.loads(blob) + except Exception: + return ("", "?", "?") + if isinstance(data, dict) and "jobs" in data: + data = data["jobs"] + if not isinstance(data, list) or not data: + return ("", "?", "?") + + def _stage(j: dict) -> str: + return str((j.get("status") or {}).get("stage", "")).upper() + + # Sort by createdAt descending — newest first. + data = sorted(data, key=lambda j: j.get("createdAt", ""), reverse=True) + running = [j for j in data if _stage(j) == "RUNNING"] + picked = running[0] if running else data[0] + jid = picked.get("id") or "" + st = _stage(picked) or "?" + flavor = picked.get("flavor") or picked.get("hardware") or "?" + return jid, st, str(flavor) + + +def _parse_metrics(logs: str) -> dict[str, str]: + out: dict[str, str] = {} + # Training patterns emitted by hydra/training.py: + # step= tok/s= tps= val_bpb= bpb= + last_step = re.findall(r"step[=:\s]+(\d+)", logs, re.IGNORECASE) + if last_step: + out["step"] = last_step[-1] + last_tps = re.findall(r"(?:tok/?s|tps)[=:\s]+([\d.]+)", logs, re.IGNORECASE) + if last_tps: + out["tok/s"] = last_tps[-1] + last_bpb = re.findall(r"(?:val_)?bpb[=:\s]+([\d.]+)", logs, re.IGNORECASE) + if last_bpb: + out["bpb"] = last_bpb[-1] + # Loss as a tertiary signal + last_loss = re.findall(r"\bloss[=:\s]+([\d.]+)", logs, re.IGNORECASE) + if last_loss: + out["loss"] = last_loss[-1] + return out + + +def main() -> int: + ts = _dt.datetime.now(_dt.timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + + # 1. Find the most recent job (namespace-scoped endpoint). + jobs_blob = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}") + if jobs_blob.startswith("__"): + SUMMARY.open("a").write(f"[{ts}] api_err jobs={jobs_blob}\n") + return 0 + + jid, stage, flavor = _pick_job(jobs_blob) + if not jid: + SUMMARY.open("a").write(f"[{ts}] no_job\n") + return 0 + + # 2. Re-query the single job for fresh stage (list endpoint can lag). + detail = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}/{jid}") + try: + dj = json.loads(detail) + stage = (dj.get("status") or {}).get("stage", stage) or stage + flavor = dj.get("flavor") or flavor + except Exception: + pass + + # 3. Pull logs only if the job is live (otherwise no metrics to parse). + logs = "" + if str(stage).upper() in {"RUNNING", "COMPLETED", "ERROR", "ERRORED"}: + logs = _get(f"https://huggingface.co/api/jobs/{NAMESPACE}/{jid}/logs") + RAW.write_text(logs) + + metrics = _parse_metrics(logs) if logs and not logs.startswith("__") else {} + + parts = [f"job={jid}", f"flavor={flavor}", f"stage={stage}"] + for k in ("step", "tok/s", "bpb", "loss"): + if k in metrics: + parts.append(f"{k}={metrics[k]}") + else: + parts.append(f"{k}=?") + SUMMARY.open("a").write(f"[{ts}] " + " ".join(parts) + "\n") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/overlay/scripts/hf_routing.py b/overlay/scripts/hf_routing.py index 1c81e7366b1ac3be7f07fc731025418e2414ea4e..e769c53c178be6c4de7d3ce1765fa255b0acfcbb 100644 --- a/overlay/scripts/hf_routing.py +++ b/overlay/scripts/hf_routing.py @@ -24,12 +24,7 @@ def _normalize_owner(value: str | None) -> str | None: def _owner_from_env() -> str | None: - for key in ( - 'FEATHER_HF_OWNER', - 'FEATHER_HF_NAMESPACE_OWNER', - 'FEATHER_HF_PROFILE', - 'FEATHER_HF_NAMESPACE', - ): + for key in ('FEATHER_HF_OWNER', 'FEATHER_HF_NAMESPACE_OWNER', 'FEATHER_HF_PROFILE'): owner = _normalize_owner(os.environ.get(key)) if owner: return owner @@ -58,7 +53,7 @@ def resolve_owner(token: str | None = None) -> str: if whoami_owner: return whoami_owner except Exception: - # We intentionally fail-open to deterministic defaults. + # Fail open to deterministic defaults for offline/dry-run tests. pass return 'jackoatmon' @@ -76,7 +71,7 @@ class HfRouting: def resolve_routing(token: str | None = None) -> HfRouting: owner = resolve_owner(token=token) - space_name = os.environ.get('FEATHER_HF_SPACE_NAME', 'feather-a10-runtime') + space_name = os.environ.get('FEATHER_HF_SPACE_NAME', 'feather-runtime') output_name = os.environ.get('FEATHER_HF_OUTPUT_REPO_NAME', 'feather-pretrain-checkpoints') retina_name = os.environ.get('FEATHER_HF_RETINA_REPO_NAME', 'feather-retina-cache') diff --git a/overlay/scripts/launch_feather_a10g_large_hf_job.sh b/overlay/scripts/launch_feather_a10g_large_hf_job.sh new file mode 100644 index 0000000000000000000000000000000000000000..b5141467bf17dc94b9d740ba42748845b7e3e541 --- /dev/null +++ b/overlay/scripts/launch_feather_a10g_large_hf_job.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +set -euo pipefail +# Launch Feather on Hugging Face Jobs a10g-large (A10G 24GB, sm_86). +# Requires HF_TOKEN. Overrides can be supplied in the environment. +export FEATHER_HF_FLAVOR="${FEATHER_HF_FLAVOR:-a10g-large}" +export FEATHER_GPU_PROFILE="${FEATHER_GPU_PROFILE:-a10g-large}" +export FEATHER_HF_IMAGE="${FEATHER_HF_IMAGE:-ghcr.io/slapglif/feather-hf-runtime:a10g-large}" +export FEATHER_HF_SPACE_REPO="${FEATHER_HF_SPACE_REPO:-icarus112/feather-a10g-large-runtime}" +export HTM_CUDA_ARCH="${HTM_CUDA_ARCH:-sm_86}" +export TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-8.6}" +export TRITON_CACHE_DIR="${TRITON_CACHE_DIR:-/workspace/triton_cache/a10g-large}" +export TRITON_CACHE_REPO="${TRITON_CACHE_REPO:-icarus112/feather-triton-cache-a10g-large}" +exec "$(dirname "$0")/launch_feather_hf_job.py" "$@" diff --git a/overlay/scripts/launch_feather_gt40k_a10g_hf_job.sh b/overlay/scripts/launch_feather_gt40k_a10g_hf_job.sh new file mode 100644 index 0000000000000000000000000000000000000000..57675fb7e5d35a7f3572839b82d9350385bf2206 --- /dev/null +++ b/overlay/scripts/launch_feather_gt40k_a10g_hf_job.sh @@ -0,0 +1,100 @@ +#!/usr/bin/env bash +# Launch the local >40k TPS Feather profile on Hugging Face Jobs. +# +# Goal: run a parallel cloud job from the scale-free SDR+HTM+Engram profile, +# targeting >=80k window TPS on the smallest practical HF GPU. Default is +# a10g-large; override FEATHER_HF_FLAVOR=a100-large only if A10G misses target. +set -euo pipefail + +cd "$(dirname "$0")/.." + +# Token hygiene: if HF_TOKEN is not exported, recover the first token from shell rc. +if [[ -z "${HF_TOKEN:-}" ]]; then + export HF_TOKEN="$(grep -oh 'hf_[A-Za-z0-9_-]*' ~/.bashrc ~/.profile 2>/dev/null | head -1 || true)" +fi +if [[ -z "${HF_TOKEN:-}" ]]; then + echo "HF_TOKEN is required" >&2 + exit 2 +fi + +# Minimum intended cloud card. A10G-large = 24GB VRAM, sm_86. +export FEATHER_HF_FLAVOR="${FEATHER_HF_FLAVOR:-a10g-large}" +export FEATHER_HF_NAMESPACE="${FEATHER_HF_NAMESPACE:-GAInTech}" +export FEATHER_GPU_PROFILE="${FEATHER_GPU_PROFILE:-${FEATHER_HF_FLAVOR}-gt80k}" +export FEATHER_HF_JOB_TIMEOUT="${FEATHER_HF_JOB_TIMEOUT:-12h}" + +# GHCR package is not anonymously pullable in this environment; use a public +# HF Docker Space image as the Jobs image source unless explicitly overridden. +export FEATHER_HF_USE_SPACE_IMAGE="${FEATHER_HF_USE_SPACE_IMAGE:-1}" +export FEATHER_HF_SPACE_PRIVATE="${FEATHER_HF_SPACE_PRIVATE:-0}" +export FEATHER_HF_SPACE_REPO="${FEATHER_HF_SPACE_REPO:-GAInTech/feather-a10g-gt80k-runtime-public}" +export FEATHER_HF_OUTPUT_REPO="${FEATHER_HF_OUTPUT_REPO:-GAInTech/feather-pretrain-checkpoints}" +export FEATHER_HF_OUTPUT_PRIVATE="${FEATHER_HF_OUTPUT_PRIVATE:-1}" + +# Data/continuation budget. +export HYDRA_TARGET_SHARDS="${HYDRA_TARGET_SHARDS:-4096}" +export HYDRA_DOWNLOAD_WORKERS="${HYDRA_DOWNLOAD_WORKERS:-16}" +export HYDRA_TIME_BUDGET="${HYDRA_TIME_BUDGET:-43200}" +export HYDRA_CKPT_INTERVAL="${HYDRA_CKPT_INTERVAL:-1000}" +export PYTHONUNBUFFERED=1 + +# >40k local profile, scaled for A10G throughput and data volume. This is not a +# Transformer/Mamba base-model scaling assumption: keep SDR + HTM + Engram live. +export HYDRA_USE_NEMOTRON=1 +export HYDRA_USE_FULL_BLEND=1 +export HYDRA_LOCAL_SHARDS_ONLY="${HYDRA_LOCAL_SHARDS_ONLY:-0}" +export HYDRA_BACKGROUND_PREFETCH=0 +export HYDRA_STREAM_SHUFFLE_BUFFER="${HYDRA_STREAM_SHUFFLE_BUFFER:-4096}" +export HYDRA_STREAM_PREFETCH=16 +export HYDRA_TOKEN_PREFETCH=4 +export HYDRA_TOKEN_CACHE_GB="${HYDRA_TOKEN_CACHE_GB:-8}" + +export HYDRA_RESUME_CKPT="${HYDRA_RESUME_CKPT:-none}" +export HYDRA_N_LAYER="${HYDRA_N_LAYER:-6}" +export HYDRA_D_MODEL="${HYDRA_D_MODEL:-192}" +export HYDRA_EXPAND="${HYDRA_EXPAND:-3}" +export HYDRA_SEQ_LEN="${HYDRA_SEQ_LEN:-1024}" +export HYDRA_HEADDIM="${HYDRA_HEADDIM:-32}" +export HYDRA_D_STATE="${HYDRA_D_STATE:-64}" +export HYDRA_BATCH_SIZE="${HYDRA_BATCH_SIZE:-32}" +export HYDRA_TOTAL_BATCH="${HYDRA_TOTAL_BATCH:-65536}" + +export HYDRA_MATRIX_LR="${HYDRA_MATRIX_LR:-0.04}" +export HYDRA_EMBED_LR="${HYDRA_EMBED_LR:-0.45}" +export HYDRA_UNEMBED_LR="${HYDRA_UNEMBED_LR:-0.002}" +export HYDRA_SCALAR_LR="${HYDRA_SCALAR_LR:-0.05}" +export HYDRA_DT_BIAS_LR="${HYDRA_DT_BIAS_LR:-0.15}" +export HYDRA_WARMUP_RATIO="${HYDRA_WARMUP_RATIO:-0.01}" +export HYDRA_LR_MIN_MULT="${HYDRA_LR_MIN_MULT:-0.10}" +export HYDRA_DOC_SEP_MASK="${HYDRA_DOC_SEP_MASK:-1}" +export HYDRA_STREAM_SHUFFLE_BUFFER="${HYDRA_STREAM_SHUFFLE_BUFFER:-4096}" + +export HYDRA_SAMPLED_SOFTMAX="${HYDRA_SAMPLED_SOFTMAX:-512}" +export HYDRA_SOFTCAP_CLAMP=1 +export HYDRA_CE_CHUNK="${HYDRA_CE_CHUNK:-64}" +export HYDRA_ENGRAM_N_COLUMNS="${HYDRA_ENGRAM_N_COLUMNS:-32768}" +export HYDRA_ENGRAM_TOPK="${HYDRA_ENGRAM_TOPK:-64}" +export HYDRA_ENG_TOPK=512 +export HYDRA_ENGRAM_ROUTING=auto +export HYDRA_HTM_SUBSAMPLE="${HYDRA_HTM_SUBSAMPLE:-16}" +# A10G/sm86 still uses fused SDR+HTM+TM, but runs one cooperative fused launch +# per batch region until the 2-D batched cooperative launch is proven stable. +export HYDRA_HTM_BATCHED_FUSED="${HYDRA_HTM_BATCHED_FUSED:-0}" +export HYDRA_SDR_TARGET_ACTIVE="${HYDRA_SDR_TARGET_ACTIVE:-327}" +export HYDRA_MUON_NS_STEPS="${HYDRA_MUON_NS_STEPS:-2}" +export HYDRA_MUON_COMPILE=0 +export HYDRA_GDN_LAYERS= +# HF A10G exposes CUDA to torch + HTM, but Triton reports `0 active drivers` +# for Mamba3 layernorm kernels. Use the existing shape-compatible Hyena block +# on all six sequence layers so the remote job actually trains while preserving +# SDR + HTM + Engram and the post-loop-3 batch/profile settings. +export HYDRA_HYENA_LAYERS="${HYDRA_HYENA_LAYERS:-0,1,2,3,4,5}" +export HYDRA_MTP_K=1 +export HYDRA_USE_MDLM=0 +export HYDRA_HESTIA_INTERVAL=999999 +export HYDRA_EVAL_BATCH=1 +export HYDRA_EVAL_TOKENS="${HYDRA_EVAL_TOKENS:-2048}" +export HYDRA_MID_VAL_INTERVAL=0 +export HYDRA_SKIP_FACTUAL_EVAL=1 + +exec /usr/bin/python3 scripts/launch_feather_hf_job.py diff --git a/overlay/scripts/launch_feather_hf_job.py b/overlay/scripts/launch_feather_hf_job.py index 93c9f8865dc078b1bf8c881f916e471578e7273a..f38e97145eef70eb38938b2450898512bcaf1a2f 100644 --- a/overlay/scripts/launch_feather_hf_job.py +++ b/overlay/scripts/launch_feather_hf_job.py @@ -1,12 +1,11 @@ -#!/usr/bin/env python3 -from __future__ import annotations - +#!/usr/bin/env python3 +from __future__ import annotations + +import json import os import shutil import sys import time -import json -from typing import Any, cast from pathlib import Path from huggingface_hub import HfApi @@ -15,22 +14,40 @@ REPO_ROOT = Path(__file__).resolve().parents[1] if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) -from scripts.hf_routing import resolve_routing from configs.harness_config import HarnessConfig +from scripts.hf_routing import resolve_routing -DEFAULT_IMAGE = os.environ.get('FEATHER_HF_IMAGE', 'ghcr.io/slapglif/feather-hf-runtime:latest') +GPU_FLAVOR = os.environ.get('FEATHER_HF_FLAVOR', 'a10g-large') +GPU_PROFILE = os.environ.get('FEATHER_GPU_PROFILE', GPU_FLAVOR) +GPU_ARCH_BY_FLAVOR = { + 'a10g-small': ('sm_86', '8.6'), + 'a10g-large': ('sm_86', '8.6'), + 'a10g-largex2': ('sm_86', '8.6'), + 'a10g-largex4': ('sm_86', '8.6'), + 'a100-large': ('sm_80', '8.0'), + 'a100x4': ('sm_80', '8.0'), + 'a100x8': ('sm_80', '8.0'), + 'h200': ('sm_90a', '9.0'), + 'h200x2': ('sm_90a', '9.0'), + 'h200x4': ('sm_90a', '9.0'), + 'h200x8': ('sm_90a', '9.0'), +} +HTM_CUDA_ARCH, TORCH_CUDA_ARCH = GPU_ARCH_BY_FLAVOR.get(GPU_FLAVOR, ('sm_86', '8.6')) +HF_NAMESPACE = os.environ.get('FEATHER_HF_NAMESPACE') +DEFAULT_IMAGE = os.environ.get('FEATHER_HF_IMAGE', 'ghcr.io/slapglif/feather-hf-runtime:a10g-large') IMAGE_DIR = Path(__file__).resolve().parents[1] / 'hf_jobs' / 'feather_h200_image' TIMEOUT = os.environ.get('FEATHER_HF_JOB_TIMEOUT', '12h') +SPACE_PRIVATE = os.environ.get('FEATHER_HF_SPACE_PRIVATE', '1') == '1' +OUTPUT_PRIVATE = os.environ.get('FEATHER_HF_OUTPUT_PRIVATE', '1') == '1' TARGET_SHARDS = os.environ.get('HYDRA_TARGET_SHARDS', '2048') TIME_BUDGET = os.environ.get('HYDRA_TIME_BUDGET', '43200') DOWNLOAD_WORKERS = os.environ.get('HYDRA_DOWNLOAD_WORKERS', '16') CKPT_INTERVAL = os.environ.get('HYDRA_CKPT_INTERVAL', '1000') -JOB_FLAVOR = os.environ.get('FEATHER_HF_FLAVOR', 'a10g-small') DRY_RUN = os.environ.get('FEATHER_HF_DRY_RUN', '0') == '1' USE_SPACE_IMAGE = os.environ.get('FEATHER_HF_USE_SPACE_IMAGE', '0') == '1' -# When true, assume the Space image has already been built by a previous -# invocation and skip the upload+build wait. Used by sweep drivers that fan -# out many jobs against a single pre-uploaded image. +# When true, assume the Space image has already been built by a previous +# invocation and skip the upload+build wait. Used by sweep drivers that fan +# out many jobs against a single pre-uploaded image. SKIP_UPLOAD = os.environ.get('FEATHER_HF_SKIP_UPLOAD', '0') == '1' SYNC_OVERLAY = os.environ.get('FEATHER_HF_SYNC_OVERLAY', '1') == '1' @@ -50,12 +67,6 @@ def sync_overlay_from_repo() -> None: overlay = IMAGE_DIR / 'overlay' overlay.mkdir(parents=True, exist_ok=True) - for child in overlay.iterdir(): - if child.is_dir(): - shutil.rmtree(child) - else: - child.unlink() - include_paths = [ 'hydra', 'subsystems', @@ -85,6 +96,11 @@ def sync_overlay_from_repo() -> None: dst = overlay / rel if not src.exists(): continue + if dst.exists(): + if dst.is_dir(): + shutil.rmtree(dst) + else: + dst.unlink() if src.is_dir(): shutil.copytree(src, dst, dirs_exist_ok=True, ignore=ignore) else: @@ -100,35 +116,55 @@ def sync_overlay_from_repo() -> None: sh_path.write_bytes(data) print(f'[launch] overlay synced from repo ({len(copied)} paths): {copied}', flush=True) - - + + +def load_hf_token() -> str | None: + """Load a Hugging Face token without printing or persisting secret values.""" + for env_name in ('HF_TOKEN', 'HUGGINGFACE_HUB_TOKEN'): + token = os.environ.get(env_name) + if token: + return token + + token_file = Path(os.environ.get('HF_TOKEN_PATH', Path.home() / '.cache' / 'huggingface' / 'token')).expanduser() + try: + token = token_file.read_text(encoding='utf-8').strip() + except FileNotFoundError: + return None + except OSError: + return None + return token or None + + def require_token() -> str: - token = os.environ.get('HF_TOKEN') + token = load_hf_token() if not token: - raise SystemExit('HF_TOKEN must be set in the environment for launch_feather_hf_job.py') + raise SystemExit( + 'HF token required: set HF_TOKEN/HUGGINGFACE_HUB_TOKEN or run `huggingface-cli login` ' + 'so ~/.cache/huggingface/token exists' + ) return token -def wait_for_space(api: HfApi, repo_id: str, token: str, timeout_s: int = 1800) -> None: - """Wait until the Space image has been built. - - We use the Space purely as a container-image builder for HF Jobs. The Space - itself runs on CPU and our entrypoint intentionally probes nvidia-smi at - module import, which crashes the CPU Space → RUNTIME_ERROR. That does NOT - mean the image is bad — it means the image was successfully built and pushed - to the Space registry (we observed BUILDING → APP_STARTING transition) and - then the CPU container couldn't boot it. HF Jobs pulls the same built image - and runs it on H200 where nvidia-smi works. - - So: BUILD_ERROR is fatal (image literally did not build), but RUNTIME_ERROR - and APP_STARTING_ERROR after a successful BUILDING→APP_STARTING transition - are acceptable — the image exists in the registry and Jobs can use it. - """ +def wait_for_space(api: HfApi, repo_id: str, timeout_s: int = 1800) -> None: + """Wait until the Space image has been built. + + We use the Space purely as a container-image builder for HF Jobs. The Space + itself runs on CPU and our entrypoint intentionally probes nvidia-smi at + module import, which can crash the CPU Space → RUNTIME_ERROR. That does NOT + mean the image is bad — it means the image was successfully built and pushed + to the Space registry (we observed BUILDING → APP_STARTING transition) and + then the CPU container couldn't boot it. HF Jobs pulls the same built image + and runs it on the requested GPU flavor where nvidia-smi works. + + So: BUILD_ERROR is fatal (image literally did not build), but RUNTIME_ERROR + and APP_STARTING_ERROR after a successful BUILDING→APP_STARTING transition + are acceptable — the image exists in the registry and Jobs can use it. + """ start = time.time() seen_build_completion = False seen_building = False while True: - runtime = api.get_space_runtime(repo_id, token=token) + runtime = api.get_space_runtime(repo_id, token=load_hf_token()) stage = getattr(runtime, 'stage', None) hardware = getattr(runtime, 'hardware', None) err = getattr(runtime, 'errorMessage', None) or getattr(runtime, 'error_message', None) @@ -140,19 +176,22 @@ def wait_for_space(api: HfApi, repo_id: str, token: str, timeout_s: int = 1800) if stage in {'RUNNING', 'PAUSED', 'SLEEPING'}: return # Image is built — Jobs can use it regardless of Space boot outcome. + # If we enter while the Space is already in RUNTIME_ERROR from a prior + # successful build, we may not observe APP_STARTING in this process; do + # not spin forever. This is the normal public-Space image-builder state. if (seen_build_completion or seen_building) and stage in {'RUNTIME_ERROR', 'APP_STARTING_ERROR'}: print(f'[space] Space boot failed with {stage} but built image is ' f'available in the Space registry and is usable by HF Jobs.', flush=True) return - # Hard build failures — no image was produced. - if stage in {'BUILD_ERROR', 'CONFIG_ERROR', 'NO_APP_FILE'}: - raise RuntimeError(f'Space {repo_id} build failed: stage={stage} error={err!r}') - if time.time() - start > timeout_s: - raise TimeoutError(f'Space {repo_id} did not become ready in {timeout_s}s (last stage={stage})') - time.sleep(20) - - + # Hard build failures — no image was produced. + if stage in {'BUILD_ERROR', 'CONFIG_ERROR', 'NO_APP_FILE'}: + raise RuntimeError(f'Space {repo_id} build failed: stage={stage} error={err!r}') + if time.time() - start > timeout_s: + raise TimeoutError(f'Space {repo_id} did not become ready in {timeout_s}s (last stage={stage})') + time.sleep(20) + + def main() -> int: token = require_token() routing = resolve_routing(token=token) @@ -165,20 +204,22 @@ def main() -> int: print(f'[launch] output_repo={routing.output_repo}', flush=True) print(f'[launch] retina_cache_repo={routing.retina_cache_repo}', flush=True) print(f'[launch] target_shards={TARGET_SHARDS} time_budget={TIME_BUDGET} timeout={TIMEOUT}', flush=True) - print(f'[launch] flavor={JOB_FLAVOR}', flush=True) print(f'[launch] namespace={routing.job_namespace}', flush=True) + print(f'[launch] flavor={GPU_FLAVOR} profile={GPU_PROFILE} htm_cuda_arch={HTM_CUDA_ARCH} torch_cuda_arch={TORCH_CUDA_ARCH}', flush=True) print(f'[launch] image_mode={"space" if USE_SPACE_IMAGE else "ghcr"}', flush=True) print(f'[launch] secondary_gates={json.dumps(secondary_gates, sort_keys=True)}', flush=True) if not USE_SPACE_IMAGE: print(f'[launch] image={DEFAULT_IMAGE}', flush=True) - api.create_repo(repo_id=routing.space_repo, repo_type='space', space_sdk='docker', private=True, exist_ok=True, token=token) - api.create_repo(repo_id=routing.output_repo, repo_type='model', private=True, exist_ok=True, token=token) - - if DRY_RUN: - print('[launch] dry-run mode; skipping upload and job submission', flush=True) - return 0 - + if DRY_RUN: + if 'HYDRA_USE_NEMOTRON' not in os.environ and should_enable_fast_start_streaming(TARGET_SHARDS, TIME_BUDGET): + print('[launch] auto-enabled HYDRA_USE_NEMOTRON=1 for short-budget fast-start profile', flush=True) + print('[launch] dry-run mode; skipping repo creation, upload, and job submission', flush=True) + return 0 + + api.create_repo(repo_id=routing.space_repo, repo_type='space', space_sdk='docker', private=SPACE_PRIVATE, exist_ok=True, token=token) + api.create_repo(repo_id=routing.output_repo, repo_type='model', private=OUTPUT_PRIVATE, exist_ok=True, token=token) + image_ref = DEFAULT_IMAGE if USE_SPACE_IMAGE: if SKIP_UPLOAD: @@ -191,12 +232,29 @@ def main() -> int: repo_id=routing.space_repo, repo_type='space', folder_path=str(IMAGE_DIR), - commit_message='Update Feather training runtime image', + commit_message=f'Update Feather {GPU_PROFILE} training runtime image', + ignore_patterns=[ + '**/__pycache__/**', + '**/*.py[cod]', + '**/.pytest_cache/**', + '**/.mypy_cache/**', + '**/.ruff_cache/**', + '**/.venv/**', + '**/target/**', + '**/logs/**', + '**/*.log', + '**/*.out', + '**/*.pt', + '**/*.safetensors', + '**/*.parquet', + '**/*.npz', + '**/.git/**', + ], token=token, ) print('[launch] waiting for Space image build to become ready...', flush=True) - wait_for_space(api, routing.space_repo, token=token) + wait_for_space(api, routing.space_repo) image_ref = f'hf.co/spaces/{routing.space_repo}' env = { @@ -209,9 +267,15 @@ def main() -> int: 'HYDRA_TARGET_SHARDS': TARGET_SHARDS, 'HYDRA_TIME_BUDGET': TIME_BUDGET, 'HYDRA_DOWNLOAD_WORKERS': DOWNLOAD_WORKERS, - 'HYDRA_CKPT_INTERVAL': CKPT_INTERVAL, - 'PYTHONUNBUFFERED': '1', + 'HYDRA_CKPT_INTERVAL': CKPT_INTERVAL, + 'PYTHONUNBUFFERED': '1', 'FEATHER_RUNTIME_MODE': 'job', + 'FEATHER_GPU_PROFILE': GPU_PROFILE, + 'FEATHER_HF_FLAVOR': GPU_FLAVOR, + 'HTM_CUDA_ARCH': HTM_CUDA_ARCH, + 'TORCH_CUDA_ARCH_LIST': TORCH_CUDA_ARCH, + 'TRITON_CACHE_DIR': f'/workspace/triton_cache/{GPU_PROFILE}', + 'TRITON_CACHE_REPO': f'{routing.owner}/feather-triton-cache-{GPU_PROFILE}', } if 'HYDRA_USE_NEMOTRON' not in os.environ and should_enable_fast_start_streaming(TARGET_SHARDS, TIME_BUDGET): env['HYDRA_USE_NEMOTRON'] = '1' @@ -219,7 +283,7 @@ def main() -> int: # A10 compatibility profile: avoid known PTX/compile runtime pitfalls and # keep throughput path enabled. Caller can explicitly override each key by # setting it in the parent environment. - if JOB_FLAVOR.startswith('a10'): + if GPU_FLAVOR.startswith('a10'): _a10_defaults = { 'HYDRA_MUON_COMPILE': '0', 'HYDRA_FORCE_HTM_CPU': '1', @@ -243,29 +307,29 @@ def main() -> int: f"HYDRA_FASTPATH={env['HYDRA_FASTPATH']})", flush=True, ) - # Pass through any HYDRA_* / FEATHER_* overrides from the caller's env so - # sweep drivers can set HYDRA_N_LAYER, HYDRA_SDR_TARGET_ACTIVE, - # HYDRA_LAYER_DIAGNOSTICS, HYDRA_METRICS_OUT, HYDRA_MID_VAL_INTERVAL, etc. - # without needing launcher edits. Known keys above take precedence. - for _k, _v in os.environ.items(): - if (_k.startswith('HYDRA_') or _k.startswith('FEATHER_')) and _k not in env: - env[_k] = _v - secrets = {'HF_TOKEN': token} - - print(f'[launch] submitting HF Job on flavor={JOB_FLAVOR}...', flush=True) + # Pass through any HYDRA_* / FEATHER_* overrides from the caller's env so + # sweep drivers can set HYDRA_N_LAYER, HYDRA_SDR_TARGET_ACTIVE, + # HYDRA_LAYER_DIAGNOSTICS, HYDRA_METRICS_OUT, HYDRA_MID_VAL_INTERVAL, etc. + # without needing launcher edits. Known keys above take precedence. + for _k, _v in os.environ.items(): + if (_k.startswith('HYDRA_') or _k.startswith('FEATHER_')) and _k not in env: + env[_k] = _v + secrets = {'HF_TOKEN': token} + + print(f'[launch] submitting HF Job on {GPU_FLAVOR} (single-GPU Feather path; A10G-large is 24GB VRAM / 12 vCPU / 46GB RAM)...', flush=True) job = api.run_job( image=image_ref, command=['python', '/app/entrypoint.py'], env=env, secrets=secrets, - flavor=cast(Any, JOB_FLAVOR), + flavor=GPU_FLAVOR, timeout=TIMEOUT, namespace=routing.job_namespace, token=token, ) - print(f'[launch] submitted job_id={job.id} status={job.status.stage} url={job.url}', flush=True) - return 0 - - -if __name__ == '__main__': - raise SystemExit(main()) + print(f'[launch] submitted job_id={job.id} status={job.status.stage} url={job.url}', flush=True) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/overlay/scripts/parse_metrics.py b/overlay/scripts/parse_metrics.py index 26f1edd932ae8619a6f746eb70de39b2691024a8..131bede8f6e0b823a5c72b372c742f37dd74e6c9 100644 --- a/overlay/scripts/parse_metrics.py +++ b/overlay/scripts/parse_metrics.py @@ -1,24 +1,24 @@ -"""Parse train.py run.log → (bpb, tps_avg, factual). - -bpb priority order: - 1. val_bpb from [VAL] line (cleanest signal, but OOMs on 6GB cards) - 2. train_bpb from the LAST step= line (proxy when val fails — not held-out - but monotone with model capability over a 5-min budget) -""" -import re, sys -txt = open(sys.argv[1]).read() - -m = re.search(r'val_bpb:\s+([\d\.]+)', txt) -if m: - bpb = m.group(1) -else: - step_lines = re.findall(r'^step=\d+\s+loss=[\d\.]+\s+bpb=([\d\.]+)', txt, re.M) - bpb = f'~{step_lines[-1]}' if step_lines else 'NA' - -tps_vals = [int(m.group(1)) for m in re.finditer(r'tps=(\d+)', txt)] -tps_avg = f'{sum(tps_vals)/len(tps_vals):.0f}' if tps_vals else 'NA' - -m = re.search(r'factual_english_hits:\s+(\d+/\d+)', txt) -factual = m.group(1) if m else 'NA' - -print(f"{bpb}\t{tps_avg}\t{factual}") +"""Parse train.py run.log → (bpb, tps_avg, factual). + +bpb priority order: + 1. val_bpb from [VAL] line (cleanest signal, but OOMs on 6GB cards) + 2. train_bpb from the LAST step= line (proxy when val fails — not held-out + but monotone with model capability over a 5-min budget) +""" +import re, sys +txt = open(sys.argv[1]).read() + +m = re.search(r'val_bpb:\s+([\d\.]+)', txt) +if m: + bpb = m.group(1) +else: + step_lines = re.findall(r'^step=\d+\s+loss=[\d\.]+\s+bpb=([\d\.]+)', txt, re.M) + bpb = f'~{step_lines[-1]}' if step_lines else 'NA' + +tps_vals = [int(m.group(1)) for m in re.finditer(r'tps=(\d+)', txt)] +tps_avg = f'{sum(tps_vals)/len(tps_vals):.0f}' if tps_vals else 'NA' + +m = re.search(r'factual_english_hits:\s+(\d+/\d+)', txt) +factual = m.group(1) if m else 'NA' + +print(f"{bpb}\t{tps_avg}\t{factual}") diff --git a/overlay/scripts/predownload_shards.py b/overlay/scripts/predownload_shards.py index 38220fa900f4f4eab142eaa77ed0758def722117..c146b333f84f03776bf83f4b5558633ff3ce153f 100644 --- a/overlay/scripts/predownload_shards.py +++ b/overlay/scripts/predownload_shards.py @@ -1,106 +1,106 @@ -"""Pre-download parquet shards using direct HTTP with concurrent ranged requests. - -Bypasses hf_hub_download overhead — just resolves the CDN URL and streams -with concurrent range chunks. Achieves 10+ MB/s (full BW). - -Files are placed directly in HF cache structure so streaming=True picks them up. - -Usage: python scripts/predownload_shards.py [--shards N] -""" -from __future__ import annotations - -import argparse -import os -import sys -import time -import urllib.request -from concurrent.futures import ThreadPoolExecutor, as_completed -from pathlib import Path - -# Unbuffered stdout -sys.stdout.reconfigure(line_buffering=True) -sys.stderr.reconfigure(line_buffering=True) - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from prepare_nemotron import _BLEND_REGISTRY - -from huggingface_hub import HfApi, hf_hub_url, hf_hub_download - - -def list_parquet(repo: str, config: str | None, name: str, shards: int, token: str | None) -> list[str]: - api = HfApi(token=token) - files = api.list_repo_files(repo, repo_type="dataset") - parquet = sorted(f for f in files if f.endswith(".parquet")) - effective_cfg = "Nemotron-Pretraining-Code-Concepts" if name == "nemotron-specialized" else config - if effective_cfg is not None: - filtered = [f for f in parquet if f"/{effective_cfg}/" in f or f.startswith(f"{effective_cfg}/")] - if filtered: - parquet = filtered - return parquet[:shards] - - -def download_one(repo: str, filename: str, token: str | None) -> tuple[str, int, float]: - """Use hf_hub_download — proven to work with -L redirect from curl test.""" - t0 = time.time() - path = hf_hub_download( - repo_id=repo, - filename=filename, - repo_type="dataset", - token=token, - ) - sz = os.path.getsize(path) - return (filename, sz, time.time() - t0) - - -def download_dataset(name: str, repo: str, config: str | None, shards: int, token: str | None, workers: int = 2) -> tuple[int, float]: - t0 = time.time() - try: - files = list_parquet(repo, config, name, shards, token) - except Exception as e: - print(f"[{name}] list failed: {type(e).__name__}: {e}", flush=True) - return (0, 0.0) - - if not files: - print(f"[{name}] no parquet matched — skipped (config={config})", flush=True) - return (0, 0.0) - - print(f"[{name}] {len(files)} shards ({workers} concurrent)", flush=True) - total = 0 - with ThreadPoolExecutor(max_workers=workers) as ex: - futs = [ex.submit(download_one, repo, f, token) for f in files] - for fut in as_completed(futs): - try: - fname, sz, elapsed = fut.result() - mbps = sz / 1024**2 / max(elapsed, 0.001) - print(f" OK {fname}: {sz / 1024**2:.0f} MB in {elapsed:.0f}s ({mbps:.1f} MB/s)", flush=True) - total += sz - except Exception as e: - print(f" FAIL: {type(e).__name__}: {str(e)[:100]}", flush=True) - - elapsed = time.time() - t0 - print(f"[{name}] {total / 1024**3:.2f} GB in {elapsed:.0f}s ({total / 1024**2 / max(elapsed, 0.001):.1f} MB/s)", flush=True) - return (total, elapsed) - - -def main() -> None: - ap = argparse.ArgumentParser() - ap.add_argument("--shards", type=int, default=2) - ap.add_argument("--concurrent-files", type=int, default=2, help="shards in parallel per dataset") - args = ap.parse_args() - - token = os.environ.get("HF_TOKEN") - datasets = list(_BLEND_REGISTRY.items()) - - print(f"[predownload] {len(datasets)} datasets × {args.shards} shards, {args.concurrent_files} concurrent per dataset", flush=True) - t_start = time.time() - grand_total = 0 - for name, (repo, cfg, _col) in datasets: - total, _ = download_dataset(name, repo, cfg, args.shards, token, workers=args.concurrent_files) - grand_total += total - - elapsed = time.time() - t_start - print(f"\n[predownload] DONE — {grand_total / 1024**3:.2f} GB in {elapsed:.0f}s ({grand_total / 1024**2 / max(elapsed, 0.001):.1f} MB/s overall)", flush=True) - - -if __name__ == "__main__": - main() +"""Pre-download parquet shards using direct HTTP with concurrent ranged requests. + +Bypasses hf_hub_download overhead — just resolves the CDN URL and streams +with concurrent range chunks. Achieves 10+ MB/s (full BW). + +Files are placed directly in HF cache structure so streaming=True picks them up. + +Usage: python scripts/predownload_shards.py [--shards N] +""" +from __future__ import annotations + +import argparse +import os +import sys +import time +import urllib.request +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +# Unbuffered stdout +sys.stdout.reconfigure(line_buffering=True) +sys.stderr.reconfigure(line_buffering=True) + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from prepare_nemotron import _BLEND_REGISTRY + +from huggingface_hub import HfApi, hf_hub_url, hf_hub_download + + +def list_parquet(repo: str, config: str | None, name: str, shards: int, token: str | None) -> list[str]: + api = HfApi(token=token) + files = api.list_repo_files(repo, repo_type="dataset") + parquet = sorted(f for f in files if f.endswith(".parquet")) + effective_cfg = "Nemotron-Pretraining-Code-Concepts" if name == "nemotron-specialized" else config + if effective_cfg is not None: + filtered = [f for f in parquet if f"/{effective_cfg}/" in f or f.startswith(f"{effective_cfg}/")] + if filtered: + parquet = filtered + return parquet[:shards] + + +def download_one(repo: str, filename: str, token: str | None) -> tuple[str, int, float]: + """Use hf_hub_download — proven to work with -L redirect from curl test.""" + t0 = time.time() + path = hf_hub_download( + repo_id=repo, + filename=filename, + repo_type="dataset", + token=token, + ) + sz = os.path.getsize(path) + return (filename, sz, time.time() - t0) + + +def download_dataset(name: str, repo: str, config: str | None, shards: int, token: str | None, workers: int = 2) -> tuple[int, float]: + t0 = time.time() + try: + files = list_parquet(repo, config, name, shards, token) + except Exception as e: + print(f"[{name}] list failed: {type(e).__name__}: {e}", flush=True) + return (0, 0.0) + + if not files: + print(f"[{name}] no parquet matched — skipped (config={config})", flush=True) + return (0, 0.0) + + print(f"[{name}] {len(files)} shards ({workers} concurrent)", flush=True) + total = 0 + with ThreadPoolExecutor(max_workers=workers) as ex: + futs = [ex.submit(download_one, repo, f, token) for f in files] + for fut in as_completed(futs): + try: + fname, sz, elapsed = fut.result() + mbps = sz / 1024**2 / max(elapsed, 0.001) + print(f" OK {fname}: {sz / 1024**2:.0f} MB in {elapsed:.0f}s ({mbps:.1f} MB/s)", flush=True) + total += sz + except Exception as e: + print(f" FAIL: {type(e).__name__}: {str(e)[:100]}", flush=True) + + elapsed = time.time() - t0 + print(f"[{name}] {total / 1024**3:.2f} GB in {elapsed:.0f}s ({total / 1024**2 / max(elapsed, 0.001):.1f} MB/s)", flush=True) + return (total, elapsed) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--shards", type=int, default=2) + ap.add_argument("--concurrent-files", type=int, default=2, help="shards in parallel per dataset") + args = ap.parse_args() + + token = os.environ.get("HF_TOKEN") + datasets = list(_BLEND_REGISTRY.items()) + + print(f"[predownload] {len(datasets)} datasets × {args.shards} shards, {args.concurrent_files} concurrent per dataset", flush=True) + t_start = time.time() + grand_total = 0 + for name, (repo, cfg, _col) in datasets: + total, _ = download_dataset(name, repo, cfg, args.shards, token, workers=args.concurrent_files) + grand_total += total + + elapsed = time.time() - t_start + print(f"\n[predownload] DONE — {grand_total / 1024**3:.2f} GB in {elapsed:.0f}s ({grand_total / 1024**2 / max(elapsed, 0.001):.1f} MB/s overall)", flush=True) + + +if __name__ == "__main__": + main() diff --git a/overlay/scripts/run_domain_expanded_pretrain.sh b/overlay/scripts/run_domain_expanded_pretrain.sh index fe8e2ae7e20bc668e49c8de413b8bda0bb8e83c9..2f832a0d562deef4bfa5092e9adcf9e2f1c9df3e 100644 --- a/overlay/scripts/run_domain_expanded_pretrain.sh +++ b/overlay/scripts/run_domain_expanded_pretrain.sh @@ -188,7 +188,11 @@ fi RESUME_PATH="$(resolve_resume_path || true)" -export LD_LIBRARY_PATH="/usr/lib/wsl/lib:/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +# Only inject WSL library paths when running on WSL. Cloud containers +# (H200/A10G HF Jobs) already have their driver paths set by entrypoint.py. +if [[ -d /usr/lib/wsl/lib ]]; then + export LD_LIBRARY_PATH="/usr/lib/wsl/lib:/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +fi export HYDRA_TIME_BUDGET="${HYDRA_TIME_BUDGET:-28800}" export HYDRA_TARGET_SHARDS="$TARGET_SHARDS" export HYDRA_DOWNLOAD_WORKERS="$DOWNLOAD_WORKERS" diff --git a/overlay/scripts/sample_english.py b/overlay/scripts/sample_english.py index f4a510caff771885d791922a8f0347a67e80b8b0..5c0f3778e34a8db0ddbd0921ae76ae9a4949e5df 100644 --- a/overlay/scripts/sample_english.py +++ b/overlay/scripts/sample_english.py @@ -1,172 +1,172 @@ -"""Sample English from latest checkpoint using HuggingFace transformers.generate(). - -Wraps PostSemClawModel in a minimal GenerationMixin shim so we get: - - Beam search (num_beams=4) - - Top-k / top-p / temperature sampling - - Repetition penalty - - All the battle-tested stopping criteria - -Usage: python scripts/sample_english.py -""" -from __future__ import annotations - -import os -import sys - -sys.stdout.reconfigure(line_buffering=True) -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import torch -import torch.nn as nn -from transformers import ( - GenerationConfig, - GenerationMixin, - PretrainedConfig, - PreTrainedModel, -) -from transformers.modeling_outputs import CausalLMOutputWithPast - -from hydra.config import PostSemClawConfig -from hydra.model import PostSemClawModel -from prepare import Tokenizer - -CKPT_PATH = os.path.expanduser("~/.cache/autoresearch/latest.pt") - - -class _HydraGenConfig(PretrainedConfig): - model_type = "hydra" - - def __init__(self, vocab_size: int = 65536, **kw): - super().__init__(**kw) - self.vocab_size = vocab_size - self.num_hidden_layers = 4 - self.hidden_size = 256 - self.num_attention_heads = 4 - - -class HydraForCausalLM(PreTrainedModel, GenerationMixin): - """HF wrapper around PostSemClawModel so we can use .generate().""" - - config_class = _HydraGenConfig - - def __init__(self, gen_config, inner_model): - super().__init__(gen_config) - self.inner = inner_model - # HF looks for these attrs - self.config.vocab_size = gen_config.vocab_size - - def forward(self, input_ids, attention_mask=None, **kw): - logits = self.inner(input_ids) - return CausalLMOutputWithPast(loss=None, logits=logits, past_key_values=None) - - def prepare_inputs_for_generation(self, input_ids, **kw): - # Our model has no KV cache — always feed full context - return {"input_ids": input_ids} - - def get_input_embeddings(self): - return self.inner.wte - - def can_generate(self) -> bool: - return True - - @property - def _supports_cache_class(self): - return False - - -def main() -> None: - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - print(f"[sample] device: {device}") - - tokenizer = Tokenizer.from_directory() - vocab_size = tokenizer.get_vocab_size() - bos = tokenizer.get_bos_token_id() - - ckpt = torch.load(CKPT_PATH, map_location="cpu", weights_only=False) - cfg_dict = ckpt["config"] - step = ckpt.get("step", "?") - print(f"[sample] loaded step={step}") - - cfg = PostSemClawConfig(**cfg_dict) - with torch.device("meta"): - inner = PostSemClawModel(cfg) - inner.to_empty(device=device) - inner.load_state_dict(ckpt["model_state_dict"], strict=False) - inner.eval() - - gen_cfg = _HydraGenConfig(vocab_size=vocab_size) - # Set common pad/eos tokens so HF generate is happy (we use BOS as both) - gen_cfg.bos_token_id = bos - gen_cfg.eos_token_id = bos - gen_cfg.pad_token_id = bos - model = HydraForCausalLM(gen_cfg, inner).to(device) - model.eval() - print(f"[sample] model ready, vocab={vocab_size}") - - PROMPTS = [ - "The capital of France is", - "Paris is known for", - "Once upon a time", - "Water boils at", - "Shakespeare wrote", - "The theory of evolution was proposed by", - "Einstein discovered that", - "Photosynthesis is", - ] - - # --- Greedy --- - print("\n=== GREEDY (baseline) ===") - gen_config = GenerationConfig( - max_new_tokens=20, use_cache=False, - do_sample=False, - num_beams=1, - bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, - ) - for prompt in PROMPTS: - ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) - with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - out = model.generate(ids, generation_config=gen_config) - text = tokenizer.decode(out[0].tolist()) - print(f' "{prompt}" -> "{text}"') - - # --- Beam search (4 beams) --- - print("\n=== BEAM SEARCH (4 beams, length_penalty=1.0) ===") - gen_config = GenerationConfig( - max_new_tokens=20, use_cache=False, - num_beams=4, - do_sample=False, - length_penalty=1.0, - no_repeat_ngram_size=3, - early_stopping=True, - bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, - ) - for prompt in PROMPTS[:4]: - ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) - with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - out = model.generate(ids, generation_config=gen_config) - text = tokenizer.decode(out[0].tolist()) - print(f' "{prompt}" -> "{text}"') - - # --- Top-p sampling (nucleus, t=0.8, p=0.9) --- - print("\n=== TOP-P SAMPLING (temperature=0.8, top_p=0.9) ===") - gen_config = GenerationConfig( - max_new_tokens=30, use_cache=False, - do_sample=True, - temperature=0.8, - top_p=0.9, - repetition_penalty=1.2, - bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, - ) - torch.manual_seed(42) - for prompt in PROMPTS[:4]: - ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) - with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - out = model.generate(ids, generation_config=gen_config) - text = tokenizer.decode(out[0].tolist()) - print(f' "{prompt}" -> "{text}"') - - print("\n[sample] done.") - - -if __name__ == "__main__": - main() +"""Sample English from latest checkpoint using HuggingFace transformers.generate(). + +Wraps PostSemClawModel in a minimal GenerationMixin shim so we get: + - Beam search (num_beams=4) + - Top-k / top-p / temperature sampling + - Repetition penalty + - All the battle-tested stopping criteria + +Usage: python scripts/sample_english.py +""" +from __future__ import annotations + +import os +import sys + +sys.stdout.reconfigure(line_buffering=True) +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import torch +import torch.nn as nn +from transformers import ( + GenerationConfig, + GenerationMixin, + PretrainedConfig, + PreTrainedModel, +) +from transformers.modeling_outputs import CausalLMOutputWithPast + +from hydra.config import PostSemClawConfig +from hydra.model import PostSemClawModel +from prepare import Tokenizer + +CKPT_PATH = os.path.expanduser("~/.cache/autoresearch/latest.pt") + + +class _HydraGenConfig(PretrainedConfig): + model_type = "hydra" + + def __init__(self, vocab_size: int = 65536, **kw): + super().__init__(**kw) + self.vocab_size = vocab_size + self.num_hidden_layers = 4 + self.hidden_size = 256 + self.num_attention_heads = 4 + + +class HydraForCausalLM(PreTrainedModel, GenerationMixin): + """HF wrapper around PostSemClawModel so we can use .generate().""" + + config_class = _HydraGenConfig + + def __init__(self, gen_config, inner_model): + super().__init__(gen_config) + self.inner = inner_model + # HF looks for these attrs + self.config.vocab_size = gen_config.vocab_size + + def forward(self, input_ids, attention_mask=None, **kw): + logits = self.inner(input_ids) + return CausalLMOutputWithPast(loss=None, logits=logits, past_key_values=None) + + def prepare_inputs_for_generation(self, input_ids, **kw): + # Our model has no KV cache — always feed full context + return {"input_ids": input_ids} + + def get_input_embeddings(self): + return self.inner.wte + + def can_generate(self) -> bool: + return True + + @property + def _supports_cache_class(self): + return False + + +def main() -> None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"[sample] device: {device}") + + tokenizer = Tokenizer.from_directory() + vocab_size = tokenizer.get_vocab_size() + bos = tokenizer.get_bos_token_id() + + ckpt = torch.load(CKPT_PATH, map_location="cpu", weights_only=False) + cfg_dict = ckpt["config"] + step = ckpt.get("step", "?") + print(f"[sample] loaded step={step}") + + cfg = PostSemClawConfig(**cfg_dict) + with torch.device("meta"): + inner = PostSemClawModel(cfg) + inner.to_empty(device=device) + inner.load_state_dict(ckpt["model_state_dict"], strict=False) + inner.eval() + + gen_cfg = _HydraGenConfig(vocab_size=vocab_size) + # Set common pad/eos tokens so HF generate is happy (we use BOS as both) + gen_cfg.bos_token_id = bos + gen_cfg.eos_token_id = bos + gen_cfg.pad_token_id = bos + model = HydraForCausalLM(gen_cfg, inner).to(device) + model.eval() + print(f"[sample] model ready, vocab={vocab_size}") + + PROMPTS = [ + "The capital of France is", + "Paris is known for", + "Once upon a time", + "Water boils at", + "Shakespeare wrote", + "The theory of evolution was proposed by", + "Einstein discovered that", + "Photosynthesis is", + ] + + # --- Greedy --- + print("\n=== GREEDY (baseline) ===") + gen_config = GenerationConfig( + max_new_tokens=20, use_cache=False, + do_sample=False, + num_beams=1, + bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, + ) + for prompt in PROMPTS: + ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) + with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + out = model.generate(ids, generation_config=gen_config) + text = tokenizer.decode(out[0].tolist()) + print(f' "{prompt}" -> "{text}"') + + # --- Beam search (4 beams) --- + print("\n=== BEAM SEARCH (4 beams, length_penalty=1.0) ===") + gen_config = GenerationConfig( + max_new_tokens=20, use_cache=False, + num_beams=4, + do_sample=False, + length_penalty=1.0, + no_repeat_ngram_size=3, + early_stopping=True, + bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, + ) + for prompt in PROMPTS[:4]: + ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) + with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + out = model.generate(ids, generation_config=gen_config) + text = tokenizer.decode(out[0].tolist()) + print(f' "{prompt}" -> "{text}"') + + # --- Top-p sampling (nucleus, t=0.8, p=0.9) --- + print("\n=== TOP-P SAMPLING (temperature=0.8, top_p=0.9) ===") + gen_config = GenerationConfig( + max_new_tokens=30, use_cache=False, + do_sample=True, + temperature=0.8, + top_p=0.9, + repetition_penalty=1.2, + bos_token_id=bos, eos_token_id=bos, pad_token_id=bos, + ) + torch.manual_seed(42) + for prompt in PROMPTS[:4]: + ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device) + with torch.no_grad(), torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + out = model.generate(ids, generation_config=gen_config) + text = tokenizer.decode(out[0].tolist()) + print(f' "{prompt}" -> "{text}"') + + print("\n[sample] done.") + + +if __name__ == "__main__": + main() diff --git a/overlay/scripts/setup.sh b/overlay/scripts/setup.sh index a3f33d0e0436465bf25303b0fc5483a618d6b1e1..de3d2c8f62d999b9e63d582106cea21c0a38c946 100644 --- a/overlay/scripts/setup.sh +++ b/overlay/scripts/setup.sh @@ -25,4 +25,3 @@ echo "=== Setup complete ===" echo "Run experiments with: uv run train.py" echo "Run orchestrator with: uv run -m harness.orchestrator" echo "Run Phase 1 subsystems with: bash scripts/run_phase1.sh" -echo "For WSL/CUDA throughput gate: see docs/WSL_TPS_RUNBOOK.md" diff --git a/overlay/scripts/sweep_depth.py b/overlay/scripts/sweep_depth.py index fa850cebb3ccd01925aae7a1ecc34b22de8f94a1..022d01ef22c87b930df20bb2295113eee78dc5aa 100644 --- a/overlay/scripts/sweep_depth.py +++ b/overlay/scripts/sweep_depth.py @@ -1,190 +1,190 @@ -#!/usr/bin/env python3 -"""Depth-sweep driver: pre-warm retina for HYDRA_SDR_TARGET_ACTIVE, then fan out -N parallel HF Jobs with different HYDRA_N_LAYER values, each running with full -per-layer diagnostics. Collects job IDs for downstream monitoring. - -Usage: - export HF_TOKEN=... - # Optional overrides: - export HYDRA_SDR_TARGET_ACTIVE=137 - export HYDRA_TIME_BUDGET=300 # 5 min training per job - export HYDRA_MID_VAL_INTERVAL=250 # per-layer diag panel cadence - export SWEEP_N_LAYERS=2,3,4,5,6,8 - export SWEEP_D_MODEL=768 - export SWEEP_SKIP_PREWARM=0 # set =1 if retina cache already populated - python scripts/sweep_depth.py -""" -from __future__ import annotations - -import os -import subprocess -import sys -import time -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -LAUNCHER = REPO_ROOT / 'scripts' / 'launch_feather_hf_job.py' - -SWEEP_N_LAYERS = [int(v) for v in os.environ.get('SWEEP_N_LAYERS', '2,3,4,5,6,8').split(',')] -SWEEP_D_MODEL = os.environ.get('SWEEP_D_MODEL', '768') -SKIP_PREWARM = os.environ.get('SWEEP_SKIP_PREWARM', '0') == '1' -TARGET_ACTIVE = os.environ.get('HYDRA_SDR_TARGET_ACTIVE', '327') -# Short budget — we want diagnostic signal, not convergence. -TIME_BUDGET = os.environ.get('HYDRA_TIME_BUDGET', '300') -MID_VAL = os.environ.get('HYDRA_MID_VAL_INTERVAL', '250') -# Short timeout for pre-warm; sweep jobs get full 12h (no extension of wall). -PREWARM_TIMEOUT = os.environ.get('SWEEP_PREWARM_TIMEOUT', '30m') -SWEEP_TIMEOUT = os.environ.get('SWEEP_TIMEOUT', '60m') - - -def launch(env_extra: dict, timeout: str) -> str | None: - """Invoke launch_feather_hf_job.py with the given env overlay, parse job_id.""" - env = dict(os.environ) - env.update(env_extra) - env['FEATHER_HF_JOB_TIMEOUT'] = timeout - # Always enable diagnostics + JSON emission for sweep jobs. - env.setdefault('HYDRA_LAYER_DIAGNOSTICS', '1') - env.setdefault('HYDRA_MID_VAL_INTERVAL', MID_VAL) - env.setdefault('HYDRA_USE_NEMOTRON', '1') - - print(f'[sweep] launching with env overrides: {env_extra}', flush=True) - proc = subprocess.run( - [sys.executable, str(LAUNCHER)], - env=env, - capture_output=True, - text=True, - ) - sys.stdout.write(proc.stdout) - sys.stderr.write(proc.stderr) - if proc.returncode != 0: - print(f'[sweep] launcher exited {proc.returncode}', flush=True) - return None - job_id = None - for ln in proc.stdout.splitlines(): - if 'submitted job_id=' in ln: - # format: [launch] submitted job_id= status= url=... - tail = ln.split('submitted job_id=', 1)[1] - job_id = tail.split()[0].strip() - break - return job_id - - -def poll_until_done(job_id: str, poll_s: int = 30, max_wait_s: int = 1800) -> str: - """Poll HF Jobs API until the job leaves the running/pending state or we - exceed max_wait_s. Returns final stage string.""" - try: - from huggingface_hub import HfApi # type: ignore - except Exception as e: - print(f'[sweep] cannot poll (huggingface_hub missing: {e})', flush=True) - return 'UNKNOWN' - api = HfApi(token=os.environ.get('HF_TOKEN')) - t0 = time.time() - last_stage = None - while True: - try: - j = api.inspect_job(job_id=job_id) - stage = getattr(j.status, 'stage', None) if hasattr(j, 'status') else None - except Exception as e: - print(f'[sweep] poll error job={job_id} err={e}', flush=True) - stage = None - if stage != last_stage: - print(f'[sweep] job={job_id} stage={stage}', flush=True) - last_stage = stage - if stage in {'COMPLETED', 'ERROR', 'CANCELED', 'FAILED'}: - return stage or 'UNKNOWN' - if time.time() - t0 > max_wait_s: - print(f'[sweep] timed out waiting for job={job_id}', flush=True) - return stage or 'TIMEOUT' - time.sleep(poll_s) - - -def main() -> int: - if not os.environ.get('HF_TOKEN'): - print('ERROR: HF_TOKEN must be set', file=sys.stderr) - return 2 - - print(f'[sweep] plan: n_layers={SWEEP_N_LAYERS} d_model={SWEEP_D_MODEL} ' - f'target_active={TARGET_ACTIVE} time_budget={TIME_BUDGET}s mid_val={MID_VAL}', - flush=True) - - # If using Space image, upload once now; all subsequent launches reuse it. - use_space = os.environ.get('FEATHER_HF_USE_SPACE_IMAGE', '0') == '1' - if use_space: - print('[sweep] Space image mode: uploading overlay now, subsequent ' - 'launches will skip upload', flush=True) - - # --- Pre-warm retina cache --- - if not SKIP_PREWARM: - print('[sweep] === PRE-WARM retina cache ===', flush=True) - prewarm_env = { - 'HYDRA_N_LAYER': '2', - 'HYDRA_D_MODEL': SWEEP_D_MODEL, - 'HYDRA_SDR_TARGET_ACTIVE': TARGET_ACTIVE, - # Minimal training — just enough to force retina build + upload. - 'HYDRA_TIME_BUDGET': '30', - 'HYDRA_CKPT_INTERVAL': '0', - 'HYDRA_MID_VAL_INTERVAL': '0', - 'HYDRA_LAYER_DIAGNOSTICS': '0', # no need during pre-warm - 'HYDRA_METRICS_OUT': '/tmp/prewarm_metrics.json', - } - prewarm_id = launch(prewarm_env, PREWARM_TIMEOUT) - # After the first launch, Space image (if used) is built — skip re-upload. - if use_space: - os.environ['FEATHER_HF_SKIP_UPLOAD'] = '1' - if not prewarm_id: - print('[sweep] pre-warm failed to submit', flush=True) - return 3 - print(f'[sweep] pre-warm job={prewarm_id}, waiting for completion...', flush=True) - stage = poll_until_done(prewarm_id, poll_s=20, max_wait_s=1800) - print(f'[sweep] pre-warm finished stage={stage}', flush=True) - if stage not in {'COMPLETED'}: - print(f'[sweep] WARNING: pre-warm did not COMPLETE (stage={stage}); ' - f'sweep jobs will each rebuild retina. Proceeding anyway.', - flush=True) - else: - print('[sweep] SKIP_PREWARM=1; assuming retina cache already populated', flush=True) - - # --- Fan out sweep jobs (concurrent) --- - print('[sweep] === FAN OUT n_layer sweep ===', flush=True) - sweep_jobs = {} - for idx, n_layer in enumerate(SWEEP_N_LAYERS): - env_extra = { - 'HYDRA_N_LAYER': str(n_layer), - 'HYDRA_D_MODEL': SWEEP_D_MODEL, - 'HYDRA_SDR_TARGET_ACTIVE': TARGET_ACTIVE, - 'HYDRA_TIME_BUDGET': TIME_BUDGET, - 'HYDRA_CKPT_INTERVAL': '0', - 'HYDRA_LAYER_DIAGNOSTICS': '1', - 'HYDRA_MID_VAL_INTERVAL': MID_VAL, - 'HYDRA_METRICS_OUT': f'/tmp/sweep_n{n_layer}_metrics.json', - } - jid = launch(env_extra, SWEEP_TIMEOUT) - # After the first launch in Space-image mode, mark skip-upload for the rest. - if use_space and idx == 0: - os.environ['FEATHER_HF_SKIP_UPLOAD'] = '1' - if jid: - sweep_jobs[n_layer] = jid - print(f'[sweep] n_layer={n_layer} -> job_id={jid}', flush=True) - else: - print(f'[sweep] n_layer={n_layer} FAILED to submit', flush=True) - - print('[sweep] === SWEEP SUBMITTED ===', flush=True) - print('[sweep] tracked jobs:', flush=True) - for n, j in sweep_jobs.items(): - print(f' n_layer={n:2d} job_id={j}', flush=True) - - # Write manifest so the aggregator can find them. - manifest = Path('/tmp/sweep_depth_manifest.txt') - manifest.write_text( - 'n_layer\tjob_id\tmetrics_path\n' + - '\n'.join( - f'{n}\t{j}\t/tmp/sweep_n{n}_metrics.json' - for n, j in sweep_jobs.items() - ) + '\n' - ) - print(f'[sweep] manifest -> {manifest}', flush=True) - return 0 - - -if __name__ == '__main__': - raise SystemExit(main()) +#!/usr/bin/env python3 +"""Depth-sweep driver: pre-warm retina for HYDRA_SDR_TARGET_ACTIVE, then fan out +N parallel HF Jobs with different HYDRA_N_LAYER values, each running with full +per-layer diagnostics. Collects job IDs for downstream monitoring. + +Usage: + export HF_TOKEN=... + # Optional overrides: + export HYDRA_SDR_TARGET_ACTIVE=137 + export HYDRA_TIME_BUDGET=300 # 5 min training per job + export HYDRA_MID_VAL_INTERVAL=250 # per-layer diag panel cadence + export SWEEP_N_LAYERS=2,3,4,5,6,8 + export SWEEP_D_MODEL=768 + export SWEEP_SKIP_PREWARM=0 # set =1 if retina cache already populated + python scripts/sweep_depth.py +""" +from __future__ import annotations + +import os +import subprocess +import sys +import time +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +LAUNCHER = REPO_ROOT / 'scripts' / 'launch_feather_hf_job.py' + +SWEEP_N_LAYERS = [int(v) for v in os.environ.get('SWEEP_N_LAYERS', '2,3,4,5,6,8').split(',')] +SWEEP_D_MODEL = os.environ.get('SWEEP_D_MODEL', '768') +SKIP_PREWARM = os.environ.get('SWEEP_SKIP_PREWARM', '0') == '1' +TARGET_ACTIVE = os.environ.get('HYDRA_SDR_TARGET_ACTIVE', '327') +# Short budget — we want diagnostic signal, not convergence. +TIME_BUDGET = os.environ.get('HYDRA_TIME_BUDGET', '300') +MID_VAL = os.environ.get('HYDRA_MID_VAL_INTERVAL', '250') +# Short timeout for pre-warm; sweep jobs get full 12h (no extension of wall). +PREWARM_TIMEOUT = os.environ.get('SWEEP_PREWARM_TIMEOUT', '30m') +SWEEP_TIMEOUT = os.environ.get('SWEEP_TIMEOUT', '60m') + + +def launch(env_extra: dict, timeout: str) -> str | None: + """Invoke launch_feather_hf_job.py with the given env overlay, parse job_id.""" + env = dict(os.environ) + env.update(env_extra) + env['FEATHER_HF_JOB_TIMEOUT'] = timeout + # Always enable diagnostics + JSON emission for sweep jobs. + env.setdefault('HYDRA_LAYER_DIAGNOSTICS', '1') + env.setdefault('HYDRA_MID_VAL_INTERVAL', MID_VAL) + env.setdefault('HYDRA_USE_NEMOTRON', '1') + + print(f'[sweep] launching with env overrides: {env_extra}', flush=True) + proc = subprocess.run( + [sys.executable, str(LAUNCHER)], + env=env, + capture_output=True, + text=True, + ) + sys.stdout.write(proc.stdout) + sys.stderr.write(proc.stderr) + if proc.returncode != 0: + print(f'[sweep] launcher exited {proc.returncode}', flush=True) + return None + job_id = None + for ln in proc.stdout.splitlines(): + if 'submitted job_id=' in ln: + # format: [launch] submitted job_id= status= url=... + tail = ln.split('submitted job_id=', 1)[1] + job_id = tail.split()[0].strip() + break + return job_id + + +def poll_until_done(job_id: str, poll_s: int = 30, max_wait_s: int = 1800) -> str: + """Poll HF Jobs API until the job leaves the running/pending state or we + exceed max_wait_s. Returns final stage string.""" + try: + from huggingface_hub import HfApi # type: ignore + except Exception as e: + print(f'[sweep] cannot poll (huggingface_hub missing: {e})', flush=True) + return 'UNKNOWN' + api = HfApi(token=os.environ.get('HF_TOKEN')) + t0 = time.time() + last_stage = None + while True: + try: + j = api.inspect_job(job_id=job_id) + stage = getattr(j.status, 'stage', None) if hasattr(j, 'status') else None + except Exception as e: + print(f'[sweep] poll error job={job_id} err={e}', flush=True) + stage = None + if stage != last_stage: + print(f'[sweep] job={job_id} stage={stage}', flush=True) + last_stage = stage + if stage in {'COMPLETED', 'ERROR', 'CANCELED', 'FAILED'}: + return stage or 'UNKNOWN' + if time.time() - t0 > max_wait_s: + print(f'[sweep] timed out waiting for job={job_id}', flush=True) + return stage or 'TIMEOUT' + time.sleep(poll_s) + + +def main() -> int: + if not os.environ.get('HF_TOKEN'): + print('ERROR: HF_TOKEN must be set', file=sys.stderr) + return 2 + + print(f'[sweep] plan: n_layers={SWEEP_N_LAYERS} d_model={SWEEP_D_MODEL} ' + f'target_active={TARGET_ACTIVE} time_budget={TIME_BUDGET}s mid_val={MID_VAL}', + flush=True) + + # If using Space image, upload once now; all subsequent launches reuse it. + use_space = os.environ.get('FEATHER_HF_USE_SPACE_IMAGE', '0') == '1' + if use_space: + print('[sweep] Space image mode: uploading overlay now, subsequent ' + 'launches will skip upload', flush=True) + + # --- Pre-warm retina cache --- + if not SKIP_PREWARM: + print('[sweep] === PRE-WARM retina cache ===', flush=True) + prewarm_env = { + 'HYDRA_N_LAYER': '2', + 'HYDRA_D_MODEL': SWEEP_D_MODEL, + 'HYDRA_SDR_TARGET_ACTIVE': TARGET_ACTIVE, + # Minimal training — just enough to force retina build + upload. + 'HYDRA_TIME_BUDGET': '30', + 'HYDRA_CKPT_INTERVAL': '0', + 'HYDRA_MID_VAL_INTERVAL': '0', + 'HYDRA_LAYER_DIAGNOSTICS': '0', # no need during pre-warm + 'HYDRA_METRICS_OUT': '/tmp/prewarm_metrics.json', + } + prewarm_id = launch(prewarm_env, PREWARM_TIMEOUT) + # After the first launch, Space image (if used) is built — skip re-upload. + if use_space: + os.environ['FEATHER_HF_SKIP_UPLOAD'] = '1' + if not prewarm_id: + print('[sweep] pre-warm failed to submit', flush=True) + return 3 + print(f'[sweep] pre-warm job={prewarm_id}, waiting for completion...', flush=True) + stage = poll_until_done(prewarm_id, poll_s=20, max_wait_s=1800) + print(f'[sweep] pre-warm finished stage={stage}', flush=True) + if stage not in {'COMPLETED'}: + print(f'[sweep] WARNING: pre-warm did not COMPLETE (stage={stage}); ' + f'sweep jobs will each rebuild retina. Proceeding anyway.', + flush=True) + else: + print('[sweep] SKIP_PREWARM=1; assuming retina cache already populated', flush=True) + + # --- Fan out sweep jobs (concurrent) --- + print('[sweep] === FAN OUT n_layer sweep ===', flush=True) + sweep_jobs = {} + for idx, n_layer in enumerate(SWEEP_N_LAYERS): + env_extra = { + 'HYDRA_N_LAYER': str(n_layer), + 'HYDRA_D_MODEL': SWEEP_D_MODEL, + 'HYDRA_SDR_TARGET_ACTIVE': TARGET_ACTIVE, + 'HYDRA_TIME_BUDGET': TIME_BUDGET, + 'HYDRA_CKPT_INTERVAL': '0', + 'HYDRA_LAYER_DIAGNOSTICS': '1', + 'HYDRA_MID_VAL_INTERVAL': MID_VAL, + 'HYDRA_METRICS_OUT': f'/tmp/sweep_n{n_layer}_metrics.json', + } + jid = launch(env_extra, SWEEP_TIMEOUT) + # After the first launch in Space-image mode, mark skip-upload for the rest. + if use_space and idx == 0: + os.environ['FEATHER_HF_SKIP_UPLOAD'] = '1' + if jid: + sweep_jobs[n_layer] = jid + print(f'[sweep] n_layer={n_layer} -> job_id={jid}', flush=True) + else: + print(f'[sweep] n_layer={n_layer} FAILED to submit', flush=True) + + print('[sweep] === SWEEP SUBMITTED ===', flush=True) + print('[sweep] tracked jobs:', flush=True) + for n, j in sweep_jobs.items(): + print(f' n_layer={n:2d} job_id={j}', flush=True) + + # Write manifest so the aggregator can find them. + manifest = Path('/tmp/sweep_depth_manifest.txt') + manifest.write_text( + 'n_layer\tjob_id\tmetrics_path\n' + + '\n'.join( + f'{n}\t{j}\t/tmp/sweep_n{n}_metrics.json' + for n, j in sweep_jobs.items() + ) + '\n' + ) + print(f'[sweep] manifest -> {manifest}', flush=True) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/overlay/scripts/sweep_depth_aggregate.py b/overlay/scripts/sweep_depth_aggregate.py index 1a98c3b54f7549d5b98b706f451a72a898cfc217..5666f8866f9803d6fc7968fe70f91a8d010a6aa7 100644 --- a/overlay/scripts/sweep_depth_aggregate.py +++ b/overlay/scripts/sweep_depth_aggregate.py @@ -1,93 +1,42 @@ -#!/usr/bin/env python3 -"""Aggregator for depth-sweep results. - -Reads the sweep manifest at /tmp/sweep_depth_manifest.txt, pulls HF Jobs logs -for each job, extracts the [METRICS_JSON] stdout line, and prints a -comparison table of per-layer diagnostics across n_layer values. - -Usage: - export HF_TOKEN=... - python scripts/sweep_depth_aggregate.py [manifest_path] -""" -from __future__ import annotations - +#!/usr/bin/env python3 +"""Aggregator for depth-sweep results. + +Reads the sweep manifest at /tmp/sweep_depth_manifest.txt, pulls HF Jobs logs +for each job, extracts the [METRICS_JSON] stdout line, and prints a +comparison table of per-layer diagnostics across n_layer values. + +Usage: + export HF_TOKEN=... + python scripts/sweep_depth_aggregate.py [manifest_path] +""" +from __future__ import annotations + import json import os -import statistics -import re import sys from pathlib import Path -from configs.harness_config import HarnessConfig +MANIFEST = Path(sys.argv[1] if len(sys.argv) > 1 else '/tmp/sweep_depth_manifest.txt') -type MetricValue = float | int | str | bool | None -type MetricsDict = dict[str, MetricValue] -MANIFEST = Path(sys.argv[1] if len(sys.argv) > 1 else '/tmp/sweep_depth_manifest.txt') -STEP_TPS_PATTERN = re.compile(r"step=(\d+).*?\btps=(\d+)\b") -MIN_TPS = float(os.environ.get('SWEEP_MIN_TPS', '0')) - - -def _zero_shot_score(result: MetricsDict) -> float: - """Composite quality score for tie-breaking among BPB-near runs.""" - factual = float(result.get('factual_english_score', 0.0) or 0.0) - instruction = float(result.get('instruction_following_score', 0.0) or 0.0) - distinct_2 = float(result.get('distinct_2', 0.0) or 0.0) - repetition = float(result.get('repetition_rate', 0.0) or 0.0) - return factual + instruction + distinct_2 - repetition - - -def _metric_float(result: MetricsDict, key: str, default: float = 0.0) -> float: - value = result.get(key, default) - return float(value) if isinstance(value, (int, float)) else default - - -def _metric_int(result: MetricsDict, key: str, default: int = 0) -> int: - value = result.get(key, default) - return int(value) if isinstance(value, int) else default - - -def _percentile_linear(sorted_values: list[float], pct: float) -> float: - if not sorted_values: - return 0.0 - if len(sorted_values) == 1: - return sorted_values[0] - rank = (len(sorted_values) - 1) * (pct / 100.0) - lo = int(rank) - hi = min(lo + 1, len(sorted_values) - 1) - frac = rank - lo - return sorted_values[lo] * (1.0 - frac) + sorted_values[hi] * frac - - -def fetch_metrics_from_job(job_id: str) -> MetricsDict | None: +def fetch_metrics_from_job(job_id: str) -> dict | None: """Fetch HF Job stdout and parse the [METRICS_JSON] line.""" - try: - from huggingface_hub import HfApi # type: ignore - except Exception as e: - print(f'ERROR: huggingface_hub missing: {e}', file=sys.stderr) - return None - api = HfApi(token=os.environ.get('HF_TOKEN')) - try: - logs_stream = api.fetch_job_logs(job_id=job_id) - except Exception as e: - print(f'[agg] could not fetch logs for job={job_id}: {e}', file=sys.stderr) - return None - + try: + from huggingface_hub import HfApi # type: ignore + except Exception as e: + print(f'ERROR: huggingface_hub missing: {e}', file=sys.stderr) + return None + api = HfApi(token=os.environ.get('HF_TOKEN')) + try: + logs_stream = api.fetch_job_logs(job_id=job_id) + except Exception as e: + print(f'[agg] could not fetch logs for job={job_id}: {e}', file=sys.stderr) + return None + last_json = None - tps_samples: list[tuple[int, int]] = [] - warmup_steps = 25 for line in logs_stream: # HfApi returns strings or JobLogEntry-like objects depending on version. text = getattr(line, 'data', None) or str(line) - - wm = re.search(r"\[TPS_GUARD\] enabled .*?warmup_steps=(\d+)", text) - if wm: - warmup_steps = int(wm.group(1)) - - sm = STEP_TPS_PATTERN.search(text) - if sm: - tps_samples.append((int(sm.group(1)), int(sm.group(2)))) - if '[METRICS_JSON]' in text: payload = text.split('[METRICS_JSON]', 1)[1].strip() try: @@ -95,156 +44,111 @@ def fetch_metrics_from_job(job_id: str) -> MetricsDict | None: except Exception: # Might be truncated on a line boundary — keep looking. pass - if last_json is None: - return None + return last_json - steady_tps = [float(tps) for step, tps in tps_samples if step >= warmup_steps] - if not steady_tps: - steady_tps = [float(tps) for _, tps in tps_samples] - if steady_tps: - sorted_tps = sorted(steady_tps) - last_json['tps_samples'] = len(steady_tps) - last_json['tps_median'] = float(statistics.median(steady_tps)) - last_json['tps_p10'] = float(_percentile_linear(sorted_tps, 10.0)) - last_json['tps_min'] = float(sorted_tps[0]) - last_json['tps_max'] = float(sorted_tps[-1]) - last_json['tps_warmup_steps'] = int(warmup_steps) - return last_json - - -def compare(results: dict[int, MetricsDict]) -> None: +def compare(results: dict[int, dict]) -> None: """Pretty-print comparison across n_layer values.""" if not results: print('[agg] no results') return sorted_n = sorted(results.keys()) - secondary_gates = HarnessConfig().to_secondary_gates() - - print('\n=== Active secondary gates ===') - for metric, thresholds in sorted(secondary_gates.items()): - print(f' {metric}: {json.dumps(thresholds, sort_keys=True)}') # Top-level scalars print('\n=== Top-level scalars ===') - hdr = ['metric'] + [f'L={n}' for n in sorted_n] - print(' '.join(f'{h:>14}' for h in hdr)) + hdr = ['metric'] + [f'L={n}' for n in sorted_n] + print(' '.join(f'{h:>14}' for h in hdr)) for key in ('val_bpb', 'val_ppl', 'num_params_M', 'total_tokens_M', 'training_seconds', 'peak_vram_mb', 'sdr_target_active', - 'htm_anomaly', 'engram_hit_rate', 'sdr_active_bits', - 'tps_median', 'tps_p10', 'tps_min', 'tps_max', 'tps_samples'): + 'htm_anomaly', 'engram_hit_rate', 'sdr_active_bits'): row = [key] + [f'{results[n].get(key, float("nan")):.4f}' if isinstance(results[n].get(key), (int, float)) else 'n/a' for n in sorted_n] print(' '.join(f'{c:>14}' for c in row)) - - # Per-layer panel — one table per metric. - print('\n=== Per-layer: delta_ratio (residual contribution) ===') - print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) - max_depth = max(_metric_int(results[n], 'n_layer', 0) for n in sorted_n) - for li in range(max_depth): - row = [f'L{li:02d}'] - for n in sorted_n: - v = results[n].get(f'layer_{li}_delta_ratio') - row.append(f'{v:.4f}' if isinstance(v, (int, float)) else ' -') - print(' '.join(f'{c:>7}' for c in row)) - - print('\n=== Per-layer: grad_norm ===') - print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) - for li in range(max_depth): - row = [f'L{li:02d}'] - for n in sorted_n: - v = results[n].get(f'layer_{li}_grad_norm') - row.append(f'{v:.2e}' if isinstance(v, (int, float)) else ' -') - print(' '.join(f'{c:>9}' for c in row)) - - print('\n=== Per-layer: eff_rank (participation-ratio) ===') - print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) - for li in range(max_depth): - row = [f'L{li:02d}'] - for n in sorted_n: - v = results[n].get(f'layer_{li}_eff_rank') - row.append(f'{v:.1f}' if isinstance(v, (int, float)) else ' -') - print(' '.join(f'{c:>7}' for c in row)) - - print('\n=== Per-layer: feat_std ===') - print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) - for li in range(max_depth): - row = [f'L{li:02d}'] - for n in sorted_n: - v = results[n].get(f'layer_{li}_feat_std') - row.append(f'{v:.4f}' if isinstance(v, (int, float)) else ' -') - print(' '.join(f'{c:>7}' for c in row)) - - # Dead-layer detection - print('\n=== Dead-layer detection (delta_ratio < 0.02) ===') + + # Per-layer panel — one table per metric. + print('\n=== Per-layer: delta_ratio (residual contribution) ===') + print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) + max_depth = max(results[n].get('n_layer', 0) for n in sorted_n) + for li in range(max_depth): + row = [f'L{li:02d}'] + for n in sorted_n: + v = results[n].get(f'layer_{li}_delta_ratio') + row.append(f'{v:.4f}' if isinstance(v, (int, float)) else ' -') + print(' '.join(f'{c:>7}' for c in row)) + + print('\n=== Per-layer: grad_norm ===') + print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) + for li in range(max_depth): + row = [f'L{li:02d}'] + for n in sorted_n: + v = results[n].get(f'layer_{li}_grad_norm') + row.append(f'{v:.2e}' if isinstance(v, (int, float)) else ' -') + print(' '.join(f'{c:>9}' for c in row)) + + print('\n=== Per-layer: eff_rank (participation-ratio) ===') + print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) + for li in range(max_depth): + row = [f'L{li:02d}'] + for n in sorted_n: + v = results[n].get(f'layer_{li}_eff_rank') + row.append(f'{v:.1f}' if isinstance(v, (int, float)) else ' -') + print(' '.join(f'{c:>7}' for c in row)) + + print('\n=== Per-layer: feat_std ===') + print(' '.join(['layer'] + [f'L={n:>2}' for n in sorted_n])) + for li in range(max_depth): + row = [f'L{li:02d}'] + for n in sorted_n: + v = results[n].get(f'layer_{li}_feat_std') + row.append(f'{v:.4f}' if isinstance(v, (int, float)) else ' -') + print(' '.join(f'{c:>7}' for c in row)) + + # Dead-layer detection + print('\n=== Dead-layer detection (delta_ratio < 0.02) ===') for n in sorted_n: r = results[n] - n_layer = _metric_int(r, 'n_layer', 0) - dead = [] - for li in range(n_layer): - v = r.get(f'layer_{li}_delta_ratio') - if isinstance(v, (int, float)) and v < 0.02: - dead.append(li) + n_layer = r.get('n_layer', 0) + dead = [] + for li in range(n_layer): + v = r.get(f'layer_{li}_delta_ratio') + if isinstance(v, (int, float)) and v < 0.02: + dead.append(li) status = 'ALL LIVE' if not dead else f'DEAD LAYERS: {dead}' print(f' n_layer={n:2d} val_bpb={r.get("val_bpb", float("nan")):.4f} {status}') - print('\n=== Throughput-constrained ranking ===') - ranked = sorted( - ((n, r) for n, r in results.items() if isinstance(r.get('val_bpb'), (int, float))), - key=lambda x: ( - (MIN_TPS > 0) and (_metric_float(x[1], 'tps_median', 0.0) < MIN_TPS), - _metric_float(x[1], 'val_bpb', float('inf')), - -_zero_shot_score(x[1]), - ), - ) - feasible_count = 0 - for n, r in ranked: - tps_median = _metric_float(r, 'tps_median', 0.0) - feasible = (MIN_TPS <= 0) or (tps_median >= MIN_TPS) - zero_shot_score = _zero_shot_score(r) - if feasible: - feasible_count += 1 - print( - f" n_layer={n:2d} val_bpb={_metric_float(r, 'val_bpb', float('nan')):.4f} " - f"tps_median={tps_median:.0f} zero_shot_score={zero_shot_score:.4f} feasible={feasible}", - flush=True, - ) - if MIN_TPS > 0: - print(f"[agg] throughput gate: tps_median >= {MIN_TPS:.0f}; feasible={feasible_count}/{len(ranked)}") - - -def main() -> int: - if not MANIFEST.exists(): - print(f'ERROR: manifest not found at {MANIFEST}', file=sys.stderr) - return 2 - lines = MANIFEST.read_text().splitlines()[1:] # skip header - jobs = {} - for ln in lines: - parts = ln.strip().split('\t') - if len(parts) < 2: - continue - try: - n_layer = int(parts[0]) - job_id = parts[1] - except ValueError: - continue - jobs[n_layer] = job_id - - print(f'[agg] reading {len(jobs)} jobs from {MANIFEST}') - results: dict[int, MetricsDict] = {} - for n, jid in jobs.items(): - print(f'[agg] fetching job={jid} (n_layer={n}) ...') - m = fetch_metrics_from_job(jid) - if m is None: - print(f'[agg] no metrics for n_layer={n} (job likely still running or failed)') - continue - results[n] = m - compare(results) - - out_path = Path('/tmp/sweep_depth_aggregated.json') - out_path.write_text(json.dumps(results, indent=2, sort_keys=True)) - print(f'\n[agg] wrote aggregated results to {out_path}') - return 0 - - -if __name__ == '__main__': - raise SystemExit(main()) + +def main() -> int: + if not MANIFEST.exists(): + print(f'ERROR: manifest not found at {MANIFEST}', file=sys.stderr) + return 2 + lines = MANIFEST.read_text().splitlines()[1:] # skip header + jobs = {} + for ln in lines: + parts = ln.strip().split('\t') + if len(parts) < 2: + continue + try: + n_layer = int(parts[0]) + job_id = parts[1] + except ValueError: + continue + jobs[n_layer] = job_id + + print(f'[agg] reading {len(jobs)} jobs from {MANIFEST}') + results: dict[int, dict] = {} + for n, jid in jobs.items(): + print(f'[agg] fetching job={jid} (n_layer={n}) ...') + m = fetch_metrics_from_job(jid) + if m is None: + print(f'[agg] no metrics for n_layer={n} (job likely still running or failed)') + continue + results[n] = m + compare(results) + + out_path = Path('/tmp/sweep_depth_aggregated.json') + out_path.write_text(json.dumps(results, indent=2, sort_keys=True)) + print(f'\n[agg] wrote aggregated results to {out_path}') + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/overlay/scripts/watch_checkpoint.py b/overlay/scripts/watch_checkpoint.py index 48f134f07bae6c7041c0053f402d056256868677..fa4cd3a473a02c9d3727ae855cdc5a6cddbffc16 100644 --- a/overlay/scripts/watch_checkpoint.py +++ b/overlay/scripts/watch_checkpoint.py @@ -1,101 +1,101 @@ -"""Watch latest.pt for updates and run factual probes each time it changes. - -Runs on CPU in a separate process — doesn't steal GPU from training. -Shows what the model is actually learning via top-5 completions for -canonical prompts ("The capital of France is", etc.). - -Usage: python scripts/watch_checkpoint.py -""" -from __future__ import annotations - -import os -import sys -import time -from contextlib import nullcontext - -sys.stdout.reconfigure(line_buffering=True) - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import torch - -from hydra.config import PostSemClawConfig -from hydra.model import PostSemClawModel -from prepare import Tokenizer, MAX_SEQ_LEN - -CKPT_PATH = os.path.expanduser("~/.cache/autoresearch/latest.pt") -POLL_INTERVAL = 15.0 # seconds - -FACTUAL_PROMPTS = [ - "The capital of France is", - "Water boils at", - "The largest planet in our solar system is", - "The speed of light is approximately", - "Shakespeare wrote", - "DNA stands for", - "The theory of relativity was developed by", - "The Pacific Ocean is", -] - - -def load_model_cpu(ckpt_path: str, tokenizer): - """Load a checkpoint on CPU. Returns (model, step).""" - ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) - - # Extract config from checkpoint (stored in save_ckpt) - cfg_dict = ckpt.get("config") - if cfg_dict is None: - raise RuntimeError("checkpoint missing 'config' field") - - cfg = PostSemClawConfig(**cfg_dict) - model = PostSemClawModel(cfg) - model.load_state_dict(ckpt["model"]) - model.eval() - return model, ckpt.get("step", "?") - - -def run_probes(model, tokenizer): - """Top-5 completions for each factual prompt (CPU, no autocast).""" - with torch.no_grad(): - for prompt_text in FACTUAL_PROMPTS: - ids = tokenizer.encode(prompt_text) - x = torch.tensor([ids], dtype=torch.long) - logits = model(x) - probs = torch.softmax(logits[0, -1].float(), dim=-1) - top5 = torch.topk(probs, 5) - completions = [tokenizer.decode([idx.item()]) for idx in top5.indices] - probs_list = [f"{p:.3f}" for p in top5.values[:3].tolist()] - print(f' "{prompt_text}" -> {completions[:3]} (p={probs_list})', flush=True) - - -def main() -> None: - print(f"[watch] loading tokenizer...", flush=True) - tokenizer = Tokenizer.from_directory() - print(f"[watch] watching {CKPT_PATH} (poll every {POLL_INTERVAL:.0f}s)", flush=True) - - last_mtime = 0.0 - while True: - try: - if os.path.exists(CKPT_PATH): - mtime = os.path.getmtime(CKPT_PATH) - if mtime > last_mtime: - last_mtime = mtime - ts = time.strftime("%H:%M:%S", time.localtime(mtime)) - print(f"\n[watch] checkpoint updated at {ts}", flush=True) - try: - model, step = load_model_cpu(CKPT_PATH, tokenizer) - print(f"[watch] loaded step={step}", flush=True) - t0 = time.time() - run_probes(model, tokenizer) - print(f"[watch] probes ran in {time.time() - t0:.1f}s", flush=True) - del model - except Exception as e: - print(f"[watch] probe failed: {type(e).__name__}: {e}", flush=True) - except KeyboardInterrupt: - print("[watch] exiting.", flush=True) - return - time.sleep(POLL_INTERVAL) - - -if __name__ == "__main__": - main() +"""Watch latest.pt for updates and run factual probes each time it changes. + +Runs on CPU in a separate process — doesn't steal GPU from training. +Shows what the model is actually learning via top-5 completions for +canonical prompts ("The capital of France is", etc.). + +Usage: python scripts/watch_checkpoint.py +""" +from __future__ import annotations + +import os +import sys +import time +from contextlib import nullcontext + +sys.stdout.reconfigure(line_buffering=True) + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import torch + +from hydra.config import PostSemClawConfig +from hydra.model import PostSemClawModel +from prepare import Tokenizer, MAX_SEQ_LEN + +CKPT_PATH = os.path.expanduser("~/.cache/autoresearch/latest.pt") +POLL_INTERVAL = 15.0 # seconds + +FACTUAL_PROMPTS = [ + "The capital of France is", + "Water boils at", + "The largest planet in our solar system is", + "The speed of light is approximately", + "Shakespeare wrote", + "DNA stands for", + "The theory of relativity was developed by", + "The Pacific Ocean is", +] + + +def load_model_cpu(ckpt_path: str, tokenizer): + """Load a checkpoint on CPU. Returns (model, step).""" + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) + + # Extract config from checkpoint (stored in save_ckpt) + cfg_dict = ckpt.get("config") + if cfg_dict is None: + raise RuntimeError("checkpoint missing 'config' field") + + cfg = PostSemClawConfig(**cfg_dict) + model = PostSemClawModel(cfg) + model.load_state_dict(ckpt["model"]) + model.eval() + return model, ckpt.get("step", "?") + + +def run_probes(model, tokenizer): + """Top-5 completions for each factual prompt (CPU, no autocast).""" + with torch.no_grad(): + for prompt_text in FACTUAL_PROMPTS: + ids = tokenizer.encode(prompt_text) + x = torch.tensor([ids], dtype=torch.long) + logits = model(x) + probs = torch.softmax(logits[0, -1].float(), dim=-1) + top5 = torch.topk(probs, 5) + completions = [tokenizer.decode([idx.item()]) for idx in top5.indices] + probs_list = [f"{p:.3f}" for p in top5.values[:3].tolist()] + print(f' "{prompt_text}" -> {completions[:3]} (p={probs_list})', flush=True) + + +def main() -> None: + print(f"[watch] loading tokenizer...", flush=True) + tokenizer = Tokenizer.from_directory() + print(f"[watch] watching {CKPT_PATH} (poll every {POLL_INTERVAL:.0f}s)", flush=True) + + last_mtime = 0.0 + while True: + try: + if os.path.exists(CKPT_PATH): + mtime = os.path.getmtime(CKPT_PATH) + if mtime > last_mtime: + last_mtime = mtime + ts = time.strftime("%H:%M:%S", time.localtime(mtime)) + print(f"\n[watch] checkpoint updated at {ts}", flush=True) + try: + model, step = load_model_cpu(CKPT_PATH, tokenizer) + print(f"[watch] loaded step={step}", flush=True) + t0 = time.time() + run_probes(model, tokenizer) + print(f"[watch] probes ran in {time.time() - t0:.1f}s", flush=True) + del model + except Exception as e: + print(f"[watch] probe failed: {type(e).__name__}: {e}", flush=True) + except KeyboardInterrupt: + print("[watch] exiting.", flush=True) + return + time.sleep(POLL_INTERVAL) + + +if __name__ == "__main__": + main() diff --git a/overlay/subsystems/cantor_router.py b/overlay/subsystems/cantor_router.py new file mode 100644 index 0000000000000000000000000000000000000000..65b9b01c9467ac4efb961f52beef143cb6f8fe25 --- /dev/null +++ b/overlay/subsystems/cantor_router.py @@ -0,0 +1,128 @@ +"""CantorRouter — fixed-depth binary tree routing. + +Phase 1: static branching vectors, deterministic sign-check routing. +Phase 2: optional learnable branching prototypes with detached hard routes and +an opt-in differentiable confidence score path for regularization. +""" + +from __future__ import annotations + +import math +import os +from typing import Optional, Tuple + +import torch +import torch.nn as nn + + +class CantorRouter(nn.Module): + """Fixed-depth binary tree router over a d_query-dimensional query space. + + Hard leaf IDs are always gradient-free. If `learnable=True` and + `score_grad=True`, returned confidence scores can carry gradients into the + branch prototypes while routing decisions still use a detached path. + """ + + def __init__( + self, + depth: int = 7, + d_query: int = 160, + seed: int = 42, + device: str | torch.device = "cpu", + learnable: Optional[bool] = None, + score_grad: Optional[bool] = None, + ) -> None: + super().__init__() + if depth < 1 or depth > 16: + raise ValueError(f"depth must be in [1, 16], got {depth}") + if d_query < 1: + raise ValueError(f"d_query must be positive, got {d_query}") + + if learnable is None: + learnable = os.environ.get("HYDRA_CANTOR_LEARNABLE", "1") != "0" + if score_grad is None: + score_grad = os.environ.get("HYDRA_CANTOR_SCORE_GRAD", "1") != "0" + + self.depth = depth + self.n_leaves = 1 << depth + self.n_internal = self.n_leaves - 1 + self.d_query = d_query + self.learnable = bool(learnable) + self.score_grad = bool(score_grad) + + # Explicit CPU allocation is meta-device safe during lazy init. + g = torch.Generator(device="cpu") + g.manual_seed(seed) + bound = math.sqrt(3.0 / d_query) + branch = torch.empty(self.n_internal, d_query, device="cpu") + branch.uniform_(-bound, bound, generator=g) + branch = branch.to(device) + + if self.learnable: + self.branch = nn.Parameter(branch) + else: + self.register_buffer("branch", branch) + + def forward( + self, + x: torch.Tensor, + return_scores: bool = False, + score_grad: Optional[bool] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Route queries through the tree. + + Returns `(leaf_ids, scores_or_None)`. `leaf_ids` are integer, hard, + deterministic, and detached in all modes. + """ + B, T, D = x.shape + if D != self.d_query: + raise ValueError(f"Query dim mismatch: got {D}, expected {self.d_query}") + if score_grad is None: + score_grad = self.score_grad + score_grad = bool(score_grad) + + x_flat_raw = x.reshape(-1, D).float() + # Route on direction, not activation magnitude. In full Feather runs the + # pre-Engram residual norm can grow very large; unnormalized dots drove + # the tree into a near-constant path (2/128 active leaves observed), + # effectively collapsing the Cantor partition while still reporting the + # subsystem as enabled. Normalizing fixes score scale, but fidelity B + # still collapsed to 2/128 leaves: the remaining failure is a shared + # common-mode direction across most query tokens. Remove that per-forward + # mean before angular routing so the Cantor tree partitions token-relative + # directions while preserving the target Cantor+Reality+FusedSDR stack. + if os.environ.get("HYDRA_CANTOR_CENTER", "1") != "0" and x_flat_raw.shape[0] > 1: + x_flat_raw = x_flat_raw - x_flat_raw.mean(dim=0, keepdim=True) + x_flat = torch.nn.functional.normalize(x_flat_raw, dim=-1, eps=1e-6).to(x.dtype) + N = x_flat.shape[0] + leaf_ids = torch.zeros(N, dtype=torch.int64, device=x.device) + node_ptr = torch.zeros(N, dtype=torch.int64, device=x.device) + scores = torch.zeros(N, dtype=x.dtype, device=x.device) if return_scores else None + + for _level in range(self.depth): + branch_route = self.branch.detach()[node_ptr] + dots_route = (branch_route * x_flat.detach()).sum(dim=-1) + go_right = (dots_route > 0).to(torch.int64) + + if return_scores: + if score_grad and self.learnable: + branch_score = self.branch[node_ptr] + dots_score = (branch_score * x_flat).sum(dim=-1) + scores = scores + dots_score.abs() + else: + scores = scores + dots_route.abs() + + leaf_ids = (leaf_ids << 1) | go_right + node_ptr = 2 * node_ptr + 1 + go_right + + leaf_ids = leaf_ids.reshape(B, T) + if return_scores: + return leaf_ids, scores.reshape(B, T) + return leaf_ids, None + + def extra_repr(self) -> str: + return ( + f"depth={self.depth}, n_leaves={self.n_leaves}, " + f"d_query={self.d_query}, learnable={self.learnable}, " + f"score_grad={self.score_grad}" + ) diff --git a/overlay/subsystems/fused_sdr_project.py b/overlay/subsystems/fused_sdr_project.py new file mode 100644 index 0000000000000000000000000000000000000000..bd99cb793cd3501ff72723f5cd7775b39a3c52a3 --- /dev/null +++ b/overlay/subsystems/fused_sdr_project.py @@ -0,0 +1,238 @@ +""" +Fused Triton SDR projection kernel. + +Replaces the Python-loop _SparseSDRProject autograd with a single Triton +kernel for the gather+sum forward pass and a companion kernel for the STE +backward. Eliminates 327 Python iterations per forward step. + +Forward: out[p,d] = Σ_{k} weight[d, active[p,k]] + One launch, fused gather+sum reduction over (B*T) positions × D dimensions. + +Backward: Computes grad_weight, grad_delta_u, grad_delta_v via associativity + without materializing dense grad_SDR. + +VRAM: Forward only materializes out (P×D = 8MB at P=16384, D=256). + No dense (P, N) or (P, K, D) intermediates. +""" +import torch +import triton +import triton.language as tl + + +# ── Forward kernel ────────────────────────────────────────────────── +# For each output element (p, d_block), accumulates weight[d, active[p,k]] +# across all K active bits. 1D grid over (p * n_blocks_d). + +@triton.jit +def _sdr_project_fwd_kernel( + out_ptr, # (P, D) fp32/bf16 + active_ptr, # (P, K) int32 + weight_t_ptr, # (N, D) — transposed weight, same dtype as out + P: tl.constexpr, + D: tl.constexpr, + N: tl.constexpr, + K: tl.constexpr, + BLOCK_D: tl.constexpr, +): + pid = tl.program_id(0) + p = pid // tl.cdiv(D, BLOCK_D) + d_off = (pid % tl.cdiv(D, BLOCK_D)) * BLOCK_D + d_idx = d_off + tl.arange(0, BLOCK_D) + mask_d = d_idx < D + + acc = tl.zeros([BLOCK_D], dtype=tl.float32) + for k in range(K): + col = tl.load(active_ptr + p * K + k) # int32 column index + # weight_t[col, d_idx] → weight_t_ptr[col * D + d_idx] + wt_offs = col * D + d_idx + wt_val = tl.load(weight_t_ptr + wt_offs, mask=mask_d, other=0.0) + acc += wt_val.to(tl.float32) + + out_offs = p * D + d_idx + tl.store(out_ptr + out_offs, acc.to(tl.float32), mask=mask_d) + + +# ── Backward kernel (weight grad) ─────────────────────────────────── +# For each weight element (d, n), accumulates G[p,d] over positions p +# where active[p, any_k] == n. Grid over (D). + +@triton.jit +def _sdr_project_bwd_weight_kernel( + grad_weight_ptr, # (D, N) fp32 + grad_out_ptr, # (P, D) fp32 + active_ptr, # (P, K) int32 + P: tl.constexpr, + D: tl.constexpr, + N: tl.constexpr, + K: tl.constexpr, + BLOCK_N: tl.constexpr, +): + d = tl.program_id(0) # output dimension + n_off = tl.program_id(1) * BLOCK_N + n_idx = n_off + tl.arange(0, BLOCK_N) + mask_n = n_idx < N + + acc = tl.zeros([BLOCK_N], dtype=tl.float32) + for p in range(P): + G_pd = tl.load(grad_out_ptr + p * D + d) + for k in range(K): + col = tl.load(active_ptr + p * K + k) + # If col == n_idx, add G_pd + match = (col == n_idx).to(tl.int32) & mask_n.to(tl.int32) + acc += G_pd * match.to(tl.float32) + + out_offs = d * N + n_idx + tl.store(grad_weight_ptr + out_offs, acc, mask=mask_n) + + +# ── Backward kernel (delta grad) ──────────────────────────────────── +# Uses associativity: grad_delta_u = G @ (W @ delta_v^T), index_add by token_id +# grad_delta_v = (delta_u^T @ G) @ W +# These are cheap small-matmul ops — run in Python, no Triton needed for 32-rank. + +# ── Python wrapper ────────────────────────────────────────────────── + +class FusedSDRProject(torch.autograd.Function): + """Fused gather+sum SDR projection with STE gradients to delta_u/delta_v.""" + + @staticmethod + def forward(ctx, active_indices, token_ids, sdr_proj_weight, delta_u, delta_v): + """ + active_indices: (B, T, K) int16 — active bit column indices per token + token_ids: (B, T) int64 — token IDs for delta indexing + sdr_proj_weight:(D, N) — projection weight matrix + delta_u: (V, R) — STE delta factor (vocab × rank) + delta_v: (R, N) — STE delta factor (rank × n_bits) + returns: (B, T, D) — projected features + """ + B, T, K = active_indices.shape + P = B * T + D, N = sdr_proj_weight.shape + + active = active_indices.reshape(P, K).long() + wt = sdr_proj_weight.t().contiguous() # (N, D) + + out = torch.empty(P, D, device=active.device, dtype=sdr_proj_weight.dtype) + + BLOCK_D = min(256, triton.next_power_of_2(D)) + grid = (P * triton.cdiv(D, BLOCK_D),) + + _sdr_project_fwd_kernel[grid]( + out, active, wt, P, D, N, K, BLOCK_D, + ) + + ctx.save_for_backward(active, token_ids, sdr_proj_weight, delta_u, delta_v) + return out.view(B, T, D) + + @staticmethod + def backward(ctx, grad_out): + active, token_ids, weight, delta_u, delta_v = ctx.saved_tensors + P, K = active.shape + B, T = grad_out.shape[:2] + D, N = weight.shape + flat_ids = token_ids.reshape(P) + + G = grad_out.reshape(P, D).float() + + # ── grad_weight: accumulate G[p,d] per column via index_add ── + # Use triton kernel for large N, or Python scatter for now + grad_weight = torch.zeros(D, N, device=G.device, dtype=torch.float32) + gt = G.t().contiguous() # (D, P) + for j in range(K): + cols = active[:, j].unsqueeze(0).expand(D, -1) + grad_weight.scatter_add_(1, cols, gt) + + # ── grad_delta_u ── + # projected = G @ (W @ delta_v.T) — (P, R) + W = weight.float() + projected = G @ (W @ delta_v.float().t()) + grad_delta_u = torch.zeros_like(delta_u.float()) + grad_delta_u.index_add_(0, flat_ids, projected) + + # ── grad_delta_v ── + # (delta_u[id].T @ G) @ W — (R, N) + gathered_u = delta_u[flat_ids].float() + grad_delta_v = (gathered_u.t() @ G) @ W + + return ( + None, # active_indices + None, # token_ids + grad_weight.to(weight.dtype), + grad_delta_u.to(delta_u.dtype), + grad_delta_v.to(delta_v.dtype), + ) + + +# ── Validation ────────────────────────────────────────────────────── + +def _validate(): + """Verify FusedSDRProject matches dense matmul forward and backward.""" + torch.manual_seed(42) + B, T, N, K, D, V, R = 2, 4, 17, 3, 5, 11, 4 + + token_ids = torch.randint(0, V, (B, T), device='cuda') + active = torch.stack([ + torch.randperm(N, device='cuda')[:K] for _ in range(B * T) + ]).view(B, T, K).to(torch.int16) + + weight_a = torch.randn(D, N, device='cuda', requires_grad=True) + delta_u_a = (torch.randn(V, R, device='cuda') * 1e-3).requires_grad_() + delta_v_a = (torch.randn(R, N, device='cuda') * 1e-3).requires_grad_() + weight_b = weight_a.detach().clone().requires_grad_() + delta_u_b = delta_u_a.detach().clone().requires_grad_() + delta_v_b = delta_v_a.detach().clone().requires_grad_() + + upstream = torch.randn(B, T, D, device='cuda') + + # Dense reference + dense = torch.zeros(B * T, N, device='cuda') + dense.scatter_(1, active.reshape(-1, K).long(), 1.0) + dense = dense.view(B, T, N) + dense_out = dense @ weight_a.t() + + # Fused + fused_out = FusedSDRProject.apply(active, token_ids, weight_b, delta_u_b, delta_v_b) + + fwd_err = (fused_out - dense_out).abs().max().item() + print(f" forward error: {fwd_err:.2e}") + assert fwd_err < 1e-5, f"Forward error {fwd_err:.2e}" + + (dense_out * upstream).sum().backward() + (fused_out * upstream).sum().backward() + + print(f" weight_b.grad is None? {weight_b.grad is None}") + print(f" delta_u_b.grad is None? {delta_u_b.grad is None}") + print(f" delta_v_b.grad is None? {delta_v_b.grad is None}") + print(f" weight_a.grad is None? {weight_a.grad is None}") + + # For STE delta comparison: run a SECOND fused forward to validate delta grads + # against the old _SparseSDRProject path + from hydra.model import _SparseSDRProject as OldSparse + weight_c = weight_a.detach().clone().requires_grad_() + delta_u_c = delta_u_a.detach().clone().requires_grad_() + delta_v_c = delta_v_a.detach().clone().requires_grad_() + weight_d = weight_a.detach().clone().requires_grad_() + delta_u_d = delta_u_a.detach().clone().requires_grad_() + delta_v_d = delta_v_a.detach().clone().requires_grad_() + + old_out = OldSparse.apply(active, token_ids, weight_c, delta_u_c, delta_v_c) + new_out = FusedSDRProject.apply(active, token_ids, weight_d, delta_u_d, delta_v_d) + fwd2_err = (new_out - old_out).abs().max().item() + + (old_out * upstream).sum().backward() + (new_out * upstream).sum().backward() + + w_err = (weight_d.grad - weight_c.grad).abs().max().item() + du_err = (delta_u_d.grad - delta_u_c.grad).abs().max().item() + dv_err = (delta_v_d.grad - delta_v_c.grad).abs().max().item() + assert fwd2_err < 1e-5, f"Fwd2 error {fwd2_err:.2e}" + assert w_err < 1e-5, f"Weight grad error {w_err:.2e}" + assert du_err < 1e-5, f"delta_u grad error {du_err:.2e}" + assert dv_err < 1e-5, f"delta_v grad error {dv_err:.2e}" + + print("[ok] FusedSDRProject: forward {:.1e} weight_grad {:.1e} du_grad {:.1e} dv_grad {:.1e}".format( + fwd_err, w_err, du_err, dv_err)) + + +if __name__ == "__main__": + _validate() diff --git a/overlay/subsystems/hestia_mini.py b/overlay/subsystems/hestia_mini.py index 089816ca90100d85bddeab447cb643893903664c..341711fb2987bfd63cfc5c30a673d6476b869aad 100644 --- a/overlay/subsystems/hestia_mini.py +++ b/overlay/subsystems/hestia_mini.py @@ -1,79 +1,79 @@ -""" -Hestia QAT — minimal drop-in for train.py's PostSemClawModel. - -Ternary quantization ({-1, 0, +1}) applied post-optimizer-step to eligible -weight matrices (Linear weights, dim>=2). Temperature is annealed 1.0 → 0.1 -across training as a metric; the quantization itself is independent of temp. - -Usage in PostSemClawModel.__init__: - from subsystems.hestia_mini import HestiaQAT - self.hestia = HestiaQAT(enabled=True, bits=1.58) - -Usage in training loop, AFTER optimizer.step() AFTER optimizer.zero_grad(): - model.hestia.apply_to(model) - model.hestia.anneal_temperature(step / max_steps) -""" - -from __future__ import annotations - -import torch -import torch.nn as nn - - -def _ternary(w: torch.Tensor) -> torch.Tensor: - """Ternary quantization with per-tensor scale (in-place safe when cloned).""" - scale = w.abs().mean() - mask = (w.abs() > 0.5 * scale).to(w.dtype) - return torch.sign(w) * mask * scale - - -class HestiaQAT(nn.Module): - """ - Post-step ternary quantization. Applied after optimizer.step() so full- - precision gradients flow through training; at step end weights snap to - the ternary grid. This is the "enabled and active" mode required by the - HYDRA full-architecture rule. - """ - - def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: - super().__init__() - self.enabled = enabled - self.bits = bits - self.register_buffer("temperature", torch.tensor(1.0)) - - @torch.no_grad() - def apply_to(self, model: nn.Module) -> None: - """Quantize eligible weight tensors in place.""" - if not self.enabled: - return - for mod_name, module in model.named_modules(): - # Skip embeddings, norms, output head (preserves softmax stability) - if isinstance(module, (nn.Embedding, nn.LayerNorm)): - continue - if "norm" in mod_name.lower(): - continue - if "lm_head" in mod_name: - continue - if not isinstance(module, nn.Linear): - continue - q = _ternary(module.weight.data) - module.weight.data.copy_(q) - - @torch.no_grad() - def quant_error(self, model: nn.Module) -> float: - if not self.enabled: - return 0.0 - total, count = 0.0, 0 - for mod_name, module in model.named_modules(): - if not isinstance(module, nn.Linear): - continue - if "lm_head" in mod_name or "norm" in mod_name.lower(): - continue - q = _ternary(module.weight.data) - total += torch.mean((q - module.weight.data) ** 2).item() - count += 1 - return total / max(count, 1) - - def anneal_temperature(self, progress: float) -> None: - progress = max(0.0, min(1.0, progress)) - self.temperature.fill_(max(1.0 - 0.9 * progress, 0.1)) +""" +Hestia QAT — minimal drop-in for train.py's PostSemClawModel. + +Ternary quantization ({-1, 0, +1}) applied post-optimizer-step to eligible +weight matrices (Linear weights, dim>=2). Temperature is annealed 1.0 → 0.1 +across training as a metric; the quantization itself is independent of temp. + +Usage in PostSemClawModel.__init__: + from subsystems.hestia_mini import HestiaQAT + self.hestia = HestiaQAT(enabled=True, bits=1.58) + +Usage in training loop, AFTER optimizer.step() AFTER optimizer.zero_grad(): + model.hestia.apply_to(model) + model.hestia.anneal_temperature(step / max_steps) +""" + +from __future__ import annotations + +import torch +import torch.nn as nn + + +def _ternary(w: torch.Tensor) -> torch.Tensor: + """Ternary quantization with per-tensor scale (in-place safe when cloned).""" + scale = w.abs().mean() + mask = (w.abs() > 0.5 * scale).to(w.dtype) + return torch.sign(w) * mask * scale + + +class HestiaQAT(nn.Module): + """ + Post-step ternary quantization. Applied after optimizer.step() so full- + precision gradients flow through training; at step end weights snap to + the ternary grid. This is the "enabled and active" mode required by the + HYDRA full-architecture rule. + """ + + def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: + super().__init__() + self.enabled = enabled + self.bits = bits + self.register_buffer("temperature", torch.tensor(1.0)) + + @torch.no_grad() + def apply_to(self, model: nn.Module) -> None: + """Quantize eligible weight tensors in place.""" + if not self.enabled: + return + for mod_name, module in model.named_modules(): + # Skip embeddings, norms, output head (preserves softmax stability) + if isinstance(module, (nn.Embedding, nn.LayerNorm)): + continue + if "norm" in mod_name.lower(): + continue + if "lm_head" in mod_name: + continue + if not isinstance(module, nn.Linear): + continue + q = _ternary(module.weight.data) + module.weight.data.copy_(q) + + @torch.no_grad() + def quant_error(self, model: nn.Module) -> float: + if not self.enabled: + return 0.0 + total, count = 0.0, 0 + for mod_name, module in model.named_modules(): + if not isinstance(module, nn.Linear): + continue + if "lm_head" in mod_name or "norm" in mod_name.lower(): + continue + q = _ternary(module.weight.data) + total += torch.mean((q - module.weight.data) ** 2).item() + count += 1 + return total / max(count, 1) + + def anneal_temperature(self, progress: float) -> None: + progress = max(0.0, min(1.0, progress)) + self.temperature.fill_(max(1.0 - 0.9 * progress, 0.1)) diff --git a/overlay/subsystems/htm.py b/overlay/subsystems/htm.py index 0e23d6a7828d5be85c4924386a0808ad67b9cf3e..1f479c79ef5e5a60fc51eccb688ed58ff5269b5e 100644 --- a/overlay/subsystems/htm.py +++ b/overlay/subsystems/htm.py @@ -1,437 +1,430 @@ -""" -HTM torch wrapper around the pyo3 ``htm_rust`` crate. - -Exposes ``HTMLayer``, a ``torch.nn.Module`` that batches calls to -``htm_rust.HTMRegion.step`` across a ``(B, T, input_bits)`` boolean SDR stream -and returns ``(B, T, n_columns + 1)`` where the last channel is the anomaly -score. HTM learning is Hebbian (not gradient), so the wrapper runs under -``torch.no_grad()``. Downstream layers carry gradients back to the embedding -via their own learnable projection from the binary column output. - -Per-sequence state semantics ---------------------------- -Training-time forward passes are independent windows of tokens (re-sampled -every step), so carrying TM state across calls would mix unrelated contexts. -This layer calls ``reset()`` on every region at the top of ``forward``; the -TM learns within-window temporal patterns only. Users that want cross-window -continuity (e.g. eval over a long document) should instead construct the -layer and drive ``step_stream`` themselves (not implemented here; the -single-forward contract is sufficient for the autoresearch loop). - -Device handling ---------------- -``htm_rust`` runs on CPU. If ``sdr`` lives on CUDA we pay a -``sdr.cpu().numpy()`` round-trip per forward. The return tensor is cast back -to ``sdr.device``. For expected use (batch<=32, T<=2048, bits=16384) this -copy is small compared to the SP/TM compute. -""" - -from __future__ import annotations - -import time +""" +HTM torch wrapper around the pyo3 ``htm_rust`` crate. + +Exposes ``HTMLayer``, a ``torch.nn.Module`` that batches calls to +``htm_rust.HTMRegion.step`` across a ``(B, T, input_bits)`` boolean SDR stream +and returns ``(B, T, n_columns + 1)`` where the last channel is the anomaly +score. HTM learning is Hebbian (not gradient), so the wrapper runs under +``torch.no_grad()``. Downstream layers carry gradients back to the embedding +via their own learnable projection from the binary column output. + +Per-sequence state semantics +--------------------------- +Training-time forward passes are independent windows of tokens (re-sampled +every step), so carrying TM state across calls would mix unrelated contexts. +This layer calls ``reset()`` on every region at the top of ``forward``; the +TM learns within-window temporal patterns only. Users that want cross-window +continuity (e.g. eval over a long document) should instead construct the +layer and drive ``step_stream`` themselves (not implemented here; the +single-forward contract is sufficient for the autoresearch loop). + +Device handling +--------------- +``htm_rust`` runs on CPU. If ``sdr`` lives on CUDA we pay a +``sdr.cpu().numpy()`` round-trip per forward. The return tensor is cast back +to ``sdr.device``. For expected use (batch<=32, T<=2048, bits=16384) this +copy is small compared to the SP/TM compute. +""" + +from __future__ import annotations + +import time from concurrent.futures import ThreadPoolExecutor -from typing import Any - -import numpy as np -import torch -import torch.nn as nn - -import htm_rust -_HTM_REGION: Any = getattr(htm_rust, "HTMRegion", None) -_HTM_REGION_GPU: Any = getattr(htm_rust, "HTMRegionGpu", None) -_HTM_STEP_BATCH_FUSED_CUDA: Any = getattr(htm_rust, "step_batch_fused_cuda", None) +import numpy as np +import torch +import torch.nn as nn + +import htm_rust # step_many releases the GIL for the whole pass, so multiple threads can # truly run regions in parallel — wall-clock scales with B up to CPU cores. -_HTM_HAS_STEP_MANY = hasattr(_HTM_REGION, "step_many") -# GPU backend: built with `maturin develop --features gpu`. One CUDA region -# per batch slot, persistent device state for SP synapses. Transparent -# fallback to CPU when not available. +_HTM_HAS_STEP_MANY = hasattr(htm_rust.HTMRegion, "step_many") +# GPU backend: built with `maturin develop --features gpu`. One CUDA region +# per batch slot, persistent device state for SP synapses. Transparent +# fallback to CPU when not available. _HTM_HAS_GPU = hasattr(htm_rust, "HTMRegionGpu") -# Zero-copy CUDA path: consumes torch CUDA tensors directly via the -# __cuda_array_interface__ protocol, skipping the sdr.cpu()/numpy round-trip -# and the D2H of outputs. Huge win when the input SDR already lives on GPU -# (which is the train.py hot path — retina is a device buffer). -_HTM_HAS_CAI = _HTM_HAS_GPU and hasattr(_HTM_REGION_GPU, "step_many_cuda") -# Fused megakernel path: collapses all T timesteps + SP + TM into a single -# CUDA launch per forward. Replaces global top-K with per-column threshold -# inhibition (see htm_rust/docs/GPU_HTM.md §Fused Kernel). -# Opt-in via env var (default on when available). -import os as _os_fused -_HTM_HAS_FUSED = _HTM_HAS_GPU and hasattr(_HTM_REGION_GPU, "step_many_fused_cuda") -_HTM_USE_FUSED = _HTM_HAS_FUSED and bool(int(_os_fused.environ.get("HYDRA_HTM_FUSED", "1"))) - - -class HTMLayer(nn.Module): - """Batched torch wrapper around ``htm_rust.HTMRegion``. - - One independent region per batch slot so temporal memory learns - sequence-local patterns without cross-batch bleed. Regions grow - lazily if a larger batch shows up. - - Output is ``(B, T, n_columns + 1)``: first ``n_columns`` channels are - the binary active-column mask (float32 0/1) and the last channel is - the per-timestep anomaly score in [0, 1]. - """ - - def __init__( - self, - input_bits: int = 16384, - n_columns: int = 2048, - cells_per_column: int = 32, - batch_size: int = 1, - seed: int = 42, - learn: bool = True, - reset_each_forward: bool = True, - use_gpu: bool | None = None, +# Zero-copy CUDA path: consumes torch CUDA tensors directly via the +# __cuda_array_interface__ protocol, skipping the sdr.cpu()/numpy round-trip +# and the D2H of outputs. Huge win when the input SDR already lives on GPU +# (which is the train.py hot path — retina is a device buffer). +_HTM_HAS_CAI = _HTM_HAS_GPU and hasattr(htm_rust.HTMRegionGpu, "step_many_cuda") +# Fused megakernel path: collapses all T timesteps + SP + TM into a single +# CUDA launch per forward. Replaces global top-K with per-column threshold +# inhibition (see htm_rust/docs/GPU_HTM.md §Fused Kernel). +# Opt-in via env var (default on when available). +import os as _os_fused +_HTM_HAS_FUSED = _HTM_HAS_GPU and hasattr(htm_rust.HTMRegionGpu, "step_many_fused_cuda") +_HTM_USE_FUSED = _HTM_HAS_FUSED and bool(int(_os_fused.environ.get("HYDRA_HTM_FUSED", "1"))) +_HTM_USE_BATCHED_FUSED = _HTM_USE_FUSED and bool(int(_os_fused.environ.get("HYDRA_HTM_BATCHED_FUSED", "1"))) + + +class HTMLayer(nn.Module): + """Batched torch wrapper around ``htm_rust.HTMRegion``. + + One independent region per batch slot so temporal memory learns + sequence-local patterns without cross-batch bleed. Regions grow + lazily if a larger batch shows up. + + Output is ``(B, T, n_columns + 1)``: first ``n_columns`` channels are + the binary active-column mask (float32 0/1) and the last channel is + the per-timestep anomaly score in [0, 1]. + """ + + def __init__( + self, + input_bits: int = 16384, + n_columns: int = 2048, + cells_per_column: int = 32, + batch_size: int = 1, + seed: int = 42, + learn: bool = True, + reset_each_forward: bool = True, + use_gpu: bool | None = None, ) -> None: super().__init__() self.input_bits = input_bits self.n_columns = n_columns self.cells_per_column = cells_per_column - self.learn = learn - self.reset_each_forward = reset_each_forward - self._seed_base = seed - # Learn gating: HTM learn kernels (tm_punish, tm_learn_reinforce, tm_grow) - # are 56% of total HTM CUDA time. Gating them to run every N forwards - # instead of every forward cuts HTM cost ~2x. Hebbian learning still - # converges since the EMA accumulates over many calls. Env: - # HYDRA_HTM_LEARN_EVERY=N (default 1 = every forward, 0 = disabled). - import os as _os + self.learn = learn + self.reset_each_forward = reset_each_forward + self._seed_base = seed + # Learn gating: HTM learn kernels (tm_punish, tm_learn_reinforce, tm_grow) + # are 56% of total HTM CUDA time. Gating them to run every N forwards + # instead of every forward cuts HTM cost ~2x. Hebbian learning still + # converges since the EMA accumulates over many calls. Env: + # HYDRA_HTM_LEARN_EVERY=N (default 1 = every forward, 0 = disabled). + import os as _os self._learn_every = max(1, int(_os.environ.get("HYDRA_HTM_LEARN_EVERY", "1"))) self._forward_counter = 0 - force_cpu = _os.environ.get("HYDRA_FORCE_HTM_CPU", "0") == "1" # GPU backend gate. Default: auto-detect — use GPU when the pyo3 # module was built with --features gpu AND CUDA is actually usable. if use_gpu is None: - use_gpu = (not force_cpu) and _HTM_HAS_GPU and torch.cuda.is_available() + use_gpu = _HTM_HAS_GPU and torch.cuda.is_available() elif use_gpu and not _HTM_HAS_GPU: raise RuntimeError( "HTMLayer(use_gpu=True) but htm_rust was not built with " "--features gpu. Re-run `maturin develop --features gpu`." ) - elif use_gpu and force_cpu: - use_gpu = False self._use_gpu = bool(use_gpu) - cls = _HTM_REGION_GPU if self._use_gpu else _HTM_REGION + cls = htm_rust.HTMRegionGpu if self._use_gpu else htm_rust.HTMRegion self._region_cls = cls - self._regions = [ - cls(input_bits, n_columns, cells_per_column, seed + i) - for i in range(batch_size) - ] - self.register_buffer("_dummy", torch.zeros(1), persistent=False) - import os as _os - self._htm_pool = ThreadPoolExecutor(max_workers=min(_os.cpu_count() or 4, 16)) - - def _ensure_regions(self, B: int) -> None: - while len(self._regions) < B: - idx = len(self._regions) - self._regions.append( - self._region_cls( - self.input_bits, - self.n_columns, - self.cells_per_column, - self._seed_base + idx, - ) - ) - - def reset(self) -> None: - """Clear TM predictive state on every region (keeps SP synapses).""" - for r in self._regions: - r.reset() - - @torch.no_grad() - def forward(self, sdr: torch.Tensor) -> torch.Tensor: - B, T, D = sdr.shape - if D != self.input_bits: - raise ValueError(f"expected input_bits={self.input_bits}, got {D}") - self._ensure_regions(B) - if self.reset_each_forward: - self.reset() - - # Learn-gate: run learn kernels only every N forwards (skips 56% of - # HTM CUDA time on skip-forwards; Hebbian EMA still converges). - self._forward_counter += 1 - learn = bool( - self.learn - and self.training - and (self._forward_counter % self._learn_every == 0) - ) - - # Zero-copy CUDA hot path. SDR already lives on GPU (retina buffer), - # so we skip sdr.cpu()/numpy round-trip AND the output D2H. The Rust - # kernel writes directly into torch-owned CUDA tensors via CAI. - # Gives 5-10x tok/s on train.py vs the numpy path below. - if _HTM_HAS_CAI and self._use_gpu and sdr.is_cuda: - sdr_u8 = sdr.contiguous().to(torch.uint8) if sdr.dtype != torch.uint8 else sdr.contiguous() - cols_out = torch.empty((B, T, self.n_columns), dtype=torch.uint8, device=sdr.device) - anom_out = torch.empty((B, T), dtype=torch.float32, device=sdr.device) - # Pick fused (1 launch) or legacy (12*T launches) path. - if _HTM_USE_FUSED: - for b in range(B): - self._regions[b].step_many_fused_cuda( - sdr_u8[b].__cuda_array_interface__, - cols_out[b].__cuda_array_interface__, - anom_out[b].__cuda_array_interface__, - learn, - ) - else: - for b in range(B): - self._regions[b].step_many_cuda( - sdr_u8[b].__cuda_array_interface__, - cols_out[b].__cuda_array_interface__, - anom_out[b].__cuda_array_interface__, - learn, - ) - # Assemble (B, T, n_cols+1) — keep bf16-friendly float32. - return torch.cat((cols_out.to(torch.float32), anom_out.unsqueeze(-1)), dim=-1) - - # Fallback: CPU / numpy path. Kept for CPU-input case and for - # builds without CAI support. - sdr_np = sdr.detach().cpu().contiguous().numpy().view(np.bool_) - out = np.zeros((B, T, self.n_columns + 1), dtype=np.float32) - - def _process_one(b: int) -> None: - region = self._regions[b] - if self._use_gpu: - cols, anom = region.step_many_gpu(sdr_np[b], learn) - out[b, :, : self.n_columns] = cols - out[b, :, self.n_columns] = anom - elif _HTM_HAS_STEP_MANY: - # Single Rust call: T steps with GIL released for the whole pass. - cols, anom = region.step_many(sdr_np[b], learn) # cols (T, n_cols), anom (T,) - out[b, :, : self.n_columns] = cols - out[b, :, self.n_columns] = anom - else: - for t in range(T): - active_cols, _ac, _pc, anomaly = region.step(sdr_np[b, t], learn) - out[b, t, : self.n_columns] = active_cols - out[b, t, self.n_columns] = float(anomaly) - - if B == 1: - _process_one(0) - elif self._use_gpu: - # GPU regions share the CUDA context; serialise to avoid contention - # for stream 0. Per-region latency is dominated by kernel compute, - # not threadable on a single stream cheaply — future work: one - # CUDA stream per region. - for b in range(B): - _process_one(b) - else: - # Each thread runs in pure Rust under py.allow_threads, so they - # parallelise to wall-clock min(B, CPU_cores). - list(self._htm_pool.map(_process_one, range(B))) - - return torch.from_numpy(out).to(sdr.device) - - def forward_async(self, sdr: torch.Tensor): - """Submit HTM work and return a handle awaitable via ``forward_await``. - - On the CAI zero-copy path (GPU tensor in, GPU region), the Rust - CUDA kernels are launched on cudarc's internal stream and control - returns **immediately** — no device synchronization. The caller's - next GPU ops (embedding lookup, Mamba forward, etc.) are enqueued - on PyTorch's default stream and can execute while HTM kernels run - on the cudarc stream. ``forward_await`` performs the cross-stream - sync (via ``device_sync``) and assembles the output tensor only - when the result is actually consumed. - - For cooperative kernels (``step_many_fused_cuda``) the GPU can only - run one cooperative launch at a time, so kernel-level overlap with - default-stream work is limited. The win is **CPU-side launch - overlap**: instead of the CPU blocking ~10 ms waiting for HTM - before it can even enqueue wte/mamba, it enqueues everything up - front and the GPU executes back-to-back without CPU stalls. - - On the legacy CPU/numpy path, work is dispatched to a thread pool - as before.""" - B, T, D = sdr.shape - if D != self.input_bits: - raise ValueError(f"expected input_bits={self.input_bits}, got {D}") - self._ensure_regions(B) - if self.reset_each_forward: - self.reset() - learn = bool(self.learn and self.training) - - if _HTM_HAS_CAI and self._use_gpu and sdr.is_cuda: - sdr_u8 = sdr.contiguous().to(torch.uint8) if sdr.dtype != torch.uint8 else sdr.contiguous() - cols_out = torch.empty((B, T, self.n_columns), dtype=torch.uint8, device=sdr.device) - anom_out = torch.empty((B, T), dtype=torch.float32, device=sdr.device) - # ONE cooperative kernel launch for all B regions. Breaks past - # the CUDA cooperative-kernel device-level serialization (only - # one cooperative kernel runs at a time). A single launch with - # grid.y = B processes all regions concurrently — ~B× speedup. - # Falls back to sequential dispatch if the batched entry isn't - # available (older htm_rust wheel). - if _HTM_USE_FUSED and _HTM_STEP_BATCH_FUSED_CUDA is not None: - # Slice self._regions to match B: _ensure_regions may have - # allocated more regions than the current batch size needs - # (e.g. factual eval uses smaller batches than training). - try: - _HTM_STEP_BATCH_FUSED_CUDA( - self._regions[:B], - [sdr_u8[b].__cuda_array_interface__ for b in range(B)], - [cols_out[b].__cuda_array_interface__ for b in range(B)], - [anom_out[b].__cuda_array_interface__ for b in range(B)], - learn, - ) - except RuntimeError as _e: - if "COOPERATIVE_LAUNCH_TOO_LARGE" in str(_e): - # Batch too large for cooperative grid. Fall back to - # sequential per-region fused launches (each B=1). - for b in range(B): - self._regions[b].step_many_fused_cuda( - sdr_u8[b].__cuda_array_interface__, - cols_out[b].__cuda_array_interface__, - anom_out[b].__cuda_array_interface__, - learn, - ) - else: - raise - elif _HTM_USE_FUSED: - for b in range(B): - self._regions[b].step_many_fused_cuda( - sdr_u8[b].__cuda_array_interface__, - cols_out[b].__cuda_array_interface__, - anom_out[b].__cuda_array_interface__, - learn, - ) - else: - for b in range(B): - self._regions[b].step_many_cuda( - sdr_u8[b].__cuda_array_interface__, - cols_out[b].__cuda_array_interface__, - anom_out[b].__cuda_array_interface__, - learn, - ) - # NO sync here — kernels are in-flight on cudarc's stream. - # forward_await() will sync before the output is consumed. - return { - 'cuda_deferred': True, - 'cols_out': cols_out, - 'anom_out': anom_out, - 'region0': self._regions[0], - } - - sdr_np = sdr.detach().cpu().contiguous().numpy().view(np.bool_) - out = np.zeros((B, T, self.n_columns + 1), dtype=np.float32) - - def _process_one(b): - region = self._regions[b] - if self._use_gpu: - cols, anom = region.step_many_gpu(sdr_np[b], learn) - out[b, :, : self.n_columns] = cols - out[b, :, self.n_columns] = anom - elif _HTM_HAS_STEP_MANY: - cols, anom = region.step_many(sdr_np[b], learn) - out[b, :, : self.n_columns] = cols - out[b, :, self.n_columns] = anom - else: - for t in range(T): - active_cols, _ac, _pc, anomaly = region.step(sdr_np[b, t], learn) - out[b, t, : self.n_columns] = active_cols - out[b, t, self.n_columns] = float(anomaly) - - fut = self._htm_pool.submit(lambda: [_process_one(b) for b in range(B)]) - return {'fut': fut, 'out': out, 'device': sdr.device} - - def forward_await(self, handle) -> torch.Tensor: - if handle.get('cuda_deferred'): - # Cross-stream sync: block until cudarc stream finishes HTM - # kernels so the output tensors are safe to read on the - # default stream. - region0 = handle['region0'] - if hasattr(region0, "device_sync"): - region0.device_sync() - else: - torch.cuda.synchronize() - cols_out = handle['cols_out'] - anom_out = handle['anom_out'] - return torch.cat( - (cols_out.to(torch.float32), anom_out.unsqueeze(-1)), dim=-1 - ) - if 'cuda_result' in handle: - return handle['cuda_result'] - handle['fut'].result() - return torch.from_numpy(handle['out']).to(handle['device']) - - -if __name__ == "__main__": - torch.manual_seed(0) - - # Smoke test: (B=2, T=4, D=16384) random 2%-sparse SDR - B, T, D = 2, 4, 16384 - n_columns = 2048 - target_active_in = int(D * 0.02) # 327 - - layer = HTMLayer( - input_bits=D, - n_columns=n_columns, - cells_per_column=32, - batch_size=B, - seed=42, - learn=True, - ) - layer.train() - - rng = np.random.default_rng(0) - sdr = np.zeros((B, T, D), dtype=bool) - for b in range(B): - for t in range(T): - idx = rng.choice(D, size=target_active_in, replace=False) - sdr[b, t, idx] = True - sdr_t = torch.from_numpy(sdr) - - t0 = time.perf_counter() - out = layer(sdr_t) - dt_first = time.perf_counter() - t0 - - assert out.shape == (B, T, n_columns + 1), f"shape {out.shape}" - assert out.dtype == torch.float32, f"dtype {out.dtype}" - - active_cols = out[..., :n_columns] - anomaly = out[..., n_columns] - - col_sums = active_cols.sum(dim=-1) # (B, T) - mean_active = col_sums.float().mean().item() - expected = n_columns * 0.02 # ≈ 40.96 - assert 20 <= mean_active <= 60, ( - f"active columns per step out of 2% band: {mean_active:.1f} (expected ~{expected:.1f})" - ) - - # t=0 has no TM prediction → anomaly = 1.0 on every batch slot. - assert torch.allclose(anomaly[:, 0], torch.ones(B)), f"t=0 anomaly {anomaly[:, 0]}" - - # Second forward on same (reset) layer: identical shapes, deterministic re-run possible. - t0 = time.perf_counter() - out2 = layer(sdr_t) - dt_second = time.perf_counter() - t0 - assert out2.shape == out.shape - - # Repeating-sequence anomaly decay check — one region, T=8 repeats of same pattern. - rep_layer = HTMLayer( - input_bits=D, - n_columns=n_columns, - batch_size=1, - seed=7, - learn=True, - ) - rep_layer.train() - base = torch.zeros(D, dtype=torch.bool) - idx = rng.choice(D, size=target_active_in, replace=False) - base[idx] = True - rep = base.unsqueeze(0).unsqueeze(0).expand(1, 16, D).clone() - rep_out = rep_layer(rep) - rep_anom = rep_out[0, :, n_columns] - assert rep_anom[0].item() > 0.5, f"anomaly at t=0 should be high, got {rep_anom[0]:.3f}" - assert rep_anom[-1].item() < rep_anom[0].item(), ( - f"anomaly should decay on repeats: first={rep_anom[0]:.3f} last={rep_anom[-1]:.3f}" - ) - - print("[OK] shape:", tuple(out.shape)) - print(f"[OK] mean active cols/step: {mean_active:.2f} (target ~{expected:.1f})") - print(f"[OK] t=0 anomaly = 1.0 on all batch slots") - print(f"[OK] repeating-sequence anomaly: first={rep_anom[0]:.3f} -> last={rep_anom[-1]:.3f}") - print(f"[OK] forward wall-clock: first={dt_first*1000:.1f}ms second={dt_second*1000:.1f}ms " - f"on (B={B}, T={T}, D={D})") + self._regions = [ + cls(input_bits, n_columns, cells_per_column, seed + i) + for i in range(batch_size) + ] + self.register_buffer("_dummy", torch.zeros(1), persistent=False) + import os as _os + self._htm_pool = ThreadPoolExecutor(max_workers=min(_os.cpu_count() or 4, 16)) + + def _ensure_regions(self, B: int) -> None: + while len(self._regions) < B: + idx = len(self._regions) + self._regions.append( + self._region_cls( + self.input_bits, + self.n_columns, + self.cells_per_column, + self._seed_base + idx, + ) + ) + + def reset(self) -> None: + """Clear TM predictive state on every region (keeps SP synapses).""" + for r in self._regions: + r.reset() + + @torch.no_grad() + def forward(self, sdr: torch.Tensor) -> torch.Tensor: + B, T, D = sdr.shape + if D != self.input_bits: + raise ValueError(f"expected input_bits={self.input_bits}, got {D}") + self._ensure_regions(B) + if self.reset_each_forward: + self.reset() + + # Learn-gate: run learn kernels only every N forwards (skips 56% of + # HTM CUDA time on skip-forwards; Hebbian EMA still converges). + self._forward_counter += 1 + learn = bool( + self.learn + and self.training + and (self._forward_counter % self._learn_every == 0) + ) + + # Zero-copy CUDA hot path. SDR already lives on GPU (retina buffer), + # so we skip sdr.cpu()/numpy round-trip AND the output D2H. The Rust + # kernel writes directly into torch-owned CUDA tensors via CAI. + # Gives 5-10x tok/s on train.py vs the numpy path below. + if _HTM_HAS_CAI and self._use_gpu and sdr.is_cuda: + sdr_u8 = sdr.contiguous().to(torch.uint8) if sdr.dtype != torch.uint8 else sdr.contiguous() + cols_out = torch.empty((B, T, self.n_columns), dtype=torch.uint8, device=sdr.device) + anom_out = torch.empty((B, T), dtype=torch.float32, device=sdr.device) + # Pick fused (1 launch) or legacy (12*T launches) path. + if _HTM_USE_FUSED: + for b in range(B): + self._regions[b].step_many_fused_cuda( + sdr_u8[b].__cuda_array_interface__, + cols_out[b].__cuda_array_interface__, + anom_out[b].__cuda_array_interface__, + learn, + ) + else: + for b in range(B): + self._regions[b].step_many_cuda( + sdr_u8[b].__cuda_array_interface__, + cols_out[b].__cuda_array_interface__, + anom_out[b].__cuda_array_interface__, + learn, + ) + # Assemble (B, T, n_cols+1) — keep bf16-friendly float32. + return torch.cat((cols_out.to(torch.float32), anom_out.unsqueeze(-1)), dim=-1) + + # Fallback: CPU / numpy path. Kept for CPU-input case and for + # builds without CAI support. + sdr_np = sdr.detach().cpu().contiguous().to(torch.bool).numpy() + out = np.zeros((B, T, self.n_columns + 1), dtype=np.float32) + + def _process_one(b: int) -> None: + region = self._regions[b] + if self._use_gpu: + cols, anom = region.step_many_gpu(sdr_np[b], learn) + out[b, :, : self.n_columns] = cols + out[b, :, self.n_columns] = anom + elif _HTM_HAS_STEP_MANY: + # Single Rust call: T steps with GIL released for the whole pass. + cols, anom = region.step_many(sdr_np[b], learn) # cols (T, n_cols), anom (T,) + out[b, :, : self.n_columns] = cols + out[b, :, self.n_columns] = anom + else: + for t in range(T): + active_cols, _ac, _pc, anomaly = region.step(sdr_np[b, t], learn) + out[b, t, : self.n_columns] = active_cols + out[b, t, self.n_columns] = float(anomaly) + + if B == 1: + _process_one(0) + elif self._use_gpu: + # GPU regions share the CUDA context; serialise to avoid contention + # for stream 0. Per-region latency is dominated by kernel compute, + # not threadable on a single stream cheaply — future work: one + # CUDA stream per region. + for b in range(B): + _process_one(b) + else: + # Each thread runs in pure Rust under py.allow_threads, so they + # parallelise to wall-clock min(B, CPU_cores). + list(self._htm_pool.map(_process_one, range(B))) + + return torch.from_numpy(out).to(sdr.device) + + def forward_async(self, sdr: torch.Tensor): + """Submit HTM work and return a handle awaitable via ``forward_await``. + + On the CAI zero-copy path (GPU tensor in, GPU region), the Rust + CUDA kernels are launched on cudarc's internal stream and control + returns **immediately** — no device synchronization. The caller's + next GPU ops (embedding lookup, Mamba forward, etc.) are enqueued + on PyTorch's default stream and can execute while HTM kernels run + on the cudarc stream. ``forward_await`` performs the cross-stream + sync (via ``device_sync``) and assembles the output tensor only + when the result is actually consumed. + + For cooperative kernels (``step_many_fused_cuda``) the GPU can only + run one cooperative launch at a time, so kernel-level overlap with + default-stream work is limited. The win is **CPU-side launch + overlap**: instead of the CPU blocking ~10 ms waiting for HTM + before it can even enqueue wte/mamba, it enqueues everything up + front and the GPU executes back-to-back without CPU stalls. + + On the legacy CPU/numpy path, work is dispatched to a thread pool + as before.""" + B, T, D = sdr.shape + if D != self.input_bits: + raise ValueError(f"expected input_bits={self.input_bits}, got {D}") + self._ensure_regions(B) + if self.reset_each_forward: + self.reset() + learn = bool(self.learn and self.training) + + if _HTM_HAS_CAI and self._use_gpu and sdr.is_cuda: + sdr_u8 = sdr.contiguous().to(torch.uint8) if sdr.dtype != torch.uint8 else sdr.contiguous() + cols_out = torch.empty((B, T, self.n_columns), dtype=torch.uint8, device=sdr.device) + anom_out = torch.empty((B, T), dtype=torch.float32, device=sdr.device) + # ONE cooperative kernel launch for all B regions. Breaks past + # the CUDA cooperative-kernel device-level serialization (only + # one cooperative kernel runs at a time). A single launch with + # grid.y = B processes all regions concurrently — ~B× speedup. + # Falls back to sequential dispatch if the batched entry isn't + # available (older htm_rust wheel). + if _HTM_USE_BATCHED_FUSED and hasattr(htm_rust, "step_batch_fused_cuda"): + # Slice self._regions to match B: _ensure_regions may have + # allocated more regions than the current batch size needs + # (e.g. factual eval uses smaller batches than training). + try: + htm_rust.step_batch_fused_cuda( + self._regions[:B], + [sdr_u8[b].__cuda_array_interface__ for b in range(B)], + [cols_out[b].__cuda_array_interface__ for b in range(B)], + [anom_out[b].__cuda_array_interface__ for b in range(B)], + learn, + ) + except RuntimeError as _e: + if "COOPERATIVE_LAUNCH_TOO_LARGE" in str(_e): + # Batch too large for cooperative grid. Fall back to + # sequential per-region fused launches (each B=1). + for b in range(B): + self._regions[b].step_many_fused_cuda( + sdr_u8[b].__cuda_array_interface__, + cols_out[b].__cuda_array_interface__, + anom_out[b].__cuda_array_interface__, + learn, + ) + else: + raise + elif _HTM_USE_FUSED: + for b in range(B): + self._regions[b].step_many_fused_cuda( + sdr_u8[b].__cuda_array_interface__, + cols_out[b].__cuda_array_interface__, + anom_out[b].__cuda_array_interface__, + learn, + ) + else: + for b in range(B): + self._regions[b].step_many_cuda( + sdr_u8[b].__cuda_array_interface__, + cols_out[b].__cuda_array_interface__, + anom_out[b].__cuda_array_interface__, + learn, + ) + # NO sync here — kernels are in-flight on cudarc's stream. + # forward_await() will sync before the output is consumed. + return { + 'cuda_deferred': True, + 'cols_out': cols_out, + 'anom_out': anom_out, + 'region0': self._regions[0], + } + + sdr_np = sdr.detach().cpu().contiguous().to(torch.bool).numpy() + out = np.zeros((B, T, self.n_columns + 1), dtype=np.float32) + + def _process_one(b): + region = self._regions[b] + if self._use_gpu: + cols, anom = region.step_many_gpu(sdr_np[b], learn) + out[b, :, : self.n_columns] = cols + out[b, :, self.n_columns] = anom + elif _HTM_HAS_STEP_MANY: + cols, anom = region.step_many(sdr_np[b], learn) + out[b, :, : self.n_columns] = cols + out[b, :, self.n_columns] = anom + else: + for t in range(T): + active_cols, _ac, _pc, anomaly = region.step(sdr_np[b, t], learn) + out[b, t, : self.n_columns] = active_cols + out[b, t, self.n_columns] = float(anomaly) + + fut = self._htm_pool.submit(lambda: [_process_one(b) for b in range(B)]) + return {'fut': fut, 'out': out, 'device': sdr.device} + + def forward_await(self, handle) -> torch.Tensor: + if handle.get('cuda_deferred'): + # Cross-stream sync: block until cudarc stream finishes HTM + # kernels so the output tensors are safe to read on the + # default stream. + region0 = handle['region0'] + if hasattr(region0, "device_sync"): + region0.device_sync() + else: + torch.cuda.synchronize() + cols_out = handle['cols_out'] + anom_out = handle['anom_out'] + return torch.cat( + (cols_out.to(torch.float32), anom_out.unsqueeze(-1)), dim=-1 + ) + if 'cuda_result' in handle: + return handle['cuda_result'] + handle['fut'].result() + return torch.from_numpy(handle['out']).to(handle['device']) + + +if __name__ == "__main__": + torch.manual_seed(0) + + # Smoke test: (B=2, T=4, D=16384) random 2%-sparse SDR + B, T, D = 2, 4, 16384 + n_columns = 2048 + target_active_in = int(D * 0.02) # 327 + + layer = HTMLayer( + input_bits=D, + n_columns=n_columns, + cells_per_column=32, + batch_size=B, + seed=42, + learn=True, + ) + layer.train() + + rng = np.random.default_rng(0) + sdr = np.zeros((B, T, D), dtype=bool) + for b in range(B): + for t in range(T): + idx = rng.choice(D, size=target_active_in, replace=False) + sdr[b, t, idx] = True + sdr_t = torch.from_numpy(sdr) + + t0 = time.perf_counter() + out = layer(sdr_t) + dt_first = time.perf_counter() - t0 + + assert out.shape == (B, T, n_columns + 1), f"shape {out.shape}" + assert out.dtype == torch.float32, f"dtype {out.dtype}" + + active_cols = out[..., :n_columns] + anomaly = out[..., n_columns] + + col_sums = active_cols.sum(dim=-1) # (B, T) + mean_active = col_sums.float().mean().item() + expected = n_columns * 0.02 # ≈ 40.96 + assert 20 <= mean_active <= 60, ( + f"active columns per step out of 2% band: {mean_active:.1f} (expected ~{expected:.1f})" + ) + + # t=0 has no TM prediction → anomaly = 1.0 on every batch slot. + assert torch.allclose(anomaly[:, 0], torch.ones(B)), f"t=0 anomaly {anomaly[:, 0]}" + + # Second forward on same (reset) layer: identical shapes, deterministic re-run possible. + t0 = time.perf_counter() + out2 = layer(sdr_t) + dt_second = time.perf_counter() - t0 + assert out2.shape == out.shape + + # Repeating-sequence anomaly decay check — one region, T=8 repeats of same pattern. + rep_layer = HTMLayer( + input_bits=D, + n_columns=n_columns, + batch_size=1, + seed=7, + learn=True, + ) + rep_layer.train() + base = torch.zeros(D, dtype=torch.bool) + idx = rng.choice(D, size=target_active_in, replace=False) + base[idx] = True + rep = base.unsqueeze(0).unsqueeze(0).expand(1, 16, D).clone() + rep_out = rep_layer(rep) + rep_anom = rep_out[0, :, n_columns] + assert rep_anom[0].item() > 0.5, f"anomaly at t=0 should be high, got {rep_anom[0]:.3f}" + assert rep_anom[-1].item() < rep_anom[0].item(), ( + f"anomaly should decay on repeats: first={rep_anom[0]:.3f} last={rep_anom[-1]:.3f}" + ) + + print("[OK] shape:", tuple(out.shape)) + print(f"[OK] mean active cols/step: {mean_active:.2f} (target ~{expected:.1f})") + print(f"[OK] t=0 anomaly = 1.0 on all batch slots") + print(f"[OK] repeating-sequence anomaly: first={rep_anom[0]:.3f} -> last={rep_anom[-1]:.3f}") + print(f"[OK] forward wall-clock: first={dt_first*1000:.1f}ms second={dt_second*1000:.1f}ms " + f"on (B={B}, T={T}, D={D})") diff --git a/overlay/subsystems/hyena_pure.py b/overlay/subsystems/hyena_pure.py index b6d354e1a70ea1ffc5eac983ffab1da8b0348aaf..7d45a5f017ac7d6e79967ccc7e7e3cf5e3e0f4a1 100644 --- a/overlay/subsystems/hyena_pure.py +++ b/overlay/subsystems/hyena_pure.py @@ -1,872 +1,872 @@ -"""Pure-PyTorch Hyena operator — vendored from HazyResearch/safari. - -Source: https://github.com/HazyResearch/safari -File: src/models/sequence/hyena.py -Commit: 02220c69d247e5473616cd053a443ad99fd2559b (main, Apr 2026 checkout) -License: Apache 2.0 - -This is a supplement block for HYDRA, used alongside Mamba3 via the -`HYDRA_HYENA_LAYERS` env var. NO attention, NO softmax-over-seq-dim, -NO KV-cache, NO transformer imports. The operator is the one described -in the paper https://arxiv.org/pdf/2302.10866.pdf (Hyena Hierarchy). - -Strict invariants (enforced by tests/test_hyena.py): - * Causality: output[:, :t] depends only on input[:, :t]. - * Shape parity: forward(x: [B, T, D]) -> y: [B, T, D]. - * Zero transformer code paths: grep'd in test_hyena.py test #7. - -Vendored changes from the reference: - * `OptimModule.register` simplified to just register a Parameter (the - per-parameter `_optim` dict is a safari-trainer detail; HYDRA uses Muon - and doesn't key off that metadata). Semantics of the *computation* are - identical. - * `Activation` reduced to Identity/GELU/SiLU/Tanh (what Hyena actually - uses). Dropped the registry-driven instantiation path. - * `OptimModule` helper replaced with plain `nn.Module` + `register_buffer` - / `nn.Parameter`. No behavior change. - * Removed `fused_fft_conv` and `FusedDense` — those require flash-attn's - CUDA extensions. Only `fftconv_ref` (pure PyTorch) is used. - * Removed `instantiate(registry.layer, ...)`; HyenaOperator constructs - HyenaFilter directly. - * Removed `auto_assign_attrs` — attributes set explicitly. - * Removed `num_heads`, `num_blocks`, `inner_factor`, `outer_mixing`, - `post_order_ffn`, `jit_filter` — kept at their defaults (1, 1, 1, - False, False, False). Reduces forward-path complexity while - preserving the core Hyena recurrence; HYDRA uses num_heads=1 (d_model - routed as a single head). Tests confirm shape parity. - * Positional embedding: sets `bands = max(1, (emb_dim - 1) // 2)` to - avoid UnboundLocalError when emb_dim=3 (bands=1 is fine). - -All Hyena mathematics (implicit filter MLP, positional encoding, exponential -modulation, order-N recurrence via fftconv) are unchanged from the reference. -""" - -from __future__ import annotations - -import math -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - - -# --------------------------------------------------------------------------- -# fftconv_ref — pure PyTorch causal long convolution via FFT. -# -# Given input u: [B, D, L] and filter k: [D, L], computes -# y[d, t] = sum_{s=0}^{t} k[d, t-s] * u[d, s] + D_bias[d] * u[d, t] -# via zero-padded FFT of length 2L (implicitly causal because we truncate to -# the first L samples of the circular convolution's non-wrap-around region). -# -# CAUSALITY: the zero-padded FFT convolution y = IFFT(FFT(u_pad) * FFT(k_pad)) -# has length 2L. We slice [..., :L] which exactly equals the causal linear -# convolution (full-length version would be :2L-1). -# -# OPTIONAL CACHE: if `k_f` is passed non-None, we SKIP the filter rfft and -# use the provided spectrum directly. Callers (HyenaOperator) can pre-compute -# once per training step (same filter reused across micro-batches) and pass -# it in. This is instrumented by `HyenaFilter.get_cached_kf`. -# -# OPTIONAL FLASH-FFT-CONV PATH: -# HazyResearch/flash-fft-conv provides Monarch-matrix-decomposed FFT kernels -# that are ~2-3x faster than cuFFT for power-of-two seqlens. When -# HYDRA_HYENA_FLASH_FFT=1 AND `flashfftconv` is importable AND the runtime -# conditions match (power-of-2 fft_size, bf16 or fp16 dtype), we route the -# inner conv through `FlashFFTConv.forward(u, k)` instead of the pure rfft+ -# mul+irfft path. Everything else (residual D*u, gelu, dropout_mask) happens -# outside the kernel to preserve HYDRA's exact control flow. -# -# The flash-fft-conv path is OFF by default; enabling it requires both: -# (1) `pip install -e /home/mikeb/work/feather/kernels/cuda/flashfftconv` -# AND the accompanying monarch_cuda extension (see its README). -# (2) `HYDRA_HYENA_FLASH_FFT=1` at runtime. -# --------------------------------------------------------------------------- -# Test hook: monotonic counter incremented every time a FILTER rfft is -# materialized inside fftconv_ref. NOT the input rfft (which is per-batch). -# Tests read and reset this to verify caching. -_fftconv_filter_rfft_count = 0 - -# Lazy, one-shot import of flashfftconv. Returns the class or None; cached. -# Import failure is non-fatal — callers fall back to pure PyTorch. -_flash_fft_conv_cls: type | None = None -_flash_fft_conv_probed: bool = False -# Per-seqlen singleton cache: FlashFFTConv owns buffers sized for one fft_size, -# so we instantiate one per (fft_size, dtype, device) pair and reuse. -_flash_fft_conv_instances: dict = {} - - -def _try_load_flash_fft_conv(): - """Import flashfftconv lazily; return its `FlashFFTConv` class or None. - - Memoized after the first probe. Import failures are swallowed and - logged once to stderr so the fallback is transparent. - """ - global _flash_fft_conv_cls, _flash_fft_conv_probed - if _flash_fft_conv_probed: - return _flash_fft_conv_cls - _flash_fft_conv_probed = True - try: - from flashfftconv import FlashFFTConv # type: ignore[import-not-found] - _flash_fft_conv_cls = FlashFFTConv - except Exception as e: # noqa: BLE001 — any import failure must fall back - import sys - print( - f"[hyena] flashfftconv unavailable ({type(e).__name__}: {e}); " - f"using pure-PyTorch fftconv_ref. Install per " - f"kernels/cuda/flashfftconv/README.md to enable.", - file=sys.stderr, - ) - _flash_fft_conv_cls = None - return _flash_fft_conv_cls - - -# Flash-fft-conv supports only these exact fft sizes. -_FLASH_FFT_SUPPORTED_SIZES = frozenset({ - 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, - # Larger (16 * 4096 etc.) exist but HYDRA sequence lengths won't reach them. -}) - - -def _flash_fft_conv_supported(fft_size: int, dtype: torch.dtype) -> bool: - """Return True iff fft_size + dtype are on flashfftconv's supported grid.""" - return ( - fft_size in _FLASH_FFT_SUPPORTED_SIZES - and dtype in (torch.bfloat16, torch.float16) - ) - - -def _get_flash_fft_conv(fft_size: int, dtype: torch.dtype, device): - """Return a cached FlashFFTConv instance for the given (size, dtype, device).""" - cls = _try_load_flash_fft_conv() - if cls is None: - return None - key = (fft_size, dtype, str(device)) - inst = _flash_fft_conv_instances.get(key) - if inst is None: - inst = cls(seqlen=fft_size, dtype=dtype).to(device) - _flash_fft_conv_instances[key] = inst - return inst - - -def fftconv_ref(u, k, D, dropout_mask=None, gelu: bool = True, k_rev=None, k_f=None): - """Reference (pure-PyTorch) FFT convolution with residual. - - Args: - u: Input signal, shape [B, D, L] (channels-first, sequence last). - k: Filter, shape [D, L] or [C, D, L]. - D: Per-channel residual scaling, shape [D]. - dropout_mask: Optional [B, D] multiplicative mask. - gelu: Apply GELU to the output before dropout. - k_rev: Optional bidirectional reverse filter (unused in causal LM). - k_f: Optional pre-computed filter rfft of shape [..., fft_size/2 + 1]. - When provided, the internal rfft(k) is skipped. The caller is - responsible for ensuring the cache was built with the same - `fft_size = 2 * seqlen`. - - Returns: - y of shape [B, D, L] in the dtype of u. - - Optional fast path: - If HYDRA_HYENA_FLASH_FFT=1 and `flashfftconv` is importable and the - (fft_size, dtype) combination is supported, we replace the inner - `irfft(rfft(u) * k_f)` with HazyResearch flash-fft-conv. Residual - (D * u), gelu, and dropout_mask are all applied outside the kernel - to preserve behavior. Falls back silently to pure-PyTorch when any - precondition is missing. - """ - global _fftconv_filter_rfft_count - seqlen = u.shape[-1] - fft_size = 2 * seqlen - - # Fast-path gate: opt-in via env var + import + runtime preconditions. - # Preconditions: - # - HYDRA_HYENA_FLASH_FFT=1 at runtime - # - flashfftconv importable (its monarch_cuda native extension built) - # - fft_size is a power-of-2 value in the kernel's supported set - # - dtype is fp16 or bf16 (kernel constraint) - # - `k` is a plain [D, L] tensor (not the [C, D, L] multi-order shape); - # the [C, D, L] case comes from k_rev paths that HYDRA doesn't use - # but we preserve the pure path for them. - # - `u` is on CUDA (the kernel is CUDA-only) - # Any failure → fall through to pure path below. - _use_flash = ( - os.environ.get("HYDRA_HYENA_FLASH_FFT", "0") == "1" - and u.is_cuda - and k.dim() == 2 # [D, L] — the only shape the shim supports - and k_rev is None # reverse filter path stays in pure PyTorch - and _flash_fft_conv_supported(fft_size, k.dtype) - ) - if _use_flash: - mod = _get_flash_fft_conv(fft_size, k.dtype, u.device) - if mod is not None: - # FlashFFTConv forward signature: (u: [B, H, L], k: [H, L]) → [B, H, L]. - # It internally handles rfft(k, n=fft_size) so we pass `k` not `k_f`. - # Shapes: u is [B, D, L], k is [D, L] — already matches. - # Ensure the input dtype matches the kernel's configured dtype. - u_cast = u if u.dtype == k.dtype else u.to(dtype=k.dtype) - y = mod(u_cast, k) # [B, D, L] in fp16/bf16 - out = y + u_cast * D.unsqueeze(-1) - if gelu: - out = F.gelu(out) - if dropout_mask is not None: - return (out * rearrange(dropout_mask, "b H -> b H 1")).to(dtype=u.dtype) - return out.to(dtype=u.dtype) - - # Pure-PyTorch fallback (the original, always-available path). - if k_f is None: - _fftconv_filter_rfft_count += 1 - k_f = torch.fft.rfft(k, n=fft_size) / fft_size - if k_rev is not None: - k_rev_f = torch.fft.rfft(k_rev, n=fft_size) / fft_size - k_f = k_f + k_rev_f.conj() - u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size) - - if len(u.shape) > 3: - k_f = k_f.unsqueeze(1) - - y = torch.fft.irfft(u_f * k_f, n=fft_size, norm="forward")[..., :seqlen] - - out = y + u * D.unsqueeze(-1) - if gelu: - out = F.gelu(out) - if dropout_mask is not None: - return (out * rearrange(dropout_mask, "b H -> b H 1")).to(dtype=u.dtype) - else: - return out.to(dtype=u.dtype) - - -@torch.jit.script -def mul_sum(q, y): - return (q * y).sum(dim=1) - - -class Sin(nn.Module): - """Sin activation with per-dim learnable frequency. From safari.""" - def __init__(self, dim, w: float = 10.0, train_freq: bool = True): - super().__init__() - if train_freq: - self.freq = nn.Parameter(w * torch.ones(1, dim)) - else: - self.register_buffer("freq", w * torch.ones(1, dim)) - - def forward(self, x): - return torch.sin(self.freq * x) - - -class PositionalEmbedding(nn.Module): - """Complex exponential positional embeddings for Hyena filters. Safari.""" - def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float = 1e-5): - super().__init__() - self.seq_len = seq_len - - t = torch.linspace(0, 1, self.seq_len)[None, :, None] # [1, L, 1] - - # Guard against emb_dim=3 reference-bug where bands was left unbound. - # For emb_dim=3: bands=1, f=[1e-4], giving one (cos, sin) pair on top - # of t — which is what the paper prescribes. - bands = max(1, (emb_dim - 1) // 2) - - t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None] - w = 2 * math.pi * t_rescaled / seq_len # [1, L, 1] - - f = torch.linspace(1e-4, bands - 1, bands)[None, None] - z = torch.exp(-1j * f * w) - z = torch.cat([t, z.real, z.imag], dim=-1) - - # Trainable with lr=lr_pos_emb; registered as Parameter so Muon (or any - # optimizer) picks it up. Per-param LR override (`_optim`) is a safari - # convention HYDRA doesn't use. - self.z = nn.Parameter(z) - self.register_buffer("t", t) - - def forward(self, L): - return self.z[:, :L], self.t[:, :L] - - -class ExponentialModulation(nn.Module): - """Exponential decay modulation for Hyena filters. Safari.""" - def __init__( - self, - d_model, - fast_decay_pct: float = 0.3, - slow_decay_pct: float = 1.5, - target: float = 1e-2, - modulate: bool = True, - shift: float = 0.0, - ): - super().__init__() - self.modulate = modulate - self.shift = shift - max_decay = math.log(target) / fast_decay_pct - min_decay = math.log(target) / slow_decay_pct - deltas = torch.linspace(min_decay, max_decay, d_model)[None, None] - # lr=0 in safari → registered as buffer (non-trainable). - self.register_buffer("deltas", deltas) - - def forward(self, t, x): - if self.modulate: - decay = torch.exp(-t * self.deltas.abs()) - x = x * (decay + self.shift) - return x - - -class HyenaFilter(nn.Module): - """Implicit long filter with modulation (safari reference, verbatim math).""" - - def __init__( - self, - d_model: int, - emb_dim: int = 3, - order: int = 64, # width of the implicit filter MLP - seq_len: int = 1024, - lr: float = 1e-3, - lr_pos_emb: float = 1e-5, - dropout: float = 0.0, - w: float = 1.0, - wd: float = 0.0, - bias: bool = True, - num_inner_mlps: int = 2, - normalized: bool = False, - # Kwargs fed to ExponentialModulation: - fast_decay_pct: float = 0.3, - slow_decay_pct: float = 1.5, - target: float = 1e-2, - modulate: bool = True, - shift: float = 0.0, - **_unused, # eat any safari extras we don't care about - ): - super().__init__() - self.d_model = d_model - self.use_bias = bias - self.bias = nn.Parameter(torch.randn(self.d_model)) - self.dropout = nn.Dropout(dropout) - - act = Sin(dim=order, w=w) - self.emb_dim = emb_dim - assert emb_dim % 2 != 0 and emb_dim >= 3, ( - "emb_dim must be odd and >= 3 (time, sine, cosine)" - ) - self.seq_len = seq_len - - self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb) - - layers = [nn.Linear(emb_dim, order), act] - for _ in range(num_inner_mlps): - layers.append(nn.Linear(order, order)) - layers.append(act) - layers.append(nn.Linear(order, d_model, bias=False)) - self.implicit_filter = nn.Sequential(*layers) - - self.modulation = ExponentialModulation( - d_model, - fast_decay_pct=fast_decay_pct, - slow_decay_pct=slow_decay_pct, - target=target, - modulate=modulate, - shift=shift, - ) - - self.normalized = normalized - - # --- Filter-rfft cache (intra-optimizer-step reuse) --------------- - # The filter `filter(L)` is a pure function of the module's params - # (implicit_filter MLP + modulation + pos_emb). Inside an optimizer - # step, these params are FROZEN — every micro-batch produces the - # same k, and therefore the same rfft(k). We cache (k, k_f, L) keyed - # on a monotonic `_cache_version` that the training loop (or the - # parent model's `invalidate_hyena_caches()`) bumps after each - # `optimizer.step()`. - # - # Cache is OPT-IN via HYDRA_HYENA_FILTER_CACHE=1 on the parent block - # (HyenaOperator). This module exposes `get_cached_kf(L, fft_size, - # version)` unconditionally; whether it's called is up to the caller. - # Defaults: version=-1 ensures no hit on the first call. - self._cached_k: torch.Tensor | None = None - self._cached_k_f: torch.Tensor | None = None - self._cached_L: int = -1 - self._cached_fft_size: int = -1 - self._cache_version: int = -1 - - # --- Training-safe filter cache (opt-in, HYDRA_HYENA_TRAIN_CACHE=1) ---- - # The problem with the plain no_grad cache above is that it's unsafe - # during training: reusing a cached in-graph tensor across grad-accum - # micro-batches triggers - # RuntimeError: Trying to backward through the graph a second time - # because PyTorch frees intermediate buffers after the first backward. - # - # Training-safe design (Option A, "deferred gradient" pattern): - # - # 1. On first call of a step, compute `_k_graph = self.filter(L)` ONCE - # with grad tracking. This tensor lives in an autograd graph - # rooted at the filter MLP + positional-embedding params. - # 2. Publish a detached, leaf copy `_k_leaf = _k_graph.detach() - # .requires_grad_(True)` for use by downstream forwards. Because - # `_k_leaf` is a LEAF tensor, each micro-batch's backward simply - # accumulates its `dL_i/dk` into `_k_leaf.grad` (standard leaf - # gradient accumulation) and stops — it never touches the - # internal filter-MLP buffers. - # 3. Each subsequent micro-batch reuses the SAME `_k_leaf` + `_k_f` - # cache — no recomputation of the implicit filter MLP, no extra - # rfft. That's the speedup. - # 4. Just before `optimizer.step()` the caller invokes - # `flush_pending_filter_grads()` which does a ONE-TIME - # `torch.autograd.backward(_k_graph, gradient=_k_leaf.grad)`. - # This pushes the summed gradient backward through the filter - # MLP, populating filter params' `.grad` slots correctly. - # 5. `invalidate_cache()` (post-step) clears _k_graph / _k_leaf and - # bumps the version — the next step rebuilds from scratch. - # - # Invariants: - # * `_k_graph` is created once and held across all micro-batches. - # * `_k_leaf` is a LEAF (so its .grad accumulates without retain_graph). - # * The per-micro-batch backward never traverses _k_graph's internals, - # so no "backward twice" error is possible. - # * `flush_pending_filter_grads()` is called at most once per step; - # if `_k_graph` is None (no Hyena forward happened this step), it - # is a no-op. - self._k_graph: torch.Tensor | None = None # in-graph tensor, held for step-end backward - self._k_leaf: torch.Tensor | None = None # detached leaf, fed to fftconv forwards - self._use_train_cache: bool = ( - os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" - ) - - def filter(self, L: int, *args, **kwargs): - z, t = self.pos_emb(L) - h = self.implicit_filter(z) - h = self.modulation(t, h) - if self.normalized: - h = h / torch.norm(h, dim=-1, p=1, keepdim=True) - return h - - def get_cached_kf(self, L: int, fft_size: int, version: int): - """Return (k, k_f) for the given L and fft_size, caching across calls. - - Cache hits require: (version == self._cache_version) AND the L and - fft_size match the stored values. The version MUST be bumped by the - training loop after every `optimizer.step()` — otherwise cache values - will be stale. - - Returns: - (k, k_f) where k has shape [1, L, D*(order-1)] (pre-rearrange, - see HyenaOperator.forward) and k_f is the rfft at length fft_size - divided by fft_size (matches fftconv_ref's internal normalization). - """ - global _fftconv_filter_rfft_count - hit = ( - self._cached_k_f is not None - and self._cache_version == version - and self._cached_L == L - and self._cached_fft_size == fft_size - ) - if hit: - return self._cached_k, self._cached_k_f - - k = self.filter(L) - # `filter` may return a tuple in safari back-compat; normalize here. - k = k[0] if isinstance(k, tuple) else k - # Count this rfft the same way fftconv_ref does so tests can assert - # cache misses cause a visible recompute. - _fftconv_filter_rfft_count += 1 - k_f = torch.fft.rfft(k, n=fft_size) / fft_size - - # Detach the cache tensors — if the training loop forgets to invalidate - # after optimizer.step(), we still want ZERO grad to flow through a - # stale cached tensor. The invalidation hook in the parent model is - # the authoritative lifecycle; this is defense-in-depth. - # NOTE: within a SINGLE step we DO want grad flow. We keep k / k_f in - # the graph as produced; invalidation is by version bump. - self._cached_k = k - self._cached_k_f = k_f - self._cached_L = L - self._cached_fft_size = fft_size - self._cache_version = version - return k, k_f - - def invalidate_cache(self) -> None: - """Drop any cached rfft. Called from the parent model after step().""" - self._cached_k = None - self._cached_k_f = None - self._cached_L = -1 - self._cached_fft_size = -1 - # Bump version so a subsequent get_cached_kf with same version misses. - self._cache_version += 1 - # Training-safe cache: drop both the in-graph k and its detached leaf. - # Any unflushed gradient on _k_leaf at this point is discarded — this - # is by design: invalidate_cache is always called AFTER - # flush_pending_filter_grads (or after eval, where no grads accumulate). - self._k_graph = None - self._k_leaf = None - - def get_or_build_train_cache(self, L: int, fft_size: int): - """Training-safe version of get_cached_kf. - - Returns (k_leaf, k_f) where: - k_leaf — detached leaf tensor [1, L, D*(order-1)], requires_grad=True. - Micro-batch backwards accumulate dL/dk_leaf in `.grad`. - k_f — rfft of k_leaf, computed FRESH per call. It lives in a - per-forward graph rooted at k_leaf (no shared saved - tensors across micro-batches, so no backward-twice - error). Chain-rule gradients through rfft still flow - back into k_leaf.grad on each micro-batch. - - On the first call of a step this materializes the in-graph filter - tensor `_k_graph` (retained for `flush_pending_filter_grads`). The - leaf `_k_leaf` is held across subsequent calls so the implicit - filter MLP forward runs ONCE per step. - - Trade-off: we keep paying for one rfft of the small filter per - forward (the filter tensor is [1, L, D*(order-1)] — at L=2048, - D=128, order=2, that's 524288 fp32 elements, ~400 µs rfft). This - is ~0.5% of a typical forward and the alternative (caching k_f as - a leaf too) would require a second stashed graph per HyenaFilter - to connect k_f_leaf → k_leaf at flush time, substantially more - complex for tiny savings. - """ - global _fftconv_filter_rfft_count - - if self._k_leaf is not None and self._cached_L == L and self._cached_fft_size == fft_size: - # Warm cache — reuse the same k_leaf; rebuild k_f this forward - # so no saved tensors are shared across micro-batches. - _fftconv_filter_rfft_count += 1 - k_f = torch.fft.rfft(self._k_leaf, n=fft_size) / fft_size - return self._k_leaf, k_f - - # Cold start (first call this step, or L/fft_size changed). - # Step 1: compute k through the real filter path WITH grad. - k_graph = self.filter(L) - k_graph = k_graph[0] if isinstance(k_graph, tuple) else k_graph - - # Step 2: publish a detached leaf for downstream forwards. The leaf - # has its OWN autograd-leaf status, so micro-batch backwards stop - # at this boundary and accumulate dL/dk_leaf into `_k_leaf.grad`. - k_leaf = k_graph.detach().clone() - k_leaf.requires_grad_(True) - - # Step 3: rfft is computed fresh per forward (see docstring). - _fftconv_filter_rfft_count += 1 - k_f = torch.fft.rfft(k_leaf, n=fft_size) / fft_size - - # Stash the cross-micro-batch state. - self._k_graph = k_graph - self._k_leaf = k_leaf - self._cached_k = k_leaf # legacy cache shim (some callers read _cached_k) - # _cached_k_f is NOT stashed across micro-batches in this mode. - self._cached_k_f = None - self._cached_L = L - self._cached_fft_size = fft_size - return k_leaf, k_f - - def flush_pending_filter_grads(self) -> None: - """Push accumulated micro-batch grads back through the filter MLP. - - MUST be called once per optimizer step, AFTER all micro-batch - backwards have completed, BEFORE `optimizer.step()` + `invalidate_cache()`. - - Idempotent: repeated calls within the same step (e.g. L-BFGS-style - optimizers that invoke the closure multiple times) are a no-op. The - first call consumes `_k_graph` (its intermediate buffers are freed by - autograd), so we null it out to signal "done". - - No-op if `_k_graph` is None (no forwards happened this step) or if - `_k_leaf.grad is None` (no micro-batch ever backwarded, e.g. eval). - """ - if self._k_graph is None or self._k_leaf is None: - return - if self._k_leaf.grad is None: - # Nothing to push (eval pass under train-cache enabled). - return - # One-shot backward through the in-graph k. The `gradient` argument - # is dL/dk (summed across micro-batches). This populates `.grad` on - # all upstream filter params (MLP, pos_emb, bias, modulation deltas). - # After this call, `_k_graph`'s internal buffers are freed by autograd; - # invalidate_cache() must be invoked shortly after to reset state. - grad = self._k_leaf.grad - k_graph = self._k_graph - # Null out BEFORE the backward to enforce idempotency even if the - # backward somehow re-enters this method. - self._k_graph = None - torch.autograd.backward( - tensors=k_graph, - grad_tensors=grad, - ) - - def forward(self, x, L: int, k=None, bias=None, *args, **kwargs): - if k is None: - k = self.filter(L) - - # Filters may return a tuple (safari back-compat). - k = k[0] if isinstance(k, tuple) else k - if bias is None: - bias = self.bias - bias = bias if self.use_bias else 0 * bias - - # Pure-PyTorch fftconv path (no flash-attn fused kernel). - y = fftconv_ref(x, k, bias, dropout_mask=None, gelu=False) - return y - - -def _activation(name: str) -> nn.Module: - """Minimal Activation factory (subset of safari's). Identity / GELU / SiLU / Tanh.""" - if name in (None, "id", "identity", "linear"): - return nn.Identity() - if name == "tanh": - return nn.Tanh() - if name == "relu": - return nn.ReLU() - if name == "gelu": - return nn.GELU() - if name in ("swish", "silu"): - return nn.SiLU() - if name == "sigmoid": - return nn.Sigmoid() - raise NotImplementedError(f"activation '{name}' not implemented in pure Hyena") - - -class HyenaOperator(nn.Module): - """Hyena operator — order-N implicit-filter recurrence (safari reference). - - Paper: https://arxiv.org/pdf/2302.10866.pdf - - Forward signature: - x: [B, T, d_model] -> y: [B, T, d_model] - - Causal: the internal fftconv_ref uses zero-padded FFT convolution, - slicing to the first T samples of a 2T-length causal linear convolution. - Additionally, the `short_filter` Conv1d uses padding=short_filter_order-1 - and is truncated with `[..., :l_filter]` to keep the output causal. - - Strict subset of safari's HyenaOperator: - num_heads = 1, num_blocks = 1, inner_factor = 1, outer_mixing = False, - post_order_ffn = False, jit_filter = False, return_state = False, - fused_bias_fc = False. - This removes the parallel-head / block-decomposition bookkeeping the - safari version supports but HYDRA doesn't use. The *math* of the - Hyena recurrence is identical to the reference code path at those - default settings. - - Filter-rfft cache (opt-in): set `HYDRA_HYENA_FILTER_CACHE=1` in env to - re-use the filter rfft across micro-batches within an optimizer step. - The parent `PostSemClawModel.invalidate_hyena_caches()` MUST be called - after every `optimizer.step()` to bump the version, otherwise stale k_f - will be reused with updated params. Default is OFF for rollout safety. - """ - - def __init__( - self, - d_model: int, - l_max: int, - order: int = 2, - filter_order: int = 64, - dropout: float = 0.0, - filter_dropout: float = 0.0, - short_filter_order: int = 3, - activation: str = "id", - **filter_args, - ): - super().__init__() - assert order >= 2, f"Order must be at least 2 (got {order})" - - # Single-head configuration (HYDRA-style: d_model as a single head). - self.d_model = d_model - self.l_max = l_max - self.order = order - self.num_heads = 1 - self.head_dim = d_model - self.num_blocks = 1 - self.block_dim = l_max - self.inner_factor = 1 - self.filter_order = filter_order - self.short_filter_order = short_filter_order - - self.activation = _activation(activation) - self.dropout = nn.Dropout(dropout) - - # Input projection: produces (order + 1) × d_model channels to feed - # the short filter and the recurrence. - self.in_proj = nn.Linear(d_model, (order + 1) * d_model) - self.out_proj = nn.Linear(d_model, d_model) - - total_width = d_model * (order + 1) - # Depthwise short conv — causal via left-padding + truncation downstream. - self.short_filter = nn.Conv1d( - in_channels=total_width, - out_channels=total_width, - kernel_size=short_filter_order, - groups=total_width, - padding=short_filter_order - 1, - ) - - # Implicit long filter: one filter per (order - 1) × d_model channels. - # Safari uses head_dim * (order - 1). With num_heads=1, head_dim=d_model. - self.filter_fn = HyenaFilter( - d_model=d_model * (order - 1), - order=filter_order, - seq_len=l_max, - dropout=filter_dropout, - **filter_args, - ) - - # Cache gate — read once per forward from env (cheap). - self._use_filter_cache = ( - os.environ.get("HYDRA_HYENA_FILTER_CACHE", "0") == "1" - ) - # Training-safe cache gate — separate knob so rollout is incremental. - # When on, the cache ALSO activates during training forwards via the - # deferred-gradient pattern in HyenaFilter.get_or_build_train_cache. - self._use_train_cache = ( - os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" - ) - - def forward(self, u, *args, **kwargs): - """u: [B, T, d_model] -> y: [B, T, d_model]""" - global _fftconv_filter_rfft_count - l = u.size(-2) - l_filter = min(l, self.l_max) - - u = self.in_proj(u) # [B, T, (order+1)*D] - u = rearrange(u, "b l d -> b d l") # [B, (order+1)*D, T] - - uc = self.short_filter(u)[..., :l_filter] # causal truncation to T - - # Reshape: num_heads=1, num_blocks=1 → simple view. - # total_width = head_dim * (order + 1) = D * (order + 1) - # v_width_per_group = head_dim * (order + 1) = D * (order + 1) - # Split into (order + 1) groups along channel axis, each of size D. - uc = rearrange( - uc, - "b (ho v) (z l) -> b ho v z l", - z=self.num_blocks, - ho=self.num_heads, - v=self.head_dim * (self.order + 1), - ) # [B, 1, (order+1)*D, 1, T] - - # Split into (order+1) tensors of shape [B, 1, D, 1, T] - *x, v = uc.split(self.d_model, dim=2) - - # Long filter: [1, T, D*(order-1)] → [order-1, D, T] - # - # Cache-routing decision tree: - # 1. HYDRA_HYENA_TRAIN_CACHE=1 and grad enabled → train-safe cache - # (deferred-gradient pattern, see HyenaFilter.get_or_build_train_cache). - # Each micro-batch reuses _k_leaf; the filter MLP runs exactly once - # per optimizer step. Requires the training loop to call - # `model.flush_hyena_pending_grads()` before `optimizer.step()` and - # `model.invalidate_hyena_caches()` after. - # 2. HYDRA_HYENA_FILTER_CACHE=1 and grad disabled → eval cache (original). - # Filter MLP runs once per eval "version", reused across passes. - # 3. Either flag set but wrong grad mode, or both unset → plain forward. - # Filter MLP runs every call. This was the only safe mode before - # HYDRA_HYENA_TRAIN_CACHE existed. - fft_size = 2 * l_filter - grad_on = torch.is_grad_enabled() - use_train_cache = self._use_train_cache and grad_on - use_eval_cache = self._use_filter_cache and not grad_on - if use_train_cache: - # Training-safe path: returns a LEAF (k_leaf.requires_grad=True). - # Its gradient contribution is flushed back through the real - # filter MLP graph at step-end via `flush_pending_filter_grads`. - k_raw, _k_f_raw = self.filter_fn.get_or_build_train_cache( - l_filter, fft_size, - ) - elif use_eval_cache: - # Pass the filter's own version so the first call after an - # invalidate_cache() always misses. - k_raw, _k_f_raw = self.filter_fn.get_cached_kf( - l_filter, fft_size, self.filter_fn._cache_version, - ) - else: - k_raw = self.filter_fn.filter(l_filter) - k_raw = k_raw[0] if isinstance(k_raw, tuple) else k_raw - k = rearrange( - k_raw, "c l (v o) -> c o v l", - v=self.head_dim, o=self.order - 1, - )[0] # [order-1, D, T] - - # Precompute per-order rfft of the rearranged filter. - # - Under eval cache (no_grad): stored across calls keyed by version. - # Safe because no_grad forwards produce no saved tensors to free. - # - Under train cache or no cache: compute fresh each forward. For the - # train cache case, re-caching across micro-batches would share - # saved rfft intermediates and trip "backward through graph twice". - if use_eval_cache: - cache_key = (l_filter, fft_size) - cached = getattr(self, "_cached_reshaped_k_f", None) - cached_key = getattr(self, "_cached_reshaped_key", None) - cached_ver = getattr(self, "_cached_reshaped_ver", -1) - if ( - cached is not None - and cached_key == cache_key - and cached_ver == self.filter_fn._cache_version - ): - k_f_per_order = cached - else: - # Count this as a filter rfft — the test hook lumps any - # recompute of the filter spectrum so callers can observe - # cache misses after invalidation. - _fftconv_filter_rfft_count += 1 - k_f_per_order = torch.fft.rfft(k, n=fft_size) / fft_size - self._cached_reshaped_k_f = k_f_per_order - self._cached_reshaped_key = cache_key - self._cached_reshaped_ver = self.filter_fn._cache_version - else: - # Non-eval-cache path (includes train-cache): compute k_f fresh - # per forward, hoisted once so the order-1 inner loop's rfft - # inside fftconv_ref doesn't redo the same transform each iter. - # This micro-opt lives entirely within a single forward graph, - # so it's safe under grad. - _fftconv_filter_rfft_count += 1 - k_f_per_order = torch.fft.rfft(k, n=fft_size) / fft_size - - bias = rearrange( - self.filter_fn.bias, "(v o) -> o v", - v=self.head_dim, o=self.order - 1, - ) # [order-1, D] - - # Hyena recurrence (reverse-iterating over x[1:] gives o = 0..order-2) - for o, x_i in enumerate(reversed(x[1:])): - v = self.dropout(v * x_i) - # Shape to fftconv: [B, 1, D, 1, T] → rely on pre-contract. - # fftconv_ref expects [B, D, L]; collapse the 1s. - # v: [B, 1, D, 1, T] (ho=1, z=1) - B = v.size(0) - v_f = v.reshape(B, self.d_model, l_filter) - k_f_slice = None if k_f_per_order is None else k_f_per_order[o] - y_f = fftconv_ref( - v_f, k[o], bias[o], dropout_mask=None, gelu=False, - k_f=k_f_slice, - ) - v = y_f.reshape(B, 1, self.d_model, 1, l_filter) - - # Final element-wise gate with x[0]: - y = self.activation( - rearrange( - v * x[0], - "b h v z l -> b (z l) (h v)", - z=self.num_blocks, h=self.num_heads, - ) - ) # [B, T, D] - y = self.out_proj(y) - return y - - def invalidate_filter_cache(self) -> None: - """Drop cached rfft on both the filter module and this operator. - - Intended to be called from the parent model's - `invalidate_hyena_caches()` after each `optimizer.step()`. - """ - self.filter_fn.invalidate_cache() - self._cached_reshaped_k_f = None - self._cached_reshaped_key = None - self._cached_reshaped_ver = -1 - - def flush_pending_filter_grads(self) -> None: - """Push accumulated train-cache filter grads back into filter params. - - Pass-through to `HyenaFilter.flush_pending_filter_grads`. Called - from the parent model's `flush_hyena_pending_grads()` BEFORE - `optimizer.step()` (and before `invalidate_hyena_caches()`) when - HYDRA_HYENA_TRAIN_CACHE=1. - """ - self.filter_fn.flush_pending_filter_grads() +"""Pure-PyTorch Hyena operator — vendored from HazyResearch/safari. + +Source: https://github.com/HazyResearch/safari +File: src/models/sequence/hyena.py +Commit: 02220c69d247e5473616cd053a443ad99fd2559b (main, Apr 2026 checkout) +License: Apache 2.0 + +This is a supplement block for HYDRA, used alongside Mamba3 via the +`HYDRA_HYENA_LAYERS` env var. NO attention, NO softmax-over-seq-dim, +NO KV-cache, NO transformer imports. The operator is the one described +in the paper https://arxiv.org/pdf/2302.10866.pdf (Hyena Hierarchy). + +Strict invariants (enforced by tests/test_hyena.py): + * Causality: output[:, :t] depends only on input[:, :t]. + * Shape parity: forward(x: [B, T, D]) -> y: [B, T, D]. + * Zero transformer code paths: grep'd in test_hyena.py test #7. + +Vendored changes from the reference: + * `OptimModule.register` simplified to just register a Parameter (the + per-parameter `_optim` dict is a safari-trainer detail; HYDRA uses Muon + and doesn't key off that metadata). Semantics of the *computation* are + identical. + * `Activation` reduced to Identity/GELU/SiLU/Tanh (what Hyena actually + uses). Dropped the registry-driven instantiation path. + * `OptimModule` helper replaced with plain `nn.Module` + `register_buffer` + / `nn.Parameter`. No behavior change. + * Removed `fused_fft_conv` and `FusedDense` — those require flash-attn's + CUDA extensions. Only `fftconv_ref` (pure PyTorch) is used. + * Removed `instantiate(registry.layer, ...)`; HyenaOperator constructs + HyenaFilter directly. + * Removed `auto_assign_attrs` — attributes set explicitly. + * Removed `num_heads`, `num_blocks`, `inner_factor`, `outer_mixing`, + `post_order_ffn`, `jit_filter` — kept at their defaults (1, 1, 1, + False, False, False). Reduces forward-path complexity while + preserving the core Hyena recurrence; HYDRA uses num_heads=1 (d_model + routed as a single head). Tests confirm shape parity. + * Positional embedding: sets `bands = max(1, (emb_dim - 1) // 2)` to + avoid UnboundLocalError when emb_dim=3 (bands=1 is fine). + +All Hyena mathematics (implicit filter MLP, positional encoding, exponential +modulation, order-N recurrence via fftconv) are unchanged from the reference. +""" + +from __future__ import annotations + +import math +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +# --------------------------------------------------------------------------- +# fftconv_ref — pure PyTorch causal long convolution via FFT. +# +# Given input u: [B, D, L] and filter k: [D, L], computes +# y[d, t] = sum_{s=0}^{t} k[d, t-s] * u[d, s] + D_bias[d] * u[d, t] +# via zero-padded FFT of length 2L (implicitly causal because we truncate to +# the first L samples of the circular convolution's non-wrap-around region). +# +# CAUSALITY: the zero-padded FFT convolution y = IFFT(FFT(u_pad) * FFT(k_pad)) +# has length 2L. We slice [..., :L] which exactly equals the causal linear +# convolution (full-length version would be :2L-1). +# +# OPTIONAL CACHE: if `k_f` is passed non-None, we SKIP the filter rfft and +# use the provided spectrum directly. Callers (HyenaOperator) can pre-compute +# once per training step (same filter reused across micro-batches) and pass +# it in. This is instrumented by `HyenaFilter.get_cached_kf`. +# +# OPTIONAL FLASH-FFT-CONV PATH: +# HazyResearch/flash-fft-conv provides Monarch-matrix-decomposed FFT kernels +# that are ~2-3x faster than cuFFT for power-of-two seqlens. When +# HYDRA_HYENA_FLASH_FFT=1 AND `flashfftconv` is importable AND the runtime +# conditions match (power-of-2 fft_size, bf16 or fp16 dtype), we route the +# inner conv through `FlashFFTConv.forward(u, k)` instead of the pure rfft+ +# mul+irfft path. Everything else (residual D*u, gelu, dropout_mask) happens +# outside the kernel to preserve HYDRA's exact control flow. +# +# The flash-fft-conv path is OFF by default; enabling it requires both: +# (1) `pip install -e /home/mikeb/work/feather/kernels/cuda/flashfftconv` +# AND the accompanying monarch_cuda extension (see its README). +# (2) `HYDRA_HYENA_FLASH_FFT=1` at runtime. +# --------------------------------------------------------------------------- +# Test hook: monotonic counter incremented every time a FILTER rfft is +# materialized inside fftconv_ref. NOT the input rfft (which is per-batch). +# Tests read and reset this to verify caching. +_fftconv_filter_rfft_count = 0 + +# Lazy, one-shot import of flashfftconv. Returns the class or None; cached. +# Import failure is non-fatal — callers fall back to pure PyTorch. +_flash_fft_conv_cls: type | None = None +_flash_fft_conv_probed: bool = False +# Per-seqlen singleton cache: FlashFFTConv owns buffers sized for one fft_size, +# so we instantiate one per (fft_size, dtype, device) pair and reuse. +_flash_fft_conv_instances: dict = {} + + +def _try_load_flash_fft_conv(): + """Import flashfftconv lazily; return its `FlashFFTConv` class or None. + + Memoized after the first probe. Import failures are swallowed and + logged once to stderr so the fallback is transparent. + """ + global _flash_fft_conv_cls, _flash_fft_conv_probed + if _flash_fft_conv_probed: + return _flash_fft_conv_cls + _flash_fft_conv_probed = True + try: + from flashfftconv import FlashFFTConv # type: ignore[import-not-found] + _flash_fft_conv_cls = FlashFFTConv + except Exception as e: # noqa: BLE001 — any import failure must fall back + import sys + print( + f"[hyena] flashfftconv unavailable ({type(e).__name__}: {e}); " + f"using pure-PyTorch fftconv_ref. Install per " + f"kernels/cuda/flashfftconv/README.md to enable.", + file=sys.stderr, + ) + _flash_fft_conv_cls = None + return _flash_fft_conv_cls + + +# Flash-fft-conv supports only these exact fft sizes. +_FLASH_FFT_SUPPORTED_SIZES = frozenset({ + 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, + # Larger (16 * 4096 etc.) exist but HYDRA sequence lengths won't reach them. +}) + + +def _flash_fft_conv_supported(fft_size: int, dtype: torch.dtype) -> bool: + """Return True iff fft_size + dtype are on flashfftconv's supported grid.""" + return ( + fft_size in _FLASH_FFT_SUPPORTED_SIZES + and dtype in (torch.bfloat16, torch.float16) + ) + + +def _get_flash_fft_conv(fft_size: int, dtype: torch.dtype, device): + """Return a cached FlashFFTConv instance for the given (size, dtype, device).""" + cls = _try_load_flash_fft_conv() + if cls is None: + return None + key = (fft_size, dtype, str(device)) + inst = _flash_fft_conv_instances.get(key) + if inst is None: + inst = cls(seqlen=fft_size, dtype=dtype).to(device) + _flash_fft_conv_instances[key] = inst + return inst + + +def fftconv_ref(u, k, D, dropout_mask=None, gelu: bool = True, k_rev=None, k_f=None): + """Reference (pure-PyTorch) FFT convolution with residual. + + Args: + u: Input signal, shape [B, D, L] (channels-first, sequence last). + k: Filter, shape [D, L] or [C, D, L]. + D: Per-channel residual scaling, shape [D]. + dropout_mask: Optional [B, D] multiplicative mask. + gelu: Apply GELU to the output before dropout. + k_rev: Optional bidirectional reverse filter (unused in causal LM). + k_f: Optional pre-computed filter rfft of shape [..., fft_size/2 + 1]. + When provided, the internal rfft(k) is skipped. The caller is + responsible for ensuring the cache was built with the same + `fft_size = 2 * seqlen`. + + Returns: + y of shape [B, D, L] in the dtype of u. + + Optional fast path: + If HYDRA_HYENA_FLASH_FFT=1 and `flashfftconv` is importable and the + (fft_size, dtype) combination is supported, we replace the inner + `irfft(rfft(u) * k_f)` with HazyResearch flash-fft-conv. Residual + (D * u), gelu, and dropout_mask are all applied outside the kernel + to preserve behavior. Falls back silently to pure-PyTorch when any + precondition is missing. + """ + global _fftconv_filter_rfft_count + seqlen = u.shape[-1] + fft_size = 2 * seqlen + + # Fast-path gate: opt-in via env var + import + runtime preconditions. + # Preconditions: + # - HYDRA_HYENA_FLASH_FFT=1 at runtime + # - flashfftconv importable (its monarch_cuda native extension built) + # - fft_size is a power-of-2 value in the kernel's supported set + # - dtype is fp16 or bf16 (kernel constraint) + # - `k` is a plain [D, L] tensor (not the [C, D, L] multi-order shape); + # the [C, D, L] case comes from k_rev paths that HYDRA doesn't use + # but we preserve the pure path for them. + # - `u` is on CUDA (the kernel is CUDA-only) + # Any failure → fall through to pure path below. + _use_flash = ( + os.environ.get("HYDRA_HYENA_FLASH_FFT", "0") == "1" + and u.is_cuda + and k.dim() == 2 # [D, L] — the only shape the shim supports + and k_rev is None # reverse filter path stays in pure PyTorch + and _flash_fft_conv_supported(fft_size, k.dtype) + ) + if _use_flash: + mod = _get_flash_fft_conv(fft_size, k.dtype, u.device) + if mod is not None: + # FlashFFTConv forward signature: (u: [B, H, L], k: [H, L]) → [B, H, L]. + # It internally handles rfft(k, n=fft_size) so we pass `k` not `k_f`. + # Shapes: u is [B, D, L], k is [D, L] — already matches. + # Ensure the input dtype matches the kernel's configured dtype. + u_cast = u if u.dtype == k.dtype else u.to(dtype=k.dtype) + y = mod(u_cast, k) # [B, D, L] in fp16/bf16 + out = y + u_cast * D.unsqueeze(-1) + if gelu: + out = F.gelu(out) + if dropout_mask is not None: + return (out * rearrange(dropout_mask, "b H -> b H 1")).to(dtype=u.dtype) + return out.to(dtype=u.dtype) + + # Pure-PyTorch fallback (the original, always-available path). + if k_f is None: + _fftconv_filter_rfft_count += 1 + k_f = torch.fft.rfft(k, n=fft_size) / fft_size + if k_rev is not None: + k_rev_f = torch.fft.rfft(k_rev, n=fft_size) / fft_size + k_f = k_f + k_rev_f.conj() + u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size) + + if len(u.shape) > 3: + k_f = k_f.unsqueeze(1) + + y = torch.fft.irfft(u_f * k_f, n=fft_size, norm="forward")[..., :seqlen] + + out = y + u * D.unsqueeze(-1) + if gelu: + out = F.gelu(out) + if dropout_mask is not None: + return (out * rearrange(dropout_mask, "b H -> b H 1")).to(dtype=u.dtype) + else: + return out.to(dtype=u.dtype) + + +@torch.jit.script +def mul_sum(q, y): + return (q * y).sum(dim=1) + + +class Sin(nn.Module): + """Sin activation with per-dim learnable frequency. From safari.""" + def __init__(self, dim, w: float = 10.0, train_freq: bool = True): + super().__init__() + if train_freq: + self.freq = nn.Parameter(w * torch.ones(1, dim)) + else: + self.register_buffer("freq", w * torch.ones(1, dim)) + + def forward(self, x): + return torch.sin(self.freq * x) + + +class PositionalEmbedding(nn.Module): + """Complex exponential positional embeddings for Hyena filters. Safari.""" + def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float = 1e-5): + super().__init__() + self.seq_len = seq_len + + t = torch.linspace(0, 1, self.seq_len)[None, :, None] # [1, L, 1] + + # Guard against emb_dim=3 reference-bug where bands was left unbound. + # For emb_dim=3: bands=1, f=[1e-4], giving one (cos, sin) pair on top + # of t — which is what the paper prescribes. + bands = max(1, (emb_dim - 1) // 2) + + t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None] + w = 2 * math.pi * t_rescaled / seq_len # [1, L, 1] + + f = torch.linspace(1e-4, bands - 1, bands)[None, None] + z = torch.exp(-1j * f * w) + z = torch.cat([t, z.real, z.imag], dim=-1) + + # Trainable with lr=lr_pos_emb; registered as Parameter so Muon (or any + # optimizer) picks it up. Per-param LR override (`_optim`) is a safari + # convention HYDRA doesn't use. + self.z = nn.Parameter(z) + self.register_buffer("t", t) + + def forward(self, L): + return self.z[:, :L], self.t[:, :L] + + +class ExponentialModulation(nn.Module): + """Exponential decay modulation for Hyena filters. Safari.""" + def __init__( + self, + d_model, + fast_decay_pct: float = 0.3, + slow_decay_pct: float = 1.5, + target: float = 1e-2, + modulate: bool = True, + shift: float = 0.0, + ): + super().__init__() + self.modulate = modulate + self.shift = shift + max_decay = math.log(target) / fast_decay_pct + min_decay = math.log(target) / slow_decay_pct + deltas = torch.linspace(min_decay, max_decay, d_model)[None, None] + # lr=0 in safari → registered as buffer (non-trainable). + self.register_buffer("deltas", deltas) + + def forward(self, t, x): + if self.modulate: + decay = torch.exp(-t * self.deltas.abs()) + x = x * (decay + self.shift) + return x + + +class HyenaFilter(nn.Module): + """Implicit long filter with modulation (safari reference, verbatim math).""" + + def __init__( + self, + d_model: int, + emb_dim: int = 3, + order: int = 64, # width of the implicit filter MLP + seq_len: int = 1024, + lr: float = 1e-3, + lr_pos_emb: float = 1e-5, + dropout: float = 0.0, + w: float = 1.0, + wd: float = 0.0, + bias: bool = True, + num_inner_mlps: int = 2, + normalized: bool = False, + # Kwargs fed to ExponentialModulation: + fast_decay_pct: float = 0.3, + slow_decay_pct: float = 1.5, + target: float = 1e-2, + modulate: bool = True, + shift: float = 0.0, + **_unused, # eat any safari extras we don't care about + ): + super().__init__() + self.d_model = d_model + self.use_bias = bias + self.bias = nn.Parameter(torch.randn(self.d_model)) + self.dropout = nn.Dropout(dropout) + + act = Sin(dim=order, w=w) + self.emb_dim = emb_dim + assert emb_dim % 2 != 0 and emb_dim >= 3, ( + "emb_dim must be odd and >= 3 (time, sine, cosine)" + ) + self.seq_len = seq_len + + self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb) + + layers = [nn.Linear(emb_dim, order), act] + for _ in range(num_inner_mlps): + layers.append(nn.Linear(order, order)) + layers.append(act) + layers.append(nn.Linear(order, d_model, bias=False)) + self.implicit_filter = nn.Sequential(*layers) + + self.modulation = ExponentialModulation( + d_model, + fast_decay_pct=fast_decay_pct, + slow_decay_pct=slow_decay_pct, + target=target, + modulate=modulate, + shift=shift, + ) + + self.normalized = normalized + + # --- Filter-rfft cache (intra-optimizer-step reuse) --------------- + # The filter `filter(L)` is a pure function of the module's params + # (implicit_filter MLP + modulation + pos_emb). Inside an optimizer + # step, these params are FROZEN — every micro-batch produces the + # same k, and therefore the same rfft(k). We cache (k, k_f, L) keyed + # on a monotonic `_cache_version` that the training loop (or the + # parent model's `invalidate_hyena_caches()`) bumps after each + # `optimizer.step()`. + # + # Cache is OPT-IN via HYDRA_HYENA_FILTER_CACHE=1 on the parent block + # (HyenaOperator). This module exposes `get_cached_kf(L, fft_size, + # version)` unconditionally; whether it's called is up to the caller. + # Defaults: version=-1 ensures no hit on the first call. + self._cached_k: torch.Tensor | None = None + self._cached_k_f: torch.Tensor | None = None + self._cached_L: int = -1 + self._cached_fft_size: int = -1 + self._cache_version: int = -1 + + # --- Training-safe filter cache (opt-in, HYDRA_HYENA_TRAIN_CACHE=1) ---- + # The problem with the plain no_grad cache above is that it's unsafe + # during training: reusing a cached in-graph tensor across grad-accum + # micro-batches triggers + # RuntimeError: Trying to backward through the graph a second time + # because PyTorch frees intermediate buffers after the first backward. + # + # Training-safe design (Option A, "deferred gradient" pattern): + # + # 1. On first call of a step, compute `_k_graph = self.filter(L)` ONCE + # with grad tracking. This tensor lives in an autograd graph + # rooted at the filter MLP + positional-embedding params. + # 2. Publish a detached, leaf copy `_k_leaf = _k_graph.detach() + # .requires_grad_(True)` for use by downstream forwards. Because + # `_k_leaf` is a LEAF tensor, each micro-batch's backward simply + # accumulates its `dL_i/dk` into `_k_leaf.grad` (standard leaf + # gradient accumulation) and stops — it never touches the + # internal filter-MLP buffers. + # 3. Each subsequent micro-batch reuses the SAME `_k_leaf` + `_k_f` + # cache — no recomputation of the implicit filter MLP, no extra + # rfft. That's the speedup. + # 4. Just before `optimizer.step()` the caller invokes + # `flush_pending_filter_grads()` which does a ONE-TIME + # `torch.autograd.backward(_k_graph, gradient=_k_leaf.grad)`. + # This pushes the summed gradient backward through the filter + # MLP, populating filter params' `.grad` slots correctly. + # 5. `invalidate_cache()` (post-step) clears _k_graph / _k_leaf and + # bumps the version — the next step rebuilds from scratch. + # + # Invariants: + # * `_k_graph` is created once and held across all micro-batches. + # * `_k_leaf` is a LEAF (so its .grad accumulates without retain_graph). + # * The per-micro-batch backward never traverses _k_graph's internals, + # so no "backward twice" error is possible. + # * `flush_pending_filter_grads()` is called at most once per step; + # if `_k_graph` is None (no Hyena forward happened this step), it + # is a no-op. + self._k_graph: torch.Tensor | None = None # in-graph tensor, held for step-end backward + self._k_leaf: torch.Tensor | None = None # detached leaf, fed to fftconv forwards + self._use_train_cache: bool = ( + os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" + ) + + def filter(self, L: int, *args, **kwargs): + z, t = self.pos_emb(L) + h = self.implicit_filter(z) + h = self.modulation(t, h) + if self.normalized: + h = h / torch.norm(h, dim=-1, p=1, keepdim=True) + return h + + def get_cached_kf(self, L: int, fft_size: int, version: int): + """Return (k, k_f) for the given L and fft_size, caching across calls. + + Cache hits require: (version == self._cache_version) AND the L and + fft_size match the stored values. The version MUST be bumped by the + training loop after every `optimizer.step()` — otherwise cache values + will be stale. + + Returns: + (k, k_f) where k has shape [1, L, D*(order-1)] (pre-rearrange, + see HyenaOperator.forward) and k_f is the rfft at length fft_size + divided by fft_size (matches fftconv_ref's internal normalization). + """ + global _fftconv_filter_rfft_count + hit = ( + self._cached_k_f is not None + and self._cache_version == version + and self._cached_L == L + and self._cached_fft_size == fft_size + ) + if hit: + return self._cached_k, self._cached_k_f + + k = self.filter(L) + # `filter` may return a tuple in safari back-compat; normalize here. + k = k[0] if isinstance(k, tuple) else k + # Count this rfft the same way fftconv_ref does so tests can assert + # cache misses cause a visible recompute. + _fftconv_filter_rfft_count += 1 + k_f = torch.fft.rfft(k, n=fft_size) / fft_size + + # Detach the cache tensors — if the training loop forgets to invalidate + # after optimizer.step(), we still want ZERO grad to flow through a + # stale cached tensor. The invalidation hook in the parent model is + # the authoritative lifecycle; this is defense-in-depth. + # NOTE: within a SINGLE step we DO want grad flow. We keep k / k_f in + # the graph as produced; invalidation is by version bump. + self._cached_k = k + self._cached_k_f = k_f + self._cached_L = L + self._cached_fft_size = fft_size + self._cache_version = version + return k, k_f + + def invalidate_cache(self) -> None: + """Drop any cached rfft. Called from the parent model after step().""" + self._cached_k = None + self._cached_k_f = None + self._cached_L = -1 + self._cached_fft_size = -1 + # Bump version so a subsequent get_cached_kf with same version misses. + self._cache_version += 1 + # Training-safe cache: drop both the in-graph k and its detached leaf. + # Any unflushed gradient on _k_leaf at this point is discarded — this + # is by design: invalidate_cache is always called AFTER + # flush_pending_filter_grads (or after eval, where no grads accumulate). + self._k_graph = None + self._k_leaf = None + + def get_or_build_train_cache(self, L: int, fft_size: int): + """Training-safe version of get_cached_kf. + + Returns (k_leaf, k_f) where: + k_leaf — detached leaf tensor [1, L, D*(order-1)], requires_grad=True. + Micro-batch backwards accumulate dL/dk_leaf in `.grad`. + k_f — rfft of k_leaf, computed FRESH per call. It lives in a + per-forward graph rooted at k_leaf (no shared saved + tensors across micro-batches, so no backward-twice + error). Chain-rule gradients through rfft still flow + back into k_leaf.grad on each micro-batch. + + On the first call of a step this materializes the in-graph filter + tensor `_k_graph` (retained for `flush_pending_filter_grads`). The + leaf `_k_leaf` is held across subsequent calls so the implicit + filter MLP forward runs ONCE per step. + + Trade-off: we keep paying for one rfft of the small filter per + forward (the filter tensor is [1, L, D*(order-1)] — at L=2048, + D=128, order=2, that's 524288 fp32 elements, ~400 µs rfft). This + is ~0.5% of a typical forward and the alternative (caching k_f as + a leaf too) would require a second stashed graph per HyenaFilter + to connect k_f_leaf → k_leaf at flush time, substantially more + complex for tiny savings. + """ + global _fftconv_filter_rfft_count + + if self._k_leaf is not None and self._cached_L == L and self._cached_fft_size == fft_size: + # Warm cache — reuse the same k_leaf; rebuild k_f this forward + # so no saved tensors are shared across micro-batches. + _fftconv_filter_rfft_count += 1 + k_f = torch.fft.rfft(self._k_leaf, n=fft_size) / fft_size + return self._k_leaf, k_f + + # Cold start (first call this step, or L/fft_size changed). + # Step 1: compute k through the real filter path WITH grad. + k_graph = self.filter(L) + k_graph = k_graph[0] if isinstance(k_graph, tuple) else k_graph + + # Step 2: publish a detached leaf for downstream forwards. The leaf + # has its OWN autograd-leaf status, so micro-batch backwards stop + # at this boundary and accumulate dL/dk_leaf into `_k_leaf.grad`. + k_leaf = k_graph.detach().clone() + k_leaf.requires_grad_(True) + + # Step 3: rfft is computed fresh per forward (see docstring). + _fftconv_filter_rfft_count += 1 + k_f = torch.fft.rfft(k_leaf, n=fft_size) / fft_size + + # Stash the cross-micro-batch state. + self._k_graph = k_graph + self._k_leaf = k_leaf + self._cached_k = k_leaf # legacy cache shim (some callers read _cached_k) + # _cached_k_f is NOT stashed across micro-batches in this mode. + self._cached_k_f = None + self._cached_L = L + self._cached_fft_size = fft_size + return k_leaf, k_f + + def flush_pending_filter_grads(self) -> None: + """Push accumulated micro-batch grads back through the filter MLP. + + MUST be called once per optimizer step, AFTER all micro-batch + backwards have completed, BEFORE `optimizer.step()` + `invalidate_cache()`. + + Idempotent: repeated calls within the same step (e.g. L-BFGS-style + optimizers that invoke the closure multiple times) are a no-op. The + first call consumes `_k_graph` (its intermediate buffers are freed by + autograd), so we null it out to signal "done". + + No-op if `_k_graph` is None (no forwards happened this step) or if + `_k_leaf.grad is None` (no micro-batch ever backwarded, e.g. eval). + """ + if self._k_graph is None or self._k_leaf is None: + return + if self._k_leaf.grad is None: + # Nothing to push (eval pass under train-cache enabled). + return + # One-shot backward through the in-graph k. The `gradient` argument + # is dL/dk (summed across micro-batches). This populates `.grad` on + # all upstream filter params (MLP, pos_emb, bias, modulation deltas). + # After this call, `_k_graph`'s internal buffers are freed by autograd; + # invalidate_cache() must be invoked shortly after to reset state. + grad = self._k_leaf.grad + k_graph = self._k_graph + # Null out BEFORE the backward to enforce idempotency even if the + # backward somehow re-enters this method. + self._k_graph = None + torch.autograd.backward( + tensors=k_graph, + grad_tensors=grad, + ) + + def forward(self, x, L: int, k=None, bias=None, *args, **kwargs): + if k is None: + k = self.filter(L) + + # Filters may return a tuple (safari back-compat). + k = k[0] if isinstance(k, tuple) else k + if bias is None: + bias = self.bias + bias = bias if self.use_bias else 0 * bias + + # Pure-PyTorch fftconv path (no flash-attn fused kernel). + y = fftconv_ref(x, k, bias, dropout_mask=None, gelu=False) + return y + + +def _activation(name: str) -> nn.Module: + """Minimal Activation factory (subset of safari's). Identity / GELU / SiLU / Tanh.""" + if name in (None, "id", "identity", "linear"): + return nn.Identity() + if name == "tanh": + return nn.Tanh() + if name == "relu": + return nn.ReLU() + if name == "gelu": + return nn.GELU() + if name in ("swish", "silu"): + return nn.SiLU() + if name == "sigmoid": + return nn.Sigmoid() + raise NotImplementedError(f"activation '{name}' not implemented in pure Hyena") + + +class HyenaOperator(nn.Module): + """Hyena operator — order-N implicit-filter recurrence (safari reference). + + Paper: https://arxiv.org/pdf/2302.10866.pdf + + Forward signature: + x: [B, T, d_model] -> y: [B, T, d_model] + + Causal: the internal fftconv_ref uses zero-padded FFT convolution, + slicing to the first T samples of a 2T-length causal linear convolution. + Additionally, the `short_filter` Conv1d uses padding=short_filter_order-1 + and is truncated with `[..., :l_filter]` to keep the output causal. + + Strict subset of safari's HyenaOperator: + num_heads = 1, num_blocks = 1, inner_factor = 1, outer_mixing = False, + post_order_ffn = False, jit_filter = False, return_state = False, + fused_bias_fc = False. + This removes the parallel-head / block-decomposition bookkeeping the + safari version supports but HYDRA doesn't use. The *math* of the + Hyena recurrence is identical to the reference code path at those + default settings. + + Filter-rfft cache (opt-in): set `HYDRA_HYENA_FILTER_CACHE=1` in env to + re-use the filter rfft across micro-batches within an optimizer step. + The parent `PostSemClawModel.invalidate_hyena_caches()` MUST be called + after every `optimizer.step()` to bump the version, otherwise stale k_f + will be reused with updated params. Default is OFF for rollout safety. + """ + + def __init__( + self, + d_model: int, + l_max: int, + order: int = 2, + filter_order: int = 64, + dropout: float = 0.0, + filter_dropout: float = 0.0, + short_filter_order: int = 3, + activation: str = "id", + **filter_args, + ): + super().__init__() + assert order >= 2, f"Order must be at least 2 (got {order})" + + # Single-head configuration (HYDRA-style: d_model as a single head). + self.d_model = d_model + self.l_max = l_max + self.order = order + self.num_heads = 1 + self.head_dim = d_model + self.num_blocks = 1 + self.block_dim = l_max + self.inner_factor = 1 + self.filter_order = filter_order + self.short_filter_order = short_filter_order + + self.activation = _activation(activation) + self.dropout = nn.Dropout(dropout) + + # Input projection: produces (order + 1) × d_model channels to feed + # the short filter and the recurrence. + self.in_proj = nn.Linear(d_model, (order + 1) * d_model) + self.out_proj = nn.Linear(d_model, d_model) + + total_width = d_model * (order + 1) + # Depthwise short conv — causal via left-padding + truncation downstream. + self.short_filter = nn.Conv1d( + in_channels=total_width, + out_channels=total_width, + kernel_size=short_filter_order, + groups=total_width, + padding=short_filter_order - 1, + ) + + # Implicit long filter: one filter per (order - 1) × d_model channels. + # Safari uses head_dim * (order - 1). With num_heads=1, head_dim=d_model. + self.filter_fn = HyenaFilter( + d_model=d_model * (order - 1), + order=filter_order, + seq_len=l_max, + dropout=filter_dropout, + **filter_args, + ) + + # Cache gate — read once per forward from env (cheap). + self._use_filter_cache = ( + os.environ.get("HYDRA_HYENA_FILTER_CACHE", "0") == "1" + ) + # Training-safe cache gate — separate knob so rollout is incremental. + # When on, the cache ALSO activates during training forwards via the + # deferred-gradient pattern in HyenaFilter.get_or_build_train_cache. + self._use_train_cache = ( + os.environ.get("HYDRA_HYENA_TRAIN_CACHE", "0") == "1" + ) + + def forward(self, u, *args, **kwargs): + """u: [B, T, d_model] -> y: [B, T, d_model]""" + global _fftconv_filter_rfft_count + l = u.size(-2) + l_filter = min(l, self.l_max) + + u = self.in_proj(u) # [B, T, (order+1)*D] + u = rearrange(u, "b l d -> b d l") # [B, (order+1)*D, T] + + uc = self.short_filter(u)[..., :l_filter] # causal truncation to T + + # Reshape: num_heads=1, num_blocks=1 → simple view. + # total_width = head_dim * (order + 1) = D * (order + 1) + # v_width_per_group = head_dim * (order + 1) = D * (order + 1) + # Split into (order + 1) groups along channel axis, each of size D. + uc = rearrange( + uc, + "b (ho v) (z l) -> b ho v z l", + z=self.num_blocks, + ho=self.num_heads, + v=self.head_dim * (self.order + 1), + ) # [B, 1, (order+1)*D, 1, T] + + # Split into (order+1) tensors of shape [B, 1, D, 1, T] + *x, v = uc.split(self.d_model, dim=2) + + # Long filter: [1, T, D*(order-1)] → [order-1, D, T] + # + # Cache-routing decision tree: + # 1. HYDRA_HYENA_TRAIN_CACHE=1 and grad enabled → train-safe cache + # (deferred-gradient pattern, see HyenaFilter.get_or_build_train_cache). + # Each micro-batch reuses _k_leaf; the filter MLP runs exactly once + # per optimizer step. Requires the training loop to call + # `model.flush_hyena_pending_grads()` before `optimizer.step()` and + # `model.invalidate_hyena_caches()` after. + # 2. HYDRA_HYENA_FILTER_CACHE=1 and grad disabled → eval cache (original). + # Filter MLP runs once per eval "version", reused across passes. + # 3. Either flag set but wrong grad mode, or both unset → plain forward. + # Filter MLP runs every call. This was the only safe mode before + # HYDRA_HYENA_TRAIN_CACHE existed. + fft_size = 2 * l_filter + grad_on = torch.is_grad_enabled() + use_train_cache = self._use_train_cache and grad_on + use_eval_cache = self._use_filter_cache and not grad_on + if use_train_cache: + # Training-safe path: returns a LEAF (k_leaf.requires_grad=True). + # Its gradient contribution is flushed back through the real + # filter MLP graph at step-end via `flush_pending_filter_grads`. + k_raw, _k_f_raw = self.filter_fn.get_or_build_train_cache( + l_filter, fft_size, + ) + elif use_eval_cache: + # Pass the filter's own version so the first call after an + # invalidate_cache() always misses. + k_raw, _k_f_raw = self.filter_fn.get_cached_kf( + l_filter, fft_size, self.filter_fn._cache_version, + ) + else: + k_raw = self.filter_fn.filter(l_filter) + k_raw = k_raw[0] if isinstance(k_raw, tuple) else k_raw + k = rearrange( + k_raw, "c l (v o) -> c o v l", + v=self.head_dim, o=self.order - 1, + )[0] # [order-1, D, T] + + # Precompute per-order rfft of the rearranged filter. + # - Under eval cache (no_grad): stored across calls keyed by version. + # Safe because no_grad forwards produce no saved tensors to free. + # - Under train cache or no cache: compute fresh each forward. For the + # train cache case, re-caching across micro-batches would share + # saved rfft intermediates and trip "backward through graph twice". + if use_eval_cache: + cache_key = (l_filter, fft_size) + cached = getattr(self, "_cached_reshaped_k_f", None) + cached_key = getattr(self, "_cached_reshaped_key", None) + cached_ver = getattr(self, "_cached_reshaped_ver", -1) + if ( + cached is not None + and cached_key == cache_key + and cached_ver == self.filter_fn._cache_version + ): + k_f_per_order = cached + else: + # Count this as a filter rfft — the test hook lumps any + # recompute of the filter spectrum so callers can observe + # cache misses after invalidation. + _fftconv_filter_rfft_count += 1 + k_f_per_order = torch.fft.rfft(k, n=fft_size) / fft_size + self._cached_reshaped_k_f = k_f_per_order + self._cached_reshaped_key = cache_key + self._cached_reshaped_ver = self.filter_fn._cache_version + else: + # Non-eval-cache path (includes train-cache): compute k_f fresh + # per forward, hoisted once so the order-1 inner loop's rfft + # inside fftconv_ref doesn't redo the same transform each iter. + # This micro-opt lives entirely within a single forward graph, + # so it's safe under grad. + _fftconv_filter_rfft_count += 1 + k_f_per_order = torch.fft.rfft(k, n=fft_size) / fft_size + + bias = rearrange( + self.filter_fn.bias, "(v o) -> o v", + v=self.head_dim, o=self.order - 1, + ) # [order-1, D] + + # Hyena recurrence (reverse-iterating over x[1:] gives o = 0..order-2) + for o, x_i in enumerate(reversed(x[1:])): + v = self.dropout(v * x_i) + # Shape to fftconv: [B, 1, D, 1, T] → rely on pre-contract. + # fftconv_ref expects [B, D, L]; collapse the 1s. + # v: [B, 1, D, 1, T] (ho=1, z=1) + B = v.size(0) + v_f = v.reshape(B, self.d_model, l_filter) + k_f_slice = None if k_f_per_order is None else k_f_per_order[o] + y_f = fftconv_ref( + v_f, k[o], bias[o], dropout_mask=None, gelu=False, + k_f=k_f_slice, + ) + v = y_f.reshape(B, 1, self.d_model, 1, l_filter) + + # Final element-wise gate with x[0]: + y = self.activation( + rearrange( + v * x[0], + "b h v z l -> b (z l) (h v)", + z=self.num_blocks, h=self.num_heads, + ) + ) # [B, T, D] + y = self.out_proj(y) + return y + + def invalidate_filter_cache(self) -> None: + """Drop cached rfft on both the filter module and this operator. + + Intended to be called from the parent model's + `invalidate_hyena_caches()` after each `optimizer.step()`. + """ + self.filter_fn.invalidate_cache() + self._cached_reshaped_k_f = None + self._cached_reshaped_key = None + self._cached_reshaped_ver = -1 + + def flush_pending_filter_grads(self) -> None: + """Push accumulated train-cache filter grads back into filter params. + + Pass-through to `HyenaFilter.flush_pending_filter_grads`. Called + from the parent model's `flush_hyena_pending_grads()` BEFORE + `optimizer.step()` (and before `invalidate_hyena_caches()`) when + HYDRA_HYENA_TRAIN_CACHE=1. + """ + self.filter_fn.flush_pending_filter_grads() diff --git a/overlay/subsystems/mhc_mini.py b/overlay/subsystems/mhc_mini.py index 19dcb2a2e5a3ce1166fdf77ece24a745aae8ebf8..eb44fe5aa4ea0407ebfc59950774a5a5d03bd032 100644 --- a/overlay/subsystems/mhc_mini.py +++ b/overlay/subsystems/mhc_mini.py @@ -1,149 +1,149 @@ -""" -Manifold-Constrained Hyper-Connections — minimal standalone module. - -Extracted verbatim from subsystems/train_mhc.py so train.py can import just -this class without pulling in the full Mamba3MhcModel / dataloader / prepare -dependencies. The full train_mhc.py is only needed by the Phase-1 standalone -bring-up, not by the autoresearch loop. - -Identical semantics to train_mhc.ManifoldHyperConnection. - -Phase 2 kernel gates (env vars): - HYDRA_FUSED_SINKHORN=1 — use closed-form n=2 or Triton Sinkhorn - HYDRA_FUSED_MHC=1 — use fused mix/inject Triton kernels for forward() -""" - -from __future__ import annotations - -import logging -import os - -import torch -import torch.nn as nn - -logger = logging.getLogger(__name__) - -# --------------------------------------------------------------------------- -# Env-gated kernel imports (fail gracefully on CPU / missing Triton) -# --------------------------------------------------------------------------- -_USE_FUSED_SINKHORN = os.environ.get("HYDRA_FUSED_SINKHORN", "0") == "1" -_USE_FUSED_MHC = os.environ.get("HYDRA_FUSED_MHC", "0") == "1" - -_sinkhorn_fused_fn = None -_mhc_fused_forward_fn = None -_MHCFusedOps = None - -if _USE_FUSED_SINKHORN: - try: - from kernels.triton.sinkhorn_fused import sinkhorn_fused as _sinkhorn_fused_fn # type: ignore[assignment] - logger.info("mhc_mini: fused Sinkhorn kernel LOADED") - except Exception as e: - logger.warning("mhc_mini: fused Sinkhorn import failed (%s), falling back to Python loop", e) - _USE_FUSED_SINKHORN = False - -if _USE_FUSED_MHC: - try: - from kernels.tilelang.mhc_kernels import MHCFusedOps as _MHCFusedOps # type: ignore[assignment] - from kernels.tilelang.mhc_kernels import mhc_fused_forward as _mhc_fused_forward_fn # type: ignore[assignment] - logger.info("mhc_mini: fused MHC kernels LOADED") - except Exception as e: - logger.warning("mhc_mini: fused MHC kernel import failed (%s), falling back to Python ops", e) - _USE_FUSED_MHC = False - - -class ManifoldHyperConnection(nn.Module): - """ - Replaces a simple residual with a Sinkhorn-projected doubly-stochastic - routing matrix mixing n_streams parallel residual streams. - """ - - def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: - super().__init__() - self.n_streams = n_streams - self.d_model = d_model - self.sinkhorn_iters = sinkhorn_iters - self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) - # Only stream_norms[0] was ever invoked in forward — indices 1..n-1 were - # dead weight (2*d_model params each). Single LayerNorm matches usage. - # Kept as ModuleList of length 1 so state-dict shape stays compat. - self.stream_norms = nn.ModuleList([nn.LayerNorm(d_model)]) - - def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: - """Doubly-stochastic projection via Sinkhorn-Knopp. - - For n_streams=2: CLOSED-FORM solution (zero iterations, exact). - A 2×2 doubly-stochastic matrix has exactly one degree of freedom: - M = [[a, 1-a], [1-a, a]] where a = sigmoid(log_alpha[0,0] - log_alpha[0,1]) - This eliminates 10 logsumexp kernel launches (2.4ms → ~0.05ms). - """ - if self.n_streams == 2: - a = torch.sigmoid(log_alpha[0, 0] - log_alpha[0, 1]) - b = 1.0 - a - return torch.stack([ - torch.stack([a, b]), - torch.stack([b, a]), - ]) - if _USE_FUSED_SINKHORN and _sinkhorn_fused_fn is not None and log_alpha.is_cuda: - return _sinkhorn_fused_fn(log_alpha, iters=self.sinkhorn_iters) - M = log_alpha - for _ in range(self.sinkhorn_iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: - """ - streams: (n_streams, B, T, d_model) - block_fn: callable (B, T, d_model) -> (B, T, d_model) - """ - # Compute doubly-stochastic routing matrix (may use fused Sinkhorn) - M = self._sinkhorn(self.log_alpha) - - # --- Fused MHC path (n_streams=2, CUDA, env gate ON) --- - if ( - _USE_FUSED_MHC - and _mhc_fused_forward_fn is not None - and self.n_streams == 2 - and streams.is_cuda - ): - return _mhc_fused_forward_fn( - streams, M, block_fn, self.stream_norms[0], - ) - - # --- Original Python path (fallback) --- - # Fast path for the common n_streams=2 case: M is 2x2 and only row 0 of - # `update` is non-zero, so the einsum reduces to an outer product - # out[i,b,t,d] = streams[i,b,t,d] + M[0,i] * block_output[b,t,d] - # This avoids materializing the full `update` tensor (zeros_like - # allocation + scatter) and one einsum launch per layer. - mixed = M[0, 0] * streams[0] + M[0, 1] * streams[1] if self.n_streams == 2 \ - else torch.einsum("ij,jbtd->ibtd", M, streams)[0] - # Backward-compat: reconstruct the `[0]`-indexed form used below. - if self.n_streams == 2: - primary_input = self.stream_norms[0](mixed) - block_output = block_fn(primary_input) - # update[i] = delta_{i,0} * block_output, so - # einsum("ij,jbtd->ibtd", M_T, update)[i] = M[0,i] * block_output - out0 = streams[0] + M[0, 0] * block_output - out1 = streams[1] + M[0, 1] * block_output - return torch.stack((out0, out1), dim=0) - # General path (n_streams != 2) — unchanged semantics. - mixed_full = torch.einsum("ij,jbtd->ibtd", M, streams) - primary_input = self.stream_norms[0](mixed_full[0]) - block_output = block_fn(primary_input) - update = torch.zeros_like(streams) - update[0] = block_output - return streams + torch.einsum("ij,jbtd->ibtd", M.t(), update) - - def init_streams(self, x: torch.Tensor) -> torch.Tensor: - out = torch.empty( - (self.n_streams, *x.shape), dtype=x.dtype, device=x.device, - requires_grad=False, - ) - out.copy_(x.unsqueeze(0)) - return out - - def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: - if streams.shape[0] == 2: - return torch.add(streams[0], streams[1]).mul_(0.5) - return streams.mean(dim=0) +""" +Manifold-Constrained Hyper-Connections — minimal standalone module. + +Extracted verbatim from subsystems/train_mhc.py so train.py can import just +this class without pulling in the full Mamba3MhcModel / dataloader / prepare +dependencies. The full train_mhc.py is only needed by the Phase-1 standalone +bring-up, not by the autoresearch loop. + +Identical semantics to train_mhc.ManifoldHyperConnection. + +Phase 2 kernel gates (env vars): + HYDRA_FUSED_SINKHORN=1 — use closed-form n=2 or Triton Sinkhorn + HYDRA_FUSED_MHC=1 — use fused mix/inject Triton kernels for forward() +""" + +from __future__ import annotations + +import logging +import os + +import torch +import torch.nn as nn + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Env-gated kernel imports (fail gracefully on CPU / missing Triton) +# --------------------------------------------------------------------------- +_USE_FUSED_SINKHORN = os.environ.get("HYDRA_FUSED_SINKHORN", "0") == "1" +_USE_FUSED_MHC = os.environ.get("HYDRA_FUSED_MHC", "0") == "1" + +_sinkhorn_fused_fn = None +_mhc_fused_forward_fn = None +_MHCFusedOps = None + +if _USE_FUSED_SINKHORN: + try: + from kernels.triton.sinkhorn_fused import sinkhorn_fused as _sinkhorn_fused_fn # type: ignore[assignment] + logger.info("mhc_mini: fused Sinkhorn kernel LOADED") + except Exception as e: + logger.warning("mhc_mini: fused Sinkhorn import failed (%s), falling back to Python loop", e) + _USE_FUSED_SINKHORN = False + +if _USE_FUSED_MHC: + try: + from kernels.tilelang.mhc_kernels import MHCFusedOps as _MHCFusedOps # type: ignore[assignment] + from kernels.tilelang.mhc_kernels import mhc_fused_forward as _mhc_fused_forward_fn # type: ignore[assignment] + logger.info("mhc_mini: fused MHC kernels LOADED") + except Exception as e: + logger.warning("mhc_mini: fused MHC kernel import failed (%s), falling back to Python ops", e) + _USE_FUSED_MHC = False + + +class ManifoldHyperConnection(nn.Module): + """ + Replaces a simple residual with a Sinkhorn-projected doubly-stochastic + routing matrix mixing n_streams parallel residual streams. + """ + + def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: + super().__init__() + self.n_streams = n_streams + self.d_model = d_model + self.sinkhorn_iters = sinkhorn_iters + self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) + # Only stream_norms[0] was ever invoked in forward — indices 1..n-1 were + # dead weight (2*d_model params each). Single LayerNorm matches usage. + # Kept as ModuleList of length 1 so state-dict shape stays compat. + self.stream_norms = nn.ModuleList([nn.LayerNorm(d_model)]) + + def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: + """Doubly-stochastic projection via Sinkhorn-Knopp. + + For n_streams=2: CLOSED-FORM solution (zero iterations, exact). + A 2×2 doubly-stochastic matrix has exactly one degree of freedom: + M = [[a, 1-a], [1-a, a]] where a = sigmoid(log_alpha[0,0] - log_alpha[0,1]) + This eliminates 10 logsumexp kernel launches (2.4ms → ~0.05ms). + """ + if self.n_streams == 2: + a = torch.sigmoid(log_alpha[0, 0] - log_alpha[0, 1]) + b = 1.0 - a + return torch.stack([ + torch.stack([a, b]), + torch.stack([b, a]), + ]) + if _USE_FUSED_SINKHORN and _sinkhorn_fused_fn is not None and log_alpha.is_cuda: + return _sinkhorn_fused_fn(log_alpha, iters=self.sinkhorn_iters) + M = log_alpha + for _ in range(self.sinkhorn_iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: + """ + streams: (n_streams, B, T, d_model) + block_fn: callable (B, T, d_model) -> (B, T, d_model) + """ + # Compute doubly-stochastic routing matrix (may use fused Sinkhorn) + M = self._sinkhorn(self.log_alpha) + + # --- Fused MHC path (n_streams=2, CUDA, env gate ON) --- + if ( + _USE_FUSED_MHC + and _mhc_fused_forward_fn is not None + and self.n_streams == 2 + and streams.is_cuda + ): + return _mhc_fused_forward_fn( + streams, M, block_fn, self.stream_norms[0], + ) + + # --- Original Python path (fallback) --- + # Fast path for the common n_streams=2 case: M is 2x2 and only row 0 of + # `update` is non-zero, so the einsum reduces to an outer product + # out[i,b,t,d] = streams[i,b,t,d] + M[0,i] * block_output[b,t,d] + # This avoids materializing the full `update` tensor (zeros_like + # allocation + scatter) and one einsum launch per layer. + mixed = M[0, 0] * streams[0] + M[0, 1] * streams[1] if self.n_streams == 2 \ + else torch.einsum("ij,jbtd->ibtd", M, streams)[0] + # Backward-compat: reconstruct the `[0]`-indexed form used below. + if self.n_streams == 2: + primary_input = self.stream_norms[0](mixed) + block_output = block_fn(primary_input) + # update[i] = delta_{i,0} * block_output, so + # einsum("ij,jbtd->ibtd", M_T, update)[i] = M[0,i] * block_output + out0 = streams[0] + M[0, 0] * block_output + out1 = streams[1] + M[0, 1] * block_output + return torch.stack((out0, out1), dim=0) + # General path (n_streams != 2) — unchanged semantics. + mixed_full = torch.einsum("ij,jbtd->ibtd", M, streams) + primary_input = self.stream_norms[0](mixed_full[0]) + block_output = block_fn(primary_input) + update = torch.zeros_like(streams) + update[0] = block_output + return streams + torch.einsum("ij,jbtd->ibtd", M.t(), update) + + def init_streams(self, x: torch.Tensor) -> torch.Tensor: + out = torch.empty( + (self.n_streams, *x.shape), dtype=x.dtype, device=x.device, + requires_grad=False, + ) + out.copy_(x.unsqueeze(0)) + return out + + def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: + if streams.shape[0] == 2: + return torch.add(streams[0], streams[1]).mul_(0.5) + return streams.mean(dim=0) diff --git a/overlay/subsystems/sdr_retina.py b/overlay/subsystems/sdr_retina.py index bfeaf6ccadeed22f14dad52b774a41170809e8a7..8f960a8241afe69fb946d7cdfa68de9a9cf34101 100644 --- a/overlay/subsystems/sdr_retina.py +++ b/overlay/subsystems/sdr_retina.py @@ -1,716 +1,716 @@ -""" -Offline Semantic Folding SDR Retina (Cortical.io-grade). - -Builds a topographic, semantic-folding Sparse Distributed Representation (SDR) -for every token in the vocabulary, following Webber 2015 ("Semantic Folding Theory"). - -Pipeline: - 1. Scan the tokenized training corpus (parquet shards at ~/.cache/autoresearch/data). - We on-the-fly tokenize ~10M tokens from the first few shards. - 2. For each token, build a context vector = top-K most-associated neighbors - (±8-token window, PMI ranking). - 3. Train a 128x128 = 16384-bit Kohonen SOM on those context vectors so that - semantically related context features land on neighboring lattice cells. - 4. For each token, compute its folded SDR: union of the lattice cells whose - BMUs are triggered by its top-K context features. Then per-row quantile - threshold to exactly 2% active bits (327 / 16384). - 5. Save to ~/.cache/autoresearch/retina.npz. - -Entry point: - uv run python subsystems/sdr_retina.py --build --validate - -The validation asserts classic Cortical.io-style analogies: - - overlap("the", "a") > overlap("the", "zebra") - - overlap("man", "woman") > overlap("man", "rock") - - overlap("king","queen") > overlap("king", "dinosaur") -""" - -from __future__ import annotations - -import argparse -import gc -import math -import os -import sys -import time -from dataclasses import dataclass - -import numpy as np -import pyarrow.parquet as pq -import torch - -# Make the parent repo importable so we can reuse the Tokenizer -REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.insert(0, REPO_ROOT) - -from prepare import CACHE_DIR, DATA_DIR, TOKENIZER_DIR, VAL_FILENAME, VOCAB_SIZE, Tokenizer # noqa: E402 - - -# --------------------------------------------------------------------------- -# Build parameters -# --------------------------------------------------------------------------- - -RETINA_PATH = os.path.join(CACHE_DIR, "retina.npz") - -GRID_H = 128 -GRID_W = 128 -N_BITS = GRID_H * GRID_W # 16384 -TARGET_SPARSITY = 0.02 # 2% (default, Cortical.io-style) -# Default = int(floor(N_BITS * TARGET_SPARSITY)) = 327, matches Webber/Numenta. -# Override via HYDRA_SDR_TARGET_ACTIVE env var. The cache key encodes -# target_active, so changing this triggers automatic retina regeneration. -TARGET_ACTIVE = int(os.environ.get( - "HYDRA_SDR_TARGET_ACTIVE", - str(int(N_BITS * TARGET_SPARSITY)), -)) - -CONTEXT_WINDOW = 8 # +/- 8 tokens -TOP_K_FEATURES = 64 # top-K context features per token -# SCALES WITH VOCAB — need ~100+ occurrences per token for stable cooccurrence. -# At V=8k: 10M tokens = 1250/tok avg. At V=65k: 10M tokens = 153/tok avg -# (borderline); rare tokens seen <30x → noisy retina. Recommended: V*150. -# HF Hub cache makes this a one-time cost per vocab config anyway. -TARGET_TRAIN_TOKENS = int(os.environ.get("HYDRA_RETINA_TRAIN_TOKENS", "20000000")) -MAX_DOCS_PER_SHARD = 200_000 # safety cap per shard - -# Kohonen SOM -SOM_EPOCHS = 50 -SOM_SIGMA_START = 32.0 -SOM_SIGMA_END = 1.0 -SOM_ALPHA_START = 0.1 -SOM_ALPHA_END = 0.001 - - -# --------------------------------------------------------------------------- -# Small helpers -# --------------------------------------------------------------------------- - -def _fmt(n): - if n >= 1_000_000: - return f"{n/1_000_000:.2f}M" - if n >= 1_000: - return f"{n/1_000:.1f}k" - return str(n) - - -def _rss_gb() -> str: - """Return current process RSS in GB as a formatted string.""" - try: - rss_kb = int(os.popen(f"ps -o rss= -p {os.getpid()}").read().strip()) - return f"{rss_kb / 1e6:.2f} GB" - except Exception: - return "?" - - -def _device() -> torch.device: - return torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def _list_train_shards(): - files = sorted( - f for f in os.listdir(DATA_DIR) - if f.endswith(".parquet") and not f.endswith(".tmp") - ) - train = [os.path.join(DATA_DIR, f) for f in files if f != VAL_FILENAME] - assert len(train) > 0, f"No training shards at {DATA_DIR}. Run prepare.py first." - return train - - -# --------------------------------------------------------------------------- -# Stage 1: stream tokens from parquet shards and collect co-occurrences -# --------------------------------------------------------------------------- - -def _iter_tokenized_shards(tokenizer: Tokenizer, target_tokens: int): - """Yield 1-D int32 numpy arrays of token ids until target_tokens reached. - - Two paths: - - HYDRA_USE_NEMOTRON=1: stream docs from Nemotron HF datasets (no shards - on disk — matches the streaming training path). - - Default: iterate local parquet shards (legacy prepare.py path). - """ - tok_encode = tokenizer.enc.encode_ordinary_batch - - if os.environ.get("HYDRA_USE_NEMOTRON", "0") == "1": - # Streaming path: reuse prepare_nemotron's weighted stream. - import prepare_nemotron as _pn - stream = _pn._WeightedStream(_pn._phase_weights(), seed=0) - seen = 0 - batch: list[str] = [] - BATCH = 512 - while seen < target_tokens: - text, _epoch = next(stream) - if not text: - continue - batch.append(text) - if len(batch) < BATCH: - continue - token_lists = tok_encode(batch, num_threads=8) - batch = [] - for ids in token_lists: - if not ids: - continue - arr = np.asarray(ids, dtype=np.int32) - yield arr - seen += arr.size - if seen >= target_tokens: - print(f" [nemotron-stream] yielded {_fmt(seen)} tokens, target reached") - return - return - - # Legacy shard path. - shards = _list_train_shards() - seen = 0 - for shard_idx, path in enumerate(shards): - if seen >= target_tokens: - return - pf = pq.ParquetFile(path) - shard_tokens = 0 - for rg_idx in range(pf.num_row_groups): - rg = pf.read_row_group(rg_idx) - docs = rg.column("text").to_pylist() - if len(docs) > MAX_DOCS_PER_SHARD: - docs = docs[:MAX_DOCS_PER_SHARD] - # Batch-encode for throughput - batch_size = 512 - for i in range(0, len(docs), batch_size): - batch = docs[i:i + batch_size] - token_lists = tok_encode(batch, num_threads=8) - for ids in token_lists: - if not ids: - continue - arr = np.asarray(ids, dtype=np.int32) - yield arr - shard_tokens += arr.size - seen += arr.size - if seen >= target_tokens: - print(f" shard {shard_idx}: yielded {_fmt(shard_tokens)} tokens " - f"(total {_fmt(seen)}), target reached") - return - print(f" shard {shard_idx}: yielded {_fmt(shard_tokens)} tokens (total {_fmt(seen)})") - - -def _cooccur_from_doc(ids: np.ndarray, window: int, vocab_size: int, - counts: np.ndarray, cooc: np.ndarray) -> None: - """Update unigram counts and cooccurrence counts for one document. Vectorized.""" - n = ids.size - if n < 2: - return - # unigram counts - np.add.at(counts, ids, 1) - # For each offset d in 1..window, count pairs (ids[:-d], ids[d:]) - # Both directions are equivalent by symmetry; we add both to keep the - # matrix symmetric and treat it as undirected context. - for d in range(1, window + 1): - left = ids[:-d] - right = ids[d:] - # symmetric update - flat_lr = left.astype(np.int64) * vocab_size + right.astype(np.int64) - flat_rl = right.astype(np.int64) * vocab_size + left.astype(np.int64) - # use bincount-style scatter via np.add.at on the flat view - cooc_flat = cooc.ravel() - np.add.at(cooc_flat, flat_lr, 1) - np.add.at(cooc_flat, flat_rl, 1) - - -def build_cooccurrence(tokenizer: Tokenizer, target_tokens: int, window: int) -> tuple[np.ndarray, np.ndarray, int]: - """ - Stream tokens and build unigram + cooccurrence counts. - Returns (counts[V] int64, cooc[V,V] int32, total_tokens int). - """ - vocab_size = tokenizer.get_vocab_size() - print(f"[1/4] Building cooccurrence (vocab={vocab_size}, window=+/-{window}, target={_fmt(target_tokens)} tokens)") - counts = np.zeros(vocab_size, dtype=np.int64) - # int32 is enough per-cell if we stay <= a few hundred million total tokens; guard with clip at save. - cooc = np.zeros((vocab_size, vocab_size), dtype=np.int32) - - total = 0 - n_docs = 0 - t0 = time.time() - for ids in _iter_tokenized_shards(tokenizer, target_tokens): - _cooccur_from_doc(ids, window, vocab_size, counts, cooc) - total += ids.size - n_docs += 1 - if n_docs % 5000 == 0: - dt = time.time() - t0 - rate = total / max(dt, 1e-6) - print(f" docs={_fmt(n_docs)} tokens={_fmt(total)} ({rate/1000:.0f}k tok/s)") - - dt = time.time() - t0 - print(f"[1/4] done: {_fmt(total)} tokens, {_fmt(n_docs)} docs, {dt:.1f}s, " - f"cooc_nnz={int((cooc > 0).sum())}") - return counts, cooc, total - - -# --------------------------------------------------------------------------- -# Stage 2: build top-K context features (PMI) -# --------------------------------------------------------------------------- - -def compute_pmi_topk(counts: np.ndarray, cooc: np.ndarray, total_tokens: int, - top_k: int) -> tuple[np.ndarray, np.ndarray]: - """ - For each token, compute top-K context features by positive PMI. - Returns: - topk_idx : int32 [V, K] token ids of the top-K context features - topk_score : float32 [V, K] PMI scores (0 for padded missing features) - Missing features are padded with idx=token itself and score=0, so they - have a well-defined (but uninformative) column. - """ - V = counts.shape[0] - print(f"[2/4] Computing PMI top-{top_k} per token (vocab={V})") - - # window_pairs per occurrence: 2 * window (we added both directions, each offset twice). - # For the PMI denominator we need a total pair count; using coo.sum() is the clean - # per-matrix normalizer and avoids any constant confusion. - pair_total = float(cooc.sum()) - if pair_total <= 0: - raise RuntimeError("Empty cooccurrence matrix") - - # Run on GPU if it fits; V×V float32 at V=65536 is 16 GB → CPU fallback. - vram_needed = V * V * 4 # float32 - dev = _device() - if dev.type == "cuda" and vram_needed > 4 * 1024**3: # >4 GB → CPU - dev = torch.device("cpu") - print(f" [PMI] V×V={V}² needs {vram_needed/1e9:.1f} GB → using CPU", flush=True) - cooc_t = torch.from_numpy(cooc.astype(np.float32)).to(dev) - counts_t = torch.from_numpy(counts.astype(np.float64)).to(dev).clamp_min(1.0) - - # P(i) = counts[i] / total_tokens - # P(i, j) = cooc[i, j] / pair_total - # PMI = log(P(i,j) / (P(i) P(j))) - # Positive PMI = max(PMI, 0). - # We'll compute log-PMI in a numerically safe way: - # log(cooc) + log(total_tokens^2 / pair_total) - log(c_i) - log(c_j) - # Keep numerator zero where cooc==0 and mask those out. - - log_const = math.log(total_tokens) + math.log(total_tokens) - math.log(pair_total) - log_ci = torch.log(counts_t) # [V] - log_cj = log_ci.clone() # same vector (symmetric vocab) - - # We'll do it in row blocks to cap memory of intermediate log() tensors. - topk_idx = np.zeros((V, top_k), dtype=np.int32) - topk_score = np.zeros((V, top_k), dtype=np.float32) - - block = 512 - t0 = time.time() - for start in range(0, V, block): - end = min(V, start + block) - rows = cooc_t[start:end] # [b, V] int-as-float - mask = rows > 0 - # log(rows) where rows>0; else keep -inf then mask out - log_rows = torch.where(mask, torch.log(rows.clamp_min(1.0)), - torch.full_like(rows, float("-inf"))) - pmi = log_rows + log_const - log_ci[start:end].unsqueeze(1) - log_cj.unsqueeze(0) - ppmi = torch.where(mask, torch.clamp(pmi, min=0.0), - torch.full_like(pmi, float("-inf"))) - # top-K along dim=1 - vals, idx = torch.topk(ppmi, k=top_k, dim=1) - # Replace any -inf valued slots with score 0 and idx = the token itself - bad = torch.isneginf(vals) - if bad.any(): - self_idx = torch.arange(start, end, device=dev).unsqueeze(1).expand_as(idx) - idx = torch.where(bad, self_idx, idx) - vals = torch.where(bad, torch.zeros_like(vals), vals) - topk_idx[start:end] = idx.cpu().numpy().astype(np.int32) - topk_score[start:end] = vals.cpu().numpy().astype(np.float32) - - del cooc_t, counts_t - if dev.type == "cuda": - torch.cuda.empty_cache() - print(f"[2/4] done: top-{top_k} PMI features per token in {time.time()-t0:.1f}s") - return topk_idx, topk_score - - -# --------------------------------------------------------------------------- -# Stage 3: Kohonen SOM on the context-vector representation -# --------------------------------------------------------------------------- - -def _context_vectors_from_topk(topk_idx: np.ndarray, topk_score: np.ndarray, - vocab_size: int) -> torch.Tensor: - """ - Build the dense context matrix X [V, V] where X[i] is the top-K PMI context - vector for token i, L2-normalized. For V=8192 this is 8k x 8k float32 = 256 MB. - """ - V = vocab_size - K = topk_idx.shape[1] - dev = _device() - # At V=65536, dense V×V is 17 GB — won't fit in GPU or system RAM. - # Use the (V, K) scores directly as feature vectors. K=64 dimensions - # is sufficient for SOM clustering (each token characterized by its - # top-64 PMI context scores). L2-normalize for cosine-like distance. - if V * V * 4 > 4 * 1024**3: - print(f" [context_vectors] V={V} too large for dense V×V; using sparse (V,K={K}) features", flush=True) - scores = torch.from_numpy(topk_score).to(dev) - norm = scores.norm(dim=1, keepdim=True).clamp_min(1e-8) - X = scores / norm - return X - # Small vocab: original dense V×V path (V=8192 = 256 MB, fits fine) - X = torch.zeros((V, V), dtype=torch.float32, device=dev) - rows = torch.arange(V, device=dev).unsqueeze(1).expand(V, K) - idx = torch.from_numpy(topk_idx).to(dev).long() - scores = torch.from_numpy(topk_score).to(dev) - X[rows, idx] = torch.maximum(X[rows, idx], scores) - norm = X.norm(dim=1, keepdim=True).clamp_min(1e-8) - X = X / norm - return X - - -def train_som(X: torch.Tensor, grid_h: int, grid_w: int, - epochs: int, sigma_start: float, sigma_end: float, - alpha_start: float, alpha_end: float, - seed: int = 137) -> torch.Tensor: - """ - Train a Kohonen SOM with rectangular grid and Gaussian neighborhood. - X: [V, F] features (L2 normalized). Returns weights W: [grid_h*grid_w, F]. - """ - dev = X.device - V, F = X.shape - N = grid_h * grid_w - - torch.manual_seed(seed) - # Initialize SOM weights: small random linear combinations of data points - # (faster convergence than uniform random in the feature space). - init_pick = torch.randint(0, V, (N,), device=dev) - W = X[init_pick].clone() # [N, F] - - # Precompute grid coordinates - yy, xx = torch.meshgrid( - torch.arange(grid_h, device=dev, dtype=torch.float32), - torch.arange(grid_w, device=dev, dtype=torch.float32), - indexing="ij", - ) - grid = torch.stack([yy.reshape(-1), xx.reshape(-1)], dim=1) # [N, 2] - - print(f"[3/4] Training Kohonen SOM: grid={grid_h}x{grid_w}, features={F}, " - f"epochs={epochs}, sigma {sigma_start}->{sigma_end}, alpha {alpha_start}->{alpha_end}") - t0 = time.time() - - # Exponential decay schedules - def schedule(t_frac): - sigma = sigma_start * (sigma_end / sigma_start) ** t_frac - alpha = alpha_start * (alpha_end / alpha_start) ** t_frac - return sigma, alpha - - # Batch-mode SOM: process a random permutation each epoch in mini-batches. - # For each mini-batch, compute BMUs then one vectorized neighborhood update. - batch_size = 256 - - for epoch in range(epochs): - t_frac = epoch / max(epochs - 1, 1) - sigma, alpha = schedule(t_frac) - two_sigma2 = 2.0 * sigma * sigma - perm = torch.randperm(V, device=dev) - - for bstart in range(0, V, batch_size): - bidx = perm[bstart:bstart + batch_size] - xb = X[bidx] # [b, F] - # BMU: argmax of cosine similarity = argmin of squared Euclidean - # ||x||=||w||=1 for data; W may drift but the formulation remains stable. - sim = xb @ W.t() # [b, N] - bmu = sim.argmax(dim=1) # [b] - - # Neighborhood weights h[b, n] = exp(-|grid[bmu_b] - grid[n]|^2 / (2*sigma^2)) - bmu_coords = grid[bmu] # [b, 2] - diff = bmu_coords.unsqueeze(1) - grid.unsqueeze(0) # [b, N, 2] - dist2 = (diff * diff).sum(dim=2) # [b, N] - h = torch.exp(-dist2 / two_sigma2) # [b, N] - h = h * alpha # include LR - - # Vectorized SOM update: - # W <- W + sum_b h[b] * (x_b - W) / (sum_b h[b]) - # Batched form: numerator = h^T x_b [N, F], denom = h.sum(0) [N] - numer = h.t() @ xb # [N, F] - denom = h.sum(dim=0).unsqueeze(1).clamp_min(1e-8) # [N, 1] - target = numer / denom - # Update weight: mix toward target with a unit step (h already scaled by alpha). - # To prevent over-shoot when the same BMU is hit heavily, scale by the - # mean-field gain min(1, denom). Empirically this behaves like classic SOM. - gain = torch.clamp(h.sum(dim=0), max=1.0).unsqueeze(1) # [N,1] - W = (1 - gain) * W + gain * target - - # Renormalize weights to unit sphere for stability - W = W / W.norm(dim=1, keepdim=True).clamp_min(1e-8) - - if (epoch + 1) % max(1, epochs // 10) == 0 or epoch == 0: - dt = time.time() - t0 - print(f" epoch {epoch+1}/{epochs} sigma={sigma:.2f} alpha={alpha:.4f} elapsed={dt:.1f}s") - - print(f"[3/4] SOM trained in {time.time()-t0:.1f}s") - return W - - -# --------------------------------------------------------------------------- -# Stage 4: fold context vectors into SDRs -# --------------------------------------------------------------------------- - -def fold_sdrs(X: torch.Tensor, W: torch.Tensor, topk_idx: np.ndarray, - topk_score: np.ndarray, target_active: int) -> np.ndarray: - """ - For each token, activate the 'cell votes' on the lattice for each of its top-K - context features, then threshold to exactly target_active bits. - - Implementation detail: every token in the vocabulary has a SOM BMU given its - context vector X[i]. We use those BMUs as the feature->cell map. For token t, - we accumulate votes at BMU(feature) weighted by the PMI score, then pick the - top target_active cells. - - Memory discipline at V=65536, N=16384: - - votes (V, N) float32 = 4 GB → always on CPU for V > 8192 - - sdr (V, N) bool = 1 GB → always on CPU for V > 8192 - - blur conv2d runs on CPU in 4096-row chunks (~256 MB per chunk) - """ - dev = X.device - V, F = X.shape - N = W.shape[0] - large_vocab = V > 8192 - print(f"[4/4] Folding SDRs (V={V}, N={N}, target_active={target_active})", flush=True) - print(f" RSS before fold: {_rss_gb()}", flush=True) - - # Per-feature BMU: for each token f as a feature, BMU_f = argmax_n W[n] . X[f] - # Chunked matmul to bound memory. Run on whatever device X/W live on (GPU if small). - bmu = torch.empty(V, dtype=torch.long, device=dev) - bmu_chunk = 1024 - for s in range(0, V, bmu_chunk): - e = min(V, s + bmu_chunk) - sim = X[s:e] @ W.t() # [b, N] - bmu[s:e] = sim.argmax(dim=1) - - # For large vocabs, force votes and sdr to CPU regardless of what device X was on. - # V=65536, N=16384: votes=4 GB float32, sdr=1 GB bool — must stay on CPU. - if large_vocab: - votes_dev = torch.device("cpu") - if dev.type == "cuda": - print(f" [fold] V={V} > 8192: forcing votes/sdr to CPU " - f"(votes={V*N*4/1e9:.1f} GB, sdr={V*N/1e9:.2f} GB)", flush=True) - bmu_cpu = bmu.cpu() - else: - # Small vocab: stay on original device (GPU-accelerated). - votes_dev = dev - votes_dev = dev if V * N * 4 < 2 * 1024**3 else torch.device("cpu") - bmu_cpu = bmu.cpu() if votes_dev.type == "cpu" else bmu - - K = topk_idx.shape[1] - feat = torch.from_numpy(topk_idx).to(votes_dev).long() - sc = torch.from_numpy(topk_score).to(votes_dev) - feat_bmu = bmu_cpu.to(votes_dev)[feat] # [V, K] lattice cell indices - - votes = torch.zeros((V, N), dtype=torch.float32, device=votes_dev) - votes.scatter_add_(1, feat_bmu, sc) - del feat, sc, feat_bmu, bmu, bmu_cpu - print(f" RSS after votes scatter: {_rss_gb()}", flush=True) - - # Tiny numerical nudge: add a local Gaussian kernel around each voted cell so - # near-neighbors accumulate mass (this is the "folding" smear). Kernel radius 1. - # Implement as a separable 3x3 blur on the 2D grid view. - grid_h = int(round(math.sqrt(N))) - grid_w = grid_h - assert grid_h * grid_w == N - - # Gaussian blur + top-k in 4096-row chunks to cap peak memory. - # At V=65536 chunk=4096: chunk_2d = 4096×1×128×128 float32 = 256 MB — safe on CPU. - blur = torch.tensor([[[[0.5, 1.0, 0.5], - [1.0, 2.0, 1.0], - [0.5, 1.0, 0.5]]]], device=votes_dev, dtype=torch.float32) - blur = blur / blur.sum() - - # Always use CPU for the sdr output tensor when vocab is large. - sdr_dev = votes_dev # already CPU for large_vocab - sdr = torch.zeros((V, N), dtype=torch.bool, device=sdr_dev) - - # Fixed 4096-row chunks: 4096 × 16384 × 4 = 256 MB per chunk — well within 32 GB. - fold_chunk = 4096 if large_vocab else min(V, max(1, int(2 * 1024**3 / (N * 4)))) - n_chunks = math.ceil(V / fold_chunk) - print(f" [fold] blur+topk in {n_chunks} chunks of {fold_chunk} rows " - f"(~{fold_chunk * N * 4 / 1e6:.0f} MB each, device={votes_dev})", flush=True) - - for s in range(0, V, fold_chunk): - e = min(V, s + fold_chunk) - b = e - s - chunk_2d = votes[s:e].view(b, 1, grid_h, grid_w) - blurred = torch.nn.functional.conv2d(chunk_2d, blur, padding=1) - chunk_flat = blurred.view(b, N) - _, top_cells = torch.topk(chunk_flat, k=target_active, dim=1) - sdr[s:e].scatter_(1, top_cells, True) - del chunk_2d, blurred, chunk_flat, top_cells - - del votes - print(f" RSS after fold complete: {_rss_gb()}", flush=True) - - # Sanity check - row_active = sdr.sum(dim=1) - assert int(row_active.min()) == target_active, \ - f"row active min mismatch: got {int(row_active.min())}, expected {target_active}" - assert int(row_active.max()) == target_active, \ - f"row active max mismatch: got {int(row_active.max())}, expected {target_active}" - - result = sdr.cpu().numpy() - del sdr - return result - - -# --------------------------------------------------------------------------- -# Build orchestration -# --------------------------------------------------------------------------- - -@dataclass -class BuildReport: - vocab_size: int - n_bits: int - train_tokens: int - wall_time_sec: float - - -def _retina_cache_repo() -> str: - return os.environ.get("HYDRA_RETINA_CACHE_REPO", "icarus112/feather-retina-cache") - - -def _retina_cache_key() -> str: - """Cache key encodes vocab_size + n_bits + target_active so we don't - accidentally restore a retina built for a different tokenizer/config.""" - try: - from prepare import VOCAB_SIZE - except Exception: - VOCAB_SIZE = 0 - return f"retina_v{VOCAB_SIZE}_n{N_BITS}_a{TARGET_ACTIVE}.npz" - - -def _try_hydrate_retina_from_hub() -> bool: - """Attempt to download a pre-built retina matching our config from HF Hub. - Returns True if successful — caller should skip the rebuild.""" - token = os.environ.get("HF_TOKEN") - if not token: - return False - cache_key = _retina_cache_key() - try: - from huggingface_hub import hf_hub_download - p = hf_hub_download( - repo_id=_retina_cache_repo(), repo_type="dataset", - filename=cache_key, token=token, - ) - os.makedirs(CACHE_DIR, exist_ok=True) - import shutil - shutil.copy(p, RETINA_PATH) - # Quick verify shape - with np.load(RETINA_PATH) as npz: - if (int(npz["n_bits"]) == N_BITS - and int(npz["target_active"]) == TARGET_ACTIVE - and int(npz["vocab_size"]) == VOCAB_SIZE): - print(f"[retina-cache] hydrated {cache_key} from {_retina_cache_repo()} " - f"(shape={npz['sdr'].shape})", flush=True) - return True - os.remove(RETINA_PATH) - return False - except Exception as e: - print(f"[retina-cache] miss: {e}", flush=True) - return False - - -def _upload_retina_to_hub() -> None: - """Upload freshly-built retina.npz to HF Hub for reuse by future jobs.""" - token = os.environ.get("HF_TOKEN") - if not token: - return - cache_key = _retina_cache_key() - try: - from huggingface_hub import HfApi, create_repo - create_repo(_retina_cache_repo(), repo_type="dataset", private=True, - exist_ok=True, token=token) - HfApi(token=token).upload_file( - path_or_fileobj=RETINA_PATH, - path_in_repo=cache_key, - repo_id=_retina_cache_repo(), repo_type="dataset", - commit_message=f"retina build for {cache_key}", token=token, - ) - print(f"[retina-cache] uploaded {cache_key} to {_retina_cache_repo()}", flush=True) - except Exception as e: - print(f"[retina-cache] upload failed: {e}", flush=True) - - -def build_retina(target_tokens: int = TARGET_TRAIN_TOKENS) -> BuildReport: - # Try HF Hub-backed cache first — retina build takes 500+ seconds. - if os.path.exists(RETINA_PATH): - print(f"[retina-cache] using local {RETINA_PATH}", flush=True) - with np.load(RETINA_PATH) as npz: - return BuildReport( - vocab_size=int(npz["vocab_size"]), - n_bits=int(npz["n_bits"]), - train_tokens=int(npz["train_tokens"]), - wall_time_sec=0.0, - ) - elif _try_hydrate_retina_from_hub(): - # Local copy now populated; return stub report - with np.load(RETINA_PATH) as npz: - return BuildReport( - vocab_size=int(npz["vocab_size"]), - n_bits=int(npz["n_bits"]), - train_tokens=int(npz["train_tokens"]), - wall_time_sec=0.0, - ) - - tokenizer = Tokenizer.from_directory(TOKENIZER_DIR) - vocab_size = tokenizer.get_vocab_size() - - t0 = time.time() - - counts, cooc, total_tokens = build_cooccurrence( - tokenizer, target_tokens=target_tokens, window=CONTEXT_WINDOW, - ) - print(f" RSS after cooccurrence: {_rss_gb()}", flush=True) - - topk_idx, topk_score = compute_pmi_topk( - counts, cooc, total_tokens=total_tokens, top_k=TOP_K_FEATURES, - ) - print(f" RSS after PMI: {_rss_gb()}", flush=True) - - # Free the big cooccurrence matrix AND unigram counts before context_vectors/fold. - # At V=65536: cooc is (65536, 65536) int32 = 16 GB, counts is 65536*8 = 0.5 MB. - # del + gc.collect() forces Python to release the memory immediately so the - # subsequent stages (context_vectors, SOM, fold_sdrs) don't fight for RAM. - del cooc, counts - gc.collect() - print(f" RSS after del cooc+counts + gc: {_rss_gb()}", flush=True) - - X = _context_vectors_from_topk(topk_idx, topk_score, vocab_size) - print(f" RSS after context_vectors: {_rss_gb()}", flush=True) - - W = train_som( - X, grid_h=GRID_H, grid_w=GRID_W, - epochs=SOM_EPOCHS, - sigma_start=SOM_SIGMA_START, sigma_end=SOM_SIGMA_END, - alpha_start=SOM_ALPHA_START, alpha_end=SOM_ALPHA_END, - ) - print(f" RSS after SOM training: {_rss_gb()}", flush=True) - - sdr = fold_sdrs(X, W, topk_idx, topk_score, target_active=TARGET_ACTIVE) - print(f" RSS after fold_sdrs: {_rss_gb()}", flush=True) - - wall = time.time() - t0 - - os.makedirs(CACHE_DIR, exist_ok=True) - np.savez_compressed( - RETINA_PATH, - sdr=sdr, - vocab_size=np.int64(vocab_size), - n_bits=np.int64(N_BITS), - grid_h=np.int64(GRID_H), - grid_w=np.int64(GRID_W), - target_active=np.int64(TARGET_ACTIVE), - context_window=np.int64(CONTEXT_WINDOW), - top_k_features=np.int64(TOP_K_FEATURES), - train_tokens=np.int64(total_tokens), - ) - print(f"[save] wrote {RETINA_PATH} sdr.shape={sdr.shape} " - f"active_per_row={int(sdr.sum(axis=1).mean())} wall={wall:.1f}s") - - # Push to HF Hub so subsequent jobs (and parallel retina experiments) - # skip the 500+ second build entirely. - _upload_retina_to_hub() - - return BuildReport( - vocab_size=vocab_size, - n_bits=N_BITS, - train_tokens=total_tokens, - wall_time_sec=wall, - ) - - - +""" +Offline Semantic Folding SDR Retina (Cortical.io-grade). + +Builds a topographic, semantic-folding Sparse Distributed Representation (SDR) +for every token in the vocabulary, following Webber 2015 ("Semantic Folding Theory"). + +Pipeline: + 1. Scan the tokenized training corpus (parquet shards at ~/.cache/autoresearch/data). + We on-the-fly tokenize ~10M tokens from the first few shards. + 2. For each token, build a context vector = top-K most-associated neighbors + (±8-token window, PMI ranking). + 3. Train a 128x128 = 16384-bit Kohonen SOM on those context vectors so that + semantically related context features land on neighboring lattice cells. + 4. For each token, compute its folded SDR: union of the lattice cells whose + BMUs are triggered by its top-K context features. Then per-row quantile + threshold to exactly 2% active bits (327 / 16384). + 5. Save to ~/.cache/autoresearch/retina.npz. + +Entry point: + uv run python subsystems/sdr_retina.py --build --validate + +The validation asserts classic Cortical.io-style analogies: + - overlap("the", "a") > overlap("the", "zebra") + - overlap("man", "woman") > overlap("man", "rock") + - overlap("king","queen") > overlap("king", "dinosaur") +""" + +from __future__ import annotations + +import argparse +import gc +import math +import os +import sys +import time +from dataclasses import dataclass + +import numpy as np +import pyarrow.parquet as pq +import torch + +# Make the parent repo importable so we can reuse the Tokenizer +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, REPO_ROOT) + +from prepare import CACHE_DIR, DATA_DIR, TOKENIZER_DIR, VAL_FILENAME, VOCAB_SIZE, Tokenizer # noqa: E402 + + +# --------------------------------------------------------------------------- +# Build parameters +# --------------------------------------------------------------------------- + +RETINA_PATH = os.path.join(CACHE_DIR, "retina.npz") + +GRID_H = 128 +GRID_W = 128 +N_BITS = GRID_H * GRID_W # 16384 +TARGET_SPARSITY = 0.02 # 2% (default, Cortical.io-style) +# Default = int(floor(N_BITS * TARGET_SPARSITY)) = 327, matches Webber/Numenta. +# Override via HYDRA_SDR_TARGET_ACTIVE env var. The cache key encodes +# target_active, so changing this triggers automatic retina regeneration. +TARGET_ACTIVE = int(os.environ.get( + "HYDRA_SDR_TARGET_ACTIVE", + str(int(N_BITS * TARGET_SPARSITY)), +)) + +CONTEXT_WINDOW = 8 # +/- 8 tokens +TOP_K_FEATURES = 64 # top-K context features per token +# SCALES WITH VOCAB — need ~100+ occurrences per token for stable cooccurrence. +# At V=8k: 10M tokens = 1250/tok avg. At V=65k: 10M tokens = 153/tok avg +# (borderline); rare tokens seen <30x → noisy retina. Recommended: V*150. +# HF Hub cache makes this a one-time cost per vocab config anyway. +TARGET_TRAIN_TOKENS = int(os.environ.get("HYDRA_RETINA_TRAIN_TOKENS", "20000000")) +MAX_DOCS_PER_SHARD = 200_000 # safety cap per shard + +# Kohonen SOM +SOM_EPOCHS = 50 +SOM_SIGMA_START = 32.0 +SOM_SIGMA_END = 1.0 +SOM_ALPHA_START = 0.1 +SOM_ALPHA_END = 0.001 + + +# --------------------------------------------------------------------------- +# Small helpers +# --------------------------------------------------------------------------- + +def _fmt(n): + if n >= 1_000_000: + return f"{n/1_000_000:.2f}M" + if n >= 1_000: + return f"{n/1_000:.1f}k" + return str(n) + + +def _rss_gb() -> str: + """Return current process RSS in GB as a formatted string.""" + try: + rss_kb = int(os.popen(f"ps -o rss= -p {os.getpid()}").read().strip()) + return f"{rss_kb / 1e6:.2f} GB" + except Exception: + return "?" + + +def _device() -> torch.device: + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def _list_train_shards(): + files = sorted( + f for f in os.listdir(DATA_DIR) + if f.endswith(".parquet") and not f.endswith(".tmp") + ) + train = [os.path.join(DATA_DIR, f) for f in files if f != VAL_FILENAME] + assert len(train) > 0, f"No training shards at {DATA_DIR}. Run prepare.py first." + return train + + +# --------------------------------------------------------------------------- +# Stage 1: stream tokens from parquet shards and collect co-occurrences +# --------------------------------------------------------------------------- + +def _iter_tokenized_shards(tokenizer: Tokenizer, target_tokens: int): + """Yield 1-D int32 numpy arrays of token ids until target_tokens reached. + + Two paths: + - HYDRA_USE_NEMOTRON=1: stream docs from Nemotron HF datasets (no shards + on disk — matches the streaming training path). + - Default: iterate local parquet shards (legacy prepare.py path). + """ + tok_encode = tokenizer.enc.encode_ordinary_batch + + if os.environ.get("HYDRA_USE_NEMOTRON", "0") == "1": + # Streaming path: reuse prepare_nemotron's weighted stream. + import prepare_nemotron as _pn + stream = _pn._WeightedStream(_pn._phase_weights(), seed=0) + seen = 0 + batch: list[str] = [] + BATCH = 512 + while seen < target_tokens: + text, _epoch = next(stream) + if not text: + continue + batch.append(text) + if len(batch) < BATCH: + continue + token_lists = tok_encode(batch, num_threads=8) + batch = [] + for ids in token_lists: + if not ids: + continue + arr = np.asarray(ids, dtype=np.int32) + yield arr + seen += arr.size + if seen >= target_tokens: + print(f" [nemotron-stream] yielded {_fmt(seen)} tokens, target reached") + return + return + + # Legacy shard path. + shards = _list_train_shards() + seen = 0 + for shard_idx, path in enumerate(shards): + if seen >= target_tokens: + return + pf = pq.ParquetFile(path) + shard_tokens = 0 + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx) + docs = rg.column("text").to_pylist() + if len(docs) > MAX_DOCS_PER_SHARD: + docs = docs[:MAX_DOCS_PER_SHARD] + # Batch-encode for throughput + batch_size = 512 + for i in range(0, len(docs), batch_size): + batch = docs[i:i + batch_size] + token_lists = tok_encode(batch, num_threads=8) + for ids in token_lists: + if not ids: + continue + arr = np.asarray(ids, dtype=np.int32) + yield arr + shard_tokens += arr.size + seen += arr.size + if seen >= target_tokens: + print(f" shard {shard_idx}: yielded {_fmt(shard_tokens)} tokens " + f"(total {_fmt(seen)}), target reached") + return + print(f" shard {shard_idx}: yielded {_fmt(shard_tokens)} tokens (total {_fmt(seen)})") + + +def _cooccur_from_doc(ids: np.ndarray, window: int, vocab_size: int, + counts: np.ndarray, cooc: np.ndarray) -> None: + """Update unigram counts and cooccurrence counts for one document. Vectorized.""" + n = ids.size + if n < 2: + return + # unigram counts + np.add.at(counts, ids, 1) + # For each offset d in 1..window, count pairs (ids[:-d], ids[d:]) + # Both directions are equivalent by symmetry; we add both to keep the + # matrix symmetric and treat it as undirected context. + for d in range(1, window + 1): + left = ids[:-d] + right = ids[d:] + # symmetric update + flat_lr = left.astype(np.int64) * vocab_size + right.astype(np.int64) + flat_rl = right.astype(np.int64) * vocab_size + left.astype(np.int64) + # use bincount-style scatter via np.add.at on the flat view + cooc_flat = cooc.ravel() + np.add.at(cooc_flat, flat_lr, 1) + np.add.at(cooc_flat, flat_rl, 1) + + +def build_cooccurrence(tokenizer: Tokenizer, target_tokens: int, window: int) -> tuple[np.ndarray, np.ndarray, int]: + """ + Stream tokens and build unigram + cooccurrence counts. + Returns (counts[V] int64, cooc[V,V] int32, total_tokens int). + """ + vocab_size = tokenizer.get_vocab_size() + print(f"[1/4] Building cooccurrence (vocab={vocab_size}, window=+/-{window}, target={_fmt(target_tokens)} tokens)") + counts = np.zeros(vocab_size, dtype=np.int64) + # int32 is enough per-cell if we stay <= a few hundred million total tokens; guard with clip at save. + cooc = np.zeros((vocab_size, vocab_size), dtype=np.int32) + + total = 0 + n_docs = 0 + t0 = time.time() + for ids in _iter_tokenized_shards(tokenizer, target_tokens): + _cooccur_from_doc(ids, window, vocab_size, counts, cooc) + total += ids.size + n_docs += 1 + if n_docs % 5000 == 0: + dt = time.time() - t0 + rate = total / max(dt, 1e-6) + print(f" docs={_fmt(n_docs)} tokens={_fmt(total)} ({rate/1000:.0f}k tok/s)") + + dt = time.time() - t0 + print(f"[1/4] done: {_fmt(total)} tokens, {_fmt(n_docs)} docs, {dt:.1f}s, " + f"cooc_nnz={int((cooc > 0).sum())}") + return counts, cooc, total + + +# --------------------------------------------------------------------------- +# Stage 2: build top-K context features (PMI) +# --------------------------------------------------------------------------- + +def compute_pmi_topk(counts: np.ndarray, cooc: np.ndarray, total_tokens: int, + top_k: int) -> tuple[np.ndarray, np.ndarray]: + """ + For each token, compute top-K context features by positive PMI. + Returns: + topk_idx : int32 [V, K] token ids of the top-K context features + topk_score : float32 [V, K] PMI scores (0 for padded missing features) + Missing features are padded with idx=token itself and score=0, so they + have a well-defined (but uninformative) column. + """ + V = counts.shape[0] + print(f"[2/4] Computing PMI top-{top_k} per token (vocab={V})") + + # window_pairs per occurrence: 2 * window (we added both directions, each offset twice). + # For the PMI denominator we need a total pair count; using coo.sum() is the clean + # per-matrix normalizer and avoids any constant confusion. + pair_total = float(cooc.sum()) + if pair_total <= 0: + raise RuntimeError("Empty cooccurrence matrix") + + # Run on GPU if it fits; V×V float32 at V=65536 is 16 GB → CPU fallback. + vram_needed = V * V * 4 # float32 + dev = _device() + if dev.type == "cuda" and vram_needed > 4 * 1024**3: # >4 GB → CPU + dev = torch.device("cpu") + print(f" [PMI] V×V={V}² needs {vram_needed/1e9:.1f} GB → using CPU", flush=True) + cooc_t = torch.from_numpy(cooc.astype(np.float32)).to(dev) + counts_t = torch.from_numpy(counts.astype(np.float64)).to(dev).clamp_min(1.0) + + # P(i) = counts[i] / total_tokens + # P(i, j) = cooc[i, j] / pair_total + # PMI = log(P(i,j) / (P(i) P(j))) + # Positive PMI = max(PMI, 0). + # We'll compute log-PMI in a numerically safe way: + # log(cooc) + log(total_tokens^2 / pair_total) - log(c_i) - log(c_j) + # Keep numerator zero where cooc==0 and mask those out. + + log_const = math.log(total_tokens) + math.log(total_tokens) - math.log(pair_total) + log_ci = torch.log(counts_t) # [V] + log_cj = log_ci.clone() # same vector (symmetric vocab) + + # We'll do it in row blocks to cap memory of intermediate log() tensors. + topk_idx = np.zeros((V, top_k), dtype=np.int32) + topk_score = np.zeros((V, top_k), dtype=np.float32) + + block = 512 + t0 = time.time() + for start in range(0, V, block): + end = min(V, start + block) + rows = cooc_t[start:end] # [b, V] int-as-float + mask = rows > 0 + # log(rows) where rows>0; else keep -inf then mask out + log_rows = torch.where(mask, torch.log(rows.clamp_min(1.0)), + torch.full_like(rows, float("-inf"))) + pmi = log_rows + log_const - log_ci[start:end].unsqueeze(1) - log_cj.unsqueeze(0) + ppmi = torch.where(mask, torch.clamp(pmi, min=0.0), + torch.full_like(pmi, float("-inf"))) + # top-K along dim=1 + vals, idx = torch.topk(ppmi, k=top_k, dim=1) + # Replace any -inf valued slots with score 0 and idx = the token itself + bad = torch.isneginf(vals) + if bad.any(): + self_idx = torch.arange(start, end, device=dev).unsqueeze(1).expand_as(idx) + idx = torch.where(bad, self_idx, idx) + vals = torch.where(bad, torch.zeros_like(vals), vals) + topk_idx[start:end] = idx.cpu().numpy().astype(np.int32) + topk_score[start:end] = vals.cpu().numpy().astype(np.float32) + + del cooc_t, counts_t + if dev.type == "cuda": + torch.cuda.empty_cache() + print(f"[2/4] done: top-{top_k} PMI features per token in {time.time()-t0:.1f}s") + return topk_idx, topk_score + + +# --------------------------------------------------------------------------- +# Stage 3: Kohonen SOM on the context-vector representation +# --------------------------------------------------------------------------- + +def _context_vectors_from_topk(topk_idx: np.ndarray, topk_score: np.ndarray, + vocab_size: int) -> torch.Tensor: + """ + Build the dense context matrix X [V, V] where X[i] is the top-K PMI context + vector for token i, L2-normalized. For V=8192 this is 8k x 8k float32 = 256 MB. + """ + V = vocab_size + K = topk_idx.shape[1] + dev = _device() + # At V=65536, dense V×V is 17 GB — won't fit in GPU or system RAM. + # Use the (V, K) scores directly as feature vectors. K=64 dimensions + # is sufficient for SOM clustering (each token characterized by its + # top-64 PMI context scores). L2-normalize for cosine-like distance. + if V * V * 4 > 4 * 1024**3: + print(f" [context_vectors] V={V} too large for dense V×V; using sparse (V,K={K}) features", flush=True) + scores = torch.from_numpy(topk_score).to(dev) + norm = scores.norm(dim=1, keepdim=True).clamp_min(1e-8) + X = scores / norm + return X + # Small vocab: original dense V×V path (V=8192 = 256 MB, fits fine) + X = torch.zeros((V, V), dtype=torch.float32, device=dev) + rows = torch.arange(V, device=dev).unsqueeze(1).expand(V, K) + idx = torch.from_numpy(topk_idx).to(dev).long() + scores = torch.from_numpy(topk_score).to(dev) + X[rows, idx] = torch.maximum(X[rows, idx], scores) + norm = X.norm(dim=1, keepdim=True).clamp_min(1e-8) + X = X / norm + return X + + +def train_som(X: torch.Tensor, grid_h: int, grid_w: int, + epochs: int, sigma_start: float, sigma_end: float, + alpha_start: float, alpha_end: float, + seed: int = 137) -> torch.Tensor: + """ + Train a Kohonen SOM with rectangular grid and Gaussian neighborhood. + X: [V, F] features (L2 normalized). Returns weights W: [grid_h*grid_w, F]. + """ + dev = X.device + V, F = X.shape + N = grid_h * grid_w + + torch.manual_seed(seed) + # Initialize SOM weights: small random linear combinations of data points + # (faster convergence than uniform random in the feature space). + init_pick = torch.randint(0, V, (N,), device=dev) + W = X[init_pick].clone() # [N, F] + + # Precompute grid coordinates + yy, xx = torch.meshgrid( + torch.arange(grid_h, device=dev, dtype=torch.float32), + torch.arange(grid_w, device=dev, dtype=torch.float32), + indexing="ij", + ) + grid = torch.stack([yy.reshape(-1), xx.reshape(-1)], dim=1) # [N, 2] + + print(f"[3/4] Training Kohonen SOM: grid={grid_h}x{grid_w}, features={F}, " + f"epochs={epochs}, sigma {sigma_start}->{sigma_end}, alpha {alpha_start}->{alpha_end}") + t0 = time.time() + + # Exponential decay schedules + def schedule(t_frac): + sigma = sigma_start * (sigma_end / sigma_start) ** t_frac + alpha = alpha_start * (alpha_end / alpha_start) ** t_frac + return sigma, alpha + + # Batch-mode SOM: process a random permutation each epoch in mini-batches. + # For each mini-batch, compute BMUs then one vectorized neighborhood update. + batch_size = 256 + + for epoch in range(epochs): + t_frac = epoch / max(epochs - 1, 1) + sigma, alpha = schedule(t_frac) + two_sigma2 = 2.0 * sigma * sigma + perm = torch.randperm(V, device=dev) + + for bstart in range(0, V, batch_size): + bidx = perm[bstart:bstart + batch_size] + xb = X[bidx] # [b, F] + # BMU: argmax of cosine similarity = argmin of squared Euclidean + # ||x||=||w||=1 for data; W may drift but the formulation remains stable. + sim = xb @ W.t() # [b, N] + bmu = sim.argmax(dim=1) # [b] + + # Neighborhood weights h[b, n] = exp(-|grid[bmu_b] - grid[n]|^2 / (2*sigma^2)) + bmu_coords = grid[bmu] # [b, 2] + diff = bmu_coords.unsqueeze(1) - grid.unsqueeze(0) # [b, N, 2] + dist2 = (diff * diff).sum(dim=2) # [b, N] + h = torch.exp(-dist2 / two_sigma2) # [b, N] + h = h * alpha # include LR + + # Vectorized SOM update: + # W <- W + sum_b h[b] * (x_b - W) / (sum_b h[b]) + # Batched form: numerator = h^T x_b [N, F], denom = h.sum(0) [N] + numer = h.t() @ xb # [N, F] + denom = h.sum(dim=0).unsqueeze(1).clamp_min(1e-8) # [N, 1] + target = numer / denom + # Update weight: mix toward target with a unit step (h already scaled by alpha). + # To prevent over-shoot when the same BMU is hit heavily, scale by the + # mean-field gain min(1, denom). Empirically this behaves like classic SOM. + gain = torch.clamp(h.sum(dim=0), max=1.0).unsqueeze(1) # [N,1] + W = (1 - gain) * W + gain * target + + # Renormalize weights to unit sphere for stability + W = W / W.norm(dim=1, keepdim=True).clamp_min(1e-8) + + if (epoch + 1) % max(1, epochs // 10) == 0 or epoch == 0: + dt = time.time() - t0 + print(f" epoch {epoch+1}/{epochs} sigma={sigma:.2f} alpha={alpha:.4f} elapsed={dt:.1f}s") + + print(f"[3/4] SOM trained in {time.time()-t0:.1f}s") + return W + + +# --------------------------------------------------------------------------- +# Stage 4: fold context vectors into SDRs +# --------------------------------------------------------------------------- + +def fold_sdrs(X: torch.Tensor, W: torch.Tensor, topk_idx: np.ndarray, + topk_score: np.ndarray, target_active: int) -> np.ndarray: + """ + For each token, activate the 'cell votes' on the lattice for each of its top-K + context features, then threshold to exactly target_active bits. + + Implementation detail: every token in the vocabulary has a SOM BMU given its + context vector X[i]. We use those BMUs as the feature->cell map. For token t, + we accumulate votes at BMU(feature) weighted by the PMI score, then pick the + top target_active cells. + + Memory discipline at V=65536, N=16384: + - votes (V, N) float32 = 4 GB → always on CPU for V > 8192 + - sdr (V, N) bool = 1 GB → always on CPU for V > 8192 + - blur conv2d runs on CPU in 4096-row chunks (~256 MB per chunk) + """ + dev = X.device + V, F = X.shape + N = W.shape[0] + large_vocab = V > 8192 + print(f"[4/4] Folding SDRs (V={V}, N={N}, target_active={target_active})", flush=True) + print(f" RSS before fold: {_rss_gb()}", flush=True) + + # Per-feature BMU: for each token f as a feature, BMU_f = argmax_n W[n] . X[f] + # Chunked matmul to bound memory. Run on whatever device X/W live on (GPU if small). + bmu = torch.empty(V, dtype=torch.long, device=dev) + bmu_chunk = 1024 + for s in range(0, V, bmu_chunk): + e = min(V, s + bmu_chunk) + sim = X[s:e] @ W.t() # [b, N] + bmu[s:e] = sim.argmax(dim=1) + + # For large vocabs, force votes and sdr to CPU regardless of what device X was on. + # V=65536, N=16384: votes=4 GB float32, sdr=1 GB bool — must stay on CPU. + if large_vocab: + votes_dev = torch.device("cpu") + if dev.type == "cuda": + print(f" [fold] V={V} > 8192: forcing votes/sdr to CPU " + f"(votes={V*N*4/1e9:.1f} GB, sdr={V*N/1e9:.2f} GB)", flush=True) + bmu_cpu = bmu.cpu() + else: + # Small vocab: stay on original device (GPU-accelerated). + votes_dev = dev + votes_dev = dev if V * N * 4 < 2 * 1024**3 else torch.device("cpu") + bmu_cpu = bmu.cpu() if votes_dev.type == "cpu" else bmu + + K = topk_idx.shape[1] + feat = torch.from_numpy(topk_idx).to(votes_dev).long() + sc = torch.from_numpy(topk_score).to(votes_dev) + feat_bmu = bmu_cpu.to(votes_dev)[feat] # [V, K] lattice cell indices + + votes = torch.zeros((V, N), dtype=torch.float32, device=votes_dev) + votes.scatter_add_(1, feat_bmu, sc) + del feat, sc, feat_bmu, bmu, bmu_cpu + print(f" RSS after votes scatter: {_rss_gb()}", flush=True) + + # Tiny numerical nudge: add a local Gaussian kernel around each voted cell so + # near-neighbors accumulate mass (this is the "folding" smear). Kernel radius 1. + # Implement as a separable 3x3 blur on the 2D grid view. + grid_h = int(round(math.sqrt(N))) + grid_w = grid_h + assert grid_h * grid_w == N + + # Gaussian blur + top-k in 4096-row chunks to cap peak memory. + # At V=65536 chunk=4096: chunk_2d = 4096×1×128×128 float32 = 256 MB — safe on CPU. + blur = torch.tensor([[[[0.5, 1.0, 0.5], + [1.0, 2.0, 1.0], + [0.5, 1.0, 0.5]]]], device=votes_dev, dtype=torch.float32) + blur = blur / blur.sum() + + # Always use CPU for the sdr output tensor when vocab is large. + sdr_dev = votes_dev # already CPU for large_vocab + sdr = torch.zeros((V, N), dtype=torch.bool, device=sdr_dev) + + # Fixed 4096-row chunks: 4096 × 16384 × 4 = 256 MB per chunk — well within 32 GB. + fold_chunk = 4096 if large_vocab else min(V, max(1, int(2 * 1024**3 / (N * 4)))) + n_chunks = math.ceil(V / fold_chunk) + print(f" [fold] blur+topk in {n_chunks} chunks of {fold_chunk} rows " + f"(~{fold_chunk * N * 4 / 1e6:.0f} MB each, device={votes_dev})", flush=True) + + for s in range(0, V, fold_chunk): + e = min(V, s + fold_chunk) + b = e - s + chunk_2d = votes[s:e].view(b, 1, grid_h, grid_w) + blurred = torch.nn.functional.conv2d(chunk_2d, blur, padding=1) + chunk_flat = blurred.view(b, N) + _, top_cells = torch.topk(chunk_flat, k=target_active, dim=1) + sdr[s:e].scatter_(1, top_cells, True) + del chunk_2d, blurred, chunk_flat, top_cells + + del votes + print(f" RSS after fold complete: {_rss_gb()}", flush=True) + + # Sanity check + row_active = sdr.sum(dim=1) + assert int(row_active.min()) == target_active, \ + f"row active min mismatch: got {int(row_active.min())}, expected {target_active}" + assert int(row_active.max()) == target_active, \ + f"row active max mismatch: got {int(row_active.max())}, expected {target_active}" + + result = sdr.cpu().numpy() + del sdr + return result + + +# --------------------------------------------------------------------------- +# Build orchestration +# --------------------------------------------------------------------------- + +@dataclass +class BuildReport: + vocab_size: int + n_bits: int + train_tokens: int + wall_time_sec: float + + +def _retina_cache_repo() -> str: + return os.environ.get("HYDRA_RETINA_CACHE_REPO", "icarus112/feather-retina-cache") + + +def _retina_cache_key() -> str: + """Cache key encodes vocab_size + n_bits + target_active so we don't + accidentally restore a retina built for a different tokenizer/config.""" + try: + from prepare import VOCAB_SIZE + except Exception: + VOCAB_SIZE = 0 + return f"retina_v{VOCAB_SIZE}_n{N_BITS}_a{TARGET_ACTIVE}.npz" + + +def _try_hydrate_retina_from_hub() -> bool: + """Attempt to download a pre-built retina matching our config from HF Hub. + Returns True if successful — caller should skip the rebuild.""" + token = os.environ.get("HF_TOKEN") + if not token: + return False + cache_key = _retina_cache_key() + try: + from huggingface_hub import hf_hub_download + p = hf_hub_download( + repo_id=_retina_cache_repo(), repo_type="dataset", + filename=cache_key, token=token, + ) + os.makedirs(CACHE_DIR, exist_ok=True) + import shutil + shutil.copy(p, RETINA_PATH) + # Quick verify shape + with np.load(RETINA_PATH) as npz: + if (int(npz["n_bits"]) == N_BITS + and int(npz["target_active"]) == TARGET_ACTIVE + and int(npz["vocab_size"]) == VOCAB_SIZE): + print(f"[retina-cache] hydrated {cache_key} from {_retina_cache_repo()} " + f"(shape={npz['sdr'].shape})", flush=True) + return True + os.remove(RETINA_PATH) + return False + except Exception as e: + print(f"[retina-cache] miss: {e}", flush=True) + return False + + +def _upload_retina_to_hub() -> None: + """Upload freshly-built retina.npz to HF Hub for reuse by future jobs.""" + token = os.environ.get("HF_TOKEN") + if not token: + return + cache_key = _retina_cache_key() + try: + from huggingface_hub import HfApi, create_repo + create_repo(_retina_cache_repo(), repo_type="dataset", private=True, + exist_ok=True, token=token) + HfApi(token=token).upload_file( + path_or_fileobj=RETINA_PATH, + path_in_repo=cache_key, + repo_id=_retina_cache_repo(), repo_type="dataset", + commit_message=f"retina build for {cache_key}", token=token, + ) + print(f"[retina-cache] uploaded {cache_key} to {_retina_cache_repo()}", flush=True) + except Exception as e: + print(f"[retina-cache] upload failed: {e}", flush=True) + + +def build_retina(target_tokens: int = TARGET_TRAIN_TOKENS) -> BuildReport: + # Try HF Hub-backed cache first — retina build takes 500+ seconds. + if os.path.exists(RETINA_PATH): + print(f"[retina-cache] using local {RETINA_PATH}", flush=True) + with np.load(RETINA_PATH) as npz: + return BuildReport( + vocab_size=int(npz["vocab_size"]), + n_bits=int(npz["n_bits"]), + train_tokens=int(npz["train_tokens"]), + wall_time_sec=0.0, + ) + elif _try_hydrate_retina_from_hub(): + # Local copy now populated; return stub report + with np.load(RETINA_PATH) as npz: + return BuildReport( + vocab_size=int(npz["vocab_size"]), + n_bits=int(npz["n_bits"]), + train_tokens=int(npz["train_tokens"]), + wall_time_sec=0.0, + ) + + tokenizer = Tokenizer.from_directory(TOKENIZER_DIR) + vocab_size = tokenizer.get_vocab_size() + + t0 = time.time() + + counts, cooc, total_tokens = build_cooccurrence( + tokenizer, target_tokens=target_tokens, window=CONTEXT_WINDOW, + ) + print(f" RSS after cooccurrence: {_rss_gb()}", flush=True) + + topk_idx, topk_score = compute_pmi_topk( + counts, cooc, total_tokens=total_tokens, top_k=TOP_K_FEATURES, + ) + print(f" RSS after PMI: {_rss_gb()}", flush=True) + + # Free the big cooccurrence matrix AND unigram counts before context_vectors/fold. + # At V=65536: cooc is (65536, 65536) int32 = 16 GB, counts is 65536*8 = 0.5 MB. + # del + gc.collect() forces Python to release the memory immediately so the + # subsequent stages (context_vectors, SOM, fold_sdrs) don't fight for RAM. + del cooc, counts + gc.collect() + print(f" RSS after del cooc+counts + gc: {_rss_gb()}", flush=True) + + X = _context_vectors_from_topk(topk_idx, topk_score, vocab_size) + print(f" RSS after context_vectors: {_rss_gb()}", flush=True) + + W = train_som( + X, grid_h=GRID_H, grid_w=GRID_W, + epochs=SOM_EPOCHS, + sigma_start=SOM_SIGMA_START, sigma_end=SOM_SIGMA_END, + alpha_start=SOM_ALPHA_START, alpha_end=SOM_ALPHA_END, + ) + print(f" RSS after SOM training: {_rss_gb()}", flush=True) + + sdr = fold_sdrs(X, W, topk_idx, topk_score, target_active=TARGET_ACTIVE) + print(f" RSS after fold_sdrs: {_rss_gb()}", flush=True) + + wall = time.time() - t0 + + os.makedirs(CACHE_DIR, exist_ok=True) + np.savez_compressed( + RETINA_PATH, + sdr=sdr, + vocab_size=np.int64(vocab_size), + n_bits=np.int64(N_BITS), + grid_h=np.int64(GRID_H), + grid_w=np.int64(GRID_W), + target_active=np.int64(TARGET_ACTIVE), + context_window=np.int64(CONTEXT_WINDOW), + top_k_features=np.int64(TOP_K_FEATURES), + train_tokens=np.int64(total_tokens), + ) + print(f"[save] wrote {RETINA_PATH} sdr.shape={sdr.shape} " + f"active_per_row={int(sdr.sum(axis=1).mean())} wall={wall:.1f}s") + + # Push to HF Hub so subsequent jobs (and parallel retina experiments) + # skip the 500+ second build entirely. + _upload_retina_to_hub() + + return BuildReport( + vocab_size=vocab_size, + n_bits=N_BITS, + train_tokens=total_tokens, + wall_time_sec=wall, + ) + + + diff --git a/overlay/subsystems/sdr_semantic.py b/overlay/subsystems/sdr_semantic.py index 752cdf9328f327e3012022614da12519d9a59f59..53875ed6cdbb56b997a2a3c1aba764470e3c2dd6 100644 --- a/overlay/subsystems/sdr_semantic.py +++ b/overlay/subsystems/sdr_semantic.py @@ -1,421 +1,443 @@ -""" -SemanticFoldingSDR — differentiable torch wrapper over the offline semantic retina. - -Forward: token_ids (B, T) int64 -> binary SDR (B, T, n_bits) float32, exactly -`target_active` bits on per (b, t). Straight-through estimator provides gradients -via a low-rank learnable residual (`delta_u @ delta_v`) while the forward output -remains EXACTLY the retina lookup — sparsity guaranteed. - -Online SOM fine-tune: `maybe_som_update` is a no-grad hook the training loop may -call after optimizer.step() every `som_update_interval` steps (post-warmup) to -nudge retina rows toward observed context co-occurrence patterns. -""" - -from __future__ import annotations - -import os -import time -from pathlib import Path - -import numpy as np -import torch -import torch.nn as nn - - -DEFAULT_RETINA_PATH = os.path.expanduser("~/.cache/autoresearch/retina.npz") -# Default 327 = 2% of 16384 (Webber/Numenta canonical). -# Override via HYDRA_SDR_TARGET_ACTIVE env var (must match the value used when -# the retina cache was built — sdr_retina.py TARGET_ACTIVE reads the same var). -DEFAULT_TARGET_ACTIVE = int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")) - - -class _SDRSTE(torch.autograd.Function): - """Memory-efficient STE: forward=sdr_binary exactly, backward routes upstream - gradient through delta=delta_u[ids]@delta_v via index_add_ (V, n_bits) buffer - instead of materializing a (B, T, n_bits) intermediate.""" - - @staticmethod - def forward(ctx, sdr_binary, delta_u, delta_v, token_ids): - ctx.save_for_backward(delta_u, delta_v, token_ids) - return sdr_binary - - @staticmethod - def backward(ctx, grad_out): - delta_u, delta_v, token_ids = ctx.saved_tensors - B, T, n_bits = grad_out.shape - flat_grad = grad_out.reshape(B * T, n_bits).to(delta_v.dtype) - flat_ids = token_ids.reshape(B * T) - V = delta_u.shape[0] - per_tok = torch.zeros(V, n_bits, device=flat_grad.device, dtype=delta_v.dtype) - per_tok.index_add_(0, flat_ids, flat_grad) - grad_delta_u = per_tok @ delta_v.t() - grad_delta_v = delta_u.t() @ per_tok - return None, grad_delta_u, grad_delta_v, None - - -class SemanticFoldingSDR(nn.Module): - """ - Token-level semantic folding SDR with straight-through estimator. - - Parameters - ---------- - vocab_size : int - Must match retina's stored vocab_size. - n_bits : int - Must match retina's stored n_bits. - retina_path : str | None - Path to the retina .npz built by subsystems/sdr_retina.py. - target_active : int | None - Exact number of active bits per SDR. If None, inferred from retina. - som_update_interval : int - Steps between online SOM updates (post-warmup). - som_warmup_steps : int - Steps before SOM updates begin. - delta_rank : int - Rank of the learnable low-rank residual used for gradient flow. - som_alpha : float - SOM blend factor (0..1) — how far retina rows move toward context pattern. - """ - - def __init__( - self, - vocab_size: int, - n_bits: int = 16384, - retina_path: str | None = None, - target_active: int | None = None, - som_update_interval: int = 100, - som_warmup_steps: int = 500, - delta_rank: int = 32, - som_alpha: float = 0.05, - ) -> None: - super().__init__() - self.vocab_size = vocab_size - self.n_bits = n_bits - self.som_update_interval = int(som_update_interval) - self.som_warmup_steps = int(som_warmup_steps) - self.som_alpha = float(som_alpha) - - path = retina_path or DEFAULT_RETINA_PATH - if not Path(path).exists(): - raise FileNotFoundError( - f"Retina not found at {path}. Run subsystems/sdr_retina.py first." - ) - - with np.load(path) as f: - retina_sdr = f["sdr"] # bool[V, n_bits] - stored_vocab = int(f["vocab_size"]) if "vocab_size" in f.files else retina_sdr.shape[0] - stored_nbits = int(f["n_bits"]) if "n_bits" in f.files else retina_sdr.shape[1] - stored_target = int(f["target_active"]) if "target_active" in f.files else int(retina_sdr[0].sum()) - - if retina_sdr.shape != (vocab_size, n_bits): - raise ValueError( - f"retina shape {retina_sdr.shape} != expected ({vocab_size}, {n_bits}). " - f"Stored metadata: vocab_size={stored_vocab}, n_bits={stored_nbits}" - ) - - self.target_active = int(target_active) if target_active is not None else stored_target - # Validate every row has exactly target_active bits - row_sums = retina_sdr.sum(axis=1) - if not np.all(row_sums == self.target_active): - bad = int(np.sum(row_sums != self.target_active)) - raise ValueError( - f"{bad}/{vocab_size} retina rows do not have exactly {self.target_active} active bits." - ) - - # CSR storage: store only the column indices of active bits per row. - # (V, target_active) int16 = 42 MB vs (V, n_bits) uint8 = 1 GB → 24× reduction. - # Stored as a plain Python attribute (NOT a registered buffer) so - # torch.compile cannot place guards on its content. SOM updates - # mutate indices in-place; if it were a buffer, dynamo would detect the - # mutation and recompile the forward graph on every subsequent call, - # dropping tps from 69k -> 20k. Checkpoint save/load handled by - # overridden state_dict / load_state_dict below. - self._retina_indices: torch.Tensor = self._dense_to_indices(retina_sdr) # [V, K] int16 - - # Low-rank learnable residual for gradient flow. - # Forward output is EXACTLY the retina lookup (STE), so delta init does - # not distort the forward at any step. Both factors get a tiny nonzero - # init so gradients flow to BOTH parameters from step 0 (otherwise a - # zero-init delta_u starves delta_v of gradient via the chain rule). - self.delta_u = nn.Parameter(torch.randn(vocab_size, delta_rank) * 1e-4) - self.delta_v = nn.Parameter(torch.randn(delta_rank, n_bits) * 1e-4) - - # Step counter as plain Python int — avoids GPU buffer mutation that - # invalidates torch.compile guards on every maybe_som_update call. - # Persisted manually via state_dict hooks for checkpointing. - self._som_step: int = 0 - - # ------------------------------------------------------------------ - # Dense ↔ CSR conversion helpers - # ------------------------------------------------------------------ - @staticmethod - def _dense_to_indices(dense: np.ndarray) -> torch.Tensor: - """Convert dense bool/uint8 (V, n_bits) → CSR int16 (V, K) indices.""" - # argsort-based: vectorised, no Python loop over V rows. - # For each row, sort column indices so that active bits come first, - # then slice [:K]. ~2× faster than per-row np.nonzero loop. - dense_bool = dense.astype(np.bool_) - K = int(dense_bool[0].sum()) - # Descending sort of each row: active (True=1) columns first - # np.argsort is ascending, so negate to get descending - order = np.argsort(~dense_bool, axis=1, kind="stable") # (V, n_bits) - indices = order[:, :K].astype(np.int16) - return torch.from_numpy(np.ascontiguousarray(indices)) - - # ------------------------------------------------------------------ - # Device movement: _retina_indices is a plain attribute, so nn.Module's - # .to() / .cuda() / to_empty() won't touch it. Override _apply to - # move it alongside registered parameters. - # ------------------------------------------------------------------ - def _apply(self, fn, recurse=True): - result = super()._apply(fn, recurse) - self._retina_indices = fn(self._retina_indices) - return result - - # ------------------------------------------------------------------ - # Checkpoint support: _retina_indices is a plain attribute, not a - # buffer, so we manually include it in state_dict for save/load. - # Backward-compat: old checkpoints store dense "_retina_data" — - # convert on load. - # ------------------------------------------------------------------ - def state_dict(self, *args, **kwargs): - sd = super().state_dict(*args, **kwargs) - sd["_retina_indices"] = self._retina_indices - return sd - - def load_state_dict(self, state_dict, *args, **kwargs): - if "_retina_indices" in state_dict: - self._retina_indices = state_dict.pop("_retina_indices") - elif "_retina_data" in state_dict: - # Backward compat: old dense (V, n_bits) uint8 → CSR (V, K) int16 - dense = state_dict.pop("_retina_data").cpu().numpy() - self._retina_indices = self._dense_to_indices(dense) - super().load_state_dict(state_dict, *args, **kwargs) - - # ------------------------------------------------------------------ - # Convenience property so existing code using `self.retina` outside - # this module (e.g. smoke tests) keeps working. Reconstructs dense - # (V, n_bits) uint8 on the fly from CSR indices. Cheap for small - # slices; callers needing the full matrix should cache the result. - # ------------------------------------------------------------------ - @property - def retina(self) -> torch.Tensor: - dense = torch.zeros( - self.vocab_size, self.n_bits, - dtype=torch.uint8, device=self._retina_indices.device, - ) - dense.scatter_(1, self._retina_indices.long(), 1) - return dense - - # ------------------------------------------------------------------ - # Forward - # ------------------------------------------------------------------ - def forward(self, token_ids: torch.Tensor) -> torch.Tensor: - """ - token_ids : (B, T) int64 - returns : (B, T, n_bits) autocast-aware dtype. Forward = exact binary - retina lookup, backward = identity to delta projection (STE) - via custom autograd.Function (no materialization of - `delta - delta.detach()`). - """ - if token_ids.dim() != 2: - raise ValueError(f"expected (B, T) token_ids, got shape {tuple(token_ids.shape)}") - - # Autocast-aware output dtype (saves 50% vs forcing fp32 under bf16 amp). - if torch.is_autocast_enabled(): - out_dtype = torch.get_autocast_gpu_dtype() - else: - out_dtype = self.delta_v.dtype - # Reconstruct dense binary SDR from CSR indices. Because - # _retina_indices is NOT a registered buffer, torch.compile/dynamo - # cannot place content-guards on it. SOM mutations therefore never - # trigger recompilation. - B, T = token_ids.shape - K = self.target_active - idx = self._retina_indices[token_ids.reshape(-1)] # (B*T, K) int16 - sdr_binary = torch.zeros( - B * T, self.n_bits, dtype=out_dtype, device=token_ids.device, - ) - sdr_binary.scatter_(1, idx.long(), 1) - sdr_binary = sdr_binary.view(B, T, self.n_bits) - return _SDRSTE.apply(sdr_binary, self.delta_u, self.delta_v, token_ids) - - @torch.no_grad() - def binary_only(self, token_ids: torch.Tensor) -> torch.Tensor: - """uint8 retina view — no STE, no autocast cost. For HTM/consumers that - only need the binary pattern. Reconstructs dense from CSR indices.""" - B, T = token_ids.shape - idx = self._retina_indices[token_ids.reshape(-1)] # (B*T, K) int16 - sdr = torch.zeros( - B * T, self.n_bits, dtype=torch.uint8, device=token_ids.device, - ) - sdr.scatter_(1, idx.long(), 1) - return sdr.view(B, T, self.n_bits) - - # ------------------------------------------------------------------ - # Validation helpers - # ------------------------------------------------------------------ - def overlap(self, tok_a: int, tok_b: int) -> float: - """Jaccard overlap of two tokens' retina SDRs — for sanity checks. - Uses set intersection on CSR indices (no dense reconstruction).""" - a = set(self._retina_indices[tok_a].tolist()) - b = set(self._retina_indices[tok_b].tolist()) - inter = len(a & b) - union = len(a | b) - return inter / max(1, union) - - # ------------------------------------------------------------------ - # Online SOM fine-tune hook (no grad) - # ------------------------------------------------------------------ - @torch.no_grad() - def maybe_som_update( - self, - token_ids: torch.Tensor, - recent_context_sdr: torch.Tensor, - ) -> bool: - """ - Move retina rows for the given tokens a small fraction toward the average - context SDR pattern, then re-binarize to exactly `target_active` bits. - - Returns True when an update occurred, False when skipped (warmup / interval). - Safe to call every step — internal counter gates it. - """ - self._som_step += 1 - if self._som_step < self.som_warmup_steps: - return False - if self._som_step % self.som_update_interval != 0: - return False - - if token_ids.numel() == 0 or recent_context_sdr.numel() == 0: - return False - - # Unique tokens in the batch - flat_ids = token_ids.view(-1).unique() - if flat_ids.numel() == 0: - return False - - # SOM runs on CPU to avoid large transient fp32 alloc on GPU. - flat_ids_cpu = flat_ids.cpu() - target = recent_context_sdr.cpu().float().reshape(-1, self.n_bits).mean(dim=0).clamp(0.0, 1.0) - - alpha = self.som_alpha - k = self.target_active - # Reconstruct dense rows from CSR indices for the affected tokens only - indices_cpu = self._retina_indices.cpu() - rows_idx = indices_cpu[flat_ids_cpu] # (U, K) int16 - rows = torch.zeros(flat_ids_cpu.numel(), self.n_bits, dtype=torch.float32) - rows.scatter_(1, rows_idx.long(), 1.0) - mixed = (1.0 - alpha) * rows + alpha * target.unsqueeze(0) # (U, n_bits) - top_idx = mixed.topk(k, dim=-1).indices # (U, k) - # Store back as CSR int16 indices — no dense matrix persisted - new_indices = top_idx.to(torch.int16) - self._retina_indices[flat_ids] = new_indices.to(self._retina_indices.device) - return True - - # ------------------------------------------------------------------ - # Convenience / introspection - # ------------------------------------------------------------------ - def extra_repr(self) -> str: - return ( - f"vocab_size={self.vocab_size}, n_bits={self.n_bits}, " - f"target_active={self.target_active}, delta_rank={self.delta_u.shape[1]}" - ) - - -# --------------------------------------------------------------------------- -# Smoke test (CLI) -# --------------------------------------------------------------------------- -def _smoke_test() -> None: - torch.manual_seed(0) - vocab_size = 8192 - n_bits = 16384 - - sdr = SemanticFoldingSDR(vocab_size=vocab_size, n_bits=n_bits) - print(f"[ok] instantiated: {sdr}") - - # --- Forward shape + sparsity ------------------------------------- - ids = torch.randint(0, vocab_size, (2, 4), dtype=torch.long) - out = sdr(ids) - assert out.shape == (2, 4, n_bits), f"shape {out.shape}" - assert out.dtype == torch.float32, f"dtype {out.dtype}" - # Forward sparsity: at step 0, delta_u is zero -> forward output is literally - # 0/1 from the retina. Verify exactly target_active bits per (b,t). - nonzero_per_bt = (out != 0).sum(dim=-1) - assert torch.all(nonzero_per_bt == sdr.target_active), ( - f"sparsity violated: nonzero counts {nonzero_per_bt.tolist()} " - f"!= target_active={sdr.target_active}" - ) - assert torch.all((out == 0) | (out == 1)), "forward output not strictly binary" - print(f"[ok] forward shape={tuple(out.shape)} dtype={out.dtype} " - f"active_bits_per_token={sdr.target_active} (exact)") - - # --- Gradient flow through STE ------------------------------------ - # Zero delta_u init => backward gradient through STE still produces a - # non-None grad on delta_u and delta_v because identity d(out)/d(delta)=1. - out2 = sdr(ids) - loss = out2.sum() - loss.backward() - assert sdr.delta_u.grad is not None and torch.isfinite(sdr.delta_u.grad).all() - assert sdr.delta_v.grad is not None and torch.isfinite(sdr.delta_v.grad).all() - assert sdr.delta_u.grad.abs().sum().item() > 0, "delta_u got no gradient" - assert sdr.delta_v.grad.abs().sum().item() > 0, "delta_v got no gradient" - # Buffer must NOT accumulate grad. - assert not sdr.retina.requires_grad - print(f"[ok] STE grads: |d delta_u|_1={sdr.delta_u.grad.abs().sum().item():.3e}, " - f"|d delta_v|_1={sdr.delta_v.grad.abs().sum().item():.3e}") - - # --- Overlap method basic validity ------------------------------ - pairs = [(10, 11), (10, 500), (100, 200), (0, 1), (7000, 7001)] - overlaps = [] - for a, b in pairs: - ov = sdr.overlap(a, b) - assert 0.0 <= ov <= 1.0 - overlaps.append(ov) - print(f"[ok] overlap() returns valid Jaccard for {len(pairs)} sample pairs " - f"(range: {min(overlaps):.4f}..{max(overlaps):.4f})") - - # Self-overlap must be 1.0 - for t in (0, 100, 8191): - assert abs(sdr.overlap(t, t) - 1.0) < 1e-9, f"self-overlap for {t} != 1" - print(f"[ok] self-overlap == 1.0 for sample tokens") - - # --- SOM update hook: no-op during warmup, then runs -------------- - # Force past warmup for this smoke test. - sdr._som_step = sdr.som_warmup_steps - 1 - ctx = sdr(ids).detach() - did = sdr.maybe_som_update(ids, ctx) # first call: step bumps to warmup -> may run - # Drive interval alignment - for _ in range(sdr.som_update_interval): - did = sdr.maybe_som_update(ids, ctx) - if did: - break - # After any update, all retina rows must still have exactly target_active bits. - row_sums = sdr.retina.sum(dim=-1) - assert torch.all(row_sums == sdr.target_active), "SOM update broke sparsity" - print(f"[ok] SOM update preserves exact sparsity (ran={did})") - - # --- Wall-clock forward at training shape ------------------------- - # Training shape target: (B=4, T=512, n_bits=16384) - dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") - sdr_dev = SemanticFoldingSDR(vocab_size=vocab_size, n_bits=n_bits).to(dev) - ids_big = torch.randint(0, vocab_size, (4, 512), dtype=torch.long, device=dev) - # Warmup - for _ in range(3): - _ = sdr_dev(ids_big) - if dev.type == "cuda": - torch.cuda.synchronize() - t0 = time.perf_counter() - for _ in range(10): - y = sdr_dev(ids_big) - if dev.type == "cuda": - torch.cuda.synchronize() - elapsed_ms = (time.perf_counter() - t0) * 1000.0 / 10.0 - mem_mb = y.numel() * 4 / (1024 * 1024) - print(f"[ok] forward (B=4, T=512, n_bits={n_bits}) on {dev}: " - f"{elapsed_ms:.2f} ms/iter, output tensor ~{mem_mb:.1f} MB") - - print("\n[SMOKE TEST PASSED]") - - -if __name__ == "__main__": - _smoke_test() +""" +SemanticFoldingSDR — differentiable torch wrapper over the offline semantic retina. + +Forward: token_ids (B, T) int64 -> binary SDR (B, T, n_bits) float32, exactly +`target_active` bits on per (b, t). Straight-through estimator provides gradients +via a low-rank learnable residual (`delta_u @ delta_v`) while the forward output +remains EXACTLY the retina lookup — sparsity guaranteed. + +Online SOM fine-tune: `maybe_som_update` is a no-grad hook the training loop may +call after optimizer.step() every `som_update_interval` steps (post-warmup) to +nudge retina rows toward observed context co-occurrence patterns. +""" + +from __future__ import annotations + +import os +import time +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn + + +DEFAULT_RETINA_PATH = os.path.expanduser("~/.cache/autoresearch/retina.npz") +# Default 327 = 2% of 16384 (Webber/Numenta canonical). +# Override via HYDRA_SDR_TARGET_ACTIVE env var (must match the value used when +# the retina cache was built — sdr_retina.py TARGET_ACTIVE reads the same var). +DEFAULT_TARGET_ACTIVE = int(os.environ.get("HYDRA_SDR_TARGET_ACTIVE", "327")) + + +class _SDRSTE(torch.autograd.Function): + """Memory-efficient STE: forward=sdr_binary exactly, backward routes upstream + gradient through delta=delta_u[ids]@delta_v via index_add_ (V, n_bits) buffer + instead of materializing a (B, T, n_bits) intermediate.""" + + @staticmethod + def forward(ctx, sdr_binary, delta_u, delta_v, token_ids): + ctx.save_for_backward(delta_u, delta_v, token_ids) + return sdr_binary + + @staticmethod + def backward(ctx, grad_out): + delta_u, delta_v, token_ids = ctx.saved_tensors + B, T, n_bits = grad_out.shape + flat_grad = grad_out.reshape(B * T, n_bits).to(delta_v.dtype) + flat_ids = token_ids.reshape(B * T) + V = delta_u.shape[0] + R = delta_u.shape[1] # delta_rank — typically 32 + # OOM fix: old code allocated (V, n_bits) = 4GB buffer via index_add. + # Instead, project to rank-R space first (small), then scatter. + # grad_delta_u[t, r] = sum_{pos: id=flat_ids[pos]=t} (flat_grad[pos] @ delta_v[r]) + # = index_add(V, R, flat_ids, flat_grad @ delta_v.T) + projected = flat_grad @ delta_v.t() # (B*T, R) — ~1MB at B=8,T=1024,R=32 + per_tok_u = torch.zeros(V, R, device=flat_grad.device, dtype=delta_v.dtype) + per_tok_u.index_add_(0, flat_ids, projected) + grad_delta_u = per_tok_u # (V, R) — ~8MB at V=65536 + # grad_delta_v = sum_{pos} delta_u[flat_ids[pos]]^T @ flat_grad[pos] + # = delta_u[flat_ids].T @ flat_grad — no intermediate buffer + gathered_u = delta_u[flat_ids] # (B*T, R) — ~1MB + grad_delta_v = gathered_u.t() @ flat_grad # (R, n_bits) — ~2MB + return None, grad_delta_u, grad_delta_v, None + + +class SemanticFoldingSDR(nn.Module): + """ + Token-level semantic folding SDR with straight-through estimator. + + Parameters + ---------- + vocab_size : int + Must match retina's stored vocab_size. + n_bits : int + Must match retina's stored n_bits. + retina_path : str | None + Path to the retina .npz built by subsystems/sdr_retina.py. + target_active : int | None + Exact number of active bits per SDR. If None, inferred from retina. + som_update_interval : int + Steps between online SOM updates (post-warmup). + som_warmup_steps : int + Steps before SOM updates begin. + delta_rank : int + Rank of the learnable low-rank residual used for gradient flow. + som_alpha : float + SOM blend factor (0..1) — how far retina rows move toward context pattern. + """ + + def __init__( + self, + vocab_size: int, + n_bits: int = 16384, + retina_path: str | None = None, + target_active: int | None = None, + som_update_interval: int = 100, + som_warmup_steps: int = 500, + delta_rank: int = 32, + som_alpha: float = 0.05, + ) -> None: + super().__init__() + self.vocab_size = vocab_size + self.n_bits = n_bits + self.som_update_interval = int(som_update_interval) + self.som_warmup_steps = int(som_warmup_steps) + self.som_alpha = float(som_alpha) + + path = retina_path or DEFAULT_RETINA_PATH + if not Path(path).exists(): + raise FileNotFoundError( + f"Retina not found at {path}. Run subsystems/sdr_retina.py first." + ) + + with np.load(path) as f: + retina_sdr = f["sdr"] # bool[V, n_bits] + stored_vocab = int(f["vocab_size"]) if "vocab_size" in f.files else retina_sdr.shape[0] + stored_nbits = int(f["n_bits"]) if "n_bits" in f.files else retina_sdr.shape[1] + stored_target = int(f["target_active"]) if "target_active" in f.files else int(retina_sdr[0].sum()) + + if retina_sdr.shape != (vocab_size, n_bits): + raise ValueError( + f"retina shape {retina_sdr.shape} != expected ({vocab_size}, {n_bits}). " + f"Stored metadata: vocab_size={stored_vocab}, n_bits={stored_nbits}" + ) + + self.target_active = int(target_active) if target_active is not None else stored_target + # Validate every row has exactly target_active bits + row_sums = retina_sdr.sum(axis=1) + if not np.all(row_sums == self.target_active): + bad = int(np.sum(row_sums != self.target_active)) + raise ValueError( + f"{bad}/{vocab_size} retina rows do not have exactly {self.target_active} active bits." + ) + + # CSR storage: store only the column indices of active bits per row. + # (V, target_active) int16 = 42 MB vs (V, n_bits) uint8 = 1 GB → 24× reduction. + # Stored as a plain Python attribute (NOT a registered buffer) so + # torch.compile cannot place guards on its content. SOM updates + # mutate indices in-place; if it were a buffer, dynamo would detect the + # mutation and recompile the forward graph on every subsequent call, + # dropping tps from 69k -> 20k. Checkpoint save/load handled by + # overridden state_dict / load_state_dict below. + self._retina_indices: torch.Tensor = self._dense_to_indices(retina_sdr) # [V, K] int16 + + # Low-rank learnable residual for gradient flow. + # Forward output is EXACTLY the retina lookup (STE), so delta init does + # not distort the forward at any step. Both factors get a tiny nonzero + # init so gradients flow to BOTH parameters from step 0 (otherwise a + # zero-init delta_u starves delta_v of gradient via the chain rule). + self.delta_u = nn.Parameter(torch.randn(vocab_size, delta_rank) * 1e-4) + self.delta_v = nn.Parameter(torch.randn(delta_rank, n_bits) * 1e-4) + + # Step counter as plain Python int — avoids GPU buffer mutation that + # invalidates torch.compile guards on every maybe_som_update call. + # Persisted manually via state_dict hooks for checkpointing. + self._som_step: int = 0 + + # ------------------------------------------------------------------ + # Dense ↔ CSR conversion helpers + # ------------------------------------------------------------------ + @staticmethod + def _dense_to_indices(dense: np.ndarray) -> torch.Tensor: + """Convert dense bool/uint8 (V, n_bits) → CSR int16 (V, K) indices.""" + # argsort-based: vectorised, no Python loop over V rows. + # For each row, sort column indices so that active bits come first, + # then slice [:K]. ~2× faster than per-row np.nonzero loop. + dense_bool = dense.astype(np.bool_) + K = int(dense_bool[0].sum()) + # Descending sort of each row: active (True=1) columns first + # np.argsort is ascending, so negate to get descending + order = np.argsort(~dense_bool, axis=1, kind="stable") # (V, n_bits) + indices = order[:, :K].astype(np.int16) + return torch.from_numpy(np.ascontiguousarray(indices)) + + # ------------------------------------------------------------------ + # Device movement: _retina_indices is a plain attribute, so nn.Module's + # .to() / .cuda() / to_empty() won't touch it. Override _apply to + # move it alongside registered parameters. + # ------------------------------------------------------------------ + def _apply(self, fn, recurse=True): + result = super()._apply(fn, recurse) + self._retina_indices = fn(self._retina_indices) + return result + + # ------------------------------------------------------------------ + # Checkpoint support: _retina_indices is a plain attribute, not a + # buffer, so we manually include it in state_dict for save/load. + # Backward-compat: old checkpoints store dense "_retina_data" — + # convert on load. + # ------------------------------------------------------------------ + def state_dict(self, *args, **kwargs): + sd = super().state_dict(*args, **kwargs) + sd["_retina_indices"] = self._retina_indices + return sd + + def load_state_dict(self, state_dict, *args, **kwargs): + if "_retina_indices" in state_dict: + self._retina_indices = state_dict.pop("_retina_indices") + elif "_retina_data" in state_dict: + # Backward compat: old dense (V, n_bits) uint8 → CSR (V, K) int16 + dense = state_dict.pop("_retina_data").cpu().numpy() + self._retina_indices = self._dense_to_indices(dense) + super().load_state_dict(state_dict, *args, **kwargs) + + # ------------------------------------------------------------------ + # Convenience property so existing code using `self.retina` outside + # this module (e.g. smoke tests) keeps working. Reconstructs dense + # (V, n_bits) uint8 on the fly from CSR indices. Cheap for small + # slices; callers needing the full matrix should cache the result. + # ------------------------------------------------------------------ + @property + def retina(self) -> torch.Tensor: + dense = torch.zeros( + self.vocab_size, self.n_bits, + dtype=torch.uint8, device=self._retina_indices.device, + ) + dense.scatter_(1, self._retina_indices.long(), 1) + return dense + + # ------------------------------------------------------------------ + # Forward + # ------------------------------------------------------------------ + def forward(self, token_ids: torch.Tensor) -> torch.Tensor: + """ + token_ids : (B, T) int64 + returns : (B, T, n_bits) autocast-aware dtype. Forward = exact binary + retina lookup, backward = identity to delta projection (STE) + via custom autograd.Function (no materialization of + `delta - delta.detach()`). + """ + if token_ids.dim() != 2: + raise ValueError(f"expected (B, T) token_ids, got shape {tuple(token_ids.shape)}") + + # Autocast-aware output dtype (saves 50% vs forcing fp32 under bf16 amp). + if torch.is_autocast_enabled(): + out_dtype = torch.get_autocast_gpu_dtype() + else: + out_dtype = self.delta_v.dtype + # Reconstruct dense binary SDR from CSR indices. Because + # _retina_indices is NOT a registered buffer, torch.compile/dynamo + # cannot place content-guards on it. SOM mutations therefore never + # trigger recompilation. + B, T = token_ids.shape + K = self.target_active + idx = self._retina_indices[token_ids.reshape(-1)] # (B*T, K) int16 + sdr_binary = torch.zeros( + B * T, self.n_bits, dtype=out_dtype, device=token_ids.device, + ) + sdr_binary.scatter_(1, idx.long(), 1) + sdr_binary = sdr_binary.view(B, T, self.n_bits) + return _SDRSTE.apply(sdr_binary, self.delta_u, self.delta_v, token_ids) + + @torch.no_grad() + def active_indices(self, token_ids: torch.Tensor) -> torch.Tensor: + """Compact int16 Reality Buffer view: (B,T,K) active retina offsets. + + This is the production discrete bridge for Cantor/Engram routing. It + avoids reconstructing dense (B,T,n_bits) masks when consumers only need + the L0 support set. + """ + if token_ids.dim() != 2: + raise ValueError(f"expected (B, T) token_ids, got shape {tuple(token_ids.shape)}") + B, T = token_ids.shape + return self._retina_indices[token_ids.reshape(-1)].view(B, T, self.target_active) + + @torch.no_grad() + def binary_only(self, token_ids: torch.Tensor) -> torch.Tensor: + """uint8 retina view — no STE, no autocast cost. For HTM/consumers that + only need the binary pattern. Reconstructs dense from CSR indices.""" + B, T = token_ids.shape + idx = self.active_indices(token_ids).reshape(B * T, self.target_active) + sdr = torch.zeros( + B * T, self.n_bits, dtype=torch.uint8, device=token_ids.device, + ) + sdr.scatter_(1, idx.long(), 1) + return sdr.view(B, T, self.n_bits) + + # ------------------------------------------------------------------ + # Validation helpers + # ------------------------------------------------------------------ + def overlap(self, tok_a: int, tok_b: int) -> float: + """Jaccard overlap of two tokens' retina SDRs — for sanity checks. + Uses set intersection on CSR indices (no dense reconstruction).""" + a = set(self._retina_indices[tok_a].tolist()) + b = set(self._retina_indices[tok_b].tolist()) + inter = len(a & b) + union = len(a | b) + return inter / max(1, union) + + # ------------------------------------------------------------------ + # Online SOM fine-tune hook (no grad) + # ------------------------------------------------------------------ + @torch.no_grad() + def maybe_som_update( + self, + token_ids: torch.Tensor, + recent_context_sdr: torch.Tensor, + ) -> bool: + """ + Move retina rows for the given tokens a small fraction toward the average + context SDR pattern, then re-binarize to exactly `target_active` bits. + + Returns True when an update occurred, False when skipped (warmup / interval). + Safe to call every step — internal counter gates it. + """ + self._som_step += 1 + if self._som_step < self.som_warmup_steps: + return False + if self._som_step % self.som_update_interval != 0: + return False + + if token_ids.numel() == 0 or recent_context_sdr.numel() == 0: + return False + + # Unique tokens in the batch + flat_ids = token_ids.view(-1).unique() + if flat_ids.numel() == 0: + return False + + # SOM runs on CPU to avoid large transient fp32 alloc on GPU. + flat_ids_cpu = flat_ids.cpu() + target = recent_context_sdr.cpu().float().reshape(-1, self.n_bits).mean(dim=0).clamp(0.0, 1.0) + + alpha = self.som_alpha + k = self.target_active + # Reconstruct dense rows from CSR indices for the affected tokens only + indices_cpu = self._retina_indices.cpu() + rows_idx = indices_cpu[flat_ids_cpu] # (U, K) int16 + rows = torch.zeros(flat_ids_cpu.numel(), self.n_bits, dtype=torch.float32) + rows.scatter_(1, rows_idx.long(), 1.0) + mixed = (1.0 - alpha) * rows + alpha * target.unsqueeze(0) # (U, n_bits) + top_idx = mixed.topk(k, dim=-1).indices # (U, k) + # Store back as CSR int16 indices — no dense matrix persisted + new_indices = top_idx.to(torch.int16) + self._retina_indices[flat_ids] = new_indices.to(self._retina_indices.device) + return True + + # ------------------------------------------------------------------ + # Convenience / introspection + # ------------------------------------------------------------------ + def extra_repr(self) -> str: + return ( + f"vocab_size={self.vocab_size}, n_bits={self.n_bits}, " + f"target_active={self.target_active}, delta_rank={self.delta_u.shape[1]}" + ) + + +# --------------------------------------------------------------------------- +# Smoke test (CLI) +# --------------------------------------------------------------------------- +def _smoke_test() -> None: + torch.manual_seed(0) + vocab_size = 8192 + n_bits = 16384 + + sdr = SemanticFoldingSDR(vocab_size=vocab_size, n_bits=n_bits) + print(f"[ok] instantiated: {sdr}") + + # --- Forward shape + sparsity ------------------------------------- + ids = torch.randint(0, vocab_size, (2, 4), dtype=torch.long) + out = sdr(ids) + assert out.shape == (2, 4, n_bits), f"shape {out.shape}" + assert out.dtype == torch.float32, f"dtype {out.dtype}" + # Forward sparsity: at step 0, delta_u is zero -> forward output is literally + # 0/1 from the retina. Verify exactly target_active bits per (b,t). + nonzero_per_bt = (out != 0).sum(dim=-1) + assert torch.all(nonzero_per_bt == sdr.target_active), ( + f"sparsity violated: nonzero counts {nonzero_per_bt.tolist()} " + f"!= target_active={sdr.target_active}" + ) + assert torch.all((out == 0) | (out == 1)), "forward output not strictly binary" + print(f"[ok] forward shape={tuple(out.shape)} dtype={out.dtype} " + f"active_bits_per_token={sdr.target_active} (exact)") + + # --- Gradient flow through STE ------------------------------------ + # Zero delta_u init => backward gradient through STE still produces a + # non-None grad on delta_u and delta_v because identity d(out)/d(delta)=1. + out2 = sdr(ids) + loss = out2.sum() + loss.backward() + assert sdr.delta_u.grad is not None and torch.isfinite(sdr.delta_u.grad).all() + assert sdr.delta_v.grad is not None and torch.isfinite(sdr.delta_v.grad).all() + assert sdr.delta_u.grad.abs().sum().item() > 0, "delta_u got no gradient" + assert sdr.delta_v.grad.abs().sum().item() > 0, "delta_v got no gradient" + # Buffer must NOT accumulate grad. + assert not sdr.retina.requires_grad + print(f"[ok] STE grads: |d delta_u|_1={sdr.delta_u.grad.abs().sum().item():.3e}, " + f"|d delta_v|_1={sdr.delta_v.grad.abs().sum().item():.3e}") + + # --- Overlap method basic validity ------------------------------ + pairs = [(10, 11), (10, 500), (100, 200), (0, 1), (7000, 7001)] + overlaps = [] + for a, b in pairs: + ov = sdr.overlap(a, b) + assert 0.0 <= ov <= 1.0 + overlaps.append(ov) + print(f"[ok] overlap() returns valid Jaccard for {len(pairs)} sample pairs " + f"(range: {min(overlaps):.4f}..{max(overlaps):.4f})") + + # Self-overlap must be 1.0 + for t in (0, 100, 8191): + assert abs(sdr.overlap(t, t) - 1.0) < 1e-9, f"self-overlap for {t} != 1" + print(f"[ok] self-overlap == 1.0 for sample tokens") + + # --- SOM update hook: no-op during warmup, then runs -------------- + # Force past warmup for this smoke test. + sdr._som_step = sdr.som_warmup_steps - 1 + ctx = sdr(ids).detach() + did = sdr.maybe_som_update(ids, ctx) # first call: step bumps to warmup -> may run + # Drive interval alignment + for _ in range(sdr.som_update_interval): + did = sdr.maybe_som_update(ids, ctx) + if did: + break + # After any update, all retina rows must still have exactly target_active bits. + row_sums = sdr.retina.sum(dim=-1) + assert torch.all(row_sums == sdr.target_active), "SOM update broke sparsity" + print(f"[ok] SOM update preserves exact sparsity (ran={did})") + + # --- Wall-clock forward at training shape ------------------------- + # Training shape target: (B=4, T=512, n_bits=16384) + dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + sdr_dev = SemanticFoldingSDR(vocab_size=vocab_size, n_bits=n_bits).to(dev) + ids_big = torch.randint(0, vocab_size, (4, 512), dtype=torch.long, device=dev) + # Warmup + for _ in range(3): + _ = sdr_dev(ids_big) + if dev.type == "cuda": + torch.cuda.synchronize() + t0 = time.perf_counter() + for _ in range(10): + y = sdr_dev(ids_big) + if dev.type == "cuda": + torch.cuda.synchronize() + elapsed_ms = (time.perf_counter() - t0) * 1000.0 / 10.0 + mem_mb = y.numel() * 4 / (1024 * 1024) + print(f"[ok] forward (B=4, T=512, n_bits={n_bits}) on {dev}: " + f"{elapsed_ms:.2f} ms/iter, output tensor ~{mem_mb:.1f} MB") + + print("\n[SMOKE TEST PASSED]") + + +if __name__ == "__main__": + _smoke_test() diff --git a/overlay/subsystems/train_engram.py b/overlay/subsystems/train_engram.py index 2cad74a6f76b9b84f2187566bdc62f77bb8b69f3..d3b75f61b700c679efa3d9ab246c36e51ed6a4ff 100644 --- a/overlay/subsystems/train_engram.py +++ b/overlay/subsystems/train_engram.py @@ -1,809 +1,809 @@ -""" -Subsystem bring-up: Mamba-3 + mHC + Engram memory. -Branch: autoresearch/phase1-engram - -Adds EngramModule (O(1) conditional memory) to the Mamba-3 + mHC stack. -No Hestia, no SDR. -""" - -import os -os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import gc -import math -import time -from dataclasses import dataclass, asdict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class Mamba3MhcEngramConfig: - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 - - # Mamba-3 SSM - n_layer: int = 4 - d_model: int = 256 - d_state: int = 64 - headdim: int = 32 - n_heads: int = 8 - expand: int = 2 - - # mHC - mhc_n_streams: int = 4 - mhc_sinkhorn_iters: int = 5 - - # Engram - engram_n_columns: int = 4096 - engram_key_dim: int = 64 - engram_layer_idx: int = 1 - - -# --------------------------------------------------------------------------- -# Utility Functions -# --------------------------------------------------------------------------- - -def norm(x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, (x.size(-1),)) - - -def complex_rope_freqs( - seq_len: int, - headdim: int, - base: float = 10000.0, - device: torch.device | None = None, -) -> tuple[torch.Tensor, torch.Tensor]: - half = headdim // 2 - freqs = 1.0 / ( - base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) - ) - t = torch.arange(seq_len, dtype=torch.float32, device=device) - angles = torch.outer(t, freqs) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - return cos, sin - - -def apply_rope_ssm( - x: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, -) -> torch.Tensor: - d = x.shape[-1] // 2 - x1, x2 = x[..., :d], x[..., d:] - cos = cos[: x.shape[-2]] - sin = sin[: x.shape[-2]] - y1 = x1 * cos + x2 * sin - y2 = x1 * (-sin) + x2 * cos - return torch.cat([y1, y2], dim=-1) - - -# --------------------------------------------------------------------------- -# Mamba-3 SSM Block -# --------------------------------------------------------------------------- - -class BCNorm(nn.Module): - def __init__(self, dim: int) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.bias = nn.Parameter(torch.zeros(dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) - - -class Mamba3Block(nn.Module): - def __init__(self, config: Mamba3MhcEngramConfig) -> None: - super().__init__() - self.d_model = config.d_model - self.d_state = config.d_state - self.headdim = config.headdim - self.n_heads = config.n_heads - inner_dim = config.expand * config.d_model - - self.in_proj = nn.Linear( - config.d_model, - inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, - bias=False, - ) - self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) - self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) - self.D = nn.Parameter(torch.ones(config.n_heads)) - self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) - self.bc_norm = BCNorm(config.d_state) - self.conv1d = nn.Conv1d( - inner_dim, inner_dim, - kernel_size=4, padding=3, - groups=inner_dim, bias=True, - ) - - def forward( - self, - x: torch.Tensor, - cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - B, T, D = x.shape - inner_dim = self.d_model * 2 - - proj = self.in_proj(x) - z = proj[..., :inner_dim] - x_ssm = proj[..., inner_dim : 2 * inner_dim] - B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] - C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] - dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] - - x_ssm = x_ssm.transpose(1, 2) - x_ssm = self.conv1d(x_ssm)[..., :T] - x_ssm = x_ssm.transpose(1, 2) - x_ssm = F.silu(x_ssm) - - B_proj = self.bc_norm(B_proj) - C_proj = self.bc_norm(C_proj) - - if cos_sin is not None: - cos, sin = cos_sin - B_proj = apply_rope_ssm(B_proj, cos, sin) - C_proj = apply_rope_ssm(C_proj, cos, sin) - - A = -torch.exp(self.A_log) - dt = F.softplus(dt_proj) - x_heads = x_ssm.view(B, T, self.n_heads, -1) - alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) - Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) - - lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) - - h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) - Bx_prev = torch.zeros_like(Bx[:, 0]) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1) - Bx_t = Bx[:, t] - h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - Bx_prev = Bx_t - C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) - y_t = (C_t * h).sum(dim=-1) - y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) - y_list.append(y_t) - - y_ssm = torch.stack(y_list, dim=1) - y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) - y_ssm = y_ssm.reshape(B, T, inner_dim) - y = y_ssm * F.silu(z) - y = self.out_proj(y) - return y - - -# --------------------------------------------------------------------------- -# Manifold Hyper-Connection (mHC) -# --------------------------------------------------------------------------- - -class ManifoldHyperConnection(nn.Module): - def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: - super().__init__() - self.n_streams = n_streams - self.d_model = d_model - self.sinkhorn_iters = sinkhorn_iters - self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) - self.stream_norms = nn.ModuleList([ - nn.LayerNorm(d_model) for _ in range(n_streams) - ]) - - def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: - M = log_alpha - for _ in range(self.sinkhorn_iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: - M = self._sinkhorn(self.log_alpha) - mixed = torch.einsum("ij,jbtd->ibtd", M, streams) - primary_input = mixed[0] - primary_input = self.stream_norms[0](primary_input) - block_output = block_fn(primary_input) - M_T = M.t() - update = torch.zeros_like(streams) - update[0] = block_output - streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) - return streams - - def init_streams(self, x: torch.Tensor) -> torch.Tensor: - return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() - - def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: - return streams.mean(dim=0) - - -# --------------------------------------------------------------------------- -# Engram Module -# --------------------------------------------------------------------------- - -class EngramModule(nn.Module): - """ - DeepSeek Engram: O(1) conditional memory lookup with soft gating. - - Hash-based lookup into a fixed-size memory table. - """ - - def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: - super().__init__() - self.d_model = d_model - self.n_columns = n_columns - self.key_dim = key_dim - - self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) - self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) - self.key_proj = nn.Linear(d_model, key_dim, bias=False) - self.gate_proj = nn.Linear(d_model, 1, bias=True) - nn.init.constant_(self.gate_proj.bias, -2.0) - - def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: - """x: (B, T, d_model) -> (B, T, d_model), hit_rate""" - B, T, D = x.shape - - query = self.key_proj(x) - sim = torch.matmul(query, self.memory_keys.t()) - attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) - retrieved = torch.matmul(attn, self.memory_values) - alpha = torch.sigmoid(self.gate_proj(x)) - output = x + alpha * retrieved - hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() - - return output, hit_rate - - -# --------------------------------------------------------------------------- -# Mamba3MhcEngramModel -# --------------------------------------------------------------------------- - -class Mamba3MhcEngramModel(nn.Module): - """ - Mamba-3 + mHC + Engram memory. No Hestia, no SDR. - - Architecture: - Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer - (+ Engram at engram_layer_idx) -> merge_streams -> norm -> LM head - """ - - def __init__(self, config: Mamba3MhcEngramConfig) -> None: - super().__init__() - self.config = config - - self.wte = nn.Embedding(config.vocab_size, config.d_model) - self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) - self.mhc_layers = nn.ModuleList([ - ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) - for _ in range(config.n_layer) - ]) - self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) - self.engram_layer_idx = config.engram_layer_idx - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - self.softcap = 30.0 - - self.rope_seq_len = config.sequence_len * 2 - cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) - self.register_buffer("rope_cos", cos, persistent=False) - self.register_buffer("rope_sin", sin, persistent=False) - - self._metrics: dict = {} - - @torch.no_grad() - def init_weights(self) -> None: - s = 3**0.5 * self.config.d_model**-0.5 - nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) - for block in self.blocks: - nn.init.uniform_(block.in_proj.weight, -s, s) - nn.init.zeros_(block.out_proj.weight) - nn.init.ones_(block.conv1d.weight) - nn.init.zeros_(block.conv1d.bias) - for mhc in self.mhc_layers: - nn.init.eye_(mhc.log_alpha.data) - self.wte.to(dtype=torch.bfloat16) - - def estimate_flops(self) -> float: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict[str, int]: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - mhc = sum(p.numel() for p in self.mhc_layers.parameters()) - engram = sum(p.numel() for p in self.engram.parameters()) - total = sum(p.numel() for p in self.parameters()) - return { - "wte": wte, "lm_head": lm_head, "blocks": blocks, - "mhc": mhc, "engram": engram, "total": total, - } - - def get_secondary_metrics(self) -> dict: - return self._metrics - - def setup_optimizer( - self, - unembedding_lr: float = 0.004, - embedding_lr: float = 0.6, - matrix_lr: float = 0.04, - weight_decay: float = 0.2, - adam_betas: tuple[float, float] = (0.8, 0.95), - scalar_lr: float = 0.5, - ) -> "MuonAdamW": - model_dim = self.config.d_model - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - matrix_params = [] - for p in self.blocks.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.mhc_layers.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.engram.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [p for p in self.parameters() if id(p) not in assigned] - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind="adamw", params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind="adamw", params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - if scalar_params: - param_groups.append( - dict(kind="adamw", params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - param_groups.append(dict( - kind="muon", params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward( - self, - idx: torch.Tensor, - targets: torch.Tensor | None = None, - reduction: str = "mean", - ) -> torch.Tensor: - B, T = idx.shape - cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) - - x = self.wte(idx) - x = norm(x) - - streams = self.mhc_layers[0].init_streams(x) - spectral_norms = [] - - for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): - def block_fn(inp, _block=block, _cos_sin=cos_sin): - return _block(inp, cos_sin=_cos_sin) - - streams = mhc(streams, block_fn) - - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - spectral_norms.append(torch.linalg.norm(M, ord=2).item()) - - if i == self.engram_layer_idx: - primary = streams[0] - primary, hit_rate = self.engram(primary) - streams[0] = primary - self._metrics["engram_hit_rate"] = hit_rate - - x = self.mhc_layers[-1].merge_streams(streams) - x = norm(x) - - self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 - - logits = self.lm_head(x) - logits = logits.float() - logits = self.softcap * torch.tanh(logits / self.softcap) - - if targets is not None: - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.view(-1), - ignore_index=-1, - reduction=reduction, - ) - return loss - return logits - - -# --------------------------------------------------------------------------- -# Optimizer (MuonAdamW) -# --------------------------------------------------------------------------- - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused( - stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, -): - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - beta2 = beta2_t.to(g.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p) - state["exp_avg_sq"] = torch.zeros_like(p) - state["step"] += 1 - self._adamw_step_t.fill_(state["step"]) - self._adamw_lr_t.fill_(group["lr"]) - self._adamw_beta1_t.fill_(group["betas"][0]) - self._adamw_beta2_t.fill_(group["betas"][1]) - self._adamw_eps_t.fill_(group["eps"]) - self._adamw_wd_t.fill_(group["weight_decay"]) - adamw_step_fused( - p, grad, state["exp_avg"], state["exp_avg_sq"], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, - ) - - def _step_muon(self, group): - params = group["params"] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - if "second_momentum_buffer" not in state: - state_shape = ( - (num_params, shape[-2], 1) if shape[-2] >= shape[-1] - else (num_params, 1, shape[-1]) - ) - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused( - stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim, - ) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group["kind"] == "adamw": - self._step_adamw(group) - elif group["kind"] == "muon": - self._step_muon(group) - - -# --------------------------------------------------------------------------- -# Hyperparameters -# --------------------------------------------------------------------------- - -D_MODEL = 256 -N_LAYER = 4 -D_STATE = 64 -HEADDIM = 32 -N_HEADS = D_MODEL // HEADDIM -EXPAND = 2 -MHC_N_STREAMS = 4 -MHC_SINKHORN_ITERS = 5 -ENGRAM_N_COLUMNS = 4096 -ENGRAM_KEY_DIM = 64 -ENGRAM_LAYER_IDX = 1 - -# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential -# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get -# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. -# The autoresearch agent can increase this if it finds faster architectures. -TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) -DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB -MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) -EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch -UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch -SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch -WEIGHT_DECAY = 0.2 -ADAM_BETAS = (0.8, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = 0.0 - -# --------------------------------------------------------------------------- -# Setup -# --------------------------------------------------------------------------- - -t_start = time.time() -torch.manual_seed(42) -torch.cuda.manual_seed(42) -torch.set_float32_matmul_precision("high") -device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -RTX3060_FP32_PEAK_FLOPS = 12.74e12 - -tokenizer = Tokenizer.from_directory() -vocab_size = tokenizer.get_vocab_size() -print(f"Vocab size: {vocab_size:,}") - -config = Mamba3MhcEngramConfig( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, - mhc_n_streams=MHC_N_STREAMS, - mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, - engram_n_columns=ENGRAM_N_COLUMNS, - engram_key_dim=ENGRAM_KEY_DIM, - engram_layer_idx=ENGRAM_LAYER_IDX, -) -print(f"Model config: {asdict(config)}") - -with torch.device("meta"): - model = Mamba3MhcEngramModel(config) -model.to_empty(device=device) -model.init_weights() - -param_counts = model.num_scaling_params() -print("Parameter counts:") -for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") -num_params = param_counts["total"] -num_flops_per_token = model.estimate_flops() -print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN -assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 -grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - -optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, -) - -model = torch.compile(model, dynamic=False) - -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") -x, y, epoch = next(train_loader) - -print(f"Time budget: {TIME_BUDGET}s") -print(f"Gradient accumulation steps: {grad_accum_steps}") - - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - elif progress < 1.0 - WARMDOWN_RATIO: - return 1.0 - else: - cooldown = (1.0 - progress) / WARMDOWN_RATIO - return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- -# Training loop -# --------------------------------------------------------------------------- - -t_start_training = time.time() -smooth_train_loss = 0.0 -total_training_time = 0.0 -step = 0 - -while True: - torch.cuda.synchronize() - t0 = time.time() - for micro_step in range(grad_accum_steps): - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - x, y, epoch = next(train_loader) - - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group["kind"] == "muon": - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - optimizer.step() - model.zero_grad(set_to_none=True) - - train_loss_f = train_loss.item() - - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - exit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - print( - f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " - f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " - f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", - end="", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - elif (step + 1) % 5000 == 0: - gc.collect() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - -print() - -total_tokens = step * TOTAL_BATCH_SIZE - -model.eval() -with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - -t_end = time.time() -steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS - if total_training_time > 0 else 0 -) -peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - -metrics = model.get_secondary_metrics() - -print("---") -print(f"val_bpb: {val_bpb:.6f}") -print(f"training_seconds: {total_training_time:.1f}") -print(f"total_seconds: {t_end - t_start:.1f}") -print(f"peak_vram_mb: {peak_vram_mb:.1f}") -print(f"mfu_percent: {steady_state_mfu:.2f}") -print(f"total_tokens_M: {total_tokens / 1e6:.1f}") -print(f"num_steps: {step}") -print(f"num_params_M: {num_params / 1e6:.1f}") -print(f"n_layer: {N_LAYER}") -print(f"d_model: {D_MODEL}") -print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") -print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") +""" +Subsystem bring-up: Mamba-3 + mHC + Engram memory. +Branch: autoresearch/phase1-engram + +Adds EngramModule (O(1) conditional memory) to the Mamba-3 + mHC stack. +No Hestia, no SDR. +""" + +import os +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class Mamba3MhcEngramConfig: + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 + + # Mamba-3 SSM + n_layer: int = 4 + d_model: int = 256 + d_state: int = 64 + headdim: int = 32 + n_heads: int = 8 + expand: int = 2 + + # mHC + mhc_n_streams: int = 4 + mhc_sinkhorn_iters: int = 5 + + # Engram + engram_n_columns: int = 4096 + engram_key_dim: int = 64 + engram_layer_idx: int = 1 + + +# --------------------------------------------------------------------------- +# Utility Functions +# --------------------------------------------------------------------------- + +def norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +def complex_rope_freqs( + seq_len: int, + headdim: int, + base: float = 10000.0, + device: torch.device | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + half = headdim // 2 + freqs = 1.0 / ( + base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) + ) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + angles = torch.outer(t, freqs) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + return cos, sin + + +def apply_rope_ssm( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + cos = cos[: x.shape[-2]] + sin = sin[: x.shape[-2]] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], dim=-1) + + +# --------------------------------------------------------------------------- +# Mamba-3 SSM Block +# --------------------------------------------------------------------------- + +class BCNorm(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) + + +class Mamba3Block(nn.Module): + def __init__(self, config: Mamba3MhcEngramConfig) -> None: + super().__init__() + self.d_model = config.d_model + self.d_state = config.d_state + self.headdim = config.headdim + self.n_heads = config.n_heads + inner_dim = config.expand * config.d_model + + self.in_proj = nn.Linear( + config.d_model, + inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, + bias=False, + ) + self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) + self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) + self.D = nn.Parameter(torch.ones(config.n_heads)) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + self.bc_norm = BCNorm(config.d_state) + self.conv1d = nn.Conv1d( + inner_dim, inner_dim, + kernel_size=4, padding=3, + groups=inner_dim, bias=True, + ) + + def forward( + self, + x: torch.Tensor, + cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, + ) -> torch.Tensor: + B, T, D = x.shape + inner_dim = self.d_model * 2 + + proj = self.in_proj(x) + z = proj[..., :inner_dim] + x_ssm = proj[..., inner_dim : 2 * inner_dim] + B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] + C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] + dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] + + x_ssm = x_ssm.transpose(1, 2) + x_ssm = self.conv1d(x_ssm)[..., :T] + x_ssm = x_ssm.transpose(1, 2) + x_ssm = F.silu(x_ssm) + + B_proj = self.bc_norm(B_proj) + C_proj = self.bc_norm(C_proj) + + if cos_sin is not None: + cos, sin = cos_sin + B_proj = apply_rope_ssm(B_proj, cos, sin) + C_proj = apply_rope_ssm(C_proj, cos, sin) + + A = -torch.exp(self.A_log) + dt = F.softplus(dt_proj) + x_heads = x_ssm.view(B, T, self.n_heads, -1) + alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) + Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) + + lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) + + h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) + Bx_prev = torch.zeros_like(Bx[:, 0]) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1) + Bx_t = Bx[:, t] + h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + Bx_prev = Bx_t + C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) + y_t = (C_t * h).sum(dim=-1) + y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) + y_list.append(y_t) + + y_ssm = torch.stack(y_list, dim=1) + y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) + y_ssm = y_ssm.reshape(B, T, inner_dim) + y = y_ssm * F.silu(z) + y = self.out_proj(y) + return y + + +# --------------------------------------------------------------------------- +# Manifold Hyper-Connection (mHC) +# --------------------------------------------------------------------------- + +class ManifoldHyperConnection(nn.Module): + def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: + super().__init__() + self.n_streams = n_streams + self.d_model = d_model + self.sinkhorn_iters = sinkhorn_iters + self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) + self.stream_norms = nn.ModuleList([ + nn.LayerNorm(d_model) for _ in range(n_streams) + ]) + + def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: + M = log_alpha + for _ in range(self.sinkhorn_iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: + M = self._sinkhorn(self.log_alpha) + mixed = torch.einsum("ij,jbtd->ibtd", M, streams) + primary_input = mixed[0] + primary_input = self.stream_norms[0](primary_input) + block_output = block_fn(primary_input) + M_T = M.t() + update = torch.zeros_like(streams) + update[0] = block_output + streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) + return streams + + def init_streams(self, x: torch.Tensor) -> torch.Tensor: + return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() + + def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: + return streams.mean(dim=0) + + +# --------------------------------------------------------------------------- +# Engram Module +# --------------------------------------------------------------------------- + +class EngramModule(nn.Module): + """ + DeepSeek Engram: O(1) conditional memory lookup with soft gating. + + Hash-based lookup into a fixed-size memory table. + """ + + def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: + super().__init__() + self.d_model = d_model + self.n_columns = n_columns + self.key_dim = key_dim + + self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) + self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) + self.key_proj = nn.Linear(d_model, key_dim, bias=False) + self.gate_proj = nn.Linear(d_model, 1, bias=True) + nn.init.constant_(self.gate_proj.bias, -2.0) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: + """x: (B, T, d_model) -> (B, T, d_model), hit_rate""" + B, T, D = x.shape + + query = self.key_proj(x) + sim = torch.matmul(query, self.memory_keys.t()) + attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) + retrieved = torch.matmul(attn, self.memory_values) + alpha = torch.sigmoid(self.gate_proj(x)) + output = x + alpha * retrieved + hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() + + return output, hit_rate + + +# --------------------------------------------------------------------------- +# Mamba3MhcEngramModel +# --------------------------------------------------------------------------- + +class Mamba3MhcEngramModel(nn.Module): + """ + Mamba-3 + mHC + Engram memory. No Hestia, no SDR. + + Architecture: + Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer + (+ Engram at engram_layer_idx) -> merge_streams -> norm -> LM head + """ + + def __init__(self, config: Mamba3MhcEngramConfig) -> None: + super().__init__() + self.config = config + + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) + self.mhc_layers = nn.ModuleList([ + ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) + for _ in range(config.n_layer) + ]) + self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) + self.engram_layer_idx = config.engram_layer_idx + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.softcap = 30.0 + + self.rope_seq_len = config.sequence_len * 2 + cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) + self.register_buffer("rope_cos", cos, persistent=False) + self.register_buffer("rope_sin", sin, persistent=False) + + self._metrics: dict = {} + + @torch.no_grad() + def init_weights(self) -> None: + s = 3**0.5 * self.config.d_model**-0.5 + nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + for block in self.blocks: + nn.init.uniform_(block.in_proj.weight, -s, s) + nn.init.zeros_(block.out_proj.weight) + nn.init.ones_(block.conv1d.weight) + nn.init.zeros_(block.conv1d.bias) + for mhc in self.mhc_layers: + nn.init.eye_(mhc.log_alpha.data) + self.wte.to(dtype=torch.bfloat16) + + def estimate_flops(self) -> float: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict[str, int]: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + mhc = sum(p.numel() for p in self.mhc_layers.parameters()) + engram = sum(p.numel() for p in self.engram.parameters()) + total = sum(p.numel() for p in self.parameters()) + return { + "wte": wte, "lm_head": lm_head, "blocks": blocks, + "mhc": mhc, "engram": engram, "total": total, + } + + def get_secondary_metrics(self) -> dict: + return self._metrics + + def setup_optimizer( + self, + unembedding_lr: float = 0.004, + embedding_lr: float = 0.6, + matrix_lr: float = 0.04, + weight_decay: float = 0.2, + adam_betas: tuple[float, float] = (0.8, 0.95), + scalar_lr: float = 0.5, + ) -> "MuonAdamW": + model_dim = self.config.d_model + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + matrix_params = [] + for p in self.blocks.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.mhc_layers.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.engram.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + scalar_params = [p for p in self.parameters() if id(p) not in assigned] + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind="adamw", params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind="adamw", params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + if scalar_params: + param_groups.append( + dict(kind="adamw", params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append(dict( + kind="muon", params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward( + self, + idx: torch.Tensor, + targets: torch.Tensor | None = None, + reduction: str = "mean", + ) -> torch.Tensor: + B, T = idx.shape + cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) + + x = self.wte(idx) + x = norm(x) + + streams = self.mhc_layers[0].init_streams(x) + spectral_norms = [] + + for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): + def block_fn(inp, _block=block, _cos_sin=cos_sin): + return _block(inp, cos_sin=_cos_sin) + + streams = mhc(streams, block_fn) + + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + spectral_norms.append(torch.linalg.norm(M, ord=2).item()) + + if i == self.engram_layer_idx: + primary = streams[0] + primary, hit_rate = self.engram(primary) + streams[0] = primary + self._metrics["engram_hit_rate"] = hit_rate + + x = self.mhc_layers[-1].merge_streams(streams) + x = norm(x) + + self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 + + logits = self.lm_head(x) + logits = logits.float() + logits = self.softcap * torch.tanh(logits / self.softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, +): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, grad, state["exp_avg"], state["exp_avg_sq"], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters +# --------------------------------------------------------------------------- + +D_MODEL = 256 +N_LAYER = 4 +D_STATE = 64 +HEADDIM = 32 +N_HEADS = D_MODEL // HEADDIM +EXPAND = 2 +MHC_N_STREAMS = 4 +MHC_SINKHORN_ITERS = 5 +ENGRAM_N_COLUMNS = 4096 +ENGRAM_KEY_DIM = 64 +ENGRAM_LAYER_IDX = 1 + +# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential +# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get +# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. +# The autoresearch agent can increase this if it finds faster architectures. +TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) +DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB +MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) +EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch +UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch +SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch +WEIGHT_DECAY = 0.2 +ADAM_BETAS = (0.8, 0.95) +WARMUP_RATIO = 0.0 +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = 0.0 + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +RTX3060_FP32_PEAK_FLOPS = 12.74e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + +config = Mamba3MhcEngramConfig( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, + mhc_n_streams=MHC_N_STREAMS, + mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, + engram_n_columns=ENGRAM_N_COLUMNS, + engram_key_dim=ENGRAM_KEY_DIM, + engram_layer_idx=ENGRAM_LAYER_IDX, +) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = Mamba3MhcEngramModel(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0.0 +total_training_time = 0.0 +step = 0 + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + train_loss_f = train_loss.item() + + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " + f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " + f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() + +total_tokens = step * TOTAL_BATCH_SIZE + +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +t_end = time.time() +steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS + if total_training_time > 0 else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +metrics = model.get_secondary_metrics() + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"n_layer: {N_LAYER}") +print(f"d_model: {D_MODEL}") +print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") +print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") diff --git a/overlay/subsystems/train_hestia.py b/overlay/subsystems/train_hestia.py index ca71a562d721e90849378bea3c471523cbe1be1c..73395a7002221a9f3f7da475ce34aab4616e55c1 100644 --- a/overlay/subsystems/train_hestia.py +++ b/overlay/subsystems/train_hestia.py @@ -1,877 +1,877 @@ -""" -Subsystem bring-up: Mamba-3 + mHC + Engram + Hestia QAT. -Branch: autoresearch/phase1-hestia - -Adds HestiaQAT (quantization-aware training) with temperature annealing. -No SDR. -""" - -import os -os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import gc -import math -import time -from dataclasses import dataclass, asdict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class Mamba3HestiaConfig: - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 - - # Mamba-3 SSM - n_layer: int = 4 - d_model: int = 256 - d_state: int = 64 - headdim: int = 32 - n_heads: int = 8 - expand: int = 2 - - # mHC - mhc_n_streams: int = 4 - mhc_sinkhorn_iters: int = 5 - - # Engram - engram_n_columns: int = 4096 - engram_key_dim: int = 64 - engram_layer_idx: int = 1 - - # Hestia QAT (ENABLED in this subsystem) - hestia_enabled: bool = True - hestia_bits: float = 1.58 - - -# --------------------------------------------------------------------------- -# Utility Functions -# --------------------------------------------------------------------------- - -def norm(x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, (x.size(-1),)) - - -def complex_rope_freqs( - seq_len: int, - headdim: int, - base: float = 10000.0, - device: torch.device | None = None, -) -> tuple[torch.Tensor, torch.Tensor]: - half = headdim // 2 - freqs = 1.0 / ( - base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) - ) - t = torch.arange(seq_len, dtype=torch.float32, device=device) - angles = torch.outer(t, freqs) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - return cos, sin - - -def apply_rope_ssm( - x: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, -) -> torch.Tensor: - d = x.shape[-1] // 2 - x1, x2 = x[..., :d], x[..., d:] - cos = cos[: x.shape[-2]] - sin = sin[: x.shape[-2]] - y1 = x1 * cos + x2 * sin - y2 = x1 * (-sin) + x2 * cos - return torch.cat([y1, y2], dim=-1) - - -# --------------------------------------------------------------------------- -# Mamba-3 SSM Block -# --------------------------------------------------------------------------- - -class BCNorm(nn.Module): - def __init__(self, dim: int) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.bias = nn.Parameter(torch.zeros(dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) - - -class Mamba3Block(nn.Module): - def __init__(self, config: Mamba3HestiaConfig) -> None: - super().__init__() - self.d_model = config.d_model - self.d_state = config.d_state - self.headdim = config.headdim - self.n_heads = config.n_heads - inner_dim = config.expand * config.d_model - - self.in_proj = nn.Linear( - config.d_model, - inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, - bias=False, - ) - self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) - self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) - self.D = nn.Parameter(torch.ones(config.n_heads)) - self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) - self.bc_norm = BCNorm(config.d_state) - self.conv1d = nn.Conv1d( - inner_dim, inner_dim, - kernel_size=4, padding=3, - groups=inner_dim, bias=True, - ) - - def forward( - self, - x: torch.Tensor, - cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - B, T, D = x.shape - inner_dim = self.d_model * 2 - - proj = self.in_proj(x) - z = proj[..., :inner_dim] - x_ssm = proj[..., inner_dim : 2 * inner_dim] - B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] - C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] - dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] - - x_ssm = x_ssm.transpose(1, 2) - x_ssm = self.conv1d(x_ssm)[..., :T] - x_ssm = x_ssm.transpose(1, 2) - x_ssm = F.silu(x_ssm) - - B_proj = self.bc_norm(B_proj) - C_proj = self.bc_norm(C_proj) - - if cos_sin is not None: - cos, sin = cos_sin - B_proj = apply_rope_ssm(B_proj, cos, sin) - C_proj = apply_rope_ssm(C_proj, cos, sin) - - A = -torch.exp(self.A_log) - dt = F.softplus(dt_proj) - x_heads = x_ssm.view(B, T, self.n_heads, -1) - alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) - Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) - - lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) - - h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) - Bx_prev = torch.zeros_like(Bx[:, 0]) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1) - Bx_t = Bx[:, t] - h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - Bx_prev = Bx_t - C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) - y_t = (C_t * h).sum(dim=-1) - y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) - y_list.append(y_t) - - y_ssm = torch.stack(y_list, dim=1) - y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) - y_ssm = y_ssm.reshape(B, T, inner_dim) - y = y_ssm * F.silu(z) - y = self.out_proj(y) - return y - - -# --------------------------------------------------------------------------- -# Manifold Hyper-Connection (mHC) -# --------------------------------------------------------------------------- - -class ManifoldHyperConnection(nn.Module): - def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: - super().__init__() - self.n_streams = n_streams - self.d_model = d_model - self.sinkhorn_iters = sinkhorn_iters - self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) - self.stream_norms = nn.ModuleList([ - nn.LayerNorm(d_model) for _ in range(n_streams) - ]) - - def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: - M = log_alpha - for _ in range(self.sinkhorn_iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: - M = self._sinkhorn(self.log_alpha) - mixed = torch.einsum("ij,jbtd->ibtd", M, streams) - primary_input = mixed[0] - primary_input = self.stream_norms[0](primary_input) - block_output = block_fn(primary_input) - M_T = M.t() - update = torch.zeros_like(streams) - update[0] = block_output - streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) - return streams - - def init_streams(self, x: torch.Tensor) -> torch.Tensor: - return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() - - def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: - return streams.mean(dim=0) - - -# --------------------------------------------------------------------------- -# Engram Module -# --------------------------------------------------------------------------- - -class EngramModule(nn.Module): - def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: - super().__init__() - self.d_model = d_model - self.n_columns = n_columns - self.key_dim = key_dim - - self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) - self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) - self.key_proj = nn.Linear(d_model, key_dim, bias=False) - self.gate_proj = nn.Linear(d_model, 1, bias=True) - nn.init.constant_(self.gate_proj.bias, -2.0) - - def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: - B, T, D = x.shape - query = self.key_proj(x) - sim = torch.matmul(query, self.memory_keys.t()) - attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) - retrieved = torch.matmul(attn, self.memory_values) - alpha = torch.sigmoid(self.gate_proj(x)) - output = x + alpha * retrieved - hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() - return output, hit_rate - - -# --------------------------------------------------------------------------- -# Hestia QAT -# --------------------------------------------------------------------------- - -class HestiaQAT(nn.Module): - """ - Hestia Quantization-Aware Training. - - Ternary quantization ({-1, 0, +1}) with straight-through estimator. - Temperature annealing drives weights toward discrete values over training. - """ - - def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: - super().__init__() - self.enabled = enabled - self.bits = bits - self.temperature = nn.Parameter(torch.tensor(1.0), requires_grad=False) - - def quantize_weight(self, w: torch.Tensor) -> torch.Tensor: - """Ternary quantization with straight-through estimator.""" - if not self.enabled: - return w - scale = w.abs().mean() - w_ternary = torch.sign(w) * (w.abs() > 0.5 * scale).float() * scale - return w + (w_ternary - w).detach() - - def forward(self, module: nn.Module) -> None: - """Apply quantization to all weight matrices in module.""" - if not self.enabled: - return - for name, param in module.named_parameters(): - if "weight" in name and param.dim() >= 2: - param.data = self.quantize_weight(param.data) - - def get_quant_error(self, module: nn.Module) -> float: - """Compute MSE between full-precision and quantized weights.""" - if not self.enabled: - return 0.0 - total_mse = 0.0 - count = 0 - for name, param in module.named_parameters(): - if "weight" in name and param.dim() >= 2: - q = self.quantize_weight(param.data) - total_mse += F.mse_loss(q, param.data).item() - count += 1 - return total_mse / max(count, 1) - - def anneal_temperature(self, progress: float) -> None: - """Anneal temperature from 1.0 to 0.1 over training.""" - if not self.enabled: - return - new_temp = 1.0 - 0.9 * progress - self.temperature.fill_(max(new_temp, 0.1)) - - -# --------------------------------------------------------------------------- -# Mamba3HestiaModel -# --------------------------------------------------------------------------- - -class Mamba3HestiaModel(nn.Module): - """ - Mamba-3 + mHC + Engram + Hestia QAT. No SDR. - - Architecture: - Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer - (+ Engram at engram_layer_idx) -> merge_streams -> norm -> LM head - Hestia QAT applied after each optimizer step. - """ - - def __init__(self, config: Mamba3HestiaConfig) -> None: - super().__init__() - self.config = config - - self.wte = nn.Embedding(config.vocab_size, config.d_model) - self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) - self.mhc_layers = nn.ModuleList([ - ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) - for _ in range(config.n_layer) - ]) - self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) - self.engram_layer_idx = config.engram_layer_idx - self.hestia = HestiaQAT(enabled=config.hestia_enabled, bits=config.hestia_bits) - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - self.softcap = 30.0 - - self.rope_seq_len = config.sequence_len * 2 - cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) - self.register_buffer("rope_cos", cos, persistent=False) - self.register_buffer("rope_sin", sin, persistent=False) - - self._metrics: dict = {} - - @torch.no_grad() - def init_weights(self) -> None: - s = 3**0.5 * self.config.d_model**-0.5 - nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) - for block in self.blocks: - nn.init.uniform_(block.in_proj.weight, -s, s) - nn.init.zeros_(block.out_proj.weight) - nn.init.ones_(block.conv1d.weight) - nn.init.zeros_(block.conv1d.bias) - for mhc in self.mhc_layers: - nn.init.eye_(mhc.log_alpha.data) - self.wte.to(dtype=torch.bfloat16) - - def estimate_flops(self) -> float: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict[str, int]: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - mhc = sum(p.numel() for p in self.mhc_layers.parameters()) - engram = sum(p.numel() for p in self.engram.parameters()) - total = sum(p.numel() for p in self.parameters()) - return { - "wte": wte, "lm_head": lm_head, "blocks": blocks, - "mhc": mhc, "engram": engram, "total": total, - } - - def get_secondary_metrics(self) -> dict: - return self._metrics - - def setup_optimizer( - self, - unembedding_lr: float = 0.004, - embedding_lr: float = 0.6, - matrix_lr: float = 0.04, - weight_decay: float = 0.2, - adam_betas: tuple[float, float] = (0.8, 0.95), - scalar_lr: float = 0.5, - ) -> "MuonAdamW": - model_dim = self.config.d_model - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - matrix_params = [] - for p in self.blocks.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.mhc_layers.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.engram.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [p for p in self.parameters() if id(p) not in assigned] - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind="adamw", params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind="adamw", params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - if scalar_params: - param_groups.append( - dict(kind="adamw", params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - param_groups.append(dict( - kind="muon", params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward( - self, - idx: torch.Tensor, - targets: torch.Tensor | None = None, - reduction: str = "mean", - ) -> torch.Tensor: - B, T = idx.shape - cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) - - x = self.wte(idx) - x = norm(x) - - streams = self.mhc_layers[0].init_streams(x) - spectral_norms = [] - - for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): - def block_fn(inp, _block=block, _cos_sin=cos_sin): - return _block(inp, cos_sin=_cos_sin) - - streams = mhc(streams, block_fn) - - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - spectral_norms.append(torch.linalg.norm(M, ord=2).item()) - - if i == self.engram_layer_idx: - primary = streams[0] - primary, hit_rate = self.engram(primary) - streams[0] = primary - self._metrics["engram_hit_rate"] = hit_rate - - x = self.mhc_layers[-1].merge_streams(streams) - x = norm(x) - - self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 - self._metrics["hestia_quant_error"] = self.hestia.get_quant_error(self) - - logits = self.lm_head(x) - logits = logits.float() - logits = self.softcap * torch.tanh(logits / self.softcap) - - if targets is not None: - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.view(-1), - ignore_index=-1, - reduction=reduction, - ) - return loss - return logits - - -# --------------------------------------------------------------------------- -# Optimizer (MuonAdamW) -# --------------------------------------------------------------------------- - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused( - stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, -): - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - beta2 = beta2_t.to(g.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p) - state["exp_avg_sq"] = torch.zeros_like(p) - state["step"] += 1 - self._adamw_step_t.fill_(state["step"]) - self._adamw_lr_t.fill_(group["lr"]) - self._adamw_beta1_t.fill_(group["betas"][0]) - self._adamw_beta2_t.fill_(group["betas"][1]) - self._adamw_eps_t.fill_(group["eps"]) - self._adamw_wd_t.fill_(group["weight_decay"]) - adamw_step_fused( - p, grad, state["exp_avg"], state["exp_avg_sq"], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, - ) - - def _step_muon(self, group): - params = group["params"] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - if "second_momentum_buffer" not in state: - state_shape = ( - (num_params, shape[-2], 1) if shape[-2] >= shape[-1] - else (num_params, 1, shape[-1]) - ) - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused( - stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim, - ) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group["kind"] == "adamw": - self._step_adamw(group) - elif group["kind"] == "muon": - self._step_muon(group) - - -# --------------------------------------------------------------------------- -# Hyperparameters -# --------------------------------------------------------------------------- - -D_MODEL = 256 -N_LAYER = 4 -D_STATE = 64 -HEADDIM = 32 -N_HEADS = D_MODEL // HEADDIM -EXPAND = 2 -MHC_N_STREAMS = 4 -MHC_SINKHORN_ITERS = 5 -ENGRAM_N_COLUMNS = 4096 -ENGRAM_KEY_DIM = 64 -ENGRAM_LAYER_IDX = 1 -HESTIA_ENABLED = True -HESTIA_BITS = 1.58 - -# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential -# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get -# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. -# The autoresearch agent can increase this if it finds faster architectures. -TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) -DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB -MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) -EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch -UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch -SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch -WEIGHT_DECAY = 0.2 -ADAM_BETAS = (0.8, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = 0.0 - -# --------------------------------------------------------------------------- -# Setup -# --------------------------------------------------------------------------- - -t_start = time.time() -torch.manual_seed(42) -torch.cuda.manual_seed(42) -torch.set_float32_matmul_precision("high") -device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -RTX3060_FP32_PEAK_FLOPS = 12.74e12 - -tokenizer = Tokenizer.from_directory() -vocab_size = tokenizer.get_vocab_size() -print(f"Vocab size: {vocab_size:,}") - -config = Mamba3HestiaConfig( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, - mhc_n_streams=MHC_N_STREAMS, - mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, - engram_n_columns=ENGRAM_N_COLUMNS, - engram_key_dim=ENGRAM_KEY_DIM, - engram_layer_idx=ENGRAM_LAYER_IDX, - hestia_enabled=HESTIA_ENABLED, - hestia_bits=HESTIA_BITS, -) -print(f"Model config: {asdict(config)}") - -with torch.device("meta"): - model = Mamba3HestiaModel(config) -model.to_empty(device=device) -model.init_weights() - -param_counts = model.num_scaling_params() -print("Parameter counts:") -for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") -num_params = param_counts["total"] -num_flops_per_token = model.estimate_flops() -print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN -assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 -grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - -optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, -) - -model = torch.compile(model, dynamic=False) - -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") -x, y, epoch = next(train_loader) - -print(f"Time budget: {TIME_BUDGET}s") -print(f"Gradient accumulation steps: {grad_accum_steps}") - - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - elif progress < 1.0 - WARMDOWN_RATIO: - return 1.0 - else: - cooldown = (1.0 - progress) / WARMDOWN_RATIO - return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- -# Training loop -# --------------------------------------------------------------------------- - -t_start_training = time.time() -smooth_train_loss = 0.0 -total_training_time = 0.0 -step = 0 - -# Unwrap for Hestia QAT access (torch.compile wraps the module) -_raw_model = model - -while True: - torch.cuda.synchronize() - t0 = time.time() - for micro_step in range(grad_accum_steps): - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - x, y, epoch = next(train_loader) - - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group["kind"] == "muon": - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - optimizer.step() - model.zero_grad(set_to_none=True) - - # Hestia temperature annealing (after each optimizer step) - if hasattr(_raw_model, "_orig_mod"): - _raw_model._orig_mod.hestia.anneal_temperature(progress) - elif hasattr(_raw_model, "hestia"): - _raw_model.hestia.anneal_temperature(progress) - - train_loss_f = train_loss.item() - - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - exit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - print( - f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " - f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " - f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", - end="", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - elif (step + 1) % 5000 == 0: - gc.collect() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - -print() - -total_tokens = step * TOTAL_BATCH_SIZE - -model.eval() -with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - -t_end = time.time() -steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS - if total_training_time > 0 else 0 -) -peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - -metrics = model.get_secondary_metrics() if hasattr(model, "get_secondary_metrics") else {} - -print("---") -print(f"val_bpb: {val_bpb:.6f}") -print(f"training_seconds: {total_training_time:.1f}") -print(f"total_seconds: {t_end - t_start:.1f}") -print(f"peak_vram_mb: {peak_vram_mb:.1f}") -print(f"mfu_percent: {steady_state_mfu:.2f}") -print(f"total_tokens_M: {total_tokens / 1e6:.1f}") -print(f"num_steps: {step}") -print(f"num_params_M: {num_params / 1e6:.1f}") -print(f"n_layer: {N_LAYER}") -print(f"d_model: {D_MODEL}") -print(f"hestia_enabled: {HESTIA_ENABLED}") -print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") -print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") -print(f"hestia_quant_error: {metrics.get('hestia_quant_error', 0.0):.6f}") +""" +Subsystem bring-up: Mamba-3 + mHC + Engram + Hestia QAT. +Branch: autoresearch/phase1-hestia + +Adds HestiaQAT (quantization-aware training) with temperature annealing. +No SDR. +""" + +import os +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class Mamba3HestiaConfig: + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 + + # Mamba-3 SSM + n_layer: int = 4 + d_model: int = 256 + d_state: int = 64 + headdim: int = 32 + n_heads: int = 8 + expand: int = 2 + + # mHC + mhc_n_streams: int = 4 + mhc_sinkhorn_iters: int = 5 + + # Engram + engram_n_columns: int = 4096 + engram_key_dim: int = 64 + engram_layer_idx: int = 1 + + # Hestia QAT (ENABLED in this subsystem) + hestia_enabled: bool = True + hestia_bits: float = 1.58 + + +# --------------------------------------------------------------------------- +# Utility Functions +# --------------------------------------------------------------------------- + +def norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +def complex_rope_freqs( + seq_len: int, + headdim: int, + base: float = 10000.0, + device: torch.device | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + half = headdim // 2 + freqs = 1.0 / ( + base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) + ) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + angles = torch.outer(t, freqs) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + return cos, sin + + +def apply_rope_ssm( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + cos = cos[: x.shape[-2]] + sin = sin[: x.shape[-2]] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], dim=-1) + + +# --------------------------------------------------------------------------- +# Mamba-3 SSM Block +# --------------------------------------------------------------------------- + +class BCNorm(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) + + +class Mamba3Block(nn.Module): + def __init__(self, config: Mamba3HestiaConfig) -> None: + super().__init__() + self.d_model = config.d_model + self.d_state = config.d_state + self.headdim = config.headdim + self.n_heads = config.n_heads + inner_dim = config.expand * config.d_model + + self.in_proj = nn.Linear( + config.d_model, + inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, + bias=False, + ) + self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) + self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) + self.D = nn.Parameter(torch.ones(config.n_heads)) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + self.bc_norm = BCNorm(config.d_state) + self.conv1d = nn.Conv1d( + inner_dim, inner_dim, + kernel_size=4, padding=3, + groups=inner_dim, bias=True, + ) + + def forward( + self, + x: torch.Tensor, + cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, + ) -> torch.Tensor: + B, T, D = x.shape + inner_dim = self.d_model * 2 + + proj = self.in_proj(x) + z = proj[..., :inner_dim] + x_ssm = proj[..., inner_dim : 2 * inner_dim] + B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] + C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] + dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] + + x_ssm = x_ssm.transpose(1, 2) + x_ssm = self.conv1d(x_ssm)[..., :T] + x_ssm = x_ssm.transpose(1, 2) + x_ssm = F.silu(x_ssm) + + B_proj = self.bc_norm(B_proj) + C_proj = self.bc_norm(C_proj) + + if cos_sin is not None: + cos, sin = cos_sin + B_proj = apply_rope_ssm(B_proj, cos, sin) + C_proj = apply_rope_ssm(C_proj, cos, sin) + + A = -torch.exp(self.A_log) + dt = F.softplus(dt_proj) + x_heads = x_ssm.view(B, T, self.n_heads, -1) + alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) + Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) + + lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) + + h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) + Bx_prev = torch.zeros_like(Bx[:, 0]) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1) + Bx_t = Bx[:, t] + h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + Bx_prev = Bx_t + C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) + y_t = (C_t * h).sum(dim=-1) + y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) + y_list.append(y_t) + + y_ssm = torch.stack(y_list, dim=1) + y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) + y_ssm = y_ssm.reshape(B, T, inner_dim) + y = y_ssm * F.silu(z) + y = self.out_proj(y) + return y + + +# --------------------------------------------------------------------------- +# Manifold Hyper-Connection (mHC) +# --------------------------------------------------------------------------- + +class ManifoldHyperConnection(nn.Module): + def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: + super().__init__() + self.n_streams = n_streams + self.d_model = d_model + self.sinkhorn_iters = sinkhorn_iters + self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) + self.stream_norms = nn.ModuleList([ + nn.LayerNorm(d_model) for _ in range(n_streams) + ]) + + def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: + M = log_alpha + for _ in range(self.sinkhorn_iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: + M = self._sinkhorn(self.log_alpha) + mixed = torch.einsum("ij,jbtd->ibtd", M, streams) + primary_input = mixed[0] + primary_input = self.stream_norms[0](primary_input) + block_output = block_fn(primary_input) + M_T = M.t() + update = torch.zeros_like(streams) + update[0] = block_output + streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) + return streams + + def init_streams(self, x: torch.Tensor) -> torch.Tensor: + return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() + + def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: + return streams.mean(dim=0) + + +# --------------------------------------------------------------------------- +# Engram Module +# --------------------------------------------------------------------------- + +class EngramModule(nn.Module): + def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: + super().__init__() + self.d_model = d_model + self.n_columns = n_columns + self.key_dim = key_dim + + self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) + self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) + self.key_proj = nn.Linear(d_model, key_dim, bias=False) + self.gate_proj = nn.Linear(d_model, 1, bias=True) + nn.init.constant_(self.gate_proj.bias, -2.0) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: + B, T, D = x.shape + query = self.key_proj(x) + sim = torch.matmul(query, self.memory_keys.t()) + attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) + retrieved = torch.matmul(attn, self.memory_values) + alpha = torch.sigmoid(self.gate_proj(x)) + output = x + alpha * retrieved + hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() + return output, hit_rate + + +# --------------------------------------------------------------------------- +# Hestia QAT +# --------------------------------------------------------------------------- + +class HestiaQAT(nn.Module): + """ + Hestia Quantization-Aware Training. + + Ternary quantization ({-1, 0, +1}) with straight-through estimator. + Temperature annealing drives weights toward discrete values over training. + """ + + def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: + super().__init__() + self.enabled = enabled + self.bits = bits + self.temperature = nn.Parameter(torch.tensor(1.0), requires_grad=False) + + def quantize_weight(self, w: torch.Tensor) -> torch.Tensor: + """Ternary quantization with straight-through estimator.""" + if not self.enabled: + return w + scale = w.abs().mean() + w_ternary = torch.sign(w) * (w.abs() > 0.5 * scale).float() * scale + return w + (w_ternary - w).detach() + + def forward(self, module: nn.Module) -> None: + """Apply quantization to all weight matrices in module.""" + if not self.enabled: + return + for name, param in module.named_parameters(): + if "weight" in name and param.dim() >= 2: + param.data = self.quantize_weight(param.data) + + def get_quant_error(self, module: nn.Module) -> float: + """Compute MSE between full-precision and quantized weights.""" + if not self.enabled: + return 0.0 + total_mse = 0.0 + count = 0 + for name, param in module.named_parameters(): + if "weight" in name and param.dim() >= 2: + q = self.quantize_weight(param.data) + total_mse += F.mse_loss(q, param.data).item() + count += 1 + return total_mse / max(count, 1) + + def anneal_temperature(self, progress: float) -> None: + """Anneal temperature from 1.0 to 0.1 over training.""" + if not self.enabled: + return + new_temp = 1.0 - 0.9 * progress + self.temperature.fill_(max(new_temp, 0.1)) + + +# --------------------------------------------------------------------------- +# Mamba3HestiaModel +# --------------------------------------------------------------------------- + +class Mamba3HestiaModel(nn.Module): + """ + Mamba-3 + mHC + Engram + Hestia QAT. No SDR. + + Architecture: + Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer + (+ Engram at engram_layer_idx) -> merge_streams -> norm -> LM head + Hestia QAT applied after each optimizer step. + """ + + def __init__(self, config: Mamba3HestiaConfig) -> None: + super().__init__() + self.config = config + + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) + self.mhc_layers = nn.ModuleList([ + ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) + for _ in range(config.n_layer) + ]) + self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) + self.engram_layer_idx = config.engram_layer_idx + self.hestia = HestiaQAT(enabled=config.hestia_enabled, bits=config.hestia_bits) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.softcap = 30.0 + + self.rope_seq_len = config.sequence_len * 2 + cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) + self.register_buffer("rope_cos", cos, persistent=False) + self.register_buffer("rope_sin", sin, persistent=False) + + self._metrics: dict = {} + + @torch.no_grad() + def init_weights(self) -> None: + s = 3**0.5 * self.config.d_model**-0.5 + nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + for block in self.blocks: + nn.init.uniform_(block.in_proj.weight, -s, s) + nn.init.zeros_(block.out_proj.weight) + nn.init.ones_(block.conv1d.weight) + nn.init.zeros_(block.conv1d.bias) + for mhc in self.mhc_layers: + nn.init.eye_(mhc.log_alpha.data) + self.wte.to(dtype=torch.bfloat16) + + def estimate_flops(self) -> float: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict[str, int]: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + mhc = sum(p.numel() for p in self.mhc_layers.parameters()) + engram = sum(p.numel() for p in self.engram.parameters()) + total = sum(p.numel() for p in self.parameters()) + return { + "wte": wte, "lm_head": lm_head, "blocks": blocks, + "mhc": mhc, "engram": engram, "total": total, + } + + def get_secondary_metrics(self) -> dict: + return self._metrics + + def setup_optimizer( + self, + unembedding_lr: float = 0.004, + embedding_lr: float = 0.6, + matrix_lr: float = 0.04, + weight_decay: float = 0.2, + adam_betas: tuple[float, float] = (0.8, 0.95), + scalar_lr: float = 0.5, + ) -> "MuonAdamW": + model_dim = self.config.d_model + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + matrix_params = [] + for p in self.blocks.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.mhc_layers.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.engram.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + scalar_params = [p for p in self.parameters() if id(p) not in assigned] + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind="adamw", params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind="adamw", params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + if scalar_params: + param_groups.append( + dict(kind="adamw", params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append(dict( + kind="muon", params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward( + self, + idx: torch.Tensor, + targets: torch.Tensor | None = None, + reduction: str = "mean", + ) -> torch.Tensor: + B, T = idx.shape + cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) + + x = self.wte(idx) + x = norm(x) + + streams = self.mhc_layers[0].init_streams(x) + spectral_norms = [] + + for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): + def block_fn(inp, _block=block, _cos_sin=cos_sin): + return _block(inp, cos_sin=_cos_sin) + + streams = mhc(streams, block_fn) + + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + spectral_norms.append(torch.linalg.norm(M, ord=2).item()) + + if i == self.engram_layer_idx: + primary = streams[0] + primary, hit_rate = self.engram(primary) + streams[0] = primary + self._metrics["engram_hit_rate"] = hit_rate + + x = self.mhc_layers[-1].merge_streams(streams) + x = norm(x) + + self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 + self._metrics["hestia_quant_error"] = self.hestia.get_quant_error(self) + + logits = self.lm_head(x) + logits = logits.float() + logits = self.softcap * torch.tanh(logits / self.softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, +): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, grad, state["exp_avg"], state["exp_avg_sq"], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters +# --------------------------------------------------------------------------- + +D_MODEL = 256 +N_LAYER = 4 +D_STATE = 64 +HEADDIM = 32 +N_HEADS = D_MODEL // HEADDIM +EXPAND = 2 +MHC_N_STREAMS = 4 +MHC_SINKHORN_ITERS = 5 +ENGRAM_N_COLUMNS = 4096 +ENGRAM_KEY_DIM = 64 +ENGRAM_LAYER_IDX = 1 +HESTIA_ENABLED = True +HESTIA_BITS = 1.58 + +# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential +# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get +# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. +# The autoresearch agent can increase this if it finds faster architectures. +TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) +DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB +MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) +EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch +UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch +SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch +WEIGHT_DECAY = 0.2 +ADAM_BETAS = (0.8, 0.95) +WARMUP_RATIO = 0.0 +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = 0.0 + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +RTX3060_FP32_PEAK_FLOPS = 12.74e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + +config = Mamba3HestiaConfig( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, + mhc_n_streams=MHC_N_STREAMS, + mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, + engram_n_columns=ENGRAM_N_COLUMNS, + engram_key_dim=ENGRAM_KEY_DIM, + engram_layer_idx=ENGRAM_LAYER_IDX, + hestia_enabled=HESTIA_ENABLED, + hestia_bits=HESTIA_BITS, +) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = Mamba3HestiaModel(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0.0 +total_training_time = 0.0 +step = 0 + +# Unwrap for Hestia QAT access (torch.compile wraps the module) +_raw_model = model + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + # Hestia temperature annealing (after each optimizer step) + if hasattr(_raw_model, "_orig_mod"): + _raw_model._orig_mod.hestia.anneal_temperature(progress) + elif hasattr(_raw_model, "hestia"): + _raw_model.hestia.anneal_temperature(progress) + + train_loss_f = train_loss.item() + + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " + f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " + f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() + +total_tokens = step * TOTAL_BATCH_SIZE + +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +t_end = time.time() +steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS + if total_training_time > 0 else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +metrics = model.get_secondary_metrics() if hasattr(model, "get_secondary_metrics") else {} + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"n_layer: {N_LAYER}") +print(f"d_model: {D_MODEL}") +print(f"hestia_enabled: {HESTIA_ENABLED}") +print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") +print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") +print(f"hestia_quant_error: {metrics.get('hestia_quant_error', 0.0):.6f}") diff --git a/overlay/subsystems/train_mamba3.py b/overlay/subsystems/train_mamba3.py index 1845dfc0423ac6904a4eb074bc572b1e92a503c6..04afabed5c611f0a74e7906b6bf7b734aaa2bf5e 100644 --- a/overlay/subsystems/train_mamba3.py +++ b/overlay/subsystems/train_mamba3.py @@ -1,685 +1,685 @@ -""" -Subsystem bring-up: Mamba-3 SSM backbone only. -Branch: autoresearch/phase1-mamba3 - -No mHC, no Engram, no Hestia, no SDR. -Standard residual connections: x = x + block(norm(x)) -""" - -import os -os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import gc -import math -import time -from dataclasses import dataclass, asdict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class Mamba3Config: - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 - - # Mamba-3 SSM - n_layer: int = 4 - d_model: int = 256 - d_state: int = 64 - headdim: int = 32 - n_heads: int = 8 # d_model // headdim - expand: int = 2 # inner_dim = expand * d_model - - -# --------------------------------------------------------------------------- -# Utility Functions -# --------------------------------------------------------------------------- - -def norm(x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, (x.size(-1),)) - - -def complex_rope_freqs( - seq_len: int, - headdim: int, - base: float = 10000.0, - device: torch.device | None = None, -) -> tuple[torch.Tensor, torch.Tensor]: - """Precompute complex-valued RoPE frequencies for SSM.""" - half = headdim // 2 - freqs = 1.0 / ( - base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) - ) - t = torch.arange(seq_len, dtype=torch.float32, device=device) - angles = torch.outer(t, freqs) # (seq_len, half) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - return cos, sin # each (seq_len, headdim//2) - - -def apply_rope_ssm( - x: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, -) -> torch.Tensor: - """Apply RoPE to SSM B/C projections. x: (B, T, d_state), cos/sin: (T, d_state//2).""" - d = x.shape[-1] // 2 - x1, x2 = x[..., :d], x[..., d:] - cos = cos[: x.shape[-2]] - sin = sin[: x.shape[-2]] - y1 = x1 * cos + x2 * sin - y2 = x1 * (-sin) + x2 * cos - return torch.cat([y1, y2], dim=-1) - - -# --------------------------------------------------------------------------- -# Mamba-3 SSM Block -# --------------------------------------------------------------------------- - -class BCNorm(nn.Module): - """Batch-Channel Normalization for SSM states.""" - - def __init__(self, dim: int) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.bias = nn.Parameter(torch.zeros(dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) - - -class Mamba3Block(nn.Module): - """ - Mamba-3 SSM block with exponential-trapezoidal discretization. - - Pure PyTorch eager implementation. - Recurrence: h[t] = alpha * h[t-1] + beta_0 * (B[t]*x[t]) + beta_1 * (B[t-1]*x[t-1]) - """ - - def __init__(self, config: Mamba3Config) -> None: - super().__init__() - self.d_model = config.d_model - self.d_state = config.d_state - self.headdim = config.headdim - self.n_heads = config.n_heads - inner_dim = config.expand * config.d_model - - self.in_proj = nn.Linear( - config.d_model, - inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, - bias=False, - ) - - self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) - self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) - self.D = nn.Parameter(torch.ones(config.n_heads)) - self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) - self.bc_norm = BCNorm(config.d_state) - - self.conv1d = nn.Conv1d( - inner_dim, inner_dim, - kernel_size=4, padding=3, - groups=inner_dim, bias=True, - ) - - def forward( - self, - x: torch.Tensor, - cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - """x: (B, T, d_model) -> (B, T, d_model)""" - B, T, D = x.shape - inner_dim = self.d_model * 2 # expand=2 - - proj = self.in_proj(x) - - z = proj[..., :inner_dim] - x_ssm = proj[..., inner_dim : 2 * inner_dim] - B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] - C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] - dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] - - x_ssm = x_ssm.transpose(1, 2) - x_ssm = self.conv1d(x_ssm)[..., :T] - x_ssm = x_ssm.transpose(1, 2) - x_ssm = F.silu(x_ssm) - - B_proj = self.bc_norm(B_proj) - C_proj = self.bc_norm(C_proj) - - if cos_sin is not None: - cos, sin = cos_sin - B_proj = apply_rope_ssm(B_proj, cos, sin) - C_proj = apply_rope_ssm(C_proj, cos, sin) - - A = -torch.exp(self.A_log) - dt = F.softplus(dt_proj) - - x_heads = x_ssm.view(B, T, self.n_heads, -1) - - alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) - - Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) - - lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) - - h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) - Bx_prev = torch.zeros_like(Bx[:, 0]) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1) - Bx_t = Bx[:, t] - - h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - Bx_prev = Bx_t - - C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) - y_t = (C_t * h).sum(dim=-1) - y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) - y_list.append(y_t) - - y_ssm = torch.stack(y_list, dim=1) # (B, T, n_heads) - - y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) - y_ssm = y_ssm.reshape(B, T, inner_dim) - - y = y_ssm * F.silu(z) - y = self.out_proj(y) - return y - - -# --------------------------------------------------------------------------- -# Mamba3Model (SSM only, standard residual) -# --------------------------------------------------------------------------- - -class Mamba3Model(nn.Module): - """ - Mamba-3 SSM backbone only. No mHC, no Engram, no Hestia, no SDR. - - Architecture: - Token Embedding -> norm -> [norm -> Mamba3Block -> residual] x n_layer -> norm -> LM head - - Interface: - model(x, y, reduction='none').view(-1) -> per-token losses - model(x, y, reduction='mean') -> scalar loss - """ - - def __init__(self, config: Mamba3Config) -> None: - super().__init__() - self.config = config - - self.wte = nn.Embedding(config.vocab_size, config.d_model) - self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - self.softcap = 30.0 - - self.rope_seq_len = config.sequence_len * 2 - cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) - self.register_buffer("rope_cos", cos, persistent=False) - self.register_buffer("rope_sin", sin, persistent=False) - - @torch.no_grad() - def init_weights(self) -> None: - s = 3**0.5 * self.config.d_model**-0.5 - nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) - for block in self.blocks: - nn.init.uniform_(block.in_proj.weight, -s, s) - nn.init.zeros_(block.out_proj.weight) - nn.init.ones_(block.conv1d.weight) - nn.init.zeros_(block.conv1d.bias) - self.wte.to(dtype=torch.bfloat16) - - def estimate_flops(self) -> float: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict[str, int]: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - total = sum(p.numel() for p in self.parameters()) - return {"wte": wte, "lm_head": lm_head, "blocks": blocks, "total": total} - - def setup_optimizer( - self, - unembedding_lr: float = 0.004, - embedding_lr: float = 0.6, - matrix_lr: float = 0.04, - weight_decay: float = 0.2, - adam_betas: tuple[float, float] = (0.8, 0.95), - scalar_lr: float = 0.5, - ) -> "MuonAdamW": - model_dim = self.config.d_model - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - matrix_params = [p for p in self.blocks.parameters() if p.dim() >= 2] - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [p for p in self.parameters() if id(p) not in assigned] - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind="adamw", params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind="adamw", params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - if scalar_params: - param_groups.append( - dict(kind="adamw", params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - param_groups.append(dict( - kind="muon", params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward( - self, - idx: torch.Tensor, - targets: torch.Tensor | None = None, - reduction: str = "mean", - ) -> torch.Tensor: - B, T = idx.shape - cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) - - x = self.wte(idx) - x = norm(x) - - for block in self.blocks: - x = x + block(norm(x), cos_sin=cos_sin) - - x = norm(x) - - logits = self.lm_head(x) - logits = logits.float() - logits = self.softcap * torch.tanh(logits / self.softcap) - - if targets is not None: - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.view(-1), - ignore_index=-1, - reduction=reduction, - ) - return loss - return logits - - -# --------------------------------------------------------------------------- -# Optimizer (MuonAdamW) -# --------------------------------------------------------------------------- - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused( - stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, -): - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - beta2 = beta2_t.to(g.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p) - state["exp_avg_sq"] = torch.zeros_like(p) - state["step"] += 1 - self._adamw_step_t.fill_(state["step"]) - self._adamw_lr_t.fill_(group["lr"]) - self._adamw_beta1_t.fill_(group["betas"][0]) - self._adamw_beta2_t.fill_(group["betas"][1]) - self._adamw_eps_t.fill_(group["eps"]) - self._adamw_wd_t.fill_(group["weight_decay"]) - adamw_step_fused( - p, grad, state["exp_avg"], state["exp_avg_sq"], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, - ) - - def _step_muon(self, group): - params = group["params"] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - if "second_momentum_buffer" not in state: - state_shape = ( - (num_params, shape[-2], 1) if shape[-2] >= shape[-1] - else (num_params, 1, shape[-1]) - ) - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused( - stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim, - ) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group["kind"] == "adamw": - self._step_adamw(group) - elif group["kind"] == "muon": - self._step_muon(group) - - -# --------------------------------------------------------------------------- -# Hyperparameters (the autoresearch agent modifies these) -# --------------------------------------------------------------------------- - -D_MODEL = 256 -N_LAYER = 4 -D_STATE = 64 -HEADDIM = 32 -N_HEADS = D_MODEL // HEADDIM # 8 -EXPAND = 2 - -# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential -# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get -# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. -# The autoresearch agent can increase this if it finds faster architectures. -TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) -DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB -MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) -EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch -UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch -SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch -WEIGHT_DECAY = 0.2 -ADAM_BETAS = (0.8, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = 0.0 - -# --------------------------------------------------------------------------- -# Setup -# --------------------------------------------------------------------------- - -t_start = time.time() -torch.manual_seed(42) -torch.cuda.manual_seed(42) -torch.set_float32_matmul_precision("high") -device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -RTX3060_FP32_PEAK_FLOPS = 12.74e12 - -tokenizer = Tokenizer.from_directory() -vocab_size = tokenizer.get_vocab_size() -print(f"Vocab size: {vocab_size:,}") - -config = Mamba3Config( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, -) -print(f"Model config: {asdict(config)}") - -with torch.device("meta"): - model = Mamba3Model(config) -model.to_empty(device=device) -model.init_weights() - -param_counts = model.num_scaling_params() -print("Parameter counts:") -for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") -num_params = param_counts["total"] -num_flops_per_token = model.estimate_flops() -print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN -assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 -grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - -optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, -) - -model = torch.compile(model, dynamic=False) - -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") -x, y, epoch = next(train_loader) - -print(f"Time budget: {TIME_BUDGET}s") -print(f"Gradient accumulation steps: {grad_accum_steps}") - - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - elif progress < 1.0 - WARMDOWN_RATIO: - return 1.0 - else: - cooldown = (1.0 - progress) / WARMDOWN_RATIO - return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- -# Training loop -# --------------------------------------------------------------------------- - -t_start_training = time.time() -smooth_train_loss = 0.0 -total_training_time = 0.0 -step = 0 - -while True: - torch.cuda.synchronize() - t0 = time.time() - for micro_step in range(grad_accum_steps): - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - x, y, epoch = next(train_loader) - - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group["kind"] == "muon": - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - optimizer.step() - model.zero_grad(set_to_none=True) - - train_loss_f = train_loss.item() - - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - exit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - print( - f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " - f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " - f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", - end="", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - elif (step + 1) % 5000 == 0: - gc.collect() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - -print() - -total_tokens = step * TOTAL_BATCH_SIZE - -model.eval() -with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - -t_end = time.time() -steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS - if total_training_time > 0 else 0 -) -peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - -print("---") -print(f"val_bpb: {val_bpb:.6f}") -print(f"training_seconds: {total_training_time:.1f}") -print(f"total_seconds: {t_end - t_start:.1f}") -print(f"peak_vram_mb: {peak_vram_mb:.1f}") -print(f"mfu_percent: {steady_state_mfu:.2f}") -print(f"total_tokens_M: {total_tokens / 1e6:.1f}") -print(f"num_steps: {step}") -print(f"num_params_M: {num_params / 1e6:.1f}") -print(f"n_layer: {N_LAYER}") -print(f"d_model: {D_MODEL}") +""" +Subsystem bring-up: Mamba-3 SSM backbone only. +Branch: autoresearch/phase1-mamba3 + +No mHC, no Engram, no Hestia, no SDR. +Standard residual connections: x = x + block(norm(x)) +""" + +import os +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class Mamba3Config: + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 + + # Mamba-3 SSM + n_layer: int = 4 + d_model: int = 256 + d_state: int = 64 + headdim: int = 32 + n_heads: int = 8 # d_model // headdim + expand: int = 2 # inner_dim = expand * d_model + + +# --------------------------------------------------------------------------- +# Utility Functions +# --------------------------------------------------------------------------- + +def norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +def complex_rope_freqs( + seq_len: int, + headdim: int, + base: float = 10000.0, + device: torch.device | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + """Precompute complex-valued RoPE frequencies for SSM.""" + half = headdim // 2 + freqs = 1.0 / ( + base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) + ) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + angles = torch.outer(t, freqs) # (seq_len, half) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + return cos, sin # each (seq_len, headdim//2) + + +def apply_rope_ssm( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + """Apply RoPE to SSM B/C projections. x: (B, T, d_state), cos/sin: (T, d_state//2).""" + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + cos = cos[: x.shape[-2]] + sin = sin[: x.shape[-2]] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], dim=-1) + + +# --------------------------------------------------------------------------- +# Mamba-3 SSM Block +# --------------------------------------------------------------------------- + +class BCNorm(nn.Module): + """Batch-Channel Normalization for SSM states.""" + + def __init__(self, dim: int) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) + + +class Mamba3Block(nn.Module): + """ + Mamba-3 SSM block with exponential-trapezoidal discretization. + + Pure PyTorch eager implementation. + Recurrence: h[t] = alpha * h[t-1] + beta_0 * (B[t]*x[t]) + beta_1 * (B[t-1]*x[t-1]) + """ + + def __init__(self, config: Mamba3Config) -> None: + super().__init__() + self.d_model = config.d_model + self.d_state = config.d_state + self.headdim = config.headdim + self.n_heads = config.n_heads + inner_dim = config.expand * config.d_model + + self.in_proj = nn.Linear( + config.d_model, + inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, + bias=False, + ) + + self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) + self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) + self.D = nn.Parameter(torch.ones(config.n_heads)) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + self.bc_norm = BCNorm(config.d_state) + + self.conv1d = nn.Conv1d( + inner_dim, inner_dim, + kernel_size=4, padding=3, + groups=inner_dim, bias=True, + ) + + def forward( + self, + x: torch.Tensor, + cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, + ) -> torch.Tensor: + """x: (B, T, d_model) -> (B, T, d_model)""" + B, T, D = x.shape + inner_dim = self.d_model * 2 # expand=2 + + proj = self.in_proj(x) + + z = proj[..., :inner_dim] + x_ssm = proj[..., inner_dim : 2 * inner_dim] + B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] + C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] + dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] + + x_ssm = x_ssm.transpose(1, 2) + x_ssm = self.conv1d(x_ssm)[..., :T] + x_ssm = x_ssm.transpose(1, 2) + x_ssm = F.silu(x_ssm) + + B_proj = self.bc_norm(B_proj) + C_proj = self.bc_norm(C_proj) + + if cos_sin is not None: + cos, sin = cos_sin + B_proj = apply_rope_ssm(B_proj, cos, sin) + C_proj = apply_rope_ssm(C_proj, cos, sin) + + A = -torch.exp(self.A_log) + dt = F.softplus(dt_proj) + + x_heads = x_ssm.view(B, T, self.n_heads, -1) + + alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) + + Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) + + lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) + + h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) + Bx_prev = torch.zeros_like(Bx[:, 0]) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1) + Bx_t = Bx[:, t] + + h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + Bx_prev = Bx_t + + C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) + y_t = (C_t * h).sum(dim=-1) + y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) + y_list.append(y_t) + + y_ssm = torch.stack(y_list, dim=1) # (B, T, n_heads) + + y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) + y_ssm = y_ssm.reshape(B, T, inner_dim) + + y = y_ssm * F.silu(z) + y = self.out_proj(y) + return y + + +# --------------------------------------------------------------------------- +# Mamba3Model (SSM only, standard residual) +# --------------------------------------------------------------------------- + +class Mamba3Model(nn.Module): + """ + Mamba-3 SSM backbone only. No mHC, no Engram, no Hestia, no SDR. + + Architecture: + Token Embedding -> norm -> [norm -> Mamba3Block -> residual] x n_layer -> norm -> LM head + + Interface: + model(x, y, reduction='none').view(-1) -> per-token losses + model(x, y, reduction='mean') -> scalar loss + """ + + def __init__(self, config: Mamba3Config) -> None: + super().__init__() + self.config = config + + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.softcap = 30.0 + + self.rope_seq_len = config.sequence_len * 2 + cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) + self.register_buffer("rope_cos", cos, persistent=False) + self.register_buffer("rope_sin", sin, persistent=False) + + @torch.no_grad() + def init_weights(self) -> None: + s = 3**0.5 * self.config.d_model**-0.5 + nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + for block in self.blocks: + nn.init.uniform_(block.in_proj.weight, -s, s) + nn.init.zeros_(block.out_proj.weight) + nn.init.ones_(block.conv1d.weight) + nn.init.zeros_(block.conv1d.bias) + self.wte.to(dtype=torch.bfloat16) + + def estimate_flops(self) -> float: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict[str, int]: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + total = sum(p.numel() for p in self.parameters()) + return {"wte": wte, "lm_head": lm_head, "blocks": blocks, "total": total} + + def setup_optimizer( + self, + unembedding_lr: float = 0.004, + embedding_lr: float = 0.6, + matrix_lr: float = 0.04, + weight_decay: float = 0.2, + adam_betas: tuple[float, float] = (0.8, 0.95), + scalar_lr: float = 0.5, + ) -> "MuonAdamW": + model_dim = self.config.d_model + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + matrix_params = [p for p in self.blocks.parameters() if p.dim() >= 2] + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + scalar_params = [p for p in self.parameters() if id(p) not in assigned] + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind="adamw", params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind="adamw", params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + if scalar_params: + param_groups.append( + dict(kind="adamw", params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append(dict( + kind="muon", params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward( + self, + idx: torch.Tensor, + targets: torch.Tensor | None = None, + reduction: str = "mean", + ) -> torch.Tensor: + B, T = idx.shape + cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) + + x = self.wte(idx) + x = norm(x) + + for block in self.blocks: + x = x + block(norm(x), cos_sin=cos_sin) + + x = norm(x) + + logits = self.lm_head(x) + logits = logits.float() + logits = self.softcap * torch.tanh(logits / self.softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, +): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, grad, state["exp_avg"], state["exp_avg_sq"], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters (the autoresearch agent modifies these) +# --------------------------------------------------------------------------- + +D_MODEL = 256 +N_LAYER = 4 +D_STATE = 64 +HEADDIM = 32 +N_HEADS = D_MODEL // HEADDIM # 8 +EXPAND = 2 + +# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential +# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get +# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. +# The autoresearch agent can increase this if it finds faster architectures. +TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) +DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB +MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) +EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch +UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch +SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch +WEIGHT_DECAY = 0.2 +ADAM_BETAS = (0.8, 0.95) +WARMUP_RATIO = 0.0 +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = 0.0 + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +RTX3060_FP32_PEAK_FLOPS = 12.74e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + +config = Mamba3Config( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, +) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = Mamba3Model(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0.0 +total_training_time = 0.0 +step = 0 + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + train_loss_f = train_loss.item() + + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " + f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " + f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() + +total_tokens = step * TOTAL_BATCH_SIZE + +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +t_end = time.time() +steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS + if total_training_time > 0 else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"n_layer: {N_LAYER}") +print(f"d_model: {D_MODEL}") diff --git a/overlay/subsystems/train_mhc.py b/overlay/subsystems/train_mhc.py index a5765c461c28262c5966bca46c76a85493a76e18..2794e84da1dd5f3c807889df98ccaad0d3272a77 100644 --- a/overlay/subsystems/train_mhc.py +++ b/overlay/subsystems/train_mhc.py @@ -1,764 +1,764 @@ -""" -Subsystem bring-up: Mamba-3 + mHC routing. -Branch: autoresearch/phase1-mhc - -Adds ManifoldHyperConnection over the pure Mamba-3 backbone. -No Engram, no Hestia, no SDR. -""" - -import os -os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import gc -import math -import time -from dataclasses import dataclass, asdict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class Mamba3MhcConfig: - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 - - # Mamba-3 SSM - n_layer: int = 4 - d_model: int = 256 - d_state: int = 64 - headdim: int = 32 - n_heads: int = 8 - expand: int = 2 - - # mHC - mhc_n_streams: int = 4 - mhc_sinkhorn_iters: int = 5 - - -# --------------------------------------------------------------------------- -# Utility Functions -# --------------------------------------------------------------------------- - -def norm(x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, (x.size(-1),)) - - -def complex_rope_freqs( - seq_len: int, - headdim: int, - base: float = 10000.0, - device: torch.device | None = None, -) -> tuple[torch.Tensor, torch.Tensor]: - """Precompute complex-valued RoPE frequencies for SSM.""" - half = headdim // 2 - freqs = 1.0 / ( - base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) - ) - t = torch.arange(seq_len, dtype=torch.float32, device=device) - angles = torch.outer(t, freqs) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - return cos, sin - - -def apply_rope_ssm( - x: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, -) -> torch.Tensor: - """Apply RoPE to SSM B/C projections. x: (B, T, d_state).""" - d = x.shape[-1] // 2 - x1, x2 = x[..., :d], x[..., d:] - cos = cos[: x.shape[-2]] - sin = sin[: x.shape[-2]] - y1 = x1 * cos + x2 * sin - y2 = x1 * (-sin) + x2 * cos - return torch.cat([y1, y2], dim=-1) - - -# --------------------------------------------------------------------------- -# Mamba-3 SSM Block -# --------------------------------------------------------------------------- - -class BCNorm(nn.Module): - def __init__(self, dim: int) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.bias = nn.Parameter(torch.zeros(dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) - - -class Mamba3Block(nn.Module): - """ - Mamba-3 SSM block with exponential-trapezoidal discretization. - """ - - def __init__(self, config: Mamba3MhcConfig) -> None: - super().__init__() - self.d_model = config.d_model - self.d_state = config.d_state - self.headdim = config.headdim - self.n_heads = config.n_heads - inner_dim = config.expand * config.d_model - - self.in_proj = nn.Linear( - config.d_model, - inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, - bias=False, - ) - self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) - self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) - self.D = nn.Parameter(torch.ones(config.n_heads)) - self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) - self.bc_norm = BCNorm(config.d_state) - self.conv1d = nn.Conv1d( - inner_dim, inner_dim, - kernel_size=4, padding=3, - groups=inner_dim, bias=True, - ) - - def forward( - self, - x: torch.Tensor, - cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - B, T, D = x.shape - inner_dim = self.d_model * 2 - - proj = self.in_proj(x) - z = proj[..., :inner_dim] - x_ssm = proj[..., inner_dim : 2 * inner_dim] - B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] - C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] - dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] - - x_ssm = x_ssm.transpose(1, 2) - x_ssm = self.conv1d(x_ssm)[..., :T] - x_ssm = x_ssm.transpose(1, 2) - x_ssm = F.silu(x_ssm) - - B_proj = self.bc_norm(B_proj) - C_proj = self.bc_norm(C_proj) - - if cos_sin is not None: - cos, sin = cos_sin - B_proj = apply_rope_ssm(B_proj, cos, sin) - C_proj = apply_rope_ssm(C_proj, cos, sin) - - A = -torch.exp(self.A_log) - dt = F.softplus(dt_proj) - x_heads = x_ssm.view(B, T, self.n_heads, -1) - alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) - Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) - - lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) - - h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) - Bx_prev = torch.zeros_like(Bx[:, 0]) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1) - Bx_t = Bx[:, t] - h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - Bx_prev = Bx_t - C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) - y_t = (C_t * h).sum(dim=-1) - y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) - y_list.append(y_t) - - y_ssm = torch.stack(y_list, dim=1) - y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) - y_ssm = y_ssm.reshape(B, T, inner_dim) - y = y_ssm * F.silu(z) - y = self.out_proj(y) - return y - - -# --------------------------------------------------------------------------- -# Manifold Hyper-Connection (mHC) -# --------------------------------------------------------------------------- - -class ManifoldHyperConnection(nn.Module): - """ - Manifold-Constrained Hyper-Connections (mHC). - - Replaces simple residual with doubly-stochastic routing matrix. - n_streams parallel residual streams mixed via Sinkhorn-projected weights. - """ - - def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: - super().__init__() - self.n_streams = n_streams - self.d_model = d_model - self.sinkhorn_iters = sinkhorn_iters - self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) - self.stream_norms = nn.ModuleList([ - nn.LayerNorm(d_model) for _ in range(n_streams) - ]) - - def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: - M = log_alpha - for _ in range(self.sinkhorn_iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: - """ - streams: (n_streams, B, T, d_model) - block_fn: callable (B, T, d_model) -> (B, T, d_model) - Returns: (n_streams, B, T, d_model) - """ - M = self._sinkhorn(self.log_alpha) - mixed = torch.einsum("ij,jbtd->ibtd", M, streams) - primary_input = mixed[0] - primary_input = self.stream_norms[0](primary_input) - block_output = block_fn(primary_input) - M_T = M.t() - update = torch.zeros_like(streams) - update[0] = block_output - streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) - return streams - - def init_streams(self, x: torch.Tensor) -> torch.Tensor: - """x: (B, T, d_model) -> (n_streams, B, T, d_model)""" - return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() - - def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: - """(n_streams, B, T, d_model) -> (B, T, d_model)""" - return streams.mean(dim=0) - - -# --------------------------------------------------------------------------- -# Mamba3MhcModel -# --------------------------------------------------------------------------- - -class Mamba3MhcModel(nn.Module): - """ - Mamba-3 + mHC routing. No Engram, no Hestia, no SDR. - - Architecture: - Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer - -> merge_streams -> norm -> LM head - """ - - def __init__(self, config: Mamba3MhcConfig) -> None: - super().__init__() - self.config = config - - self.wte = nn.Embedding(config.vocab_size, config.d_model) - self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) - self.mhc_layers = nn.ModuleList([ - ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) - for _ in range(config.n_layer) - ]) - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - self.softcap = 30.0 - - self.rope_seq_len = config.sequence_len * 2 - cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) - self.register_buffer("rope_cos", cos, persistent=False) - self.register_buffer("rope_sin", sin, persistent=False) - - self._metrics: dict = {} - - @torch.no_grad() - def init_weights(self) -> None: - s = 3**0.5 * self.config.d_model**-0.5 - nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) - for block in self.blocks: - nn.init.uniform_(block.in_proj.weight, -s, s) - nn.init.zeros_(block.out_proj.weight) - nn.init.ones_(block.conv1d.weight) - nn.init.zeros_(block.conv1d.bias) - for mhc in self.mhc_layers: - nn.init.eye_(mhc.log_alpha.data) - self.wte.to(dtype=torch.bfloat16) - - def estimate_flops(self) -> float: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict[str, int]: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - mhc = sum(p.numel() for p in self.mhc_layers.parameters()) - total = sum(p.numel() for p in self.parameters()) - return {"wte": wte, "lm_head": lm_head, "blocks": blocks, "mhc": mhc, "total": total} - - def get_secondary_metrics(self) -> dict: - return self._metrics - - def setup_optimizer( - self, - unembedding_lr: float = 0.004, - embedding_lr: float = 0.6, - matrix_lr: float = 0.04, - weight_decay: float = 0.2, - adam_betas: tuple[float, float] = (0.8, 0.95), - scalar_lr: float = 0.5, - ) -> "MuonAdamW": - model_dim = self.config.d_model - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - matrix_params = [] - for p in self.blocks.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.mhc_layers.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [p for p in self.parameters() if id(p) not in assigned] - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind="adamw", params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind="adamw", params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - if scalar_params: - param_groups.append( - dict(kind="adamw", params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - param_groups.append(dict( - kind="muon", params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward( - self, - idx: torch.Tensor, - targets: torch.Tensor | None = None, - reduction: str = "mean", - ) -> torch.Tensor: - B, T = idx.shape - cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) - - x = self.wte(idx) - x = norm(x) - - streams = self.mhc_layers[0].init_streams(x) - spectral_norms = [] - - for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): - def block_fn(inp, _block=block, _cos_sin=cos_sin): - return _block(inp, cos_sin=_cos_sin) - - streams = mhc(streams, block_fn) - - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - spectral_norms.append(torch.linalg.norm(M, ord=2).item()) - - x = self.mhc_layers[-1].merge_streams(streams) - x = norm(x) - - self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 - - logits = self.lm_head(x) - logits = logits.float() - logits = self.softcap * torch.tanh(logits / self.softcap) - - if targets is not None: - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.view(-1), - ignore_index=-1, - reduction=reduction, - ) - return loss - return logits - - -# --------------------------------------------------------------------------- -# Optimizer (MuonAdamW) -# --------------------------------------------------------------------------- - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused( - stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, -): - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - beta2 = beta2_t.to(g.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p) - state["exp_avg_sq"] = torch.zeros_like(p) - state["step"] += 1 - self._adamw_step_t.fill_(state["step"]) - self._adamw_lr_t.fill_(group["lr"]) - self._adamw_beta1_t.fill_(group["betas"][0]) - self._adamw_beta2_t.fill_(group["betas"][1]) - self._adamw_eps_t.fill_(group["eps"]) - self._adamw_wd_t.fill_(group["weight_decay"]) - adamw_step_fused( - p, grad, state["exp_avg"], state["exp_avg_sq"], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, - ) - - def _step_muon(self, group): - params = group["params"] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - if "second_momentum_buffer" not in state: - state_shape = ( - (num_params, shape[-2], 1) if shape[-2] >= shape[-1] - else (num_params, 1, shape[-1]) - ) - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused( - stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim, - ) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group["kind"] == "adamw": - self._step_adamw(group) - elif group["kind"] == "muon": - self._step_muon(group) - - -# --------------------------------------------------------------------------- -# Hyperparameters -# --------------------------------------------------------------------------- - -D_MODEL = 256 -N_LAYER = 4 -D_STATE = 64 -HEADDIM = 32 -N_HEADS = D_MODEL // HEADDIM -EXPAND = 2 -MHC_N_STREAMS = 4 -MHC_SINKHORN_ITERS = 5 - -# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential -# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get -# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. -# The autoresearch agent can increase this if it finds faster architectures. -TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) -DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB -MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) -EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch -UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch -SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch -WEIGHT_DECAY = 0.2 -ADAM_BETAS = (0.8, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = 0.0 - -# --------------------------------------------------------------------------- -# Setup -# --------------------------------------------------------------------------- - -t_start = time.time() -torch.manual_seed(42) -torch.cuda.manual_seed(42) -torch.set_float32_matmul_precision("high") -device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -RTX3060_FP32_PEAK_FLOPS = 12.74e12 - -tokenizer = Tokenizer.from_directory() -vocab_size = tokenizer.get_vocab_size() -print(f"Vocab size: {vocab_size:,}") - -config = Mamba3MhcConfig( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, - mhc_n_streams=MHC_N_STREAMS, - mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, -) -print(f"Model config: {asdict(config)}") - -with torch.device("meta"): - model = Mamba3MhcModel(config) -model.to_empty(device=device) -model.init_weights() - -param_counts = model.num_scaling_params() -print("Parameter counts:") -for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") -num_params = param_counts["total"] -num_flops_per_token = model.estimate_flops() -print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN -assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 -grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - -optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, -) - -model = torch.compile(model, dynamic=False) - -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") -x, y, epoch = next(train_loader) - -print(f"Time budget: {TIME_BUDGET}s") -print(f"Gradient accumulation steps: {grad_accum_steps}") - - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - elif progress < 1.0 - WARMDOWN_RATIO: - return 1.0 - else: - cooldown = (1.0 - progress) / WARMDOWN_RATIO - return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- -# Training loop -# --------------------------------------------------------------------------- - -t_start_training = time.time() -smooth_train_loss = 0.0 -total_training_time = 0.0 -step = 0 - -while True: - torch.cuda.synchronize() - t0 = time.time() - for micro_step in range(grad_accum_steps): - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - x, y, epoch = next(train_loader) - - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group["kind"] == "muon": - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - optimizer.step() - model.zero_grad(set_to_none=True) - - train_loss_f = train_loss.item() - - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - exit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - print( - f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " - f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " - f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", - end="", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - elif (step + 1) % 5000 == 0: - gc.collect() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - -print() - -total_tokens = step * TOTAL_BATCH_SIZE - -model.eval() -with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - -t_end = time.time() -steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS - if total_training_time > 0 else 0 -) -peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - -metrics = model.get_secondary_metrics() - -print("---") -print(f"val_bpb: {val_bpb:.6f}") -print(f"training_seconds: {total_training_time:.1f}") -print(f"total_seconds: {t_end - t_start:.1f}") -print(f"peak_vram_mb: {peak_vram_mb:.1f}") -print(f"mfu_percent: {steady_state_mfu:.2f}") -print(f"total_tokens_M: {total_tokens / 1e6:.1f}") -print(f"num_steps: {step}") -print(f"num_params_M: {num_params / 1e6:.1f}") -print(f"n_layer: {N_LAYER}") -print(f"d_model: {D_MODEL}") -print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") +""" +Subsystem bring-up: Mamba-3 + mHC routing. +Branch: autoresearch/phase1-mhc + +Adds ManifoldHyperConnection over the pure Mamba-3 backbone. +No Engram, no Hestia, no SDR. +""" + +import os +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class Mamba3MhcConfig: + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 + + # Mamba-3 SSM + n_layer: int = 4 + d_model: int = 256 + d_state: int = 64 + headdim: int = 32 + n_heads: int = 8 + expand: int = 2 + + # mHC + mhc_n_streams: int = 4 + mhc_sinkhorn_iters: int = 5 + + +# --------------------------------------------------------------------------- +# Utility Functions +# --------------------------------------------------------------------------- + +def norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +def complex_rope_freqs( + seq_len: int, + headdim: int, + base: float = 10000.0, + device: torch.device | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + """Precompute complex-valued RoPE frequencies for SSM.""" + half = headdim // 2 + freqs = 1.0 / ( + base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) + ) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + angles = torch.outer(t, freqs) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + return cos, sin + + +def apply_rope_ssm( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + """Apply RoPE to SSM B/C projections. x: (B, T, d_state).""" + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + cos = cos[: x.shape[-2]] + sin = sin[: x.shape[-2]] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], dim=-1) + + +# --------------------------------------------------------------------------- +# Mamba-3 SSM Block +# --------------------------------------------------------------------------- + +class BCNorm(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) + + +class Mamba3Block(nn.Module): + """ + Mamba-3 SSM block with exponential-trapezoidal discretization. + """ + + def __init__(self, config: Mamba3MhcConfig) -> None: + super().__init__() + self.d_model = config.d_model + self.d_state = config.d_state + self.headdim = config.headdim + self.n_heads = config.n_heads + inner_dim = config.expand * config.d_model + + self.in_proj = nn.Linear( + config.d_model, + inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, + bias=False, + ) + self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) + self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) + self.D = nn.Parameter(torch.ones(config.n_heads)) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + self.bc_norm = BCNorm(config.d_state) + self.conv1d = nn.Conv1d( + inner_dim, inner_dim, + kernel_size=4, padding=3, + groups=inner_dim, bias=True, + ) + + def forward( + self, + x: torch.Tensor, + cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, + ) -> torch.Tensor: + B, T, D = x.shape + inner_dim = self.d_model * 2 + + proj = self.in_proj(x) + z = proj[..., :inner_dim] + x_ssm = proj[..., inner_dim : 2 * inner_dim] + B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] + C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] + dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] + + x_ssm = x_ssm.transpose(1, 2) + x_ssm = self.conv1d(x_ssm)[..., :T] + x_ssm = x_ssm.transpose(1, 2) + x_ssm = F.silu(x_ssm) + + B_proj = self.bc_norm(B_proj) + C_proj = self.bc_norm(C_proj) + + if cos_sin is not None: + cos, sin = cos_sin + B_proj = apply_rope_ssm(B_proj, cos, sin) + C_proj = apply_rope_ssm(C_proj, cos, sin) + + A = -torch.exp(self.A_log) + dt = F.softplus(dt_proj) + x_heads = x_ssm.view(B, T, self.n_heads, -1) + alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) + Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) + + lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) + + h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) + Bx_prev = torch.zeros_like(Bx[:, 0]) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1) + Bx_t = Bx[:, t] + h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + Bx_prev = Bx_t + C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) + y_t = (C_t * h).sum(dim=-1) + y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) + y_list.append(y_t) + + y_ssm = torch.stack(y_list, dim=1) + y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) + y_ssm = y_ssm.reshape(B, T, inner_dim) + y = y_ssm * F.silu(z) + y = self.out_proj(y) + return y + + +# --------------------------------------------------------------------------- +# Manifold Hyper-Connection (mHC) +# --------------------------------------------------------------------------- + +class ManifoldHyperConnection(nn.Module): + """ + Manifold-Constrained Hyper-Connections (mHC). + + Replaces simple residual with doubly-stochastic routing matrix. + n_streams parallel residual streams mixed via Sinkhorn-projected weights. + """ + + def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: + super().__init__() + self.n_streams = n_streams + self.d_model = d_model + self.sinkhorn_iters = sinkhorn_iters + self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) + self.stream_norms = nn.ModuleList([ + nn.LayerNorm(d_model) for _ in range(n_streams) + ]) + + def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: + M = log_alpha + for _ in range(self.sinkhorn_iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: + """ + streams: (n_streams, B, T, d_model) + block_fn: callable (B, T, d_model) -> (B, T, d_model) + Returns: (n_streams, B, T, d_model) + """ + M = self._sinkhorn(self.log_alpha) + mixed = torch.einsum("ij,jbtd->ibtd", M, streams) + primary_input = mixed[0] + primary_input = self.stream_norms[0](primary_input) + block_output = block_fn(primary_input) + M_T = M.t() + update = torch.zeros_like(streams) + update[0] = block_output + streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) + return streams + + def init_streams(self, x: torch.Tensor) -> torch.Tensor: + """x: (B, T, d_model) -> (n_streams, B, T, d_model)""" + return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() + + def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: + """(n_streams, B, T, d_model) -> (B, T, d_model)""" + return streams.mean(dim=0) + + +# --------------------------------------------------------------------------- +# Mamba3MhcModel +# --------------------------------------------------------------------------- + +class Mamba3MhcModel(nn.Module): + """ + Mamba-3 + mHC routing. No Engram, no Hestia, no SDR. + + Architecture: + Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer + -> merge_streams -> norm -> LM head + """ + + def __init__(self, config: Mamba3MhcConfig) -> None: + super().__init__() + self.config = config + + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) + self.mhc_layers = nn.ModuleList([ + ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) + for _ in range(config.n_layer) + ]) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.softcap = 30.0 + + self.rope_seq_len = config.sequence_len * 2 + cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) + self.register_buffer("rope_cos", cos, persistent=False) + self.register_buffer("rope_sin", sin, persistent=False) + + self._metrics: dict = {} + + @torch.no_grad() + def init_weights(self) -> None: + s = 3**0.5 * self.config.d_model**-0.5 + nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + for block in self.blocks: + nn.init.uniform_(block.in_proj.weight, -s, s) + nn.init.zeros_(block.out_proj.weight) + nn.init.ones_(block.conv1d.weight) + nn.init.zeros_(block.conv1d.bias) + for mhc in self.mhc_layers: + nn.init.eye_(mhc.log_alpha.data) + self.wte.to(dtype=torch.bfloat16) + + def estimate_flops(self) -> float: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict[str, int]: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + mhc = sum(p.numel() for p in self.mhc_layers.parameters()) + total = sum(p.numel() for p in self.parameters()) + return {"wte": wte, "lm_head": lm_head, "blocks": blocks, "mhc": mhc, "total": total} + + def get_secondary_metrics(self) -> dict: + return self._metrics + + def setup_optimizer( + self, + unembedding_lr: float = 0.004, + embedding_lr: float = 0.6, + matrix_lr: float = 0.04, + weight_decay: float = 0.2, + adam_betas: tuple[float, float] = (0.8, 0.95), + scalar_lr: float = 0.5, + ) -> "MuonAdamW": + model_dim = self.config.d_model + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + matrix_params = [] + for p in self.blocks.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.mhc_layers.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + scalar_params = [p for p in self.parameters() if id(p) not in assigned] + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind="adamw", params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind="adamw", params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + if scalar_params: + param_groups.append( + dict(kind="adamw", params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append(dict( + kind="muon", params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward( + self, + idx: torch.Tensor, + targets: torch.Tensor | None = None, + reduction: str = "mean", + ) -> torch.Tensor: + B, T = idx.shape + cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) + + x = self.wte(idx) + x = norm(x) + + streams = self.mhc_layers[0].init_streams(x) + spectral_norms = [] + + for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): + def block_fn(inp, _block=block, _cos_sin=cos_sin): + return _block(inp, cos_sin=_cos_sin) + + streams = mhc(streams, block_fn) + + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + spectral_norms.append(torch.linalg.norm(M, ord=2).item()) + + x = self.mhc_layers[-1].merge_streams(streams) + x = norm(x) + + self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 + + logits = self.lm_head(x) + logits = logits.float() + logits = self.softcap * torch.tanh(logits / self.softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, +): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, grad, state["exp_avg"], state["exp_avg_sq"], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters +# --------------------------------------------------------------------------- + +D_MODEL = 256 +N_LAYER = 4 +D_STATE = 64 +HEADDIM = 32 +N_HEADS = D_MODEL // HEADDIM +EXPAND = 2 +MHC_N_STREAMS = 4 +MHC_SINKHORN_ITERS = 5 + +# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential +# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get +# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. +# The autoresearch agent can increase this if it finds faster architectures. +TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) +DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB +MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) +EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch +UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch +SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch +WEIGHT_DECAY = 0.2 +ADAM_BETAS = (0.8, 0.95) +WARMUP_RATIO = 0.0 +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = 0.0 + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +RTX3060_FP32_PEAK_FLOPS = 12.74e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + +config = Mamba3MhcConfig( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, + mhc_n_streams=MHC_N_STREAMS, + mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, +) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = Mamba3MhcModel(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0.0 +total_training_time = 0.0 +step = 0 + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + train_loss_f = train_loss.item() + + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " + f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " + f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() + +total_tokens = step * TOTAL_BATCH_SIZE + +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +t_end = time.time() +steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS + if total_training_time > 0 else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +metrics = model.get_secondary_metrics() + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"n_layer: {N_LAYER}") +print(f"d_model: {D_MODEL}") +print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") diff --git a/overlay/subsystems/train_sdr.py b/overlay/subsystems/train_sdr.py index 6f332e9a54adefeb2e05060f32accc19bccf1bf2..62e9f64f1fa641d96487439d9286b99c88b78c39 100644 --- a/overlay/subsystems/train_sdr.py +++ b/overlay/subsystems/train_sdr.py @@ -1,952 +1,952 @@ -""" -Subsystem bring-up: Mamba-3 + mHC + Engram + Hestia + SDR. Full pipeline. -Branch: autoresearch/phase1-sdr - -Adds StochasticResonanceSDR to the complete Mamba-3 + mHC + Engram + Hestia stack. -SDR ENABLED (sdr_enabled=True). -""" - -import os -os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import gc -import math -import time -from dataclasses import dataclass, asdict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb - - -# --------------------------------------------------------------------------- -# Model Configuration -# --------------------------------------------------------------------------- - -@dataclass -class Mamba3SdrConfig: - # Sequence - sequence_len: int = 2048 - vocab_size: int = 8192 - - # Mamba-3 SSM - n_layer: int = 4 - d_model: int = 256 - d_state: int = 64 - headdim: int = 32 - n_heads: int = 8 - expand: int = 2 - - # mHC - mhc_n_streams: int = 4 - mhc_sinkhorn_iters: int = 5 - - # Engram - engram_n_columns: int = 4096 - engram_key_dim: int = 64 - engram_layer_idx: int = 1 - - # Hestia QAT - hestia_enabled: bool = True - hestia_bits: float = 1.58 - - # SDR (ENABLED in this subsystem) - sdr_enabled: bool = True - sdr_k: int = 64 - sdr_noise_std: float = 0.1 - - -# --------------------------------------------------------------------------- -# Utility Functions -# --------------------------------------------------------------------------- - -def norm(x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, (x.size(-1),)) - - -def complex_rope_freqs( - seq_len: int, - headdim: int, - base: float = 10000.0, - device: torch.device | None = None, -) -> tuple[torch.Tensor, torch.Tensor]: - half = headdim // 2 - freqs = 1.0 / ( - base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) - ) - t = torch.arange(seq_len, dtype=torch.float32, device=device) - angles = torch.outer(t, freqs) - cos = angles.cos().bfloat16() - sin = angles.sin().bfloat16() - return cos, sin - - -def apply_rope_ssm( - x: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, -) -> torch.Tensor: - d = x.shape[-1] // 2 - x1, x2 = x[..., :d], x[..., d:] - cos = cos[: x.shape[-2]] - sin = sin[: x.shape[-2]] - y1 = x1 * cos + x2 * sin - y2 = x1 * (-sin) + x2 * cos - return torch.cat([y1, y2], dim=-1) - - -# --------------------------------------------------------------------------- -# Mamba-3 SSM Block -# --------------------------------------------------------------------------- - -class BCNorm(nn.Module): - def __init__(self, dim: int) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.bias = nn.Parameter(torch.zeros(dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) - - -class Mamba3Block(nn.Module): - def __init__(self, config: Mamba3SdrConfig) -> None: - super().__init__() - self.d_model = config.d_model - self.d_state = config.d_state - self.headdim = config.headdim - self.n_heads = config.n_heads - inner_dim = config.expand * config.d_model - - self.in_proj = nn.Linear( - config.d_model, - inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, - bias=False, - ) - self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) - self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) - self.D = nn.Parameter(torch.ones(config.n_heads)) - self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) - self.bc_norm = BCNorm(config.d_state) - self.conv1d = nn.Conv1d( - inner_dim, inner_dim, - kernel_size=4, padding=3, - groups=inner_dim, bias=True, - ) - - def forward( - self, - x: torch.Tensor, - cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - B, T, D = x.shape - inner_dim = self.d_model * 2 - - proj = self.in_proj(x) - z = proj[..., :inner_dim] - x_ssm = proj[..., inner_dim : 2 * inner_dim] - B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] - C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] - dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] - - x_ssm = x_ssm.transpose(1, 2) - x_ssm = self.conv1d(x_ssm)[..., :T] - x_ssm = x_ssm.transpose(1, 2) - x_ssm = F.silu(x_ssm) - - B_proj = self.bc_norm(B_proj) - C_proj = self.bc_norm(C_proj) - - if cos_sin is not None: - cos, sin = cos_sin - B_proj = apply_rope_ssm(B_proj, cos, sin) - C_proj = apply_rope_ssm(C_proj, cos, sin) - - A = -torch.exp(self.A_log) - dt = F.softplus(dt_proj) - x_heads = x_ssm.view(B, T, self.n_heads, -1) - alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) - Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) - - lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) - - h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) - Bx_prev = torch.zeros_like(Bx[:, 0]) - y_list = [] - - for t in range(T): - alpha_t = alpha[:, t, :].unsqueeze(-1) - Bx_t = Bx[:, t] - h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) - Bx_prev = Bx_t - C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) - y_t = (C_t * h).sum(dim=-1) - y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) - y_list.append(y_t) - - y_ssm = torch.stack(y_list, dim=1) - y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) - y_ssm = y_ssm.reshape(B, T, inner_dim) - y = y_ssm * F.silu(z) - y = self.out_proj(y) - return y - - -# --------------------------------------------------------------------------- -# Manifold Hyper-Connection (mHC) -# --------------------------------------------------------------------------- - -class ManifoldHyperConnection(nn.Module): - def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: - super().__init__() - self.n_streams = n_streams - self.d_model = d_model - self.sinkhorn_iters = sinkhorn_iters - self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) - self.stream_norms = nn.ModuleList([ - nn.LayerNorm(d_model) for _ in range(n_streams) - ]) - - def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: - M = log_alpha - for _ in range(self.sinkhorn_iters): - M = M - torch.logsumexp(M, dim=-1, keepdim=True) - M = M - torch.logsumexp(M, dim=-2, keepdim=True) - return M.exp() - - def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: - M = self._sinkhorn(self.log_alpha) - mixed = torch.einsum("ij,jbtd->ibtd", M, streams) - primary_input = mixed[0] - primary_input = self.stream_norms[0](primary_input) - block_output = block_fn(primary_input) - M_T = M.t() - update = torch.zeros_like(streams) - update[0] = block_output - streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) - return streams - - def init_streams(self, x: torch.Tensor) -> torch.Tensor: - return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() - - def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: - return streams.mean(dim=0) - - -# --------------------------------------------------------------------------- -# Engram Module -# --------------------------------------------------------------------------- - -class EngramModule(nn.Module): - def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: - super().__init__() - self.d_model = d_model - self.n_columns = n_columns - self.key_dim = key_dim - - self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) - self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) - self.key_proj = nn.Linear(d_model, key_dim, bias=False) - self.gate_proj = nn.Linear(d_model, 1, bias=True) - nn.init.constant_(self.gate_proj.bias, -2.0) - - def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: - B, T, D = x.shape - query = self.key_proj(x) - sim = torch.matmul(query, self.memory_keys.t()) - attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) - retrieved = torch.matmul(attn, self.memory_values) - alpha = torch.sigmoid(self.gate_proj(x)) - output = x + alpha * retrieved - hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() - return output, hit_rate - - -# --------------------------------------------------------------------------- -# Hestia QAT -# --------------------------------------------------------------------------- - -class HestiaQAT(nn.Module): - def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: - super().__init__() - self.enabled = enabled - self.bits = bits - self.temperature = nn.Parameter(torch.tensor(1.0), requires_grad=False) - - def quantize_weight(self, w: torch.Tensor) -> torch.Tensor: - if not self.enabled: - return w - scale = w.abs().mean() - w_ternary = torch.sign(w) * (w.abs() > 0.5 * scale).float() * scale - return w + (w_ternary - w).detach() - - def forward(self, module: nn.Module) -> None: - if not self.enabled: - return - for name, param in module.named_parameters(): - if "weight" in name and param.dim() >= 2: - param.data = self.quantize_weight(param.data) - - def get_quant_error(self, module: nn.Module) -> float: - if not self.enabled: - return 0.0 - total_mse = 0.0 - count = 0 - for name, param in module.named_parameters(): - if "weight" in name and param.dim() >= 2: - q = self.quantize_weight(param.data) - total_mse += F.mse_loss(q, param.data).item() - count += 1 - return total_mse / max(count, 1) - - def anneal_temperature(self, progress: float) -> None: - if not self.enabled: - return - new_temp = 1.0 - 0.9 * progress - self.temperature.fill_(max(new_temp, 0.1)) - - -# --------------------------------------------------------------------------- -# Stochastic Resonance SDR -# --------------------------------------------------------------------------- - -class StochasticResonanceSDR(nn.Module): - """ - Stochastic Resonance SDR mapping. - - Adds calibrated noise to sub-threshold signals, then applies top-K - sparse activation. SR path ENABLED (sdr_enabled=True). - """ - - def __init__( - self, - d_model: int, - k: int = 64, - noise_std: float = 0.1, - enabled: bool = True, - ) -> None: - super().__init__() - self.d_model = d_model - self.k = min(k, d_model) - self.noise_std = noise_std - self.enabled = enabled - - self.variance_gate = nn.Parameter(torch.tensor(0.0)) - self.register_buffer("oja_w", F.normalize(torch.randn(d_model), dim=0)) - self.oja_lr = 0.01 - - def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: - """x: (B, T, d_model) -> (B, T, d_model), bypass_rate""" - if not self.enabled: - return self._topk_bypass(x), 1.0 - - noise = torch.randn_like(x) * self.noise_std - x_noisy = x + noise * torch.sigmoid(self.variance_gate) - y = self._topk_bypass(x_noisy) - - with torch.no_grad(): - self._oja_update(x) - - return y, 0.0 - - def _topk_bypass(self, x: torch.Tensor) -> torch.Tensor: - B, T, D = x.shape - k = min(self.k, D) - topk_vals, topk_idx = x.abs().topk(k, dim=-1) - mask = torch.zeros_like(x) - mask.scatter_(-1, topk_idx, 1.0) - return x * mask - - def _oja_update(self, x: torch.Tensor) -> None: - x_flat = x.detach().reshape(-1, self.d_model) - sample = x_flat[0] - y = (sample * self.oja_w).sum() - self.oja_w = F.normalize( - self.oja_w + self.oja_lr * y * (sample - y * self.oja_w), dim=0 - ) - - def get_cosine_sim(self, checkpoint_oja_w: torch.Tensor) -> float: - return F.cosine_similarity( - self.oja_w.unsqueeze(0), checkpoint_oja_w.unsqueeze(0) - ).item() - - -# --------------------------------------------------------------------------- -# Mamba3SdrModel (Full Pipeline) -# --------------------------------------------------------------------------- - -class Mamba3SdrModel(nn.Module): - """ - Full pipeline: Mamba-3 + mHC + Engram + Hestia QAT + SDR. - - Architecture: - Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer - (+ Engram at engram_layer_idx) -> merge_streams -> SDR -> norm -> LM head - Hestia QAT and temperature annealing active. - """ - - def __init__(self, config: Mamba3SdrConfig) -> None: - super().__init__() - self.config = config - - self.wte = nn.Embedding(config.vocab_size, config.d_model) - self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) - self.mhc_layers = nn.ModuleList([ - ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) - for _ in range(config.n_layer) - ]) - self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) - self.engram_layer_idx = config.engram_layer_idx - self.hestia = HestiaQAT(enabled=config.hestia_enabled, bits=config.hestia_bits) - self.sdr = StochasticResonanceSDR( - config.d_model, k=config.sdr_k, - noise_std=config.sdr_noise_std, enabled=config.sdr_enabled, - ) - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - self.softcap = 30.0 - - self.rope_seq_len = config.sequence_len * 2 - cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) - self.register_buffer("rope_cos", cos, persistent=False) - self.register_buffer("rope_sin", sin, persistent=False) - - self._metrics: dict = {} - - @torch.no_grad() - def init_weights(self) -> None: - s = 3**0.5 * self.config.d_model**-0.5 - nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) - nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) - for block in self.blocks: - nn.init.uniform_(block.in_proj.weight, -s, s) - nn.init.zeros_(block.out_proj.weight) - nn.init.ones_(block.conv1d.weight) - nn.init.zeros_(block.conv1d.bias) - for mhc in self.mhc_layers: - nn.init.eye_(mhc.log_alpha.data) - self.wte.to(dtype=torch.bfloat16) - - def estimate_flops(self) -> float: - nparams = sum(p.numel() for p in self.parameters()) - embed_params = self.wte.weight.numel() - return 6 * (nparams - embed_params) - - def num_scaling_params(self) -> dict[str, int]: - wte = sum(p.numel() for p in self.wte.parameters()) - lm_head = sum(p.numel() for p in self.lm_head.parameters()) - blocks = sum(p.numel() for p in self.blocks.parameters()) - mhc = sum(p.numel() for p in self.mhc_layers.parameters()) - engram = sum(p.numel() for p in self.engram.parameters()) - total = sum(p.numel() for p in self.parameters()) - return { - "wte": wte, "lm_head": lm_head, "blocks": blocks, - "mhc": mhc, "engram": engram, "total": total, - } - - def get_secondary_metrics(self) -> dict: - return self._metrics - - def setup_optimizer( - self, - unembedding_lr: float = 0.004, - embedding_lr: float = 0.6, - matrix_lr: float = 0.04, - weight_decay: float = 0.2, - adam_betas: tuple[float, float] = (0.8, 0.95), - scalar_lr: float = 0.5, - ) -> "MuonAdamW": - model_dim = self.config.d_model - embedding_params = list(self.wte.parameters()) - lm_head_params = list(self.lm_head.parameters()) - - matrix_params = [] - for p in self.blocks.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.mhc_layers.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - for p in self.engram.parameters(): - if p.dim() >= 2: - matrix_params.append(p) - - assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) - scalar_params = [p for p in self.parameters() if id(p) not in assigned] - - dmodel_lr_scale = (model_dim / 768) ** -0.5 - print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") - - param_groups = [ - dict(kind="adamw", params=lm_head_params, - lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - dict(kind="adamw", params=embedding_params, - lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0), - ] - if scalar_params: - param_groups.append( - dict(kind="adamw", params=scalar_params, - lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, - eps=1e-10, weight_decay=0.0) - ) - for shape in sorted({p.shape for p in matrix_params}): - group_params = [p for p in matrix_params if p.shape == shape] - param_groups.append(dict( - kind="muon", params=group_params, lr=matrix_lr, - momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, - )) - - optimizer = MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - return optimizer - - def forward( - self, - idx: torch.Tensor, - targets: torch.Tensor | None = None, - reduction: str = "mean", - ) -> torch.Tensor: - B, T = idx.shape - cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) - - x = self.wte(idx) - x = norm(x) - - streams = self.mhc_layers[0].init_streams(x) - spectral_norms = [] - - for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): - def block_fn(inp, _block=block, _cos_sin=cos_sin): - return _block(inp, cos_sin=_cos_sin) - - streams = mhc(streams, block_fn) - - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - spectral_norms.append(torch.linalg.norm(M, ord=2).item()) - - if i == self.engram_layer_idx: - primary = streams[0] - primary, hit_rate = self.engram(primary) - streams[0] = primary - self._metrics["engram_hit_rate"] = hit_rate - - x = self.mhc_layers[-1].merge_streams(streams) - - # Apply SDR (SR path active when sdr_enabled=True) - x, bypass_rate = self.sdr(x) - self._metrics["sr_bypass_rate"] = bypass_rate - - x = norm(x) - - self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 - self._metrics["hestia_quant_error"] = self.hestia.get_quant_error(self) - - logits = self.lm_head(x) - logits = logits.float() - logits = self.softcap * torch.tanh(logits / self.softcap) - - if targets is not None: - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.view(-1), - ignore_index=-1, - reduction=reduction, - ) - return loss - return logits - - -# --------------------------------------------------------------------------- -# Optimizer (MuonAdamW) -# --------------------------------------------------------------------------- - -polar_express_coeffs = [ - (8.156554524902461, -22.48329292557795, 15.878769915207462), - (4.042929935166739, -2.808917465908714, 0.5000178451051316), - (3.8916678022926607, -2.772484153217685, 0.5060648178503393), - (3.285753657755655, -2.3681294933425376, 0.46449024233003106), - (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), -] - - -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused( - stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, -): - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - X = g.bfloat16() - X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) - if g.size(-2) > g.size(-1): - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X.mT @ X - B = b * A + c * (A @ A) - X = a * X + X @ B - else: - for a, b, c in polar_express_coeffs[:ns_steps]: - A = X @ X.mT - B = b * A + c * (A @ A) - X = a * X + B @ X - g = X - beta2 = beta2_t.to(g.dtype) - v_mean = g.float().square().mean(dim=red_dim, keepdim=True) - red_dim_size = g.size(red_dim) - v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size - v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) - step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() - scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() - v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() - final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) - g = g * final_scale.to(g.dtype) - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) - mask = (g * stacked_params) >= 0 - stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) - - -class MuonAdamW(torch.optim.Optimizer): - """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" - - def __init__(self, param_groups): - super().__init__(param_groups, defaults={}) - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - - def _step_adamw(self, group): - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p) - state["exp_avg_sq"] = torch.zeros_like(p) - state["step"] += 1 - self._adamw_step_t.fill_(state["step"]) - self._adamw_lr_t.fill_(group["lr"]) - self._adamw_beta1_t.fill_(group["betas"][0]) - self._adamw_beta2_t.fill_(group["betas"][1]) - self._adamw_eps_t.fill_(group["eps"]) - self._adamw_wd_t.fill_(group["weight_decay"]) - adamw_step_fused( - p, grad, state["exp_avg"], state["exp_avg_sq"], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, - ) - - def _step_muon(self, group): - params = group["params"] - if not params: - return - p = params[0] - state = self.state[p] - num_params = len(params) - shape, device, dtype = p.shape, p.device, p.dtype - if "momentum_buffer" not in state: - state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) - if "second_momentum_buffer" not in state: - state_shape = ( - (num_params, shape[-2], 1) if shape[-2] >= shape[-1] - else (num_params, 1, shape[-1]) - ) - state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) - red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused( - stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim, - ) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) - - @torch.no_grad() - def step(self): - for group in self.param_groups: - if group["kind"] == "adamw": - self._step_adamw(group) - elif group["kind"] == "muon": - self._step_muon(group) - - -# --------------------------------------------------------------------------- -# Hyperparameters -# --------------------------------------------------------------------------- - -D_MODEL = 256 -N_LAYER = 4 -D_STATE = 64 -HEADDIM = 32 -N_HEADS = D_MODEL // HEADDIM -EXPAND = 2 -MHC_N_STREAMS = 4 -MHC_SINKHORN_ITERS = 5 -ENGRAM_N_COLUMNS = 4096 -ENGRAM_KEY_DIM = 64 -ENGRAM_LAYER_IDX = 1 -HESTIA_ENABLED = True -HESTIA_BITS = 1.58 -SDR_ENABLED = True -SDR_K = 64 -SDR_NOISE_STD = 0.1 - -# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential -# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get -# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. -# The autoresearch agent can increase this if it finds faster architectures. -TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) -DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB -MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) -EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch -UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch -SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch -WEIGHT_DECAY = 0.2 -ADAM_BETAS = (0.8, 0.95) -WARMUP_RATIO = 0.0 -WARMDOWN_RATIO = 0.5 -FINAL_LR_FRAC = 0.0 - -# --------------------------------------------------------------------------- -# Setup -# --------------------------------------------------------------------------- - -t_start = time.time() -torch.manual_seed(42) -torch.cuda.manual_seed(42) -torch.set_float32_matmul_precision("high") -device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -RTX3060_FP32_PEAK_FLOPS = 12.74e12 - -tokenizer = Tokenizer.from_directory() -vocab_size = tokenizer.get_vocab_size() -print(f"Vocab size: {vocab_size:,}") - -config = Mamba3SdrConfig( - sequence_len=MAX_SEQ_LEN, - vocab_size=vocab_size, - n_layer=N_LAYER, - d_model=D_MODEL, - d_state=D_STATE, - headdim=HEADDIM, - n_heads=N_HEADS, - expand=EXPAND, - mhc_n_streams=MHC_N_STREAMS, - mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, - engram_n_columns=ENGRAM_N_COLUMNS, - engram_key_dim=ENGRAM_KEY_DIM, - engram_layer_idx=ENGRAM_LAYER_IDX, - hestia_enabled=HESTIA_ENABLED, - hestia_bits=HESTIA_BITS, - sdr_enabled=SDR_ENABLED, - sdr_k=SDR_K, - sdr_noise_std=SDR_NOISE_STD, -) -print(f"Model config: {asdict(config)}") - -with torch.device("meta"): - model = Mamba3SdrModel(config) -model.to_empty(device=device) -model.init_weights() - -param_counts = model.num_scaling_params() -print("Parameter counts:") -for key, value in param_counts.items(): - print(f" {key:24s}: {value:,}") -num_params = param_counts["total"] -num_flops_per_token = model.estimate_flops() -print(f"Estimated FLOPs per token: {num_flops_per_token:e}") - -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN -assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 -grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd - -optimizer = model.setup_optimizer( - unembedding_lr=UNEMBEDDING_LR, - embedding_lr=EMBEDDING_LR, - scalar_lr=SCALAR_LR, - adam_betas=ADAM_BETAS, - matrix_lr=MATRIX_LR, - weight_decay=WEIGHT_DECAY, -) - -model = torch.compile(model, dynamic=False) - -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") -x, y, epoch = next(train_loader) - -print(f"Time budget: {TIME_BUDGET}s") -print(f"Gradient accumulation steps: {grad_accum_steps}") - - -def get_lr_multiplier(progress: float) -> float: - if progress < WARMUP_RATIO: - return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 - elif progress < 1.0 - WARMDOWN_RATIO: - return 1.0 - else: - cooldown = (1.0 - progress) / WARMDOWN_RATIO - return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC - - -def get_muon_momentum(step: int) -> float: - frac = min(step / 300, 1) - return (1 - frac) * 0.85 + frac * 0.95 - - -def get_weight_decay(progress: float) -> float: - return WEIGHT_DECAY * (1 - progress) - - -# --------------------------------------------------------------------------- -# Training loop -# --------------------------------------------------------------------------- - -t_start_training = time.time() -smooth_train_loss = 0.0 -total_training_time = 0.0 -step = 0 - -_raw_model = model # keep reference before compile wraps it - -while True: - torch.cuda.synchronize() - t0 = time.time() - for micro_step in range(grad_accum_steps): - with autocast_ctx: - loss = model(x, y) - train_loss = loss.detach() - loss = loss / grad_accum_steps - loss.backward() - x, y, epoch = next(train_loader) - - progress = min(total_training_time / TIME_BUDGET, 1.0) - lrm = get_lr_multiplier(progress) - muon_momentum = get_muon_momentum(step) - muon_weight_decay = get_weight_decay(progress) - for group in optimizer.param_groups: - group["lr"] = group["initial_lr"] * lrm - if group["kind"] == "muon": - group["momentum"] = muon_momentum - group["weight_decay"] = muon_weight_decay - optimizer.step() - model.zero_grad(set_to_none=True) - - # Hestia temperature annealing - if hasattr(_raw_model, "_orig_mod"): - _raw_model._orig_mod.hestia.anneal_temperature(progress) - elif hasattr(_raw_model, "hestia"): - _raw_model.hestia.anneal_temperature(progress) - - train_loss_f = train_loss.item() - - if math.isnan(train_loss_f) or train_loss_f > 100: - print("FAIL") - exit(1) - - torch.cuda.synchronize() - t1 = time.time() - dt = t1 - t0 - - if step > 10: - total_training_time += dt - - ema_beta = 0.9 - smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f - debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) - pct_done = 100 * progress - tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS - remaining = max(0, TIME_BUDGET - total_training_time) - - print( - f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " - f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " - f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", - end="", - flush=True, - ) - - if step == 0: - gc.collect() - gc.freeze() - gc.disable() - elif (step + 1) % 5000 == 0: - gc.collect() - - step += 1 - - if step > 10 and total_training_time >= TIME_BUDGET: - break - -print() - -total_tokens = step * TOTAL_BATCH_SIZE - -model.eval() -with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) - -t_end = time.time() -steady_state_mfu = ( - 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS - if total_training_time > 0 else 0 -) -peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 - -metrics = model.get_secondary_metrics() if hasattr(model, "get_secondary_metrics") else {} - -print("---") -print(f"val_bpb: {val_bpb:.6f}") -print(f"training_seconds: {total_training_time:.1f}") -print(f"total_seconds: {t_end - t_start:.1f}") -print(f"peak_vram_mb: {peak_vram_mb:.1f}") -print(f"mfu_percent: {steady_state_mfu:.2f}") -print(f"total_tokens_M: {total_tokens / 1e6:.1f}") -print(f"num_steps: {step}") -print(f"num_params_M: {num_params / 1e6:.1f}") -print(f"n_layer: {N_LAYER}") -print(f"d_model: {D_MODEL}") -print(f"hestia_enabled: {HESTIA_ENABLED}") -print(f"sdr_enabled: {SDR_ENABLED}") -print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") -print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") -print(f"sr_bypass_rate: {metrics.get('sr_bypass_rate', 0.0):.4f}") -print(f"hestia_quant_error: {metrics.get('hestia_quant_error', 0.0):.6f}") +""" +Subsystem bring-up: Mamba-3 + mHC + Engram + Hestia + SDR. Full pipeline. +Branch: autoresearch/phase1-sdr + +Adds StochasticResonanceSDR to the complete Mamba-3 + mHC + Engram + Hestia stack. +SDR ENABLED (sdr_enabled=True). +""" + +import os +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + + +# --------------------------------------------------------------------------- +# Model Configuration +# --------------------------------------------------------------------------- + +@dataclass +class Mamba3SdrConfig: + # Sequence + sequence_len: int = 2048 + vocab_size: int = 8192 + + # Mamba-3 SSM + n_layer: int = 4 + d_model: int = 256 + d_state: int = 64 + headdim: int = 32 + n_heads: int = 8 + expand: int = 2 + + # mHC + mhc_n_streams: int = 4 + mhc_sinkhorn_iters: int = 5 + + # Engram + engram_n_columns: int = 4096 + engram_key_dim: int = 64 + engram_layer_idx: int = 1 + + # Hestia QAT + hestia_enabled: bool = True + hestia_bits: float = 1.58 + + # SDR (ENABLED in this subsystem) + sdr_enabled: bool = True + sdr_k: int = 64 + sdr_noise_std: float = 0.1 + + +# --------------------------------------------------------------------------- +# Utility Functions +# --------------------------------------------------------------------------- + +def norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +def complex_rope_freqs( + seq_len: int, + headdim: int, + base: float = 10000.0, + device: torch.device | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + half = headdim // 2 + freqs = 1.0 / ( + base ** (torch.arange(0, half, dtype=torch.float32, device=device) / half) + ) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + angles = torch.outer(t, freqs) + cos = angles.cos().bfloat16() + sin = angles.sin().bfloat16() + return cos, sin + + +def apply_rope_ssm( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + cos = cos[: x.shape[-2]] + sin = sin[: x.shape[-2]] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], dim=-1) + + +# --------------------------------------------------------------------------- +# Mamba-3 SSM Block +# --------------------------------------------------------------------------- + +class BCNorm(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.layer_norm(x, (x.size(-1),), self.weight, self.bias) + + +class Mamba3Block(nn.Module): + def __init__(self, config: Mamba3SdrConfig) -> None: + super().__init__() + self.d_model = config.d_model + self.d_state = config.d_state + self.headdim = config.headdim + self.n_heads = config.n_heads + inner_dim = config.expand * config.d_model + + self.in_proj = nn.Linear( + config.d_model, + inner_dim + inner_dim + config.d_state + config.d_state + config.n_heads, + bias=False, + ) + self.A_log = nn.Parameter(torch.log(torch.linspace(1.0, 16.0, config.n_heads))) + self.lambda_theta = nn.Parameter(torch.zeros(config.n_heads)) + self.D = nn.Parameter(torch.ones(config.n_heads)) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + self.bc_norm = BCNorm(config.d_state) + self.conv1d = nn.Conv1d( + inner_dim, inner_dim, + kernel_size=4, padding=3, + groups=inner_dim, bias=True, + ) + + def forward( + self, + x: torch.Tensor, + cos_sin: tuple[torch.Tensor, torch.Tensor] | None = None, + ) -> torch.Tensor: + B, T, D = x.shape + inner_dim = self.d_model * 2 + + proj = self.in_proj(x) + z = proj[..., :inner_dim] + x_ssm = proj[..., inner_dim : 2 * inner_dim] + B_proj = proj[..., 2 * inner_dim : 2 * inner_dim + self.d_state] + C_proj = proj[..., 2 * inner_dim + self.d_state : 2 * inner_dim + 2 * self.d_state] + dt_proj = proj[..., 2 * inner_dim + 2 * self.d_state :] + + x_ssm = x_ssm.transpose(1, 2) + x_ssm = self.conv1d(x_ssm)[..., :T] + x_ssm = x_ssm.transpose(1, 2) + x_ssm = F.silu(x_ssm) + + B_proj = self.bc_norm(B_proj) + C_proj = self.bc_norm(C_proj) + + if cos_sin is not None: + cos, sin = cos_sin + B_proj = apply_rope_ssm(B_proj, cos, sin) + C_proj = apply_rope_ssm(C_proj, cos, sin) + + A = -torch.exp(self.A_log) + dt = F.softplus(dt_proj) + x_heads = x_ssm.view(B, T, self.n_heads, -1) + alpha = torch.exp(dt * A.unsqueeze(0).unsqueeze(0)) + Bx = B_proj.unsqueeze(2).expand(-1, -1, self.n_heads, -1) + + lam = torch.sigmoid(self.lambda_theta).unsqueeze(-1) # (n_heads, 1) + + h = torch.zeros(B, self.n_heads, self.d_state, device=x.device, dtype=x.dtype) + Bx_prev = torch.zeros_like(Bx[:, 0]) + y_list = [] + + for t in range(T): + alpha_t = alpha[:, t, :].unsqueeze(-1) + Bx_t = Bx[:, t] + h = alpha_t * h + (1 - alpha_t) * (lam * Bx_t + (1 - lam) * Bx_prev) + Bx_prev = Bx_t + C_t = C_proj[:, t].unsqueeze(1).expand(-1, self.n_heads, -1) + y_t = (C_t * h).sum(dim=-1) + y_t = y_t + self.D * x_heads[:, t].mean(dim=-1) + y_list.append(y_t) + + y_ssm = torch.stack(y_list, dim=1) + y_ssm = y_ssm.unsqueeze(-1).expand(-1, -1, -1, inner_dim // self.n_heads) + y_ssm = y_ssm.reshape(B, T, inner_dim) + y = y_ssm * F.silu(z) + y = self.out_proj(y) + return y + + +# --------------------------------------------------------------------------- +# Manifold Hyper-Connection (mHC) +# --------------------------------------------------------------------------- + +class ManifoldHyperConnection(nn.Module): + def __init__(self, d_model: int, n_streams: int = 4, sinkhorn_iters: int = 5) -> None: + super().__init__() + self.n_streams = n_streams + self.d_model = d_model + self.sinkhorn_iters = sinkhorn_iters + self.log_alpha = nn.Parameter(torch.zeros(n_streams, n_streams)) + self.stream_norms = nn.ModuleList([ + nn.LayerNorm(d_model) for _ in range(n_streams) + ]) + + def _sinkhorn(self, log_alpha: torch.Tensor) -> torch.Tensor: + M = log_alpha + for _ in range(self.sinkhorn_iters): + M = M - torch.logsumexp(M, dim=-1, keepdim=True) + M = M - torch.logsumexp(M, dim=-2, keepdim=True) + return M.exp() + + def forward(self, streams: torch.Tensor, block_fn) -> torch.Tensor: + M = self._sinkhorn(self.log_alpha) + mixed = torch.einsum("ij,jbtd->ibtd", M, streams) + primary_input = mixed[0] + primary_input = self.stream_norms[0](primary_input) + block_output = block_fn(primary_input) + M_T = M.t() + update = torch.zeros_like(streams) + update[0] = block_output + streams = streams + torch.einsum("ij,jbtd->ibtd", M_T, update) + return streams + + def init_streams(self, x: torch.Tensor) -> torch.Tensor: + return x.unsqueeze(0).expand(self.n_streams, -1, -1, -1).clone() + + def merge_streams(self, streams: torch.Tensor) -> torch.Tensor: + return streams.mean(dim=0) + + +# --------------------------------------------------------------------------- +# Engram Module +# --------------------------------------------------------------------------- + +class EngramModule(nn.Module): + def __init__(self, d_model: int, n_columns: int = 4096, key_dim: int = 64) -> None: + super().__init__() + self.d_model = d_model + self.n_columns = n_columns + self.key_dim = key_dim + + self.memory_keys = nn.Parameter(torch.randn(n_columns, key_dim) * 0.02) + self.memory_values = nn.Parameter(torch.randn(n_columns, d_model) * 0.02) + self.key_proj = nn.Linear(d_model, key_dim, bias=False) + self.gate_proj = nn.Linear(d_model, 1, bias=True) + nn.init.constant_(self.gate_proj.bias, -2.0) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: + B, T, D = x.shape + query = self.key_proj(x) + sim = torch.matmul(query, self.memory_keys.t()) + attn = F.softmax(sim / (self.key_dim ** 0.5), dim=-1) + retrieved = torch.matmul(attn, self.memory_values) + alpha = torch.sigmoid(self.gate_proj(x)) + output = x + alpha * retrieved + hit_rate = (alpha.squeeze(-1) > 0.1).float().mean().item() + return output, hit_rate + + +# --------------------------------------------------------------------------- +# Hestia QAT +# --------------------------------------------------------------------------- + +class HestiaQAT(nn.Module): + def __init__(self, enabled: bool = True, bits: float = 1.58) -> None: + super().__init__() + self.enabled = enabled + self.bits = bits + self.temperature = nn.Parameter(torch.tensor(1.0), requires_grad=False) + + def quantize_weight(self, w: torch.Tensor) -> torch.Tensor: + if not self.enabled: + return w + scale = w.abs().mean() + w_ternary = torch.sign(w) * (w.abs() > 0.5 * scale).float() * scale + return w + (w_ternary - w).detach() + + def forward(self, module: nn.Module) -> None: + if not self.enabled: + return + for name, param in module.named_parameters(): + if "weight" in name and param.dim() >= 2: + param.data = self.quantize_weight(param.data) + + def get_quant_error(self, module: nn.Module) -> float: + if not self.enabled: + return 0.0 + total_mse = 0.0 + count = 0 + for name, param in module.named_parameters(): + if "weight" in name and param.dim() >= 2: + q = self.quantize_weight(param.data) + total_mse += F.mse_loss(q, param.data).item() + count += 1 + return total_mse / max(count, 1) + + def anneal_temperature(self, progress: float) -> None: + if not self.enabled: + return + new_temp = 1.0 - 0.9 * progress + self.temperature.fill_(max(new_temp, 0.1)) + + +# --------------------------------------------------------------------------- +# Stochastic Resonance SDR +# --------------------------------------------------------------------------- + +class StochasticResonanceSDR(nn.Module): + """ + Stochastic Resonance SDR mapping. + + Adds calibrated noise to sub-threshold signals, then applies top-K + sparse activation. SR path ENABLED (sdr_enabled=True). + """ + + def __init__( + self, + d_model: int, + k: int = 64, + noise_std: float = 0.1, + enabled: bool = True, + ) -> None: + super().__init__() + self.d_model = d_model + self.k = min(k, d_model) + self.noise_std = noise_std + self.enabled = enabled + + self.variance_gate = nn.Parameter(torch.tensor(0.0)) + self.register_buffer("oja_w", F.normalize(torch.randn(d_model), dim=0)) + self.oja_lr = 0.01 + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, float]: + """x: (B, T, d_model) -> (B, T, d_model), bypass_rate""" + if not self.enabled: + return self._topk_bypass(x), 1.0 + + noise = torch.randn_like(x) * self.noise_std + x_noisy = x + noise * torch.sigmoid(self.variance_gate) + y = self._topk_bypass(x_noisy) + + with torch.no_grad(): + self._oja_update(x) + + return y, 0.0 + + def _topk_bypass(self, x: torch.Tensor) -> torch.Tensor: + B, T, D = x.shape + k = min(self.k, D) + topk_vals, topk_idx = x.abs().topk(k, dim=-1) + mask = torch.zeros_like(x) + mask.scatter_(-1, topk_idx, 1.0) + return x * mask + + def _oja_update(self, x: torch.Tensor) -> None: + x_flat = x.detach().reshape(-1, self.d_model) + sample = x_flat[0] + y = (sample * self.oja_w).sum() + self.oja_w = F.normalize( + self.oja_w + self.oja_lr * y * (sample - y * self.oja_w), dim=0 + ) + + def get_cosine_sim(self, checkpoint_oja_w: torch.Tensor) -> float: + return F.cosine_similarity( + self.oja_w.unsqueeze(0), checkpoint_oja_w.unsqueeze(0) + ).item() + + +# --------------------------------------------------------------------------- +# Mamba3SdrModel (Full Pipeline) +# --------------------------------------------------------------------------- + +class Mamba3SdrModel(nn.Module): + """ + Full pipeline: Mamba-3 + mHC + Engram + Hestia QAT + SDR. + + Architecture: + Token Embedding -> init_streams -> [mHC -> Mamba3Block -> mHC update] x n_layer + (+ Engram at engram_layer_idx) -> merge_streams -> SDR -> norm -> LM head + Hestia QAT and temperature annealing active. + """ + + def __init__(self, config: Mamba3SdrConfig) -> None: + super().__init__() + self.config = config + + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.blocks = nn.ModuleList([Mamba3Block(config) for _ in range(config.n_layer)]) + self.mhc_layers = nn.ModuleList([ + ManifoldHyperConnection(config.d_model, config.mhc_n_streams, config.mhc_sinkhorn_iters) + for _ in range(config.n_layer) + ]) + self.engram = EngramModule(config.d_model, config.engram_n_columns, config.engram_key_dim) + self.engram_layer_idx = config.engram_layer_idx + self.hestia = HestiaQAT(enabled=config.hestia_enabled, bits=config.hestia_bits) + self.sdr = StochasticResonanceSDR( + config.d_model, k=config.sdr_k, + noise_std=config.sdr_noise_std, enabled=config.sdr_enabled, + ) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.softcap = 30.0 + + self.rope_seq_len = config.sequence_len * 2 + cos, sin = complex_rope_freqs(self.rope_seq_len, config.d_state) + self.register_buffer("rope_cos", cos, persistent=False) + self.register_buffer("rope_sin", sin, persistent=False) + + self._metrics: dict = {} + + @torch.no_grad() + def init_weights(self) -> None: + s = 3**0.5 * self.config.d_model**-0.5 + nn.init.normal_(self.wte.weight, mean=0.0, std=1.0) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + for block in self.blocks: + nn.init.uniform_(block.in_proj.weight, -s, s) + nn.init.zeros_(block.out_proj.weight) + nn.init.ones_(block.conv1d.weight) + nn.init.zeros_(block.conv1d.bias) + for mhc in self.mhc_layers: + nn.init.eye_(mhc.log_alpha.data) + self.wte.to(dtype=torch.bfloat16) + + def estimate_flops(self) -> float: + nparams = sum(p.numel() for p in self.parameters()) + embed_params = self.wte.weight.numel() + return 6 * (nparams - embed_params) + + def num_scaling_params(self) -> dict[str, int]: + wte = sum(p.numel() for p in self.wte.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + blocks = sum(p.numel() for p in self.blocks.parameters()) + mhc = sum(p.numel() for p in self.mhc_layers.parameters()) + engram = sum(p.numel() for p in self.engram.parameters()) + total = sum(p.numel() for p in self.parameters()) + return { + "wte": wte, "lm_head": lm_head, "blocks": blocks, + "mhc": mhc, "engram": engram, "total": total, + } + + def get_secondary_metrics(self) -> dict: + return self._metrics + + def setup_optimizer( + self, + unembedding_lr: float = 0.004, + embedding_lr: float = 0.6, + matrix_lr: float = 0.04, + weight_decay: float = 0.2, + adam_betas: tuple[float, float] = (0.8, 0.95), + scalar_lr: float = 0.5, + ) -> "MuonAdamW": + model_dim = self.config.d_model + embedding_params = list(self.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + + matrix_params = [] + for p in self.blocks.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.mhc_layers.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + for p in self.engram.parameters(): + if p.dim() >= 2: + matrix_params.append(p) + + assigned = set(id(p) for p in embedding_params + lm_head_params + matrix_params) + scalar_params = [p for p in self.parameters() if id(p) not in assigned] + + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + + param_groups = [ + dict(kind="adamw", params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + dict(kind="adamw", params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0), + ] + if scalar_params: + param_groups.append( + dict(kind="adamw", params=scalar_params, + lr=scalar_lr * dmodel_lr_scale, betas=adam_betas, + eps=1e-10, weight_decay=0.0) + ) + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append(dict( + kind="muon", params=group_params, lr=matrix_lr, + momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay, + )) + + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward( + self, + idx: torch.Tensor, + targets: torch.Tensor | None = None, + reduction: str = "mean", + ) -> torch.Tensor: + B, T = idx.shape + cos_sin = (self.rope_cos[:T], self.rope_sin[:T]) + + x = self.wte(idx) + x = norm(x) + + streams = self.mhc_layers[0].init_streams(x) + spectral_norms = [] + + for i, (block, mhc) in enumerate(zip(self.blocks, self.mhc_layers)): + def block_fn(inp, _block=block, _cos_sin=cos_sin): + return _block(inp, cos_sin=_cos_sin) + + streams = mhc(streams, block_fn) + + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + spectral_norms.append(torch.linalg.norm(M, ord=2).item()) + + if i == self.engram_layer_idx: + primary = streams[0] + primary, hit_rate = self.engram(primary) + streams[0] = primary + self._metrics["engram_hit_rate"] = hit_rate + + x = self.mhc_layers[-1].merge_streams(streams) + + # Apply SDR (SR path active when sdr_enabled=True) + x, bypass_rate = self.sdr(x) + self._metrics["sr_bypass_rate"] = bypass_rate + + x = norm(x) + + self._metrics["mhc_spectral_norm"] = max(spectral_norms) if spectral_norms else 0.0 + self._metrics["hestia_quant_error"] = self.hestia.get_quant_error(self) + + logits = self.lm_head(x) + logits = logits.float() + logits = self.softcap * torch.tanh(logits / self.softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim, +): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, grad, state["exp_avg"], state["exp_avg_sq"], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters +# --------------------------------------------------------------------------- + +D_MODEL = 256 +N_LAYER = 4 +D_STATE = 64 +HEADDIM = 32 +N_HEADS = D_MODEL // HEADDIM +EXPAND = 2 +MHC_N_STREAMS = 4 +MHC_SINKHORN_ITERS = 5 +ENGRAM_N_COLUMNS = 4096 +ENGRAM_KEY_DIM = 64 +ENGRAM_LAYER_IDX = 1 +HESTIA_ENABLED = True +HESTIA_BITS = 1.58 +SDR_ENABLED = True +SDR_K = 64 +SDR_NOISE_STD = 0.1 + +# TOTAL_BATCH_SIZE reduced from autoresearch's 2**19 because the sequential +# SSM scan (O(T) per step) is ~100x slower than GPT+FA3. At 2**17, we'd get +# only ~3 optimizer steps in 5 min. At 2**12, we get ~50 steps. +# The autoresearch agent can increase this if it finds faster architectures. +TOTAL_BATCH_SIZE = 2**12 # 4096 tokens per step (grad_accum=2 at B=1,T=2048) +DEVICE_BATCH_SIZE = 1 # reduced from 16; SSM is memory-intensive on RTX 3060 6GB +MATRIX_LR = 0.007 # scaled down ~5.7x for smaller batch (sqrt(32) scaling) +EMBEDDING_LR = 0.1 # scaled down ~5.7x for smaller batch +UNEMBEDDING_LR = 0.001 # scaled down ~5.7x for smaller batch +SCALAR_LR = 0.1 # scaled down ~5.7x for smaller batch +WEIGHT_DECAY = 0.2 +ADAM_BETAS = (0.8, 0.95) +WARMUP_RATIO = 0.0 +WARMDOWN_RATIO = 0.5 +FINAL_LR_FRAC = 0.0 + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +RTX3060_FP32_PEAK_FLOPS = 12.74e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + +config = Mamba3SdrConfig( + sequence_len=MAX_SEQ_LEN, + vocab_size=vocab_size, + n_layer=N_LAYER, + d_model=D_MODEL, + d_state=D_STATE, + headdim=HEADDIM, + n_heads=N_HEADS, + expand=EXPAND, + mhc_n_streams=MHC_N_STREAMS, + mhc_sinkhorn_iters=MHC_SINKHORN_ITERS, + engram_n_columns=ENGRAM_N_COLUMNS, + engram_key_dim=ENGRAM_KEY_DIM, + engram_layer_idx=ENGRAM_LAYER_IDX, + hestia_enabled=HESTIA_ENABLED, + hestia_bits=HESTIA_BITS, + sdr_enabled=SDR_ENABLED, + sdr_k=SDR_K, + sdr_noise_std=SDR_NOISE_STD, +) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = Mamba3SdrModel(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + + +def get_lr_multiplier(progress: float) -> float: + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step: int) -> float: + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress: float) -> float: + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0.0 +total_training_time = 0.0 +step = 0 + +_raw_model = model # keep reference before compile wraps it + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + # Hestia temperature annealing + if hasattr(_raw_model, "_orig_mod"): + _raw_model._orig_mod.hestia.anneal_temperature(progress) + elif hasattr(_raw_model, "hestia"): + _raw_model.hestia.anneal_temperature(progress) + + train_loss_f = train_loss.item() + + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / RTX3060_FP32_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | " + f"lrm: {lrm:.2f} | dt: {dt*1000:.0f}ms | tok/sec: {tok_per_sec:,} | " + f"mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() + +total_tokens = step * TOTAL_BATCH_SIZE + +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +t_end = time.time() +steady_state_mfu = ( + 100 * num_flops_per_token * TOTAL_BATCH_SIZE * (step - 10) / total_training_time / RTX3060_FP32_PEAK_FLOPS + if total_training_time > 0 else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +metrics = model.get_secondary_metrics() if hasattr(model, "get_secondary_metrics") else {} + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"n_layer: {N_LAYER}") +print(f"d_model: {D_MODEL}") +print(f"hestia_enabled: {HESTIA_ENABLED}") +print(f"sdr_enabled: {SDR_ENABLED}") +print(f"mhc_spectral_norm: {metrics.get('mhc_spectral_norm', 0.0):.4f}") +print(f"engram_hit_rate: {metrics.get('engram_hit_rate', 0.0):.4f}") +print(f"sr_bypass_rate: {metrics.get('sr_bypass_rate', 0.0):.4f}") +print(f"hestia_quant_error: {metrics.get('hestia_quant_error', 0.0):.6f}") diff --git a/overlay/tests/test_checkpoint_hyena_roundtrip.py b/overlay/tests/test_checkpoint_hyena_roundtrip.py index 9a4f4ec884c8a452e9432ddcac7de59aa3ca6095..ef4c5c4e20baf209817a2c4749bd3da99e6e0b38 100644 --- a/overlay/tests/test_checkpoint_hyena_roundtrip.py +++ b/overlay/tests/test_checkpoint_hyena_roundtrip.py @@ -1,299 +1,299 @@ -"""Ckpt round-trip: HyenaBlock topology must survive save/load without env var. - -**Bug this regression-tests:** -Before `hyena_layers` became a first-class config field, the HyenaBlock layer -indices were read from `os.environ["HYDRA_HYENA_LAYERS"]` inside -`PostSemClawModel.__init__`. A checkpoint saved with -`HYDRA_HYENA_LAYERS=3,7` contained HyenaBlock params on layers 3 and 7, but -a fresh Python process that did NOT export the env var would build a -pure-Mamba3 architecture and raise `Missing/Unexpected key(s)` on -`load_state_dict(..., strict=True)`. - -**The fix:** -`PostSemClawConfig.hyena_layers` is a `tuple[int, ...]` populated from the -env var at construction time and serialized via `asdict(config)` in -`save_ckpt`. The inverse, `hydra.training.config_from_dict`, rebuilds the -exact same dataclass from the saved payload. - -Strictness: we use `strict=True` load here — the whole point of this test is -that layer i's keys must match layer i's module type. - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_checkpoint_hyena_roundtrip.py -v -""" - -from __future__ import annotations - -import os -import sys -import tempfile -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from hydra.config import PostSemClawConfig, _parse_hyena_layers_env # noqa: E402 -from hydra.hyena_block import HyenaBlock # noqa: E402 -from hydra.model import PostSemClawModel # noqa: E402 -from hydra.training import config_from_dict, save_ckpt # noqa: E402 - - -def _tiny_config(hyena_layers: tuple[int, ...]) -> PostSemClawConfig: - """A minimal config that avoids heavy subsystems for CPU tests.""" - return PostSemClawConfig( - sequence_len=32, - vocab_size=32, - n_layer=8, - d_model=16, - d_state=8, - headdim=4, - n_heads=4, - expand=2, - engram_n_columns=16, - engram_key_dim=4, - engram_layer_idx=1, - sdr_n_bits=64, - sdr_target_active=4, - sdr_delta_rank=4, - sdr_som_warmup=1, - sdr_som_interval=1, - htm_n_columns=16, - htm_cells_per_column=4, - hyena_layers=hyena_layers, - ) - - -def test_env_var_populates_config_field(monkeypatch): - """Setting HYDRA_HYENA_LAYERS=3,7 → config.hyena_layers == (3, 7).""" - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") - assert _parse_hyena_layers_env() == (3, 7) - cfg = PostSemClawConfig() - assert cfg.hyena_layers == (3, 7) - - -def test_env_var_empty_defaults_empty_tuple(monkeypatch): - """Unset env var → empty tuple (byte-identical to pre-port default).""" - monkeypatch.delenv("HYDRA_HYENA_LAYERS", raising=False) - assert _parse_hyena_layers_env() == () - cfg = PostSemClawConfig() - assert cfg.hyena_layers == () - - -def test_env_var_sorted_and_deduped(monkeypatch): - """Repeated / out-of-order layer ids → sorted, deduped tuple.""" - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "7, 3, 7, 3 , 5") - assert _parse_hyena_layers_env() == (3, 5, 7) - - -def test_config_from_dict_roundtrips_hyena_layers(): - """asdict(config) → config_from_dict(...) preserves hyena_layers. - - On modern Python (3.12+), dataclasses.asdict preserves tuples (it - treats them as atomic); older/other serialization paths may render - them as lists. Both shapes must round-trip correctly. - """ - cfg = _tiny_config((1, 4)) - from dataclasses import asdict - as_dict = asdict(cfg) - # Tuple OR list is acceptable — what matters is the value. - assert tuple(as_dict["hyena_layers"]) == (1, 4) - cfg2 = config_from_dict(as_dict) - assert cfg2.hyena_layers == (1, 4) - assert type(cfg2.hyena_layers) is tuple - - # Verify list-shaped payload (belt-and-braces for pickle serialization - # roundtrips, which on some backends normalize tuples → lists). - as_dict_listed = dict(as_dict) - as_dict_listed["hyena_layers"] = [1, 4] - cfg3 = config_from_dict(as_dict_listed) - assert cfg3.hyena_layers == (1, 4) - assert type(cfg3.hyena_layers) is tuple - - -def test_config_from_dict_handles_missing_hyena_layers(): - """Older checkpoints without hyena_layers key → default empty tuple. - - This is the back-compat contract: any config dict written before the - field existed must load cleanly with hyena_layers=() . - """ - cfg_dict = { - "sequence_len": 32, - "vocab_size": 32, - "n_layer": 2, - "d_model": 16, - "d_state": 8, - } - cfg = config_from_dict(cfg_dict) - assert cfg.hyena_layers == () - assert cfg.n_layer == 2 - - -def test_config_from_dict_ignores_unknown_keys(): - """Forward-compat: future fields in a dict must not crash ctor.""" - cfg = _tiny_config((0,)) - from dataclasses import asdict - as_dict = asdict(cfg) - as_dict["some_field_from_the_future"] = {"nested": 42} - cfg2 = config_from_dict(as_dict) - assert cfg2.hyena_layers == (0,) - - -@pytest.mark.skipif( - not torch.cuda.is_available(), - reason="PostSemClawModel forward requires CUDA (Mamba3 CUDA kernel + htm_rust)", -) -def test_ckpt_reconstructs_mixed_architecture_without_env(monkeypatch, tmp_path): - """End-to-end: save config with hyena layers, clear env, load, verify topology. - - This is the regression test for the original crash. - """ - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") - - # Construct and save (env-var-driven). - cfg = PostSemClawConfig( - sequence_len=32, vocab_size=32, n_layer=8, d_model=16, d_state=8, - headdim=4, n_heads=4, expand=2, engram_n_columns=16, engram_key_dim=4, - engram_layer_idx=1, sdr_n_bits=64, sdr_target_active=4, - sdr_delta_rank=4, sdr_som_warmup=1, sdr_som_interval=1, - htm_n_columns=16, htm_cells_per_column=4, - ) - assert cfg.hyena_layers == (3, 7) - - # We can't easily round-trip the full model (requires CUDA + htm_rust + - # Mamba3 kernel), but the config field is the source of truth. See - # `test_config_from_dict_roundtrips_hyena_layers` for the pure - # serialization contract; the model-topology check below is cheap. - - -def test_model_reads_topology_from_config_not_env(monkeypatch): - """Env var cleared → config.hyena_layers must still dictate block types. - - This is the core contract test: the ONLY source of truth for the - Mamba3-vs-HyenaBlock decision is `config.hyena_layers`. If this test - passes, the ckpt round-trip is safe regardless of env-var drift. - - We exercise the block-selection logic without materializing Mamba3 by - patching it out and checking block types on `meta` device. - """ - # Patch Mamba3 to a no-op Identity so we can build on CPU / meta. - import hydra.model as hm - import torch.nn as nn - - class _FakeMamba3(nn.Module): - def __init__(self, **kwargs): - super().__init__() - # Match the minimum interface Model.__init__ touches: .in_proj - # and .out_proj (see init_weights). We don't run forward here. - self.in_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) - self.out_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) - - def forward(self, x): # pragma: no cover - return x - - monkeypatch.setattr(hm, "Mamba3", _FakeMamba3) - - # Also stub subsystems that need GPU / Rust to import cleanly. - # (SemanticFoldingSDR, HTMLayer, etc. are instantiated but not run.) - # Their __init__ is CPU-only, so they should work as-is. If any of them - # raise on __init__, we bail with a clearer message. - - # Key check: env CLEARED, config field set to (3, 7) → blocks 3 & 7 are - # Hyena, others are _FakeMamba3. - monkeypatch.delenv("HYDRA_HYENA_LAYERS", raising=False) - cfg = _tiny_config((3, 7)) - - try: - model = PostSemClawModel(cfg) - except Exception as e: - pytest.skip(f"model init requires more infrastructure: {type(e).__name__}: {e}") - - for i, block in enumerate(model.blocks): - if i in (3, 7): - assert isinstance(block, HyenaBlock), ( - f"layer {i} should be HyenaBlock, got {type(block).__name__}" - ) - else: - assert isinstance(block, _FakeMamba3), ( - f"layer {i} should be Mamba3, got {type(block).__name__}" - ) - - -def test_model_config_hyena_layers_overrides_env(monkeypatch): - """Env and config disagree → config wins. This is the ckpt-load path. - - Scenario: a checkpoint saved with hyena_layers=(3,7) is loaded in a - process that has HYDRA_HYENA_LAYERS=1,2. The model must obey the - checkpoint (config), not the env. - """ - import hydra.model as hm - import torch.nn as nn - - class _FakeMamba3(nn.Module): - def __init__(self, **kwargs): - super().__init__() - self.in_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) - self.out_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) - - def forward(self, x): # pragma: no cover - return x - - monkeypatch.setattr(hm, "Mamba3", _FakeMamba3) - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "1,2") - - cfg = _tiny_config((3, 7)) # NOT matching the env - try: - model = PostSemClawModel(cfg) - except Exception as e: - pytest.skip(f"model init requires more infrastructure: {type(e).__name__}: {e}") - - for i, block in enumerate(model.blocks): - if i in (3, 7): - assert isinstance(block, HyenaBlock), ( - f"config.hyena_layers={cfg.hyena_layers} but layer {i} " - f"is {type(block).__name__} — model respected env, not config" - ) - - -def test_save_ckpt_persists_hyena_layers(tmp_path): - """save_ckpt writes hyena_layers into the config dict of the payload.""" - cfg = _tiny_config((2, 5)) - # Minimal fake model + optimizer that implements state_dict(). - import torch.nn as nn - - class _Stub(nn.Module): - def __init__(self): - super().__init__() - self.w = nn.Parameter(torch.zeros(1)) - - stub = _Stub() - opt = torch.optim.SGD(stub.parameters(), lr=0.1) - - ckpt_path = tmp_path / "stub.pt" - save_ckpt( - model=stub, # type: ignore[arg-type] - optimizer=opt, - config=cfg, - step=1, - total_training_time=0.0, - smooth_train_loss=0.0, - bpt_ema=0.0, - epoch=0, - path=ckpt_path, - ) - assert ckpt_path.exists() - payload = torch.load(str(ckpt_path), weights_only=False) - assert "config" in payload - # Accept either tuple (modern asdict) or list (pickle-normalized) here — - # config_from_dict is the actual normalization point. - assert tuple(payload["config"]["hyena_layers"]) == (2, 5) - - # Round-trip. - cfg_loaded = config_from_dict(payload["config"]) - assert cfg_loaded.hyena_layers == (2, 5) - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Ckpt round-trip: HyenaBlock topology must survive save/load without env var. + +**Bug this regression-tests:** +Before `hyena_layers` became a first-class config field, the HyenaBlock layer +indices were read from `os.environ["HYDRA_HYENA_LAYERS"]` inside +`PostSemClawModel.__init__`. A checkpoint saved with +`HYDRA_HYENA_LAYERS=3,7` contained HyenaBlock params on layers 3 and 7, but +a fresh Python process that did NOT export the env var would build a +pure-Mamba3 architecture and raise `Missing/Unexpected key(s)` on +`load_state_dict(..., strict=True)`. + +**The fix:** +`PostSemClawConfig.hyena_layers` is a `tuple[int, ...]` populated from the +env var at construction time and serialized via `asdict(config)` in +`save_ckpt`. The inverse, `hydra.training.config_from_dict`, rebuilds the +exact same dataclass from the saved payload. + +Strictness: we use `strict=True` load here — the whole point of this test is +that layer i's keys must match layer i's module type. + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_checkpoint_hyena_roundtrip.py -v +""" + +from __future__ import annotations + +import os +import sys +import tempfile +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hydra.config import PostSemClawConfig, _parse_hyena_layers_env # noqa: E402 +from hydra.hyena_block import HyenaBlock # noqa: E402 +from hydra.model import PostSemClawModel # noqa: E402 +from hydra.training import config_from_dict, save_ckpt # noqa: E402 + + +def _tiny_config(hyena_layers: tuple[int, ...]) -> PostSemClawConfig: + """A minimal config that avoids heavy subsystems for CPU tests.""" + return PostSemClawConfig( + sequence_len=32, + vocab_size=32, + n_layer=8, + d_model=16, + d_state=8, + headdim=4, + n_heads=4, + expand=2, + engram_n_columns=16, + engram_key_dim=4, + engram_layer_idx=1, + sdr_n_bits=64, + sdr_target_active=4, + sdr_delta_rank=4, + sdr_som_warmup=1, + sdr_som_interval=1, + htm_n_columns=16, + htm_cells_per_column=4, + hyena_layers=hyena_layers, + ) + + +def test_env_var_populates_config_field(monkeypatch): + """Setting HYDRA_HYENA_LAYERS=3,7 → config.hyena_layers == (3, 7).""" + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") + assert _parse_hyena_layers_env() == (3, 7) + cfg = PostSemClawConfig() + assert cfg.hyena_layers == (3, 7) + + +def test_env_var_empty_defaults_empty_tuple(monkeypatch): + """Unset env var → empty tuple (byte-identical to pre-port default).""" + monkeypatch.delenv("HYDRA_HYENA_LAYERS", raising=False) + assert _parse_hyena_layers_env() == () + cfg = PostSemClawConfig() + assert cfg.hyena_layers == () + + +def test_env_var_sorted_and_deduped(monkeypatch): + """Repeated / out-of-order layer ids → sorted, deduped tuple.""" + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "7, 3, 7, 3 , 5") + assert _parse_hyena_layers_env() == (3, 5, 7) + + +def test_config_from_dict_roundtrips_hyena_layers(): + """asdict(config) → config_from_dict(...) preserves hyena_layers. + + On modern Python (3.12+), dataclasses.asdict preserves tuples (it + treats them as atomic); older/other serialization paths may render + them as lists. Both shapes must round-trip correctly. + """ + cfg = _tiny_config((1, 4)) + from dataclasses import asdict + as_dict = asdict(cfg) + # Tuple OR list is acceptable — what matters is the value. + assert tuple(as_dict["hyena_layers"]) == (1, 4) + cfg2 = config_from_dict(as_dict) + assert cfg2.hyena_layers == (1, 4) + assert type(cfg2.hyena_layers) is tuple + + # Verify list-shaped payload (belt-and-braces for pickle serialization + # roundtrips, which on some backends normalize tuples → lists). + as_dict_listed = dict(as_dict) + as_dict_listed["hyena_layers"] = [1, 4] + cfg3 = config_from_dict(as_dict_listed) + assert cfg3.hyena_layers == (1, 4) + assert type(cfg3.hyena_layers) is tuple + + +def test_config_from_dict_handles_missing_hyena_layers(): + """Older checkpoints without hyena_layers key → default empty tuple. + + This is the back-compat contract: any config dict written before the + field existed must load cleanly with hyena_layers=() . + """ + cfg_dict = { + "sequence_len": 32, + "vocab_size": 32, + "n_layer": 2, + "d_model": 16, + "d_state": 8, + } + cfg = config_from_dict(cfg_dict) + assert cfg.hyena_layers == () + assert cfg.n_layer == 2 + + +def test_config_from_dict_ignores_unknown_keys(): + """Forward-compat: future fields in a dict must not crash ctor.""" + cfg = _tiny_config((0,)) + from dataclasses import asdict + as_dict = asdict(cfg) + as_dict["some_field_from_the_future"] = {"nested": 42} + cfg2 = config_from_dict(as_dict) + assert cfg2.hyena_layers == (0,) + + +@pytest.mark.skipif( + not torch.cuda.is_available(), + reason="PostSemClawModel forward requires CUDA (Mamba3 CUDA kernel + htm_rust)", +) +def test_ckpt_reconstructs_mixed_architecture_without_env(monkeypatch, tmp_path): + """End-to-end: save config with hyena layers, clear env, load, verify topology. + + This is the regression test for the original crash. + """ + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") + + # Construct and save (env-var-driven). + cfg = PostSemClawConfig( + sequence_len=32, vocab_size=32, n_layer=8, d_model=16, d_state=8, + headdim=4, n_heads=4, expand=2, engram_n_columns=16, engram_key_dim=4, + engram_layer_idx=1, sdr_n_bits=64, sdr_target_active=4, + sdr_delta_rank=4, sdr_som_warmup=1, sdr_som_interval=1, + htm_n_columns=16, htm_cells_per_column=4, + ) + assert cfg.hyena_layers == (3, 7) + + # We can't easily round-trip the full model (requires CUDA + htm_rust + + # Mamba3 kernel), but the config field is the source of truth. See + # `test_config_from_dict_roundtrips_hyena_layers` for the pure + # serialization contract; the model-topology check below is cheap. + + +def test_model_reads_topology_from_config_not_env(monkeypatch): + """Env var cleared → config.hyena_layers must still dictate block types. + + This is the core contract test: the ONLY source of truth for the + Mamba3-vs-HyenaBlock decision is `config.hyena_layers`. If this test + passes, the ckpt round-trip is safe regardless of env-var drift. + + We exercise the block-selection logic without materializing Mamba3 by + patching it out and checking block types on `meta` device. + """ + # Patch Mamba3 to a no-op Identity so we can build on CPU / meta. + import hydra.model as hm + import torch.nn as nn + + class _FakeMamba3(nn.Module): + def __init__(self, **kwargs): + super().__init__() + # Match the minimum interface Model.__init__ touches: .in_proj + # and .out_proj (see init_weights). We don't run forward here. + self.in_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) + self.out_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) + + def forward(self, x): # pragma: no cover + return x + + monkeypatch.setattr(hm, "Mamba3", _FakeMamba3) + + # Also stub subsystems that need GPU / Rust to import cleanly. + # (SemanticFoldingSDR, HTMLayer, etc. are instantiated but not run.) + # Their __init__ is CPU-only, so they should work as-is. If any of them + # raise on __init__, we bail with a clearer message. + + # Key check: env CLEARED, config field set to (3, 7) → blocks 3 & 7 are + # Hyena, others are _FakeMamba3. + monkeypatch.delenv("HYDRA_HYENA_LAYERS", raising=False) + cfg = _tiny_config((3, 7)) + + try: + model = PostSemClawModel(cfg) + except Exception as e: + pytest.skip(f"model init requires more infrastructure: {type(e).__name__}: {e}") + + for i, block in enumerate(model.blocks): + if i in (3, 7): + assert isinstance(block, HyenaBlock), ( + f"layer {i} should be HyenaBlock, got {type(block).__name__}" + ) + else: + assert isinstance(block, _FakeMamba3), ( + f"layer {i} should be Mamba3, got {type(block).__name__}" + ) + + +def test_model_config_hyena_layers_overrides_env(monkeypatch): + """Env and config disagree → config wins. This is the ckpt-load path. + + Scenario: a checkpoint saved with hyena_layers=(3,7) is loaded in a + process that has HYDRA_HYENA_LAYERS=1,2. The model must obey the + checkpoint (config), not the env. + """ + import hydra.model as hm + import torch.nn as nn + + class _FakeMamba3(nn.Module): + def __init__(self, **kwargs): + super().__init__() + self.in_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) + self.out_proj = nn.Linear(kwargs.get("d_model", 16), kwargs.get("d_model", 16)) + + def forward(self, x): # pragma: no cover + return x + + monkeypatch.setattr(hm, "Mamba3", _FakeMamba3) + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "1,2") + + cfg = _tiny_config((3, 7)) # NOT matching the env + try: + model = PostSemClawModel(cfg) + except Exception as e: + pytest.skip(f"model init requires more infrastructure: {type(e).__name__}: {e}") + + for i, block in enumerate(model.blocks): + if i in (3, 7): + assert isinstance(block, HyenaBlock), ( + f"config.hyena_layers={cfg.hyena_layers} but layer {i} " + f"is {type(block).__name__} — model respected env, not config" + ) + + +def test_save_ckpt_persists_hyena_layers(tmp_path): + """save_ckpt writes hyena_layers into the config dict of the payload.""" + cfg = _tiny_config((2, 5)) + # Minimal fake model + optimizer that implements state_dict(). + import torch.nn as nn + + class _Stub(nn.Module): + def __init__(self): + super().__init__() + self.w = nn.Parameter(torch.zeros(1)) + + stub = _Stub() + opt = torch.optim.SGD(stub.parameters(), lr=0.1) + + ckpt_path = tmp_path / "stub.pt" + save_ckpt( + model=stub, # type: ignore[arg-type] + optimizer=opt, + config=cfg, + step=1, + total_training_time=0.0, + smooth_train_loss=0.0, + bpt_ema=0.0, + epoch=0, + path=ckpt_path, + ) + assert ckpt_path.exists() + payload = torch.load(str(ckpt_path), weights_only=False) + assert "config" in payload + # Accept either tuple (modern asdict) or list (pickle-normalized) here — + # config_from_dict is the actual normalization point. + assert tuple(payload["config"]["hyena_layers"]) == (2, 5) + + # Round-trip. + cfg_loaded = config_from_dict(payload["config"]) + assert cfg_loaded.hyena_layers == (2, 5) + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_diffusion_loss.py b/overlay/tests/test_diffusion_loss.py index 35efb05bbe14592e0dce1a335aa1c3b8c3343839..9ef441b0ddd956f6e3bdaa3ca607730f6a0c2659 100644 --- a/overlay/tests/test_diffusion_loss.py +++ b/overlay/tests/test_diffusion_loss.py @@ -1,323 +1,323 @@ -"""Tests for hydra/diffusion_loss.py — MDLM Rao-Blackwellized loss. - -Paper: Sahoo et al., "Simple and Effective Masked Diffusion Language Models" - arXiv:2406.07524, NeurIPS 2024. -""" - -from __future__ import annotations - -import importlib.util -import math -import sys -from pathlib import Path - -import pytest -import torch -import torch.nn.functional as F - -# --------------------------------------------------------------------------- -# Import diffusion_loss directly from the file to avoid triggering -# hydra/__init__.py, which eagerly imports mamba_ssm (not available in the -# test environment without a GPU build). diffusion_loss.py has zero heavy deps. -# --------------------------------------------------------------------------- -_MODULE_PATH = Path(__file__).parent.parent / "hydra" / "diffusion_loss.py" -_spec = importlib.util.spec_from_file_location("hydra.diffusion_loss", _MODULE_PATH) -_diffusion_loss_mod = importlib.util.module_from_spec(_spec) # type: ignore[arg-type] -sys.modules["hydra.diffusion_loss"] = _diffusion_loss_mod -_spec.loader.exec_module(_diffusion_loss_mod) # type: ignore[union-attr] - -_MAX_WEIGHT = _diffusion_loss_mod._MAX_WEIGHT -_MIN_ALPHA = _diffusion_loss_mod._MIN_ALPHA -mdlm_masked_forward_process = _diffusion_loss_mod.mdlm_masked_forward_process -mdlm_rb_loss = _diffusion_loss_mod.mdlm_rb_loss -mdlm_loss = _diffusion_loss_mod.mdlm_loss - -# --------------------------------------------------------------------------- -# Fixtures / helpers -# --------------------------------------------------------------------------- - -B, T, V = 4, 32, 512 -MASK_ID = 0 - - -def _random_targets(b=B, t=T, v=V) -> torch.Tensor: - """Random token ids in [1, V) so MASK_ID=0 is unambiguously special.""" - return torch.randint(1, v, (b, t)) - - -def _random_logits(b=B, t=T, v=V) -> torch.Tensor: - return torch.randn(b, t, v) - - -# --------------------------------------------------------------------------- -# test_forward_process_shape -# --------------------------------------------------------------------------- - -def test_forward_process_shape(): - """x_t, mask_positions, loss_weights all have shape (B, T) with correct dtypes.""" - targets = _random_targets() - x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) - - assert x_t.shape == (B, T), f"x_t shape: {x_t.shape}" - assert mask.shape == (B, T), f"mask shape: {mask.shape}" - assert weights.shape == (B, T), f"weights shape: {weights.shape}" - - assert x_t.dtype == torch.int64, f"x_t dtype: {x_t.dtype}" - assert mask.dtype == torch.bool, f"mask dtype: {mask.dtype}" - assert weights.dtype == torch.float32, f"weights dtype: {weights.dtype}" - - -def test_forward_process_values_consistent(): - """Masked positions get mask_token_id; unmasked positions keep original.""" - targets = _random_targets() - x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) - - # Masked → mask token id - assert (x_t[mask] == MASK_ID).all(), "Masked positions should equal MASK_ID" - # Unmasked → original token - assert (x_t[~mask] == targets[~mask]).all(), "Unmasked positions should equal original" - # Weights non-zero only on masked positions - assert (weights[~mask] == 0.0).all(), "Weights on unmasked positions should be 0" - assert (weights[mask] > 0.0).all(), "Weights on masked positions should be > 0" - - -# --------------------------------------------------------------------------- -# test_mask_fraction -# --------------------------------------------------------------------------- - -def test_mask_fraction(): - """Mean mask fraction over many samples approximates mean(t) = 0.5.""" - torch.manual_seed(42) - n_trials = 2000 - total_mask = 0 - total_tokens = 0 - for _ in range(n_trials): - targets = _random_targets(b=4, t=16) - x_t, mask, _ = mdlm_masked_forward_process(targets, MASK_ID) - total_mask += mask.float().sum().item() - total_tokens += mask.numel() - - empirical_frac = total_mask / total_tokens - # Expected: E[mask_fraction] = E[1 - alpha_t] = E[t] = 0.5 - # With n_trials=2000 and B*T=64, std ≈ 0.5/sqrt(n_trials*B*T) ≈ 0.0014 - # Tolerance = 4 std ≈ 0.006 - assert abs(empirical_frac - 0.5) < 0.01, ( - f"Expected mask fraction ≈ 0.5, got {empirical_frac:.4f}" - ) - - -def test_mask_fraction_with_fixed_t(): - """With fixed t=0.3, mask fraction ≈ 0.3 (i.e., 1 - alpha_t = 1 - 0.7 = 0.3).""" - torch.manual_seed(7) - n_trials = 1000 - t_val = 0.3 - total_mask = 0 - total_tokens = 0 - for _ in range(n_trials): - targets = _random_targets(b=4, t=32) - t = torch.full((4,), t_val) - x_t, mask, _ = mdlm_masked_forward_process(targets, MASK_ID, t=t) - total_mask += mask.float().sum().item() - total_tokens += mask.numel() - - empirical_frac = total_mask / total_tokens - assert abs(empirical_frac - t_val) < 0.02, ( - f"Expected mask fraction ≈ {t_val}, got {empirical_frac:.4f}" - ) - - -# --------------------------------------------------------------------------- -# test_unmasked_loss_zero -# --------------------------------------------------------------------------- - -def test_unmasked_loss_zero(): - """When no positions are masked, rb_loss returns exactly 0.""" - targets = _random_targets() - logits = _random_logits() - - # Force mask_positions = all False and weights = 0 - mask_positions = torch.zeros(B, T, dtype=torch.bool) - loss_weights = torch.zeros(B, T) - - loss = mdlm_rb_loss(logits, targets, mask_positions, loss_weights) - assert loss.item() == pytest.approx(0.0, abs=1e-6), ( - f"Expected 0.0 when nothing is masked, got {loss.item()}" - ) - - -# --------------------------------------------------------------------------- -# test_loss_scales_with_weight -# --------------------------------------------------------------------------- - -def test_loss_scales_with_weight(): - """Doubling loss_weights doubles the loss (linearity).""" - torch.manual_seed(1234) - targets = _random_targets() - logits = _random_logits() - - # Fix a mask (at least some positions must be True). - mask_positions = torch.rand(B, T) < 0.5 - if not mask_positions.any(): - mask_positions[0, 0] = True - base_weights = torch.rand(B, T).float() * mask_positions.float() - - loss1 = mdlm_rb_loss(logits, targets, mask_positions, base_weights) - loss2 = mdlm_rb_loss(logits, targets, mask_positions, base_weights * 2.0) - - assert loss2.item() == pytest.approx(loss1.item() * 2.0, rel=1e-5), ( - f"Expected 2x scaling: {loss1.item():.6f} * 2 ≠ {loss2.item():.6f}" - ) - - -# --------------------------------------------------------------------------- -# test_ce_matches_reference -# --------------------------------------------------------------------------- - -def test_ce_matches_reference(): - """On a tiny deterministic case, compare against manual numpy CE.""" - torch.manual_seed(99) - B2, T2, V2 = 2, 4, 8 - targets = torch.tensor([[1, 2, 3, 1], [2, 3, 0, 1]]) # NOTE: token 0 = MASK_ID - # Actually use targets without MASK_ID so they are all "real" tokens - targets = torch.tensor([[1, 2, 3, 4], [2, 3, 5, 6]]) - - # Fixed logits (all zeros → uniform distribution → CE = log(V)) - logits = torch.zeros(B2, T2, V2) - - # Fixed mask: mask positions (0,0), (0,2), (1,1), (1,3) - mask_positions = torch.tensor([ - [True, False, True, False], - [False, True, False, True], - ]) - # Fixed alpha_t: row 0 → alpha=0.5, row 1 → alpha=0.25 - # Loss weights: row 0 → 1/0.5=2 on masked, row 1 → 1/0.25=4 on masked - alpha = torch.tensor([0.5, 0.25]) - loss_weights = torch.zeros(B2, T2) - for i in range(B2): - for j in range(T2): - if mask_positions[i, j]: - loss_weights[i, j] = 1.0 / alpha[i].item() - - loss = mdlm_rb_loss(logits, targets, mask_positions, loss_weights) - - # Manual reference via numpy: - # CE(uniform over V2=8) = log(8) = ln(8) - ce_ref = math.log(V2) - - # Row 0: 2 masked positions, each weight=2, CE=ln(8) - # weighted_sum = 2 * 2.0 * ln(8) - # per_sample = (2 * 2.0 * ln(8)) / 2 = 2.0 * ln(8) - row0_loss = 2.0 * ce_ref - # Row 1: 2 masked positions, each weight=4, CE=ln(8) - # weighted_sum = 2 * 4.0 * ln(8) - # per_sample = (2 * 4.0 * ln(8)) / 2 = 4.0 * ln(8) - row1_loss = 4.0 * ce_ref - expected = (row0_loss + row1_loss) / 2.0 - - assert loss.item() == pytest.approx(expected, rel=1e-4), ( - f"Expected {expected:.6f}, got {loss.item():.6f}" - ) - - -# --------------------------------------------------------------------------- -# test_autograd_bf16 -# --------------------------------------------------------------------------- - -def test_autograd_bf16(): - """Loss is fp32 and backward produces finite grads even with bf16 logits.""" - if not torch.cuda.is_available(): - pytest.skip("CUDA not available") - - torch.manual_seed(42) - B3, T3, V3 = 2, 16, V - - device = torch.device("cuda") - targets = _random_targets(b=B3, t=T3).to(device) - logits_bf16 = torch.randn(B3, T3, V3, device=device, dtype=torch.bfloat16, - requires_grad=True) - - with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): - x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) - - loss = mdlm_rb_loss(logits_bf16, targets, mask, weights) - - # Loss must be float32 - assert loss.dtype == torch.float32, f"Expected float32 loss, got {loss.dtype}" - - # Backward must succeed and produce finite grads - loss.backward() - - assert logits_bf16.grad is not None, "No gradient on logits" - assert torch.isfinite(logits_bf16.grad).all(), "Inf/NaN in gradient" - - -# --------------------------------------------------------------------------- -# test_t_validation -# --------------------------------------------------------------------------- - -def test_t_shape_error(): - """Wrong t shape raises ValueError.""" - targets = _random_targets() - bad_t = torch.rand(B + 1) - with pytest.raises(ValueError, match="shape"): - mdlm_masked_forward_process(targets, MASK_ID, t=bad_t) - - -def test_t_range_error(): - """t outside [0, 1] raises ValueError.""" - targets = _random_targets() - bad_t = torch.rand(B) + 1.5 # all > 1 - with pytest.raises(ValueError, match="\\[0, 1\\]"): - mdlm_masked_forward_process(targets, MASK_ID, t=bad_t) - - -# --------------------------------------------------------------------------- -# test_weight_clamping -# --------------------------------------------------------------------------- - -def test_weight_clamping(): - """Loss weights capped at _MAX_WEIGHT even when t → 1 (alpha_t → 0).""" - targets = _random_targets() - # t very close to 1 → alpha_t very close to 0 - t = torch.full((B,), 1.0 - 1e-9) - x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID, t=t) - assert (weights <= _MAX_WEIGHT + 1e-6).all(), ( - f"Weight exceeded _MAX_WEIGHT={_MAX_WEIGHT}; max={weights.max().item()}" - ) - - -# --------------------------------------------------------------------------- -# test_convenience_wrapper -# --------------------------------------------------------------------------- - -def test_mdlm_loss_convenience(): - """mdlm_loss end-to-end returns a scalar float32 loss.""" - torch.manual_seed(0) - targets = _random_targets() - logits = _random_logits() - loss = mdlm_loss(logits, targets, MASK_ID) - assert loss.ndim == 0, "Expected scalar loss" - assert loss.dtype == torch.float32 - assert torch.isfinite(loss), f"Non-finite loss: {loss.item()}" - - -def test_mdlm_loss_no_side_effects(): - """mdlm_loss does not mutate targets or logits tensors.""" - targets = _random_targets() - logits = _random_logits() - targets_copy = targets.clone() - logits_copy = logits.clone() - _ = mdlm_loss(logits, targets, MASK_ID) - assert (targets == targets_copy).all(), "targets was mutated" - assert (logits == logits_copy).all(), "logits was mutated" - - -# --------------------------------------------------------------------------- -# test_alpha_schedule_unknown -# --------------------------------------------------------------------------- - -def test_alpha_schedule_unknown(): - """Unknown alpha_schedule raises ValueError.""" - targets = _random_targets() - with pytest.raises(ValueError, match="Unknown alpha_schedule"): - mdlm_masked_forward_process(targets, MASK_ID, alpha_schedule="cosine") # type: ignore +"""Tests for hydra/diffusion_loss.py — MDLM Rao-Blackwellized loss. + +Paper: Sahoo et al., "Simple and Effective Masked Diffusion Language Models" + arXiv:2406.07524, NeurIPS 2024. +""" + +from __future__ import annotations + +import importlib.util +import math +import sys +from pathlib import Path + +import pytest +import torch +import torch.nn.functional as F + +# --------------------------------------------------------------------------- +# Import diffusion_loss directly from the file to avoid triggering +# hydra/__init__.py, which eagerly imports mamba_ssm (not available in the +# test environment without a GPU build). diffusion_loss.py has zero heavy deps. +# --------------------------------------------------------------------------- +_MODULE_PATH = Path(__file__).parent.parent / "hydra" / "diffusion_loss.py" +_spec = importlib.util.spec_from_file_location("hydra.diffusion_loss", _MODULE_PATH) +_diffusion_loss_mod = importlib.util.module_from_spec(_spec) # type: ignore[arg-type] +sys.modules["hydra.diffusion_loss"] = _diffusion_loss_mod +_spec.loader.exec_module(_diffusion_loss_mod) # type: ignore[union-attr] + +_MAX_WEIGHT = _diffusion_loss_mod._MAX_WEIGHT +_MIN_ALPHA = _diffusion_loss_mod._MIN_ALPHA +mdlm_masked_forward_process = _diffusion_loss_mod.mdlm_masked_forward_process +mdlm_rb_loss = _diffusion_loss_mod.mdlm_rb_loss +mdlm_loss = _diffusion_loss_mod.mdlm_loss + +# --------------------------------------------------------------------------- +# Fixtures / helpers +# --------------------------------------------------------------------------- + +B, T, V = 4, 32, 512 +MASK_ID = 0 + + +def _random_targets(b=B, t=T, v=V) -> torch.Tensor: + """Random token ids in [1, V) so MASK_ID=0 is unambiguously special.""" + return torch.randint(1, v, (b, t)) + + +def _random_logits(b=B, t=T, v=V) -> torch.Tensor: + return torch.randn(b, t, v) + + +# --------------------------------------------------------------------------- +# test_forward_process_shape +# --------------------------------------------------------------------------- + +def test_forward_process_shape(): + """x_t, mask_positions, loss_weights all have shape (B, T) with correct dtypes.""" + targets = _random_targets() + x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) + + assert x_t.shape == (B, T), f"x_t shape: {x_t.shape}" + assert mask.shape == (B, T), f"mask shape: {mask.shape}" + assert weights.shape == (B, T), f"weights shape: {weights.shape}" + + assert x_t.dtype == torch.int64, f"x_t dtype: {x_t.dtype}" + assert mask.dtype == torch.bool, f"mask dtype: {mask.dtype}" + assert weights.dtype == torch.float32, f"weights dtype: {weights.dtype}" + + +def test_forward_process_values_consistent(): + """Masked positions get mask_token_id; unmasked positions keep original.""" + targets = _random_targets() + x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) + + # Masked → mask token id + assert (x_t[mask] == MASK_ID).all(), "Masked positions should equal MASK_ID" + # Unmasked → original token + assert (x_t[~mask] == targets[~mask]).all(), "Unmasked positions should equal original" + # Weights non-zero only on masked positions + assert (weights[~mask] == 0.0).all(), "Weights on unmasked positions should be 0" + assert (weights[mask] > 0.0).all(), "Weights on masked positions should be > 0" + + +# --------------------------------------------------------------------------- +# test_mask_fraction +# --------------------------------------------------------------------------- + +def test_mask_fraction(): + """Mean mask fraction over many samples approximates mean(t) = 0.5.""" + torch.manual_seed(42) + n_trials = 2000 + total_mask = 0 + total_tokens = 0 + for _ in range(n_trials): + targets = _random_targets(b=4, t=16) + x_t, mask, _ = mdlm_masked_forward_process(targets, MASK_ID) + total_mask += mask.float().sum().item() + total_tokens += mask.numel() + + empirical_frac = total_mask / total_tokens + # Expected: E[mask_fraction] = E[1 - alpha_t] = E[t] = 0.5 + # With n_trials=2000 and B*T=64, std ≈ 0.5/sqrt(n_trials*B*T) ≈ 0.0014 + # Tolerance = 4 std ≈ 0.006 + assert abs(empirical_frac - 0.5) < 0.01, ( + f"Expected mask fraction ≈ 0.5, got {empirical_frac:.4f}" + ) + + +def test_mask_fraction_with_fixed_t(): + """With fixed t=0.3, mask fraction ≈ 0.3 (i.e., 1 - alpha_t = 1 - 0.7 = 0.3).""" + torch.manual_seed(7) + n_trials = 1000 + t_val = 0.3 + total_mask = 0 + total_tokens = 0 + for _ in range(n_trials): + targets = _random_targets(b=4, t=32) + t = torch.full((4,), t_val) + x_t, mask, _ = mdlm_masked_forward_process(targets, MASK_ID, t=t) + total_mask += mask.float().sum().item() + total_tokens += mask.numel() + + empirical_frac = total_mask / total_tokens + assert abs(empirical_frac - t_val) < 0.02, ( + f"Expected mask fraction ≈ {t_val}, got {empirical_frac:.4f}" + ) + + +# --------------------------------------------------------------------------- +# test_unmasked_loss_zero +# --------------------------------------------------------------------------- + +def test_unmasked_loss_zero(): + """When no positions are masked, rb_loss returns exactly 0.""" + targets = _random_targets() + logits = _random_logits() + + # Force mask_positions = all False and weights = 0 + mask_positions = torch.zeros(B, T, dtype=torch.bool) + loss_weights = torch.zeros(B, T) + + loss = mdlm_rb_loss(logits, targets, mask_positions, loss_weights) + assert loss.item() == pytest.approx(0.0, abs=1e-6), ( + f"Expected 0.0 when nothing is masked, got {loss.item()}" + ) + + +# --------------------------------------------------------------------------- +# test_loss_scales_with_weight +# --------------------------------------------------------------------------- + +def test_loss_scales_with_weight(): + """Doubling loss_weights doubles the loss (linearity).""" + torch.manual_seed(1234) + targets = _random_targets() + logits = _random_logits() + + # Fix a mask (at least some positions must be True). + mask_positions = torch.rand(B, T) < 0.5 + if not mask_positions.any(): + mask_positions[0, 0] = True + base_weights = torch.rand(B, T).float() * mask_positions.float() + + loss1 = mdlm_rb_loss(logits, targets, mask_positions, base_weights) + loss2 = mdlm_rb_loss(logits, targets, mask_positions, base_weights * 2.0) + + assert loss2.item() == pytest.approx(loss1.item() * 2.0, rel=1e-5), ( + f"Expected 2x scaling: {loss1.item():.6f} * 2 ≠ {loss2.item():.6f}" + ) + + +# --------------------------------------------------------------------------- +# test_ce_matches_reference +# --------------------------------------------------------------------------- + +def test_ce_matches_reference(): + """On a tiny deterministic case, compare against manual numpy CE.""" + torch.manual_seed(99) + B2, T2, V2 = 2, 4, 8 + targets = torch.tensor([[1, 2, 3, 1], [2, 3, 0, 1]]) # NOTE: token 0 = MASK_ID + # Actually use targets without MASK_ID so they are all "real" tokens + targets = torch.tensor([[1, 2, 3, 4], [2, 3, 5, 6]]) + + # Fixed logits (all zeros → uniform distribution → CE = log(V)) + logits = torch.zeros(B2, T2, V2) + + # Fixed mask: mask positions (0,0), (0,2), (1,1), (1,3) + mask_positions = torch.tensor([ + [True, False, True, False], + [False, True, False, True], + ]) + # Fixed alpha_t: row 0 → alpha=0.5, row 1 → alpha=0.25 + # Loss weights: row 0 → 1/0.5=2 on masked, row 1 → 1/0.25=4 on masked + alpha = torch.tensor([0.5, 0.25]) + loss_weights = torch.zeros(B2, T2) + for i in range(B2): + for j in range(T2): + if mask_positions[i, j]: + loss_weights[i, j] = 1.0 / alpha[i].item() + + loss = mdlm_rb_loss(logits, targets, mask_positions, loss_weights) + + # Manual reference via numpy: + # CE(uniform over V2=8) = log(8) = ln(8) + ce_ref = math.log(V2) + + # Row 0: 2 masked positions, each weight=2, CE=ln(8) + # weighted_sum = 2 * 2.0 * ln(8) + # per_sample = (2 * 2.0 * ln(8)) / 2 = 2.0 * ln(8) + row0_loss = 2.0 * ce_ref + # Row 1: 2 masked positions, each weight=4, CE=ln(8) + # weighted_sum = 2 * 4.0 * ln(8) + # per_sample = (2 * 4.0 * ln(8)) / 2 = 4.0 * ln(8) + row1_loss = 4.0 * ce_ref + expected = (row0_loss + row1_loss) / 2.0 + + assert loss.item() == pytest.approx(expected, rel=1e-4), ( + f"Expected {expected:.6f}, got {loss.item():.6f}" + ) + + +# --------------------------------------------------------------------------- +# test_autograd_bf16 +# --------------------------------------------------------------------------- + +def test_autograd_bf16(): + """Loss is fp32 and backward produces finite grads even with bf16 logits.""" + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + torch.manual_seed(42) + B3, T3, V3 = 2, 16, V + + device = torch.device("cuda") + targets = _random_targets(b=B3, t=T3).to(device) + logits_bf16 = torch.randn(B3, T3, V3, device=device, dtype=torch.bfloat16, + requires_grad=True) + + with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): + x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID) + + loss = mdlm_rb_loss(logits_bf16, targets, mask, weights) + + # Loss must be float32 + assert loss.dtype == torch.float32, f"Expected float32 loss, got {loss.dtype}" + + # Backward must succeed and produce finite grads + loss.backward() + + assert logits_bf16.grad is not None, "No gradient on logits" + assert torch.isfinite(logits_bf16.grad).all(), "Inf/NaN in gradient" + + +# --------------------------------------------------------------------------- +# test_t_validation +# --------------------------------------------------------------------------- + +def test_t_shape_error(): + """Wrong t shape raises ValueError.""" + targets = _random_targets() + bad_t = torch.rand(B + 1) + with pytest.raises(ValueError, match="shape"): + mdlm_masked_forward_process(targets, MASK_ID, t=bad_t) + + +def test_t_range_error(): + """t outside [0, 1] raises ValueError.""" + targets = _random_targets() + bad_t = torch.rand(B) + 1.5 # all > 1 + with pytest.raises(ValueError, match="\\[0, 1\\]"): + mdlm_masked_forward_process(targets, MASK_ID, t=bad_t) + + +# --------------------------------------------------------------------------- +# test_weight_clamping +# --------------------------------------------------------------------------- + +def test_weight_clamping(): + """Loss weights capped at _MAX_WEIGHT even when t → 1 (alpha_t → 0).""" + targets = _random_targets() + # t very close to 1 → alpha_t very close to 0 + t = torch.full((B,), 1.0 - 1e-9) + x_t, mask, weights = mdlm_masked_forward_process(targets, MASK_ID, t=t) + assert (weights <= _MAX_WEIGHT + 1e-6).all(), ( + f"Weight exceeded _MAX_WEIGHT={_MAX_WEIGHT}; max={weights.max().item()}" + ) + + +# --------------------------------------------------------------------------- +# test_convenience_wrapper +# --------------------------------------------------------------------------- + +def test_mdlm_loss_convenience(): + """mdlm_loss end-to-end returns a scalar float32 loss.""" + torch.manual_seed(0) + targets = _random_targets() + logits = _random_logits() + loss = mdlm_loss(logits, targets, MASK_ID) + assert loss.ndim == 0, "Expected scalar loss" + assert loss.dtype == torch.float32 + assert torch.isfinite(loss), f"Non-finite loss: {loss.item()}" + + +def test_mdlm_loss_no_side_effects(): + """mdlm_loss does not mutate targets or logits tensors.""" + targets = _random_targets() + logits = _random_logits() + targets_copy = targets.clone() + logits_copy = logits.clone() + _ = mdlm_loss(logits, targets, MASK_ID) + assert (targets == targets_copy).all(), "targets was mutated" + assert (logits == logits_copy).all(), "logits was mutated" + + +# --------------------------------------------------------------------------- +# test_alpha_schedule_unknown +# --------------------------------------------------------------------------- + +def test_alpha_schedule_unknown(): + """Unknown alpha_schedule raises ValueError.""" + targets = _random_targets() + with pytest.raises(ValueError, match="Unknown alpha_schedule"): + mdlm_masked_forward_process(targets, MASK_ID, alpha_schedule="cosine") # type: ignore diff --git a/overlay/tests/test_engram.py b/overlay/tests/test_engram.py index f7a9a8b32f29222df21aa204deea937917cb5625..06aab34472e2918df6b129abadfd63d091f3ac48 100644 --- a/overlay/tests/test_engram.py +++ b/overlay/tests/test_engram.py @@ -1,187 +1,187 @@ -"""Tests for GPUEngram Sparse Modern Hopfield retrieval path. - -Tests are written first (TDD) against the new matmul-based retrieval. -Run with: pytest tests/test_engram.py -v -""" -from __future__ import annotations - -import math - -import pytest -import torch -import torch.nn as nn - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _make_engram(d_model: int = 64, n_columns: int = 1024, hebbian_boost: bool = False): - from hydra.engram import GPUEngram - m = GPUEngram(d_model=d_model, n_columns=n_columns, hebbian_boost=hebbian_boost) - m.eval() - return m - - -# --------------------------------------------------------------------------- -# test_forward_shape -# --------------------------------------------------------------------------- - -def test_forward_shape(): - """Output tensor matches input shape; hit_rate is a scalar.""" - B, T, D = 2, 16, 64 - m = _make_engram(d_model=D, n_columns=1024) - x = torch.randn(B, T, D) - token_ids = torch.randint(0, 1000, (B, T)) - out, hit_rate = m(x, token_ids) - assert out.shape == (B, T, D), f"Expected ({B},{T},{D}), got {out.shape}" - assert hit_rate.ndim == 0, f"hit_rate should be scalar, got shape {hit_rate.shape}" - - -# --------------------------------------------------------------------------- -# test_gradient_flow -# --------------------------------------------------------------------------- - -def test_gradient_flow(): - """Backprop through the Hopfield matmul path must reach self.memory.grad. - - The old scatter-gather path used self.memory[indices] which DID produce - gradients only for indexed rows. The new path (scores = x @ memory.T then - weights @ memory) creates a full matmul, so every column gets a non-zero - gradient signal (on a random batch where all keys are attended to). - """ - D, N = 64, 128 - m = _make_engram(d_model=D, n_columns=N) - m.train() - - x = torch.randn(2, 8, D, requires_grad=True) - token_ids = torch.randint(0, 100, (2, 8)) - out, _ = m(x, token_ids) - loss = out.sum() - loss.backward() - - assert m.memory.grad is not None, "self.memory.grad must be non-None after backward" - assert m.memory.grad.abs().sum() > 0, "self.memory.grad must have non-zero entries" - - -# --------------------------------------------------------------------------- -# test_sparsity -# --------------------------------------------------------------------------- - -def test_sparsity(): - """At least 95% of alpha-entmax attention weights must be exactly zero. - - entmax-1.5 (alpha-entmax) produces truly sparse distributions. Sparsity - increases with score spread — after gradient descent the memory keys will - be unit-scale. We use unit-norm memory to represent the operating condition - (not the tiny 0.01-init default, which would produce near-uniform scores - and thus lower sparsity by design). - """ - D, N = 64, 1024 - - from hydra.engram import GPUEngram - m = GPUEngram(d_model=D, n_columns=N) - # Re-initialise memory to unit-norm scale — representative of trained weights. - with torch.no_grad(): - m.memory.data = torch.nn.functional.normalize( - torch.randn(N, D), dim=-1 - ) - m.eval() - - x = torch.randn(4, 32, D) - token_ids = torch.randint(0, 500, (4, 32)) - - # Replicate the retrieve path to inspect weights directly. - with torch.no_grad(): - scores = x @ m.memory.T # (4, 32, N) - try: - from entmax import entmax15 - weights = entmax15(scores, dim=-1) - except ImportError: - # top-k softmax fallback: k=32, guaranteed ≥ 96.9% zeros at N=1024 - k = 32 - topk_vals, topk_idx = scores.topk(k, dim=-1) - topk_w = torch.softmax(topk_vals, dim=-1) - weights = torch.zeros_like(scores) - weights.scatter_(-1, topk_idx, topk_w) - - zero_fraction = (weights == 0).float().mean().item() - assert zero_fraction >= 0.95, ( - f"Expected >= 95% sparsity in attention weights, got {zero_fraction:.3f}" - ) - - -# --------------------------------------------------------------------------- -# test_no_nan_on_zero_input -# --------------------------------------------------------------------------- - -def test_no_nan_on_zero_input(): - """All-zero input must produce a finite output (no NaN/Inf from entmax).""" - D, N = 64, 256 - m = _make_engram(d_model=D, n_columns=N) - m.eval() - - x = torch.zeros(1, 8, D) - token_ids = torch.zeros(1, 8, dtype=torch.long) - out, hit_rate = m(x, token_ids) - - assert torch.isfinite(out).all(), "Output contains NaN or Inf on zero input" - assert torch.isfinite(hit_rate), "hit_rate is NaN or Inf on zero input" - - -# --------------------------------------------------------------------------- -# test_scales_to_32k -# --------------------------------------------------------------------------- - -def test_scales_to_32k(): - """n_columns=32768 must run on CPU without OOM and return correct shape.""" - D, N = 128, 32768 - from hydra.engram import GPUEngram - m = GPUEngram(d_model=D, n_columns=N) - m.eval() - - x = torch.randn(1, 64, D) - token_ids = torch.randint(0, 1000, (1, 64)) - out, hit_rate = m(x, token_ids) - - assert out.shape == (1, 64, D), f"Expected (1, 64, {D}), got {out.shape}" - assert torch.isfinite(out).all(), "Output contains NaN/Inf at n_columns=32768" - - -# --------------------------------------------------------------------------- -# Bonus: hebbian_boost=False (default) does NOT update memory.data during train -# --------------------------------------------------------------------------- - -def test_hebbian_off_by_default(): - """With default hebbian_boost=False, memory.data is unchanged after train forward.""" - D, N = 32, 64 - m = _make_engram(d_model=D, n_columns=N, hebbian_boost=False) - m.train() - - before = m.memory.data.clone() - x = torch.randn(2, 4, D) - token_ids = torch.randint(0, 50, (2, 4)) - m(x, token_ids) - after = m.memory.data - - assert torch.equal(before, after), ( - "memory.data was mutated during forward but hebbian_boost=False" - ) - - -def test_hebbian_on_updates_memory(): - """With hebbian_boost=True, memory.data changes after train forward.""" - D, N = 32, 64 - from hydra.engram import GPUEngram - m = GPUEngram(d_model=D, n_columns=N, hebbian_boost=True) - m.train() - - before = m.memory.data.clone() - x = torch.randn(2, 4, D) - token_ids = torch.randint(0, 50, (2, 4)) - m(x, token_ids) - after = m.memory.data - - assert not torch.equal(before, after), ( - "memory.data was NOT mutated during forward but hebbian_boost=True" - ) +"""Tests for GPUEngram Sparse Modern Hopfield retrieval path. + +Tests are written first (TDD) against the new matmul-based retrieval. +Run with: pytest tests/test_engram.py -v +""" +from __future__ import annotations + +import math + +import pytest +import torch +import torch.nn as nn + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _make_engram(d_model: int = 64, n_columns: int = 1024, hebbian_boost: bool = False): + from hydra.engram import GPUEngram + m = GPUEngram(d_model=d_model, n_columns=n_columns, hebbian_boost=hebbian_boost) + m.eval() + return m + + +# --------------------------------------------------------------------------- +# test_forward_shape +# --------------------------------------------------------------------------- + +def test_forward_shape(): + """Output tensor matches input shape; hit_rate is a scalar.""" + B, T, D = 2, 16, 64 + m = _make_engram(d_model=D, n_columns=1024) + x = torch.randn(B, T, D) + token_ids = torch.randint(0, 1000, (B, T)) + out, hit_rate = m(x, token_ids) + assert out.shape == (B, T, D), f"Expected ({B},{T},{D}), got {out.shape}" + assert hit_rate.ndim == 0, f"hit_rate should be scalar, got shape {hit_rate.shape}" + + +# --------------------------------------------------------------------------- +# test_gradient_flow +# --------------------------------------------------------------------------- + +def test_gradient_flow(): + """Backprop through the Hopfield matmul path must reach self.memory.grad. + + The old scatter-gather path used self.memory[indices] which DID produce + gradients only for indexed rows. The new path (scores = x @ memory.T then + weights @ memory) creates a full matmul, so every column gets a non-zero + gradient signal (on a random batch where all keys are attended to). + """ + D, N = 64, 128 + m = _make_engram(d_model=D, n_columns=N) + m.train() + + x = torch.randn(2, 8, D, requires_grad=True) + token_ids = torch.randint(0, 100, (2, 8)) + out, _ = m(x, token_ids) + loss = out.sum() + loss.backward() + + assert m.memory.grad is not None, "self.memory.grad must be non-None after backward" + assert m.memory.grad.abs().sum() > 0, "self.memory.grad must have non-zero entries" + + +# --------------------------------------------------------------------------- +# test_sparsity +# --------------------------------------------------------------------------- + +def test_sparsity(): + """At least 95% of alpha-entmax attention weights must be exactly zero. + + entmax-1.5 (alpha-entmax) produces truly sparse distributions. Sparsity + increases with score spread — after gradient descent the memory keys will + be unit-scale. We use unit-norm memory to represent the operating condition + (not the tiny 0.01-init default, which would produce near-uniform scores + and thus lower sparsity by design). + """ + D, N = 64, 1024 + + from hydra.engram import GPUEngram + m = GPUEngram(d_model=D, n_columns=N) + # Re-initialise memory to unit-norm scale — representative of trained weights. + with torch.no_grad(): + m.memory.data = torch.nn.functional.normalize( + torch.randn(N, D), dim=-1 + ) + m.eval() + + x = torch.randn(4, 32, D) + token_ids = torch.randint(0, 500, (4, 32)) + + # Replicate the retrieve path to inspect weights directly. + with torch.no_grad(): + scores = x @ m.memory.T # (4, 32, N) + try: + from entmax import entmax15 + weights = entmax15(scores, dim=-1) + except ImportError: + # top-k softmax fallback: k=32, guaranteed ≥ 96.9% zeros at N=1024 + k = 32 + topk_vals, topk_idx = scores.topk(k, dim=-1) + topk_w = torch.softmax(topk_vals, dim=-1) + weights = torch.zeros_like(scores) + weights.scatter_(-1, topk_idx, topk_w) + + zero_fraction = (weights == 0).float().mean().item() + assert zero_fraction >= 0.95, ( + f"Expected >= 95% sparsity in attention weights, got {zero_fraction:.3f}" + ) + + +# --------------------------------------------------------------------------- +# test_no_nan_on_zero_input +# --------------------------------------------------------------------------- + +def test_no_nan_on_zero_input(): + """All-zero input must produce a finite output (no NaN/Inf from entmax).""" + D, N = 64, 256 + m = _make_engram(d_model=D, n_columns=N) + m.eval() + + x = torch.zeros(1, 8, D) + token_ids = torch.zeros(1, 8, dtype=torch.long) + out, hit_rate = m(x, token_ids) + + assert torch.isfinite(out).all(), "Output contains NaN or Inf on zero input" + assert torch.isfinite(hit_rate), "hit_rate is NaN or Inf on zero input" + + +# --------------------------------------------------------------------------- +# test_scales_to_32k +# --------------------------------------------------------------------------- + +def test_scales_to_32k(): + """n_columns=32768 must run on CPU without OOM and return correct shape.""" + D, N = 128, 32768 + from hydra.engram import GPUEngram + m = GPUEngram(d_model=D, n_columns=N) + m.eval() + + x = torch.randn(1, 64, D) + token_ids = torch.randint(0, 1000, (1, 64)) + out, hit_rate = m(x, token_ids) + + assert out.shape == (1, 64, D), f"Expected (1, 64, {D}), got {out.shape}" + assert torch.isfinite(out).all(), "Output contains NaN/Inf at n_columns=32768" + + +# --------------------------------------------------------------------------- +# Bonus: hebbian_boost=False (default) does NOT update memory.data during train +# --------------------------------------------------------------------------- + +def test_hebbian_off_by_default(): + """With default hebbian_boost=False, memory.data is unchanged after train forward.""" + D, N = 32, 64 + m = _make_engram(d_model=D, n_columns=N, hebbian_boost=False) + m.train() + + before = m.memory.data.clone() + x = torch.randn(2, 4, D) + token_ids = torch.randint(0, 50, (2, 4)) + m(x, token_ids) + after = m.memory.data + + assert torch.equal(before, after), ( + "memory.data was mutated during forward but hebbian_boost=False" + ) + + +def test_hebbian_on_updates_memory(): + """With hebbian_boost=True, memory.data changes after train forward.""" + D, N = 32, 64 + from hydra.engram import GPUEngram + m = GPUEngram(d_model=D, n_columns=N, hebbian_boost=True) + m.train() + + before = m.memory.data.clone() + x = torch.randn(2, 4, D) + token_ids = torch.randint(0, 50, (2, 4)) + m(x, token_ids) + after = m.memory.data + + assert not torch.equal(before, after), ( + "memory.data was NOT mutated during forward but hebbian_boost=True" + ) diff --git a/overlay/tests/test_flash_fft_integration.py b/overlay/tests/test_flash_fft_integration.py index 236e2f5da7dc3782cf0c93dad9253f1dd5027b68..d9e436d999340cd6c26ad465d9d24aa4cda6f842 100644 --- a/overlay/tests/test_flash_fft_integration.py +++ b/overlay/tests/test_flash_fft_integration.py @@ -1,201 +1,201 @@ -"""Flash-FFT-conv integration: opt-in fast path, graceful fallback. - -**What this validates:** - * When `flashfftconv` is NOT importable, `fftconv_ref` falls back silently - to the pure-PyTorch path regardless of env-var value. - * `HYDRA_HYENA_FLASH_FFT=0` (default) always uses the pure path. - * The env-var gate + import-probe gate are independent; both must pass for - the fast path to activate. - * The vendored source tree is present and structurally sane (csrc/, - flashfftconv/, LICENSE) so offline builds remain possible. - -Numeric equivalence between the CUDA kernel and the pure path is validated -separately when flashfftconv is actually built — that requires a specific -GPU arch match and is run manually (see `test_flash_fft_vs_pytorch_fftconv`). - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_flash_fft_integration.py -v -""" - -from __future__ import annotations - -import os -import sys -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from subsystems import hyena_pure # noqa: E402 -from subsystems.hyena_pure import ( # noqa: E402 - _FLASH_FFT_SUPPORTED_SIZES, - _flash_fft_conv_supported, - _try_load_flash_fft_conv, - fftconv_ref, -) - - -def test_flash_fft_conv_supported_matrix(): - """Supported seqlens are the specific power-of-2 grid the kernel handles.""" - assert _flash_fft_conv_supported(4096, torch.bfloat16) is True - assert _flash_fft_conv_supported(4096, torch.float16) is True - # fp32 not supported (kernel requires 16-bit input). - assert _flash_fft_conv_supported(4096, torch.float32) is False - # Non-power-of-2 / off-grid. - assert _flash_fft_conv_supported(4000, torch.bfloat16) is False - # Very large — not in set. - assert _flash_fft_conv_supported(2**24, torch.bfloat16) is False - - -def test_flash_fft_supported_set_matches_expected(): - """The supported set must include every fft_size HYDRA may reach. - - HYDRA's Hyena uses fft_size = 2 * sequence_len. Sequence lengths in - practice: 512, 1024, 2048, 4096. → fft sizes 1024, 2048, 4096, 8192. - All must be in the supported set. - """ - for s in (1024, 2048, 4096, 8192): - assert s in _FLASH_FFT_SUPPORTED_SIZES, ( - f"fft_size {s} must be supported for HYDRA sequence length " - f"{s // 2}" - ) - - -def test_pure_path_used_when_env_off(monkeypatch): - """HYDRA_HYENA_FLASH_FFT=0 (or unset) → pure PyTorch path.""" - monkeypatch.delenv("HYDRA_HYENA_FLASH_FFT", raising=False) - - torch.manual_seed(0) - B, D, L = 1, 8, 16 - u = torch.randn(B, D, L) - k = torch.randn(D, L) - D_bias = torch.randn(D) - - # Count filter rfft invocations — the pure path calls it once when k_f is None. - hyena_pure._fftconv_filter_rfft_count = 0 - y = fftconv_ref(u, k, D_bias, gelu=False) - assert y.shape == (B, D, L) - # Pure path: exactly one filter rfft (k_f was None). - assert hyena_pure._fftconv_filter_rfft_count == 1 - - -def test_try_load_flash_fft_conv_memoized(): - """_try_load_flash_fft_conv probes once and memoizes the result.""" - # Reset memo so this test can observe the probe. - hyena_pure._flash_fft_conv_cls = None - hyena_pure._flash_fft_conv_probed = False - - r1 = _try_load_flash_fft_conv() - assert hyena_pure._flash_fft_conv_probed is True - r2 = _try_load_flash_fft_conv() - assert r1 is r2, "second probe must return the memoized value" - - -def test_fallback_when_flash_fft_unavailable(monkeypatch): - """HYDRA_HYENA_FLASH_FFT=1 + flashfftconv unimportable → pure path. - - Fallback must be silent (stderr warning but no crash, no behavior change). - """ - monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") - # Force the probe to record "unavailable" regardless of what's installed. - monkeypatch.setattr(hyena_pure, "_flash_fft_conv_cls", None) - monkeypatch.setattr(hyena_pure, "_flash_fft_conv_probed", True) - - torch.manual_seed(1) - B, D, L = 1, 8, 16 - u = torch.randn(B, D, L) - k = torch.randn(D, L) - D_bias = torch.randn(D) - - y = fftconv_ref(u, k, D_bias, gelu=False) - assert y.shape == (B, D, L) - assert torch.isfinite(y).all() - - -def test_fallback_when_dtype_unsupported(monkeypatch): - """fp32 input + env on → falls back even if flashfftconv present.""" - monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") - - torch.manual_seed(2) - B, D, L = 1, 8, 16 - u = torch.randn(B, D, L, dtype=torch.float32) - k = torch.randn(D, L, dtype=torch.float32) # fp32 is NOT supported - D_bias = torch.randn(D) - - y = fftconv_ref(u, k, D_bias, gelu=False) - # Pure path handles fp32 fine. - assert y.dtype == torch.float32 - assert torch.isfinite(y).all() - - -def test_fallback_when_k_is_higher_rank(monkeypatch): - """k.dim()>2 (reverse-filter path) → fall back. HYDRA doesn't use this.""" - monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") - - torch.manual_seed(3) - B, D, L = 1, 8, 16 - u = torch.randn(B, D, L) - # k shape [C, D, L] — upstream reverse-filter shape; kernel doesn't handle it. - k = torch.randn(2, D, L) - D_bias = torch.randn(D) - - # The upstream pure-path handles 3-D k by unsqueeze; we must not fast-path. - # Pass k_f=None to force the fall-through. - # Reshape to [D, L] so the pure path accepts it for this test. - y = fftconv_ref(u, k[0], D_bias, gelu=False) - assert y.shape == (B, D, L) - - -def test_vendored_source_tree_intact(): - """The vendored flash-fft-conv source files must exist at known paths.""" - root = Path(__file__).resolve().parents[1] / "kernels" / "cuda" / "flashfftconv" - assert root.exists() - assert (root / "LICENSE").exists() - assert (root / "UPSTREAM_COMMIT").exists() - assert (root / "csrc").exists() - assert (root / "csrc" / "setup.py").exists() - assert (root / "flashfftconv").exists() - assert (root / "flashfftconv" / "conv.py").exists() - # LICENSE must be Apache 2.0 (pin — if this drifts, update the vendor). - license_text = (root / "LICENSE").read_text() - assert "Apache License" in license_text - - -@pytest.mark.skipif( - _try_load_flash_fft_conv() is None or not torch.cuda.is_available(), - reason="flashfftconv not installed or CUDA unavailable", -) -def test_flash_fft_vs_pytorch_fftconv_numeric_equivalence(): - """When the kernel IS available, its output must match pure PyTorch - within bf16 tolerance. - - This test only runs on machines with a successful flashfftconv build. - See kernels/cuda/flashfftconv/README.md for setup instructions. - """ - torch.manual_seed(42) - B, D, L = 2, 16, 2048 - fft_size = 2 * L - assert fft_size in _FLASH_FFT_SUPPORTED_SIZES - - u = torch.randn(B, D, L, device="cuda", dtype=torch.bfloat16) - k = torch.randn(D, L, device="cuda", dtype=torch.bfloat16) - D_bias = torch.randn(D, device="cuda", dtype=torch.bfloat16) - - os.environ["HYDRA_HYENA_FLASH_FFT"] = "0" - y_pure = fftconv_ref(u, k, D_bias, gelu=False) - - os.environ["HYDRA_HYENA_FLASH_FFT"] = "1" - y_flash = fftconv_ref(u, k, D_bias, gelu=False) - - max_abs_diff = (y_pure - y_flash).abs().max().item() - # bf16 tolerance target from the task spec. - assert max_abs_diff < 1e-3, ( - f"flash-fft-conv vs pure-PyTorch disagree: |Δ| max = {max_abs_diff:.3e}" - ) - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Flash-FFT-conv integration: opt-in fast path, graceful fallback. + +**What this validates:** + * When `flashfftconv` is NOT importable, `fftconv_ref` falls back silently + to the pure-PyTorch path regardless of env-var value. + * `HYDRA_HYENA_FLASH_FFT=0` (default) always uses the pure path. + * The env-var gate + import-probe gate are independent; both must pass for + the fast path to activate. + * The vendored source tree is present and structurally sane (csrc/, + flashfftconv/, LICENSE) so offline builds remain possible. + +Numeric equivalence between the CUDA kernel and the pure path is validated +separately when flashfftconv is actually built — that requires a specific +GPU arch match and is run manually (see `test_flash_fft_vs_pytorch_fftconv`). + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_flash_fft_integration.py -v +""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from subsystems import hyena_pure # noqa: E402 +from subsystems.hyena_pure import ( # noqa: E402 + _FLASH_FFT_SUPPORTED_SIZES, + _flash_fft_conv_supported, + _try_load_flash_fft_conv, + fftconv_ref, +) + + +def test_flash_fft_conv_supported_matrix(): + """Supported seqlens are the specific power-of-2 grid the kernel handles.""" + assert _flash_fft_conv_supported(4096, torch.bfloat16) is True + assert _flash_fft_conv_supported(4096, torch.float16) is True + # fp32 not supported (kernel requires 16-bit input). + assert _flash_fft_conv_supported(4096, torch.float32) is False + # Non-power-of-2 / off-grid. + assert _flash_fft_conv_supported(4000, torch.bfloat16) is False + # Very large — not in set. + assert _flash_fft_conv_supported(2**24, torch.bfloat16) is False + + +def test_flash_fft_supported_set_matches_expected(): + """The supported set must include every fft_size HYDRA may reach. + + HYDRA's Hyena uses fft_size = 2 * sequence_len. Sequence lengths in + practice: 512, 1024, 2048, 4096. → fft sizes 1024, 2048, 4096, 8192. + All must be in the supported set. + """ + for s in (1024, 2048, 4096, 8192): + assert s in _FLASH_FFT_SUPPORTED_SIZES, ( + f"fft_size {s} must be supported for HYDRA sequence length " + f"{s // 2}" + ) + + +def test_pure_path_used_when_env_off(monkeypatch): + """HYDRA_HYENA_FLASH_FFT=0 (or unset) → pure PyTorch path.""" + monkeypatch.delenv("HYDRA_HYENA_FLASH_FFT", raising=False) + + torch.manual_seed(0) + B, D, L = 1, 8, 16 + u = torch.randn(B, D, L) + k = torch.randn(D, L) + D_bias = torch.randn(D) + + # Count filter rfft invocations — the pure path calls it once when k_f is None. + hyena_pure._fftconv_filter_rfft_count = 0 + y = fftconv_ref(u, k, D_bias, gelu=False) + assert y.shape == (B, D, L) + # Pure path: exactly one filter rfft (k_f was None). + assert hyena_pure._fftconv_filter_rfft_count == 1 + + +def test_try_load_flash_fft_conv_memoized(): + """_try_load_flash_fft_conv probes once and memoizes the result.""" + # Reset memo so this test can observe the probe. + hyena_pure._flash_fft_conv_cls = None + hyena_pure._flash_fft_conv_probed = False + + r1 = _try_load_flash_fft_conv() + assert hyena_pure._flash_fft_conv_probed is True + r2 = _try_load_flash_fft_conv() + assert r1 is r2, "second probe must return the memoized value" + + +def test_fallback_when_flash_fft_unavailable(monkeypatch): + """HYDRA_HYENA_FLASH_FFT=1 + flashfftconv unimportable → pure path. + + Fallback must be silent (stderr warning but no crash, no behavior change). + """ + monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") + # Force the probe to record "unavailable" regardless of what's installed. + monkeypatch.setattr(hyena_pure, "_flash_fft_conv_cls", None) + monkeypatch.setattr(hyena_pure, "_flash_fft_conv_probed", True) + + torch.manual_seed(1) + B, D, L = 1, 8, 16 + u = torch.randn(B, D, L) + k = torch.randn(D, L) + D_bias = torch.randn(D) + + y = fftconv_ref(u, k, D_bias, gelu=False) + assert y.shape == (B, D, L) + assert torch.isfinite(y).all() + + +def test_fallback_when_dtype_unsupported(monkeypatch): + """fp32 input + env on → falls back even if flashfftconv present.""" + monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") + + torch.manual_seed(2) + B, D, L = 1, 8, 16 + u = torch.randn(B, D, L, dtype=torch.float32) + k = torch.randn(D, L, dtype=torch.float32) # fp32 is NOT supported + D_bias = torch.randn(D) + + y = fftconv_ref(u, k, D_bias, gelu=False) + # Pure path handles fp32 fine. + assert y.dtype == torch.float32 + assert torch.isfinite(y).all() + + +def test_fallback_when_k_is_higher_rank(monkeypatch): + """k.dim()>2 (reverse-filter path) → fall back. HYDRA doesn't use this.""" + monkeypatch.setenv("HYDRA_HYENA_FLASH_FFT", "1") + + torch.manual_seed(3) + B, D, L = 1, 8, 16 + u = torch.randn(B, D, L) + # k shape [C, D, L] — upstream reverse-filter shape; kernel doesn't handle it. + k = torch.randn(2, D, L) + D_bias = torch.randn(D) + + # The upstream pure-path handles 3-D k by unsqueeze; we must not fast-path. + # Pass k_f=None to force the fall-through. + # Reshape to [D, L] so the pure path accepts it for this test. + y = fftconv_ref(u, k[0], D_bias, gelu=False) + assert y.shape == (B, D, L) + + +def test_vendored_source_tree_intact(): + """The vendored flash-fft-conv source files must exist at known paths.""" + root = Path(__file__).resolve().parents[1] / "kernels" / "cuda" / "flashfftconv" + assert root.exists() + assert (root / "LICENSE").exists() + assert (root / "UPSTREAM_COMMIT").exists() + assert (root / "csrc").exists() + assert (root / "csrc" / "setup.py").exists() + assert (root / "flashfftconv").exists() + assert (root / "flashfftconv" / "conv.py").exists() + # LICENSE must be Apache 2.0 (pin — if this drifts, update the vendor). + license_text = (root / "LICENSE").read_text() + assert "Apache License" in license_text + + +@pytest.mark.skipif( + _try_load_flash_fft_conv() is None or not torch.cuda.is_available(), + reason="flashfftconv not installed or CUDA unavailable", +) +def test_flash_fft_vs_pytorch_fftconv_numeric_equivalence(): + """When the kernel IS available, its output must match pure PyTorch + within bf16 tolerance. + + This test only runs on machines with a successful flashfftconv build. + See kernels/cuda/flashfftconv/README.md for setup instructions. + """ + torch.manual_seed(42) + B, D, L = 2, 16, 2048 + fft_size = 2 * L + assert fft_size in _FLASH_FFT_SUPPORTED_SIZES + + u = torch.randn(B, D, L, device="cuda", dtype=torch.bfloat16) + k = torch.randn(D, L, device="cuda", dtype=torch.bfloat16) + D_bias = torch.randn(D, device="cuda", dtype=torch.bfloat16) + + os.environ["HYDRA_HYENA_FLASH_FFT"] = "0" + y_pure = fftconv_ref(u, k, D_bias, gelu=False) + + os.environ["HYDRA_HYENA_FLASH_FFT"] = "1" + y_flash = fftconv_ref(u, k, D_bias, gelu=False) + + max_abs_diff = (y_pure - y_flash).abs().max().item() + # bf16 tolerance target from the task spec. + assert max_abs_diff < 1e-3, ( + f"flash-fft-conv vs pure-PyTorch disagree: |Δ| max = {max_abs_diff:.3e}" + ) + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_full_arch.py b/overlay/tests/test_full_arch.py index 77defedfe0fbff0fd9c131faf2eefde7eef2d1fe..752ad9207df58ed6666b7cd2f7b31bcd303c09c1 100644 --- a/overlay/tests/test_full_arch.py +++ b/overlay/tests/test_full_arch.py @@ -1,233 +1,233 @@ -""" -Integration gates for the full-architecture autoresearch loop. - -Three gates that MUST all pass before the orchestrator may mark a run "keep" -in results.tsv: - - Gate 1 (sdr_overlap_test) — semantic topology of SemanticFoldingSDR - Gate 2 (htm_anomaly_drops) — HTM TM learns a repeating sequence - Gate 3 (full_arch_end_to_end) — forward + backward through PostSemClawModel, - grads must reach the embedding (proves SDR's - straight-through estimator actually flows back) - -Run with: - cd /home/mikeb/work/feather && uv run pytest tests/test_full_arch.py -v -""" - -from __future__ import annotations - -import sys -from pathlib import Path - -import pytest -import torch -import torch.nn.functional as F - -# Make the repo root importable when pytest is invoked from anywhere. -ROOT = Path(__file__).resolve().parents[1] -if str(ROOT) not in sys.path: - sys.path.insert(0, str(ROOT)) - -from prepare import Tokenizer # noqa: E402 -from subsystems.htm import HTMLayer # noqa: E402 -from subsystems.sdr_semantic import SemanticFoldingSDR # noqa: E402 - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _encode_leading_space_first(tok: Tokenizer, word: str) -> int: - """Return the first BPE piece of ``" " + word``. - - The BPE tokenizer merges most common nouns into a single id when prefixed - with a leading space (e.g. ' man' -> 555, ' king' -> 7759). Less-common - words may split (' queen' -> [' qu', 'een'], ' dinosaur' -> [' din', - 'osaur']); for those we take the leading-space first piece, which still - carries the semantic morpheme. We deliberately avoid the bare-string first - piece ('w' from 'woman') because that's just a letter with no meaning. - """ - ids = tok.encode(" " + word) - assert ids, f"empty encoding for {word!r}" - return ids[0] - - -# --------------------------------------------------------------------------- -# Gate 1 — SDR semantic overlap -# --------------------------------------------------------------------------- - - -def test_sdr_overlap_semantic_invariant() -> None: - """SemanticFoldingSDR must place semantically related tokens closer than - unrelated ones. We prefer leading-space whole-word encodings because the - BPE tokenizer ships single-id mappings for common nouns there.""" - tok = Tokenizer.from_directory() - sdr = SemanticFoldingSDR(vocab_size=tok.get_vocab_size(), n_bits=16384) - - tok_man = _encode_leading_space_first(tok, "man") - tok_woman = _encode_leading_space_first(tok, "woman") - tok_rock = _encode_leading_space_first(tok, "rock") - tok_king = _encode_leading_space_first(tok, "king") - tok_queen = _encode_leading_space_first(tok, "queen") - tok_dino = _encode_leading_space_first(tok, "dinosaur") - - ov_man_woman = sdr.overlap(tok_man, tok_woman) - ov_man_rock = sdr.overlap(tok_man, tok_rock) - ov_king_queen = sdr.overlap(tok_king, tok_queen) - ov_king_dino = sdr.overlap(tok_king, tok_dino) - - assert ov_man_woman > ov_man_rock, ( - f"semantic invariant broken: overlap(man,woman)={ov_man_woman:.4f} " - f"is not greater than overlap(man,rock)={ov_man_rock:.4f}" - ) - assert ov_king_queen > ov_king_dino, ( - f"semantic invariant broken: overlap(king,queen)={ov_king_queen:.4f} " - f"is not greater than overlap(king,dinosaur)={ov_king_dino:.4f}" - ) - - -# --------------------------------------------------------------------------- -# Gate 2 — HTM anomaly drops on repetition -# --------------------------------------------------------------------------- - - -def test_htm_anomaly_drops_on_repetition() -> None: - """A 3-step (A,B,C) sequence repeated many times must be learned by the - HTM temporal memory: late-iteration anomaly score must be <50% of the - early anomaly score.""" - htm = HTMLayer( - input_bits=16384, - n_columns=2048, - cells_per_column=32, - batch_size=1, - reset_each_forward=False, - ) - htm.train() # enable Hebbian learning inside the wrapper - - rng = torch.Generator().manual_seed(0) - - def sparse_sdr() -> torch.Tensor: - s = torch.zeros(16384, dtype=torch.float32) - idx = torch.randperm(16384, generator=rng)[:327] - s[idx] = 1.0 - return s - - A, B, C = sparse_sdr(), sparse_sdr(), sparse_sdr() - seq = torch.stack([A, B, C], dim=0).unsqueeze(0) # (1, 3, 16384) - - htm.reset() - early_anomalies: list[float] = [] - late_anomalies: list[float] = [] - for it in range(220): - out = htm(seq) # (1, 3, 2049) - anom = out[..., -1].mean().item() - if 5 <= it < 25: - early_anomalies.append(anom) - if 200 <= it < 220: - late_anomalies.append(anom) - - early = sum(early_anomalies) / len(early_anomalies) - late = sum(late_anomalies) / len(late_anomalies) - assert late < 0.5 * early, ( - f"HTM TM did not learn repeating sequence: " - f"early={early:.3f} late={late:.3f} (require late < 0.5 * early)" - ) - - -# --------------------------------------------------------------------------- -# Gate 3 — Full architecture end-to-end forward + backward -# --------------------------------------------------------------------------- - - -def _build_full_arch_model(vocab_size: int): - """Try to construct PostSemClawModel using whichever signature train.py - currently exposes. Returns ``None`` if the model can't be built (e.g. T5 - rewire incomplete or CUDA-only kernels missing on this host). - - NOTE: importing train.py must not run training as a side-effect; T5 must - guard the script body with ``if __name__ == "__main__":``. Until then we - skip with a clear actionable message instead of OOM-ing the box.""" - try: - from train import PostSemClawModel # noqa: F401 (test of import path) - except ImportError as e: - pytest.skip(f"train.py import failed (T5 in progress): {e}") - return None - except AttributeError as e: - pytest.skip(f"PostSemClawModel not exported by train.py (T5 in progress): {e}") - return None - except Exception as e: - # Any other crash on import means train.py runs work at module-load time. - pytest.skip( - "train.py runs as a script on import (likely missing " - f"`if __name__ == \"__main__\":` guard around the training body): " - f"{type(e).__name__}: {e}" - ) - return None - from train import PostSemClawModel - - # Attempt 1: spec-style direct kwargs (what T5 SHOULD expose). - try: - return PostSemClawModel( - vocab_size=vocab_size, d_model=64, n_layer=2, - ) - except TypeError: - pass - - # Attempt 2: legacy config-object API as it stands at HEAD. - try: - from train import PostSemClawConfig - except ImportError as e: - pytest.skip(f"cannot construct PostSemClawModel (no Config): {e}") - return None - - cfg = PostSemClawConfig() - cfg.vocab_size = vocab_size - cfg.d_model = 64 - cfg.n_layer = 2 - # Trim heavy substructures so the test stays cheap. - if hasattr(cfg, "engram_n_columns"): - cfg.engram_n_columns = 256 - if hasattr(cfg, "headdim"): - cfg.headdim = 32 - if hasattr(cfg, "n_heads"): - cfg.n_heads = max(1, cfg.d_model // cfg.headdim) - if hasattr(cfg, "engram_layer_idx"): - cfg.engram_layer_idx = min(cfg.engram_layer_idx, cfg.n_layer - 1) - return PostSemClawModel(cfg) - - -def test_full_arch_forward_and_grad() -> None: - pytest.importorskip("htm_rust") - if not torch.cuda.is_available(): - pytest.skip("full-arch model requires CUDA (Mamba3 kernels are GPU-only)") - - vocab_size = 8192 - model = _build_full_arch_model(vocab_size) - if model is None: - return # pytest.skip already raised inside the helper - - model = model.cuda() - if hasattr(model, "init_weights"): - model.init_weights() - - ids = torch.randint(0, vocab_size, (2, 32), device="cuda") - targets = ids.clone() - - logits = model(ids, targets=None) - assert logits.shape == (2, 32, vocab_size), ( - f"unexpected logits shape: {tuple(logits.shape)}" - ) - - loss = F.cross_entropy(logits.view(-1, vocab_size), targets.view(-1)) - assert torch.isfinite(loss), f"loss is not finite: {loss.item()}" - - loss.backward() - - # Embedding weight must receive gradient — proves SDR's STE flows back. - assert model.wte.weight.grad is not None, ( - "no grad on embedding — SDR straight-through estimator broken" - ) - assert torch.isfinite(model.wte.weight.grad).all(), ( - "non-finite gradient on embedding" - ) +""" +Integration gates for the full-architecture autoresearch loop. + +Three gates that MUST all pass before the orchestrator may mark a run "keep" +in results.tsv: + + Gate 1 (sdr_overlap_test) — semantic topology of SemanticFoldingSDR + Gate 2 (htm_anomaly_drops) — HTM TM learns a repeating sequence + Gate 3 (full_arch_end_to_end) — forward + backward through PostSemClawModel, + grads must reach the embedding (proves SDR's + straight-through estimator actually flows back) + +Run with: + cd /home/mikeb/work/feather && uv run pytest tests/test_full_arch.py -v +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest +import torch +import torch.nn.functional as F + +# Make the repo root importable when pytest is invoked from anywhere. +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from prepare import Tokenizer # noqa: E402 +from subsystems.htm import HTMLayer # noqa: E402 +from subsystems.sdr_semantic import SemanticFoldingSDR # noqa: E402 + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _encode_leading_space_first(tok: Tokenizer, word: str) -> int: + """Return the first BPE piece of ``" " + word``. + + The BPE tokenizer merges most common nouns into a single id when prefixed + with a leading space (e.g. ' man' -> 555, ' king' -> 7759). Less-common + words may split (' queen' -> [' qu', 'een'], ' dinosaur' -> [' din', + 'osaur']); for those we take the leading-space first piece, which still + carries the semantic morpheme. We deliberately avoid the bare-string first + piece ('w' from 'woman') because that's just a letter with no meaning. + """ + ids = tok.encode(" " + word) + assert ids, f"empty encoding for {word!r}" + return ids[0] + + +# --------------------------------------------------------------------------- +# Gate 1 — SDR semantic overlap +# --------------------------------------------------------------------------- + + +def test_sdr_overlap_semantic_invariant() -> None: + """SemanticFoldingSDR must place semantically related tokens closer than + unrelated ones. We prefer leading-space whole-word encodings because the + BPE tokenizer ships single-id mappings for common nouns there.""" + tok = Tokenizer.from_directory() + sdr = SemanticFoldingSDR(vocab_size=tok.get_vocab_size(), n_bits=16384) + + tok_man = _encode_leading_space_first(tok, "man") + tok_woman = _encode_leading_space_first(tok, "woman") + tok_rock = _encode_leading_space_first(tok, "rock") + tok_king = _encode_leading_space_first(tok, "king") + tok_queen = _encode_leading_space_first(tok, "queen") + tok_dino = _encode_leading_space_first(tok, "dinosaur") + + ov_man_woman = sdr.overlap(tok_man, tok_woman) + ov_man_rock = sdr.overlap(tok_man, tok_rock) + ov_king_queen = sdr.overlap(tok_king, tok_queen) + ov_king_dino = sdr.overlap(tok_king, tok_dino) + + assert ov_man_woman > ov_man_rock, ( + f"semantic invariant broken: overlap(man,woman)={ov_man_woman:.4f} " + f"is not greater than overlap(man,rock)={ov_man_rock:.4f}" + ) + assert ov_king_queen > ov_king_dino, ( + f"semantic invariant broken: overlap(king,queen)={ov_king_queen:.4f} " + f"is not greater than overlap(king,dinosaur)={ov_king_dino:.4f}" + ) + + +# --------------------------------------------------------------------------- +# Gate 2 — HTM anomaly drops on repetition +# --------------------------------------------------------------------------- + + +def test_htm_anomaly_drops_on_repetition() -> None: + """A 3-step (A,B,C) sequence repeated many times must be learned by the + HTM temporal memory: late-iteration anomaly score must be <50% of the + early anomaly score.""" + htm = HTMLayer( + input_bits=16384, + n_columns=2048, + cells_per_column=32, + batch_size=1, + reset_each_forward=False, + ) + htm.train() # enable Hebbian learning inside the wrapper + + rng = torch.Generator().manual_seed(0) + + def sparse_sdr() -> torch.Tensor: + s = torch.zeros(16384, dtype=torch.float32) + idx = torch.randperm(16384, generator=rng)[:327] + s[idx] = 1.0 + return s + + A, B, C = sparse_sdr(), sparse_sdr(), sparse_sdr() + seq = torch.stack([A, B, C], dim=0).unsqueeze(0) # (1, 3, 16384) + + htm.reset() + early_anomalies: list[float] = [] + late_anomalies: list[float] = [] + for it in range(220): + out = htm(seq) # (1, 3, 2049) + anom = out[..., -1].mean().item() + if 5 <= it < 25: + early_anomalies.append(anom) + if 200 <= it < 220: + late_anomalies.append(anom) + + early = sum(early_anomalies) / len(early_anomalies) + late = sum(late_anomalies) / len(late_anomalies) + assert late < 0.5 * early, ( + f"HTM TM did not learn repeating sequence: " + f"early={early:.3f} late={late:.3f} (require late < 0.5 * early)" + ) + + +# --------------------------------------------------------------------------- +# Gate 3 — Full architecture end-to-end forward + backward +# --------------------------------------------------------------------------- + + +def _build_full_arch_model(vocab_size: int): + """Try to construct PostSemClawModel using whichever signature train.py + currently exposes. Returns ``None`` if the model can't be built (e.g. T5 + rewire incomplete or CUDA-only kernels missing on this host). + + NOTE: importing train.py must not run training as a side-effect; T5 must + guard the script body with ``if __name__ == "__main__":``. Until then we + skip with a clear actionable message instead of OOM-ing the box.""" + try: + from train import PostSemClawModel # noqa: F401 (test of import path) + except ImportError as e: + pytest.skip(f"train.py import failed (T5 in progress): {e}") + return None + except AttributeError as e: + pytest.skip(f"PostSemClawModel not exported by train.py (T5 in progress): {e}") + return None + except Exception as e: + # Any other crash on import means train.py runs work at module-load time. + pytest.skip( + "train.py runs as a script on import (likely missing " + f"`if __name__ == \"__main__\":` guard around the training body): " + f"{type(e).__name__}: {e}" + ) + return None + from train import PostSemClawModel + + # Attempt 1: spec-style direct kwargs (what T5 SHOULD expose). + try: + return PostSemClawModel( + vocab_size=vocab_size, d_model=64, n_layer=2, + ) + except TypeError: + pass + + # Attempt 2: legacy config-object API as it stands at HEAD. + try: + from train import PostSemClawConfig + except ImportError as e: + pytest.skip(f"cannot construct PostSemClawModel (no Config): {e}") + return None + + cfg = PostSemClawConfig() + cfg.vocab_size = vocab_size + cfg.d_model = 64 + cfg.n_layer = 2 + # Trim heavy substructures so the test stays cheap. + if hasattr(cfg, "engram_n_columns"): + cfg.engram_n_columns = 256 + if hasattr(cfg, "headdim"): + cfg.headdim = 32 + if hasattr(cfg, "n_heads"): + cfg.n_heads = max(1, cfg.d_model // cfg.headdim) + if hasattr(cfg, "engram_layer_idx"): + cfg.engram_layer_idx = min(cfg.engram_layer_idx, cfg.n_layer - 1) + return PostSemClawModel(cfg) + + +def test_full_arch_forward_and_grad() -> None: + pytest.importorskip("htm_rust") + if not torch.cuda.is_available(): + pytest.skip("full-arch model requires CUDA (Mamba3 kernels are GPU-only)") + + vocab_size = 8192 + model = _build_full_arch_model(vocab_size) + if model is None: + return # pytest.skip already raised inside the helper + + model = model.cuda() + if hasattr(model, "init_weights"): + model.init_weights() + + ids = torch.randint(0, vocab_size, (2, 32), device="cuda") + targets = ids.clone() + + logits = model(ids, targets=None) + assert logits.shape == (2, 32, vocab_size), ( + f"unexpected logits shape: {tuple(logits.shape)}" + ) + + loss = F.cross_entropy(logits.view(-1, vocab_size), targets.view(-1)) + assert torch.isfinite(loss), f"loss is not finite: {loss.item()}" + + loss.backward() + + # Embedding weight must receive gradient — proves SDR's STE flows back. + assert model.wte.weight.grad is not None, ( + "no grad on embedding — SDR straight-through estimator broken" + ) + assert torch.isfinite(model.wte.weight.grad).all(), ( + "non-finite gradient on embedding" + ) diff --git a/overlay/tests/test_gdn_block.py b/overlay/tests/test_gdn_block.py index ec47bf96f69f0670faac5cea2240e23da586d0bf..ed43df31d60e1045c84b104993a04759220177c3 100644 --- a/overlay/tests/test_gdn_block.py +++ b/overlay/tests/test_gdn_block.py @@ -1,201 +1,201 @@ -"""Tests for hydra.gdn_block.GDNBlock. - -All tests are skipped gracefully when flash-linear-attention (fla) is not -installed, so CI without a GPU/fla wheel still passes with 0 failures. - -Run with CUDA available for full coverage (Triton kernels require sm86+): - pytest tests/test_gdn_block.py -v -""" - -from __future__ import annotations - -import pytest -import torch - -# Skip entire module if fla is not importable — clean, no ImportError noise. -fla = pytest.importorskip("fla", reason="flash-linear-attention not installed; skipping GDNBlock tests") - -from hydra.gdn_block import GDNBlock # noqa: E402 (after importorskip guard) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -D_MODEL = 128 -N_HEADS = 4 # head_dim = 128 // 4 = 32, evenly divisible -B, T = 2, 64 - - -def _make_block(d_model: int = D_MODEL, n_heads: int = N_HEADS) -> GDNBlock: - return GDNBlock(d_model=d_model, n_heads=n_heads) - - -def _cuda_block(d_model: int = D_MODEL, n_heads: int = N_HEADS) -> GDNBlock: - """Return a block on CUDA in bfloat16 — required for Triton kernels.""" - return _make_block(d_model, n_heads).cuda().to(torch.bfloat16) - - -def _cuda_input(b: int = B, t: int = T, d: int = D_MODEL) -> torch.Tensor: - return torch.randn(b, t, d, device="cuda", dtype=torch.bfloat16) - - -def _requires_cuda(fn): - """Decorator: skip test if no CUDA device is available.""" - return pytest.mark.skipif( - not torch.cuda.is_available(), - reason="CUDA required for Triton kernels in GatedDeltaNet", - )(fn) - - -# --------------------------------------------------------------------------- -# test_forward_shape -# --------------------------------------------------------------------------- - -@_requires_cuda -def test_forward_shape(): - """Output tensor must have the same shape as the input.""" - block = _cuda_block() - x = _cuda_input() - with torch.no_grad(): - y = block(x) - assert y.shape == x.shape, ( - f"Expected output shape {x.shape}, got {y.shape}" - ) - assert y.dtype == x.dtype, ( - f"Expected output dtype {x.dtype}, got {y.dtype}" - ) - - -# --------------------------------------------------------------------------- -# test_gradient_flow -# --------------------------------------------------------------------------- - -@_requires_cuda -def test_gradient_flow(): - """A scalar loss on the output must produce nonzero gradients on block params.""" - block = _cuda_block() - block.train() - x = _cuda_input() - y = block(x) - loss = y.float().sum() - loss.backward() - - grad_norms = [ - p.grad.norm().item() - for p in block.parameters() - if p.grad is not None - ] - assert len(grad_norms) > 0, "No parameters received gradients" - assert any(g > 0.0 for g in grad_norms), ( - f"All gradient norms are zero: {grad_norms}" - ) - - -# --------------------------------------------------------------------------- -# test_param_count -# --------------------------------------------------------------------------- - -def test_param_count(): - """GDNBlock(d=384, n_heads=6) params must be within 2x of a Mamba3 block. - - Mamba3 rough param count at d=384: - in_proj: d * (expand*d + d_state + d_state) = 384*(768+64+64) = 344,064 - out_proj: expand*d * d = 768*384 = 294,912 - ssm misc: ~24,576 - total: ~663,552 - - GDN at d=384, n_heads=6 (head_dim=64, expand_v=2.0): - measured at ~1,190,540 (< 2 * 663,552 = 1,327,104) - """ - d_model = 384 - n_heads = 6 # head_dim = 384 // 6 = 64 - - block = GDNBlock(d_model=d_model, n_heads=n_heads) - gdn_params = sum(p.numel() for p in block.parameters()) - - # Mamba3 reference estimate at same d_model (see docstring above) - d_state = 64 - expand = 2 - mamba3_estimate = ( - d_model * (expand * d_model + d_state + d_state) # in_proj (x, b, c) - + expand * d_model * d_model # out_proj - + d_model * d_state # state params - ) - - ratio = gdn_params / mamba3_estimate - assert ratio <= 2.0, ( - f"GDNBlock has {gdn_params:,} params, which is {ratio:.2f}x " - f"the Mamba3 estimate of {mamba3_estimate:,}. " - "Must be within 2x." - ) - - -# --------------------------------------------------------------------------- -# test_does_not_leak_state -# --------------------------------------------------------------------------- - -@_requires_cuda -def test_does_not_leak_state(): - """Two sequential forward calls on the same x must produce identical outputs. - - GDNBlock must be stateless between calls (use_cache=False, no hidden - state carry-over) so gradient-accumulation loops are safe. - """ - block = _cuda_block() - block.eval() - x = _cuda_input() - - with torch.no_grad(): - y1 = block(x) - y2 = block(x) - - # Outputs must be bitwise identical — same input, same weights, no state. - assert torch.allclose(y1, y2, atol=0.0, rtol=0.0), ( - "Two forward calls on identical input produced different outputs. " - "State is leaking between calls." - ) - - -# --------------------------------------------------------------------------- -# test_no_grads_in_eval -# --------------------------------------------------------------------------- - -@_requires_cuda -def test_no_grads_in_eval(): - """In eval + no_grad mode, output must not require grad when input doesn't.""" - block = _cuda_block() - block.eval() - x = _cuda_input() - assert not x.requires_grad, "Precondition: input must not require grad" - - with torch.no_grad(): - y = block(x) - - assert not y.requires_grad, ( - "Output requires_grad=True even though input had requires_grad=False " - "and we were inside torch.no_grad(). " - "This could cause unexpected grad accumulation." - ) - - -# --------------------------------------------------------------------------- -# test_invalidate_caches_is_noop -# --------------------------------------------------------------------------- - -def test_invalidate_caches_is_noop(): - """invalidate_caches() must exist and be callable without side-effects.""" - block = _make_block() - # Should not raise - block.invalidate_caches() - block.invalidate_caches() # idempotent - - -# --------------------------------------------------------------------------- -# test_head_dim_must_divide_d_model -# --------------------------------------------------------------------------- - -def test_head_dim_must_divide_d_model(): - """GDNBlock must raise ValueError when d_model is not divisible by n_heads.""" - with pytest.raises(ValueError, match="divisible"): - GDNBlock(d_model=100, n_heads=7) # 100 % 7 != 0 +"""Tests for hydra.gdn_block.GDNBlock. + +All tests are skipped gracefully when flash-linear-attention (fla) is not +installed, so CI without a GPU/fla wheel still passes with 0 failures. + +Run with CUDA available for full coverage (Triton kernels require sm86+): + pytest tests/test_gdn_block.py -v +""" + +from __future__ import annotations + +import pytest +import torch + +# Skip entire module if fla is not importable — clean, no ImportError noise. +fla = pytest.importorskip("fla", reason="flash-linear-attention not installed; skipping GDNBlock tests") + +from hydra.gdn_block import GDNBlock # noqa: E402 (after importorskip guard) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +D_MODEL = 128 +N_HEADS = 4 # head_dim = 128 // 4 = 32, evenly divisible +B, T = 2, 64 + + +def _make_block(d_model: int = D_MODEL, n_heads: int = N_HEADS) -> GDNBlock: + return GDNBlock(d_model=d_model, n_heads=n_heads) + + +def _cuda_block(d_model: int = D_MODEL, n_heads: int = N_HEADS) -> GDNBlock: + """Return a block on CUDA in bfloat16 — required for Triton kernels.""" + return _make_block(d_model, n_heads).cuda().to(torch.bfloat16) + + +def _cuda_input(b: int = B, t: int = T, d: int = D_MODEL) -> torch.Tensor: + return torch.randn(b, t, d, device="cuda", dtype=torch.bfloat16) + + +def _requires_cuda(fn): + """Decorator: skip test if no CUDA device is available.""" + return pytest.mark.skipif( + not torch.cuda.is_available(), + reason="CUDA required for Triton kernels in GatedDeltaNet", + )(fn) + + +# --------------------------------------------------------------------------- +# test_forward_shape +# --------------------------------------------------------------------------- + +@_requires_cuda +def test_forward_shape(): + """Output tensor must have the same shape as the input.""" + block = _cuda_block() + x = _cuda_input() + with torch.no_grad(): + y = block(x) + assert y.shape == x.shape, ( + f"Expected output shape {x.shape}, got {y.shape}" + ) + assert y.dtype == x.dtype, ( + f"Expected output dtype {x.dtype}, got {y.dtype}" + ) + + +# --------------------------------------------------------------------------- +# test_gradient_flow +# --------------------------------------------------------------------------- + +@_requires_cuda +def test_gradient_flow(): + """A scalar loss on the output must produce nonzero gradients on block params.""" + block = _cuda_block() + block.train() + x = _cuda_input() + y = block(x) + loss = y.float().sum() + loss.backward() + + grad_norms = [ + p.grad.norm().item() + for p in block.parameters() + if p.grad is not None + ] + assert len(grad_norms) > 0, "No parameters received gradients" + assert any(g > 0.0 for g in grad_norms), ( + f"All gradient norms are zero: {grad_norms}" + ) + + +# --------------------------------------------------------------------------- +# test_param_count +# --------------------------------------------------------------------------- + +def test_param_count(): + """GDNBlock(d=384, n_heads=6) params must be within 2x of a Mamba3 block. + + Mamba3 rough param count at d=384: + in_proj: d * (expand*d + d_state + d_state) = 384*(768+64+64) = 344,064 + out_proj: expand*d * d = 768*384 = 294,912 + ssm misc: ~24,576 + total: ~663,552 + + GDN at d=384, n_heads=6 (head_dim=64, expand_v=2.0): + measured at ~1,190,540 (< 2 * 663,552 = 1,327,104) + """ + d_model = 384 + n_heads = 6 # head_dim = 384 // 6 = 64 + + block = GDNBlock(d_model=d_model, n_heads=n_heads) + gdn_params = sum(p.numel() for p in block.parameters()) + + # Mamba3 reference estimate at same d_model (see docstring above) + d_state = 64 + expand = 2 + mamba3_estimate = ( + d_model * (expand * d_model + d_state + d_state) # in_proj (x, b, c) + + expand * d_model * d_model # out_proj + + d_model * d_state # state params + ) + + ratio = gdn_params / mamba3_estimate + assert ratio <= 2.0, ( + f"GDNBlock has {gdn_params:,} params, which is {ratio:.2f}x " + f"the Mamba3 estimate of {mamba3_estimate:,}. " + "Must be within 2x." + ) + + +# --------------------------------------------------------------------------- +# test_does_not_leak_state +# --------------------------------------------------------------------------- + +@_requires_cuda +def test_does_not_leak_state(): + """Two sequential forward calls on the same x must produce identical outputs. + + GDNBlock must be stateless between calls (use_cache=False, no hidden + state carry-over) so gradient-accumulation loops are safe. + """ + block = _cuda_block() + block.eval() + x = _cuda_input() + + with torch.no_grad(): + y1 = block(x) + y2 = block(x) + + # Outputs must be bitwise identical — same input, same weights, no state. + assert torch.allclose(y1, y2, atol=0.0, rtol=0.0), ( + "Two forward calls on identical input produced different outputs. " + "State is leaking between calls." + ) + + +# --------------------------------------------------------------------------- +# test_no_grads_in_eval +# --------------------------------------------------------------------------- + +@_requires_cuda +def test_no_grads_in_eval(): + """In eval + no_grad mode, output must not require grad when input doesn't.""" + block = _cuda_block() + block.eval() + x = _cuda_input() + assert not x.requires_grad, "Precondition: input must not require grad" + + with torch.no_grad(): + y = block(x) + + assert not y.requires_grad, ( + "Output requires_grad=True even though input had requires_grad=False " + "and we were inside torch.no_grad(). " + "This could cause unexpected grad accumulation." + ) + + +# --------------------------------------------------------------------------- +# test_invalidate_caches_is_noop +# --------------------------------------------------------------------------- + +def test_invalidate_caches_is_noop(): + """invalidate_caches() must exist and be callable without side-effects.""" + block = _make_block() + # Should not raise + block.invalidate_caches() + block.invalidate_caches() # idempotent + + +# --------------------------------------------------------------------------- +# test_head_dim_must_divide_d_model +# --------------------------------------------------------------------------- + +def test_head_dim_must_divide_d_model(): + """GDNBlock must raise ValueError when d_model is not divisible by n_heads.""" + with pytest.raises(ValueError, match="divisible"): + GDNBlock(d_model=100, n_heads=7) # 100 % 7 != 0 diff --git a/overlay/tests/test_harness.py b/overlay/tests/test_harness.py index ceea8e1a8f8665f8dc2737b381f77bf58f7ed9e3..4462fdcd5a8c878759591cdcebd5c0f092030ee5 100644 --- a/overlay/tests/test_harness.py +++ b/overlay/tests/test_harness.py @@ -1,532 +1,532 @@ -"""Tests for HYDRA harness components. - -Covers: - - eval_agent: parse_run_log, check_secondary_alarms, should_keep - - search_strategy: diagnose, should_explore - - meta_agent: generate_directive, _strip_previous_directive - -All tests are CPU-only and create/destroy temp files as needed. - -Run: - uv run pytest tests/test_harness.py -v -""" -import os -import tempfile -import pytest - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - - -# --------------------------------------------------------------------------- -# eval_agent tests -# --------------------------------------------------------------------------- - -class TestParseRunLog: - def _write_log(self, content: str) -> str: - """Write content to a temp log file and return its path.""" - fh = tempfile.NamedTemporaryFile( - mode="w", suffix=".log", delete=False - ) - fh.write(content) - fh.flush() - fh.close() - return fh.name - - def test_parse_valid_summary_block(self): - """All fields are extracted correctly from a well-formed log.""" - from harness.eval_agent import parse_run_log - - log = ( - "step 00100 (50.0%) | loss: 3.123456\n" - "---\n" - "val_bpb: 1.234567\n" - "training_seconds: 300.100\n" - "total_seconds: 325.000\n" - "peak_vram_mb: 2048.000\n" - "mfu_percent: 12.500\n" - "total_tokens_M: 100.000\n" - "num_steps: 200\n" - "num_params_M: 7.900\n" - "n_layer: 4\n" - "d_model: 256\n" - "mhc_spectral_norm: 1.2300\n" - "engram_hit_rate: 0.4500\n" - "sr_bypass_rate: 1.0000\n" - ) - path = self._write_log(log) - try: - result = parse_run_log(path) - assert result.val_bpb == pytest.approx(1.234567) - assert result.training_seconds == pytest.approx(300.1) - assert result.total_seconds == pytest.approx(325.0) - assert result.peak_vram_mb == pytest.approx(2048.0) - assert result.mfu_percent == pytest.approx(12.5) - assert result.total_tokens_m == pytest.approx(100.0) - assert result.num_steps == 200 - assert result.num_params_m == pytest.approx(7.9) - assert result.n_layer == 4 - assert result.d_model == 256 - assert result.mhc_spectral_norm == pytest.approx(1.23) - assert result.engram_hit_rate == pytest.approx(0.45) - assert result.sr_bypass_rate == pytest.approx(1.0) - assert not result.crashed - assert result.error_message == "" - finally: - os.unlink(path) - - def test_parse_crash_traceback(self): - """Crashed run sets crashed=True and captures error_message.""" - from harness.eval_agent import parse_run_log - - log = ( - "Traceback (most recent call last):\n" - " File 'train.py', line 100, in \n" - "RuntimeError: CUDA out of memory\n" - ) - path = self._write_log(log) - try: - result = parse_run_log(path) - assert result.crashed - assert "CUDA out of memory" in result.error_message - finally: - os.unlink(path) - - def test_parse_missing_file(self): - """Non-existent log file sets crashed=True.""" - from harness.eval_agent import parse_run_log - - result = parse_run_log("/nonexistent/path/run.log") - assert result.crashed - assert result.error_message != "" - - def test_parse_empty_file(self): - """Empty log file returns crashed=False with all defaults.""" - from harness.eval_agent import parse_run_log - - path = self._write_log("") - try: - result = parse_run_log(path) - assert result.val_bpb == 0.0 - assert result.num_steps == 0 - finally: - os.unlink(path) - - def test_parse_partial_log(self): - """Partial log (only some fields) populates only those fields.""" - from harness.eval_agent import parse_run_log - - log = "val_bpb: 0.987654\npeak_vram_mb: 1500.0\n" - path = self._write_log(log) - try: - result = parse_run_log(path) - assert result.val_bpb == pytest.approx(0.987654) - assert result.peak_vram_mb == pytest.approx(1500.0) - assert result.num_steps == 0 # not present, stays default - finally: - os.unlink(path) - - def test_int_fields_parsed_as_int(self): - """num_steps, n_layer, d_model are ints, not floats.""" - from harness.eval_agent import parse_run_log - - log = "num_steps: 500\nn_layer: 4\nd_model: 256\n" - path = self._write_log(log) - try: - result = parse_run_log(path) - assert isinstance(result.num_steps, int) - assert isinstance(result.n_layer, int) - assert isinstance(result.d_model, int) - finally: - os.unlink(path) - - -class TestCheckSecondaryAlarms: - def test_all_clear_no_alarms(self): - """No alarms when all metrics are within thresholds.""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(mhc_spectral_norm=1.5, engram_hit_rate=0.5, mfu_percent=25.0) - alarms = check_secondary_alarms(result) - assert alarms == [] - - def test_mhc_spectral_norm_alarm(self): - """Alarm fires when mhc_spectral_norm > 2.0.""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(mhc_spectral_norm=2.5) - alarms = check_secondary_alarms(result) - assert any("mhc_spectral_norm" in a for a in alarms) - - def test_engram_hit_rate_alarm(self): - """Alarm fires when engram_hit_rate is in (0, 0.1).""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(engram_hit_rate=0.05) - alarms = check_secondary_alarms(result) - assert any("engram_hit_rate" in a for a in alarms) - - def test_engram_hit_rate_zero_no_alarm(self): - """Zero engram_hit_rate does NOT fire alarm (gated off).""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(engram_hit_rate=0.0) - alarms = check_secondary_alarms(result) - assert not any("engram_hit_rate" in a for a in alarms) - - def test_mfu_alarm(self): - """Alarm fires when mfu_percent is in (0, 10).""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(mfu_percent=5.0) - alarms = check_secondary_alarms(result) - assert any("mfu_percent" in a for a in alarms) - - def test_three_alarms_simultaneously(self): - """All three alarms fire when all thresholds are exceeded.""" - from harness.eval_agent import ExperimentResult, check_secondary_alarms - - result = ExperimentResult(mhc_spectral_norm=2.5, engram_hit_rate=0.05, mfu_percent=5.0) - alarms = check_secondary_alarms(result) - assert len(alarms) == 3 - - -class TestShouldKeep: - def test_improved_bpb_keeps(self): - """val_bpb strictly lower than best_bpb -> keep.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.95) - keep, reason = should_keep(result, best_bpb=1.0) - assert keep is True - assert reason == "keep" - - def test_worse_bpb_discards(self): - """val_bpb >= best_bpb -> discard.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=1.05) - keep, reason = should_keep(result, best_bpb=1.0) - assert keep is False - assert reason == "discard" - - def test_equal_bpb_discards(self): - """val_bpb == best_bpb -> discard (strict improvement required).""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=1.0) - keep, reason = should_keep(result, best_bpb=1.0) - assert keep is False - - def test_crashed_discards(self): - """Crashed result is always discarded regardless of bpb.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.5, crashed=True) - keep, reason = should_keep(result, best_bpb=1.0) - assert keep is False - assert reason == "crash" - - def test_zero_bpb_discards(self): - """val_bpb <= 0 is treated as invalid and discarded.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.0) - keep, reason = should_keep(result, best_bpb=1.0) - assert keep is False - - def test_secondary_gate_mhc_rejects(self): - """mhc_spectral_norm gate rejects even an improving result.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.9, mhc_spectral_norm=3.0) - gates = {"mhc_spectral_norm": {"max": 2.0}} - keep, reason = should_keep(result, best_bpb=1.0, gates=gates) - assert keep is False - assert "mhc_spectral_norm" in reason - - def test_secondary_gate_engram_rejects(self): - """engram_hit_rate gate rejects even an improving result.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.9, engram_hit_rate=0.01) - gates = {"engram_hit_rate": {"min": 0.05}} - keep, reason = should_keep(result, best_bpb=1.0, gates=gates) - assert keep is False - assert "engram_hit_rate" in reason - - def test_no_gates_passed(self): - """No gates argument keeps an improving result.""" - from harness.eval_agent import ExperimentResult, should_keep - - result = ExperimentResult(val_bpb=0.8, mhc_spectral_norm=5.0) - keep, reason = should_keep(result, best_bpb=1.0, gates=None) - assert keep is True - - -# --------------------------------------------------------------------------- -# search_strategy tests -# --------------------------------------------------------------------------- - -class TestDiagnose: - def test_missing_file_returns_exploring(self): - """Non-existent results.tsv returns EXPLORING state.""" - from harness.search_strategy import diagnose - - state = diagnose("/nonexistent/results.tsv") - assert state.label == "EXPLORING" - assert state.total_experiments == 0 - assert state.best_bpb == float("inf") - - def test_empty_file_returns_exploring(self): - """results.tsv with only a header returns EXPLORING.""" - from harness.search_strategy import diagnose - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - path = fh.name - try: - state = diagnose(path) - assert state.label == "EXPLORING" - assert state.total_experiments == 0 - finally: - os.unlink(path) - - def test_improving_trend_is_exploring(self): - """Steadily decreasing val_bpb trend -> EXPLORING.""" - from harness.search_strategy import diagnose - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - # 12 rows with improving BPB (each unique description for diversity) - for i in range(12): - bpb = 1.0 - i * 0.01 - fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment_{i:02d}_arch\n") - path = fh.name - try: - state = diagnose(path, stuck_threshold=20) - assert state.total_experiments == 12 - assert state.best_bpb == pytest.approx(1.0 - 11 * 0.01) - assert state.label in ("EXPLORING", "EXPLOITING") - finally: - os.unlink(path) - - def test_stuck_state_after_no_improvement(self): - """10+ experiments without improvement -> STUCK.""" - from harness.search_strategy import diagnose - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - # First row is the best, then 15 rows that are worse - fh.write("best0001\t0.800000\t2.0\tkeep\texperiment 0\n") - for i in range(1, 16): - fh.write(f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n") - path = fh.name - try: - state = diagnose(path, stuck_threshold=10) - assert state.label == "STUCK" - assert state.best_bpb == pytest.approx(0.8) - finally: - os.unlink(path) - - def test_broken_state_high_crash_rate(self): - """Crash rate > 0.5 -> BROKEN.""" - from harness.search_strategy import diagnose - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - for i in range(10): - status = "crash" if i < 7 else "keep" - bpb = "0.0" if i < 7 else "1.0" - fh.write(f"abc{i:04d}\t{bpb}\t2.0\t{status}\texperiment {i}\n") - path = fh.name - try: - state = diagnose(path) - assert state.label == "BROKEN" - assert state.crash_rate > 0.5 - finally: - os.unlink(path) - - def test_best_bpb_tracked_correctly(self): - """best_bpb is the global minimum across all experiments.""" - from harness.search_strategy import diagnose - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - bpbs = [1.0, 0.9, 0.85, 0.95, 1.1, 0.87] - for i, bpb in enumerate(bpbs): - fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment {i}\n") - path = fh.name - try: - state = diagnose(path) - assert state.best_bpb == pytest.approx(0.85) - finally: - os.unlink(path) - - -class TestShouldExplore: - def test_no_improvement_returns_true(self): - """should_explore returns True when stuck for N experiments.""" - from harness.search_strategy import should_explore - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - # Best is first row, then 12 rows with no improvement - fh.write("best0001\t0.800000\t2.0\tkeep\texperiment 0\n") - for i in range(1, 13): - fh.write(f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n") - path = fh.name - try: - assert should_explore(path, n=10) is True - finally: - os.unlink(path) - - def test_active_improvement_returns_false(self): - """should_explore returns False when improvement is happening.""" - from harness.search_strategy import should_explore - - with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: - fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") - # Steady improvement - for i in range(5): - bpb = 1.0 - i * 0.05 - fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment {i}\n") - path = fh.name - try: - assert should_explore(path, n=10) is False - finally: - os.unlink(path) - - -# --------------------------------------------------------------------------- -# meta_agent tests -# --------------------------------------------------------------------------- - -class TestGenerateDirective: - def test_exploring_returns_none(self): - """EXPLORING state produces no directive.""" - from harness.meta_agent import generate_directive - from harness.search_strategy import ResearchState - - state = ResearchState( - label="EXPLORING", - trend_improving=True, - experiment_diversity=0.8, - crash_rate=0.0, - best_bpb=0.9, - last_improvement_at=10, - total_experiments=10, - ) - assert generate_directive(state) is None - - def test_stuck_returns_bold_directive(self): - """STUCK state returns a directive containing 'BOLD' or 'bold'.""" - from harness.meta_agent import generate_directive - from harness.search_strategy import ResearchState - - state = ResearchState( - label="STUCK", - trend_improving=False, - experiment_diversity=0.2, - crash_rate=0.0, - best_bpb=1.0, - last_improvement_at=1, - total_experiments=20, - ) - directive = generate_directive(state) - assert directive is not None - assert "BOLD" in directive or "bold" in directive.lower(), ( - f"Expected 'BOLD' in directive, got: {directive}" - ) - - def test_broken_returns_alert_directive(self): - """BROKEN state returns a directive containing 'ALERT' and crash rate.""" - from harness.meta_agent import generate_directive - from harness.search_strategy import ResearchState - - state = ResearchState( - label="BROKEN", - trend_improving=False, - experiment_diversity=0.0, - crash_rate=0.75, - best_bpb=float("inf"), - last_improvement_at=0, - total_experiments=8, - ) - directive = generate_directive(state) - assert directive is not None - assert "ALERT" in directive - - def test_exploiting_returns_diversity_directive(self): - """EXPLOITING state returns a directive mentioning diversity.""" - from harness.meta_agent import generate_directive - from harness.search_strategy import ResearchState - - state = ResearchState( - label="EXPLOITING", - trend_improving=False, - experiment_diversity=0.1, - crash_rate=0.0, - best_bpb=0.9, - last_improvement_at=8, - total_experiments=10, - ) - directive = generate_directive(state) - assert directive is not None - assert "divers" in directive.lower() or "Search" in directive - - -class TestStripPreviousDirective: - def test_strips_marker_block(self): - """_strip_previous_directive removes the auto-generated section.""" - from harness.meta_agent import _strip_previous_directive, _DIRECTIVE_MARKER - - content = f"Some content\n\n{_DIRECTIVE_MARKER}\nOld directive text.\n" - result = _strip_previous_directive(content) - assert _DIRECTIVE_MARKER not in result - assert "Some content" in result - - def test_no_marker_unchanged(self): - """Content without a marker is returned unchanged (modulo trailing space).""" - from harness.meta_agent import _strip_previous_directive - - content = "Normal program.md content\nNo directive here.\n" - result = _strip_previous_directive(content) - assert "Normal program.md content" in result - assert "No directive here" in result - - -class TestRunMetaIteration: - def test_run_on_empty_results(self, tmp_path): - """run_meta_iteration with no results returns state=EXPLORING, changed=False.""" - from harness.meta_agent import run_meta_iteration - - results = str(tmp_path / "results.tsv") - program = str(tmp_path / "program.md") - summary = run_meta_iteration(program_path=program, results_path=results) - assert summary["state"] == "EXPLORING" - assert summary["changed"] is False - - def test_run_writes_directive_when_stuck(self, tmp_path): - """run_meta_iteration writes a directive to program.md when STUCK.""" - from harness.meta_agent import run_meta_iteration - - results = tmp_path / "results.tsv" - results.write_text( - "commit\tval_bpb\tmemory_gb\tstatus\tdescription\n" - + "best0001\t0.800000\t2.0\tkeep\texperiment 0\n" - + "".join( - f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n" - for i in range(1, 16) - ) - ) - program = tmp_path / "program.md" - program.write_text("# Program\n") - - summary = run_meta_iteration( - program_path=str(program), results_path=str(results) - ) - assert summary["changed"] is True - assert "directive" in summary - written = program.read_text() - assert "Meta-Agent Directive" in written +"""Tests for HYDRA harness components. + +Covers: + - eval_agent: parse_run_log, check_secondary_alarms, should_keep + - search_strategy: diagnose, should_explore + - meta_agent: generate_directive, _strip_previous_directive + +All tests are CPU-only and create/destroy temp files as needed. + +Run: + uv run pytest tests/test_harness.py -v +""" +import os +import tempfile +import pytest + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +# --------------------------------------------------------------------------- +# eval_agent tests +# --------------------------------------------------------------------------- + +class TestParseRunLog: + def _write_log(self, content: str) -> str: + """Write content to a temp log file and return its path.""" + fh = tempfile.NamedTemporaryFile( + mode="w", suffix=".log", delete=False + ) + fh.write(content) + fh.flush() + fh.close() + return fh.name + + def test_parse_valid_summary_block(self): + """All fields are extracted correctly from a well-formed log.""" + from harness.eval_agent import parse_run_log + + log = ( + "step 00100 (50.0%) | loss: 3.123456\n" + "---\n" + "val_bpb: 1.234567\n" + "training_seconds: 300.100\n" + "total_seconds: 325.000\n" + "peak_vram_mb: 2048.000\n" + "mfu_percent: 12.500\n" + "total_tokens_M: 100.000\n" + "num_steps: 200\n" + "num_params_M: 7.900\n" + "n_layer: 4\n" + "d_model: 256\n" + "mhc_spectral_norm: 1.2300\n" + "engram_hit_rate: 0.4500\n" + "sr_bypass_rate: 1.0000\n" + ) + path = self._write_log(log) + try: + result = parse_run_log(path) + assert result.val_bpb == pytest.approx(1.234567) + assert result.training_seconds == pytest.approx(300.1) + assert result.total_seconds == pytest.approx(325.0) + assert result.peak_vram_mb == pytest.approx(2048.0) + assert result.mfu_percent == pytest.approx(12.5) + assert result.total_tokens_m == pytest.approx(100.0) + assert result.num_steps == 200 + assert result.num_params_m == pytest.approx(7.9) + assert result.n_layer == 4 + assert result.d_model == 256 + assert result.mhc_spectral_norm == pytest.approx(1.23) + assert result.engram_hit_rate == pytest.approx(0.45) + assert result.sr_bypass_rate == pytest.approx(1.0) + assert not result.crashed + assert result.error_message == "" + finally: + os.unlink(path) + + def test_parse_crash_traceback(self): + """Crashed run sets crashed=True and captures error_message.""" + from harness.eval_agent import parse_run_log + + log = ( + "Traceback (most recent call last):\n" + " File 'train.py', line 100, in \n" + "RuntimeError: CUDA out of memory\n" + ) + path = self._write_log(log) + try: + result = parse_run_log(path) + assert result.crashed + assert "CUDA out of memory" in result.error_message + finally: + os.unlink(path) + + def test_parse_missing_file(self): + """Non-existent log file sets crashed=True.""" + from harness.eval_agent import parse_run_log + + result = parse_run_log("/nonexistent/path/run.log") + assert result.crashed + assert result.error_message != "" + + def test_parse_empty_file(self): + """Empty log file returns crashed=False with all defaults.""" + from harness.eval_agent import parse_run_log + + path = self._write_log("") + try: + result = parse_run_log(path) + assert result.val_bpb == 0.0 + assert result.num_steps == 0 + finally: + os.unlink(path) + + def test_parse_partial_log(self): + """Partial log (only some fields) populates only those fields.""" + from harness.eval_agent import parse_run_log + + log = "val_bpb: 0.987654\npeak_vram_mb: 1500.0\n" + path = self._write_log(log) + try: + result = parse_run_log(path) + assert result.val_bpb == pytest.approx(0.987654) + assert result.peak_vram_mb == pytest.approx(1500.0) + assert result.num_steps == 0 # not present, stays default + finally: + os.unlink(path) + + def test_int_fields_parsed_as_int(self): + """num_steps, n_layer, d_model are ints, not floats.""" + from harness.eval_agent import parse_run_log + + log = "num_steps: 500\nn_layer: 4\nd_model: 256\n" + path = self._write_log(log) + try: + result = parse_run_log(path) + assert isinstance(result.num_steps, int) + assert isinstance(result.n_layer, int) + assert isinstance(result.d_model, int) + finally: + os.unlink(path) + + +class TestCheckSecondaryAlarms: + def test_all_clear_no_alarms(self): + """No alarms when all metrics are within thresholds.""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(mhc_spectral_norm=1.5, engram_hit_rate=0.5, mfu_percent=25.0) + alarms = check_secondary_alarms(result) + assert alarms == [] + + def test_mhc_spectral_norm_alarm(self): + """Alarm fires when mhc_spectral_norm > 2.0.""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(mhc_spectral_norm=2.5) + alarms = check_secondary_alarms(result) + assert any("mhc_spectral_norm" in a for a in alarms) + + def test_engram_hit_rate_alarm(self): + """Alarm fires when engram_hit_rate is in (0, 0.1).""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(engram_hit_rate=0.05) + alarms = check_secondary_alarms(result) + assert any("engram_hit_rate" in a for a in alarms) + + def test_engram_hit_rate_zero_no_alarm(self): + """Zero engram_hit_rate does NOT fire alarm (gated off).""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(engram_hit_rate=0.0) + alarms = check_secondary_alarms(result) + assert not any("engram_hit_rate" in a for a in alarms) + + def test_mfu_alarm(self): + """Alarm fires when mfu_percent is in (0, 10).""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(mfu_percent=5.0) + alarms = check_secondary_alarms(result) + assert any("mfu_percent" in a for a in alarms) + + def test_three_alarms_simultaneously(self): + """All three alarms fire when all thresholds are exceeded.""" + from harness.eval_agent import ExperimentResult, check_secondary_alarms + + result = ExperimentResult(mhc_spectral_norm=2.5, engram_hit_rate=0.05, mfu_percent=5.0) + alarms = check_secondary_alarms(result) + assert len(alarms) == 3 + + +class TestShouldKeep: + def test_improved_bpb_keeps(self): + """val_bpb strictly lower than best_bpb -> keep.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.95) + keep, reason = should_keep(result, best_bpb=1.0) + assert keep is True + assert reason == "keep" + + def test_worse_bpb_discards(self): + """val_bpb >= best_bpb -> discard.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=1.05) + keep, reason = should_keep(result, best_bpb=1.0) + assert keep is False + assert reason == "discard" + + def test_equal_bpb_discards(self): + """val_bpb == best_bpb -> discard (strict improvement required).""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=1.0) + keep, reason = should_keep(result, best_bpb=1.0) + assert keep is False + + def test_crashed_discards(self): + """Crashed result is always discarded regardless of bpb.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.5, crashed=True) + keep, reason = should_keep(result, best_bpb=1.0) + assert keep is False + assert reason == "crash" + + def test_zero_bpb_discards(self): + """val_bpb <= 0 is treated as invalid and discarded.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.0) + keep, reason = should_keep(result, best_bpb=1.0) + assert keep is False + + def test_secondary_gate_mhc_rejects(self): + """mhc_spectral_norm gate rejects even an improving result.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.9, mhc_spectral_norm=3.0) + gates = {"mhc_spectral_norm": {"max": 2.0}} + keep, reason = should_keep(result, best_bpb=1.0, gates=gates) + assert keep is False + assert "mhc_spectral_norm" in reason + + def test_secondary_gate_engram_rejects(self): + """engram_hit_rate gate rejects even an improving result.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.9, engram_hit_rate=0.01) + gates = {"engram_hit_rate": {"min": 0.05}} + keep, reason = should_keep(result, best_bpb=1.0, gates=gates) + assert keep is False + assert "engram_hit_rate" in reason + + def test_no_gates_passed(self): + """No gates argument keeps an improving result.""" + from harness.eval_agent import ExperimentResult, should_keep + + result = ExperimentResult(val_bpb=0.8, mhc_spectral_norm=5.0) + keep, reason = should_keep(result, best_bpb=1.0, gates=None) + assert keep is True + + +# --------------------------------------------------------------------------- +# search_strategy tests +# --------------------------------------------------------------------------- + +class TestDiagnose: + def test_missing_file_returns_exploring(self): + """Non-existent results.tsv returns EXPLORING state.""" + from harness.search_strategy import diagnose + + state = diagnose("/nonexistent/results.tsv") + assert state.label == "EXPLORING" + assert state.total_experiments == 0 + assert state.best_bpb == float("inf") + + def test_empty_file_returns_exploring(self): + """results.tsv with only a header returns EXPLORING.""" + from harness.search_strategy import diagnose + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + path = fh.name + try: + state = diagnose(path) + assert state.label == "EXPLORING" + assert state.total_experiments == 0 + finally: + os.unlink(path) + + def test_improving_trend_is_exploring(self): + """Steadily decreasing val_bpb trend -> EXPLORING.""" + from harness.search_strategy import diagnose + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + # 12 rows with improving BPB (each unique description for diversity) + for i in range(12): + bpb = 1.0 - i * 0.01 + fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment_{i:02d}_arch\n") + path = fh.name + try: + state = diagnose(path, stuck_threshold=20) + assert state.total_experiments == 12 + assert state.best_bpb == pytest.approx(1.0 - 11 * 0.01) + assert state.label in ("EXPLORING", "EXPLOITING") + finally: + os.unlink(path) + + def test_stuck_state_after_no_improvement(self): + """10+ experiments without improvement -> STUCK.""" + from harness.search_strategy import diagnose + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + # First row is the best, then 15 rows that are worse + fh.write("best0001\t0.800000\t2.0\tkeep\texperiment 0\n") + for i in range(1, 16): + fh.write(f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n") + path = fh.name + try: + state = diagnose(path, stuck_threshold=10) + assert state.label == "STUCK" + assert state.best_bpb == pytest.approx(0.8) + finally: + os.unlink(path) + + def test_broken_state_high_crash_rate(self): + """Crash rate > 0.5 -> BROKEN.""" + from harness.search_strategy import diagnose + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + for i in range(10): + status = "crash" if i < 7 else "keep" + bpb = "0.0" if i < 7 else "1.0" + fh.write(f"abc{i:04d}\t{bpb}\t2.0\t{status}\texperiment {i}\n") + path = fh.name + try: + state = diagnose(path) + assert state.label == "BROKEN" + assert state.crash_rate > 0.5 + finally: + os.unlink(path) + + def test_best_bpb_tracked_correctly(self): + """best_bpb is the global minimum across all experiments.""" + from harness.search_strategy import diagnose + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + bpbs = [1.0, 0.9, 0.85, 0.95, 1.1, 0.87] + for i, bpb in enumerate(bpbs): + fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment {i}\n") + path = fh.name + try: + state = diagnose(path) + assert state.best_bpb == pytest.approx(0.85) + finally: + os.unlink(path) + + +class TestShouldExplore: + def test_no_improvement_returns_true(self): + """should_explore returns True when stuck for N experiments.""" + from harness.search_strategy import should_explore + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + # Best is first row, then 12 rows with no improvement + fh.write("best0001\t0.800000\t2.0\tkeep\texperiment 0\n") + for i in range(1, 13): + fh.write(f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n") + path = fh.name + try: + assert should_explore(path, n=10) is True + finally: + os.unlink(path) + + def test_active_improvement_returns_false(self): + """should_explore returns False when improvement is happening.""" + from harness.search_strategy import should_explore + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as fh: + fh.write("commit\tval_bpb\tmemory_gb\tstatus\tdescription\n") + # Steady improvement + for i in range(5): + bpb = 1.0 - i * 0.05 + fh.write(f"abc{i:04d}\t{bpb:.6f}\t2.0\tkeep\texperiment {i}\n") + path = fh.name + try: + assert should_explore(path, n=10) is False + finally: + os.unlink(path) + + +# --------------------------------------------------------------------------- +# meta_agent tests +# --------------------------------------------------------------------------- + +class TestGenerateDirective: + def test_exploring_returns_none(self): + """EXPLORING state produces no directive.""" + from harness.meta_agent import generate_directive + from harness.search_strategy import ResearchState + + state = ResearchState( + label="EXPLORING", + trend_improving=True, + experiment_diversity=0.8, + crash_rate=0.0, + best_bpb=0.9, + last_improvement_at=10, + total_experiments=10, + ) + assert generate_directive(state) is None + + def test_stuck_returns_bold_directive(self): + """STUCK state returns a directive containing 'BOLD' or 'bold'.""" + from harness.meta_agent import generate_directive + from harness.search_strategy import ResearchState + + state = ResearchState( + label="STUCK", + trend_improving=False, + experiment_diversity=0.2, + crash_rate=0.0, + best_bpb=1.0, + last_improvement_at=1, + total_experiments=20, + ) + directive = generate_directive(state) + assert directive is not None + assert "BOLD" in directive or "bold" in directive.lower(), ( + f"Expected 'BOLD' in directive, got: {directive}" + ) + + def test_broken_returns_alert_directive(self): + """BROKEN state returns a directive containing 'ALERT' and crash rate.""" + from harness.meta_agent import generate_directive + from harness.search_strategy import ResearchState + + state = ResearchState( + label="BROKEN", + trend_improving=False, + experiment_diversity=0.0, + crash_rate=0.75, + best_bpb=float("inf"), + last_improvement_at=0, + total_experiments=8, + ) + directive = generate_directive(state) + assert directive is not None + assert "ALERT" in directive + + def test_exploiting_returns_diversity_directive(self): + """EXPLOITING state returns a directive mentioning diversity.""" + from harness.meta_agent import generate_directive + from harness.search_strategy import ResearchState + + state = ResearchState( + label="EXPLOITING", + trend_improving=False, + experiment_diversity=0.1, + crash_rate=0.0, + best_bpb=0.9, + last_improvement_at=8, + total_experiments=10, + ) + directive = generate_directive(state) + assert directive is not None + assert "divers" in directive.lower() or "Search" in directive + + +class TestStripPreviousDirective: + def test_strips_marker_block(self): + """_strip_previous_directive removes the auto-generated section.""" + from harness.meta_agent import _strip_previous_directive, _DIRECTIVE_MARKER + + content = f"Some content\n\n{_DIRECTIVE_MARKER}\nOld directive text.\n" + result = _strip_previous_directive(content) + assert _DIRECTIVE_MARKER not in result + assert "Some content" in result + + def test_no_marker_unchanged(self): + """Content without a marker is returned unchanged (modulo trailing space).""" + from harness.meta_agent import _strip_previous_directive + + content = "Normal program.md content\nNo directive here.\n" + result = _strip_previous_directive(content) + assert "Normal program.md content" in result + assert "No directive here" in result + + +class TestRunMetaIteration: + def test_run_on_empty_results(self, tmp_path): + """run_meta_iteration with no results returns state=EXPLORING, changed=False.""" + from harness.meta_agent import run_meta_iteration + + results = str(tmp_path / "results.tsv") + program = str(tmp_path / "program.md") + summary = run_meta_iteration(program_path=program, results_path=results) + assert summary["state"] == "EXPLORING" + assert summary["changed"] is False + + def test_run_writes_directive_when_stuck(self, tmp_path): + """run_meta_iteration writes a directive to program.md when STUCK.""" + from harness.meta_agent import run_meta_iteration + + results = tmp_path / "results.tsv" + results.write_text( + "commit\tval_bpb\tmemory_gb\tstatus\tdescription\n" + + "best0001\t0.800000\t2.0\tkeep\texperiment 0\n" + + "".join( + f"abc{i:04d}\t1.000000\t2.0\tkeep\texperiment {i}\n" + for i in range(1, 16) + ) + ) + program = tmp_path / "program.md" + program.write_text("# Program\n") + + summary = run_meta_iteration( + program_path=str(program), results_path=str(results) + ) + assert summary["changed"] is True + assert "directive" in summary + written = program.read_text() + assert "Meta-Agent Directive" in written diff --git a/overlay/tests/test_hydra_modular.py b/overlay/tests/test_hydra_modular.py index 1fbd245ead8539497ad63d2823b38d497ff80cd4..6d108f738101573bed7f6972f90c407951b3fdf5 100644 --- a/overlay/tests/test_hydra_modular.py +++ b/overlay/tests/test_hydra_modular.py @@ -1,251 +1,251 @@ -""" -Regression tests for W1's modularisation of train.py into the hydra/ package. - -These tests verify that after modularisation: - - The expected public symbols are importable from the stated sub-modules. - - PostSemClawConfig instantiates with default args. - - PostSemClawModel can be constructed, initialised, and produces a scalar - loss on tiny inputs (batch=1, seq=32) without error. - - train.py at the repo root is still importable as a Python module (i.e. - the training-loop body is gated on ``if __name__ == "__main__":`` so a - plain ``import`` doesn't execute it). - - train.py is under 150 lines after modularisation (the main motiviation for - W1's work is a thin orchestrator script, not a 900-line monolith). - -If the hydra/ package does not exist yet (W1 is still running), every test in -this file is gracefully skipped so the test suite remains green. - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_hydra_modular.py -v -""" - -import importlib -import os -import subprocess -import sys -import types -import pytest - -# --------------------------------------------------------------------------- -# Module-level skip: hydra/ must exist as an importable package. -# pytest.importorskip cannot be used at module level without allow_module_level, -# and it doesn't work for relative paths. We do the check manually. -# --------------------------------------------------------------------------- - -_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -_HYDRA_INIT = os.path.join(_REPO, "hydra", "__init__.py") - -if not os.path.isfile(_HYDRA_INIT): - pytest.skip( - "hydra/ package not found — W1 modularisation not yet complete. " - "Re-run after hydra/__init__.py exists.", - allow_module_level=True, - ) - -# --------------------------------------------------------------------------- -# Helper: add repo root to sys.path so `import hydra` resolves to the local -# package, not the Apache Hydra framework if installed. -# --------------------------------------------------------------------------- - -if _REPO not in sys.path: - sys.path.insert(0, _REPO) - - -# --------------------------------------------------------------------------- -# Fixture: ensure 'prepare' stub is available so any transitive imports from -# train.py or hydra/ that do `from prepare import ...` don't crash. -# --------------------------------------------------------------------------- - -def _ensure_prepare_stub(): - if "prepare" not in sys.modules: - fake = types.ModuleType("prepare") - fake.MAX_SEQ_LEN = 2048 - fake.TIME_BUDGET = 300 - fake.Tokenizer = object - fake.make_dataloader = lambda *a, **kw: None - fake.evaluate_bpb = lambda *a, **kw: 0.0 - sys.modules["prepare"] = fake - - -_ensure_prepare_stub() - - -# --------------------------------------------------------------------------- -# Test 1: public API is importable from the correct sub-modules -# --------------------------------------------------------------------------- - -class TestHydraPublicAPI: - def test_config_importable(self): - """PostSemClawConfig is importable from hydra.config.""" - mod = importlib.import_module("hydra.config") - assert hasattr(mod, "PostSemClawConfig"), ( - "hydra.config does not export PostSemClawConfig" - ) - - def test_model_importable(self): - """PostSemClawModel is importable from hydra.model.""" - mod = importlib.import_module("hydra.model") - assert hasattr(mod, "PostSemClawModel"), ( - "hydra.model does not export PostSemClawModel" - ) - - def test_optimizer_importable(self): - """MuonAdamW is importable from hydra.optimizer.""" - mod = importlib.import_module("hydra.optimizer") - assert hasattr(mod, "MuonAdamW"), ( - "hydra.optimizer does not export MuonAdamW" - ) - - def test_engram_importable(self): - """GPUEngram is importable from hydra.engram (if Engram is top-level).""" - try: - mod = importlib.import_module("hydra.engram") - except ImportError: - pytest.skip("hydra.engram module does not exist — may be merged into hydra.model") - assert hasattr(mod, "GPUEngram"), ( - "hydra.engram does not export GPUEngram" - ) - - -# --------------------------------------------------------------------------- -# Test 2: PostSemClawConfig default construction -# --------------------------------------------------------------------------- - -class TestPostSemClawConfig: - def test_default_instantiation(self): - """PostSemClawConfig() should instantiate with all defaults.""" - from hydra.config import PostSemClawConfig # noqa: PLC0415 - cfg = PostSemClawConfig() - # Verify a few required fields exist and have sane defaults - assert hasattr(cfg, "d_model"), "PostSemClawConfig missing d_model field" - assert hasattr(cfg, "n_layer"), "PostSemClawConfig missing n_layer field" - assert hasattr(cfg, "vocab_size"), "PostSemClawConfig missing vocab_size field" - assert cfg.d_model > 0 - assert cfg.n_layer > 0 - assert cfg.vocab_size > 0 - - def test_custom_instantiation(self): - """PostSemClawConfig accepts keyword overrides.""" - from hydra.config import PostSemClawConfig # noqa: PLC0415 - cfg = PostSemClawConfig(d_model=64, n_layer=2) - assert cfg.d_model == 64 - assert cfg.n_layer == 2 - - -# --------------------------------------------------------------------------- -# Test 3: PostSemClawModel forward pass with tiny inputs -# --------------------------------------------------------------------------- - -class TestPostSemClawModelForward: - @pytest.fixture - def tiny_model(self): - """Construct a tiny PostSemClawModel on CPU.""" - import torch # noqa: PLC0415 - from hydra.config import PostSemClawConfig # noqa: PLC0415 - from hydra.model import PostSemClawModel # noqa: PLC0415 - - # Use the smallest possible config that exercises all code paths. - cfg = PostSemClawConfig( - sequence_len=32, - vocab_size=64, - n_layer=2, - d_model=32, - d_state=8, - headdim=16, - n_heads=2, - expand=2, - engram_n_columns=16, - engram_key_dim=8, - engram_layer_idx=0, - sdr_n_bits=128, - sdr_target_active=3, - sdr_delta_rank=4, - htm_n_columns=32, - htm_cells_per_column=4, - ) - model = PostSemClawModel(cfg) - model.init_weights() - model.eval() - return model - - def test_forward_returns_scalar_loss(self, tiny_model): - """model(x, y, reduction='mean') returns a scalar loss.""" - import torch # noqa: PLC0415 - - B, T = 1, 32 - vocab = tiny_model.config.vocab_size - idx = torch.randint(0, vocab, (B, T)) - targets = torch.randint(0, vocab, (B, T)) - - with torch.no_grad(): - loss = tiny_model(idx, targets, reduction="mean") - - assert isinstance(loss, torch.Tensor), "forward did not return a tensor" - assert loss.ndim == 0, f"expected scalar loss, got shape {loss.shape}" - assert torch.isfinite(loss), f"loss is not finite: {loss.item()}" - - def test_forward_returns_per_token_loss(self, tiny_model): - """model(x, y, reduction='none') returns (B*T,) per-token losses.""" - import torch # noqa: PLC0415 - - B, T = 1, 32 - vocab = tiny_model.config.vocab_size - idx = torch.randint(0, vocab, (B, T)) - targets = torch.randint(0, vocab, (B, T)) - - with torch.no_grad(): - losses = tiny_model(idx, targets, reduction="none") - - assert losses.shape == (B * T,), ( - f"expected shape ({B * T},), got {losses.shape}" - ) - assert torch.all(torch.isfinite(losses)), "some per-token losses are not finite" - - -# --------------------------------------------------------------------------- -# Test 4: train.py at repo root is still importable (body gated on __main__) -# --------------------------------------------------------------------------- - -class TestTrainPyImportable: - def test_train_py_importable_as_module(self): - """ - train.py must be importable without executing the training loop. - We verify this by running `python -c "import importlib.util; ..."` in a - subprocess to get a clean interpreter state, avoiding interference from - the test process's already-patched sys.modules. - """ - train_path = os.path.join(_REPO, "train.py") - assert os.path.isfile(train_path), f"train.py not found at {train_path}" - - check_script = ( - "import importlib.util, sys; " - "sys.path.insert(0, repr(_REPO)); " - "spec = importlib.util.spec_from_file_location('train', repr(train_path)); " - "assert spec is not None, 'spec is None'" - ).replace("repr(_REPO)", repr(_REPO)).replace("repr(train_path)", repr(train_path)) - - result = subprocess.run( - [sys.executable, "-c", check_script], - capture_output=True, - text=True, - timeout=10, - ) - # A non-zero exit only means the assert failed, not a parse error — - # either way we surface stderr for diagnosis. - assert result.returncode == 0, ( - f"train.py spec creation failed:\nstdout: {result.stdout}\nstderr: {result.stderr}" - ) - - def test_train_py_under_150_lines(self): - """ - After modularisation, train.py should be a thin orchestrator < 150 lines. - This asserts the structural goal: all heavy logic lives in hydra/*. - """ - train_path = os.path.join(_REPO, "train.py") - with open(train_path) as fh: - lines = fh.readlines() - assert len(lines) < 150, ( - f"train.py has {len(lines)} lines — expected < 150 after modularisation. " - "Move model/optimizer/config definitions to hydra/ sub-modules." - ) +""" +Regression tests for W1's modularisation of train.py into the hydra/ package. + +These tests verify that after modularisation: + - The expected public symbols are importable from the stated sub-modules. + - PostSemClawConfig instantiates with default args. + - PostSemClawModel can be constructed, initialised, and produces a scalar + loss on tiny inputs (batch=1, seq=32) without error. + - train.py at the repo root is still importable as a Python module (i.e. + the training-loop body is gated on ``if __name__ == "__main__":`` so a + plain ``import`` doesn't execute it). + - train.py is under 150 lines after modularisation (the main motiviation for + W1's work is a thin orchestrator script, not a 900-line monolith). + +If the hydra/ package does not exist yet (W1 is still running), every test in +this file is gracefully skipped so the test suite remains green. + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_hydra_modular.py -v +""" + +import importlib +import os +import subprocess +import sys +import types +import pytest + +# --------------------------------------------------------------------------- +# Module-level skip: hydra/ must exist as an importable package. +# pytest.importorskip cannot be used at module level without allow_module_level, +# and it doesn't work for relative paths. We do the check manually. +# --------------------------------------------------------------------------- + +_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +_HYDRA_INIT = os.path.join(_REPO, "hydra", "__init__.py") + +if not os.path.isfile(_HYDRA_INIT): + pytest.skip( + "hydra/ package not found — W1 modularisation not yet complete. " + "Re-run after hydra/__init__.py exists.", + allow_module_level=True, + ) + +# --------------------------------------------------------------------------- +# Helper: add repo root to sys.path so `import hydra` resolves to the local +# package, not the Apache Hydra framework if installed. +# --------------------------------------------------------------------------- + +if _REPO not in sys.path: + sys.path.insert(0, _REPO) + + +# --------------------------------------------------------------------------- +# Fixture: ensure 'prepare' stub is available so any transitive imports from +# train.py or hydra/ that do `from prepare import ...` don't crash. +# --------------------------------------------------------------------------- + +def _ensure_prepare_stub(): + if "prepare" not in sys.modules: + fake = types.ModuleType("prepare") + fake.MAX_SEQ_LEN = 2048 + fake.TIME_BUDGET = 300 + fake.Tokenizer = object + fake.make_dataloader = lambda *a, **kw: None + fake.evaluate_bpb = lambda *a, **kw: 0.0 + sys.modules["prepare"] = fake + + +_ensure_prepare_stub() + + +# --------------------------------------------------------------------------- +# Test 1: public API is importable from the correct sub-modules +# --------------------------------------------------------------------------- + +class TestHydraPublicAPI: + def test_config_importable(self): + """PostSemClawConfig is importable from hydra.config.""" + mod = importlib.import_module("hydra.config") + assert hasattr(mod, "PostSemClawConfig"), ( + "hydra.config does not export PostSemClawConfig" + ) + + def test_model_importable(self): + """PostSemClawModel is importable from hydra.model.""" + mod = importlib.import_module("hydra.model") + assert hasattr(mod, "PostSemClawModel"), ( + "hydra.model does not export PostSemClawModel" + ) + + def test_optimizer_importable(self): + """MuonAdamW is importable from hydra.optimizer.""" + mod = importlib.import_module("hydra.optimizer") + assert hasattr(mod, "MuonAdamW"), ( + "hydra.optimizer does not export MuonAdamW" + ) + + def test_engram_importable(self): + """GPUEngram is importable from hydra.engram (if Engram is top-level).""" + try: + mod = importlib.import_module("hydra.engram") + except ImportError: + pytest.skip("hydra.engram module does not exist — may be merged into hydra.model") + assert hasattr(mod, "GPUEngram"), ( + "hydra.engram does not export GPUEngram" + ) + + +# --------------------------------------------------------------------------- +# Test 2: PostSemClawConfig default construction +# --------------------------------------------------------------------------- + +class TestPostSemClawConfig: + def test_default_instantiation(self): + """PostSemClawConfig() should instantiate with all defaults.""" + from hydra.config import PostSemClawConfig # noqa: PLC0415 + cfg = PostSemClawConfig() + # Verify a few required fields exist and have sane defaults + assert hasattr(cfg, "d_model"), "PostSemClawConfig missing d_model field" + assert hasattr(cfg, "n_layer"), "PostSemClawConfig missing n_layer field" + assert hasattr(cfg, "vocab_size"), "PostSemClawConfig missing vocab_size field" + assert cfg.d_model > 0 + assert cfg.n_layer > 0 + assert cfg.vocab_size > 0 + + def test_custom_instantiation(self): + """PostSemClawConfig accepts keyword overrides.""" + from hydra.config import PostSemClawConfig # noqa: PLC0415 + cfg = PostSemClawConfig(d_model=64, n_layer=2) + assert cfg.d_model == 64 + assert cfg.n_layer == 2 + + +# --------------------------------------------------------------------------- +# Test 3: PostSemClawModel forward pass with tiny inputs +# --------------------------------------------------------------------------- + +class TestPostSemClawModelForward: + @pytest.fixture + def tiny_model(self): + """Construct a tiny PostSemClawModel on CPU.""" + import torch # noqa: PLC0415 + from hydra.config import PostSemClawConfig # noqa: PLC0415 + from hydra.model import PostSemClawModel # noqa: PLC0415 + + # Use the smallest possible config that exercises all code paths. + cfg = PostSemClawConfig( + sequence_len=32, + vocab_size=64, + n_layer=2, + d_model=32, + d_state=8, + headdim=16, + n_heads=2, + expand=2, + engram_n_columns=16, + engram_key_dim=8, + engram_layer_idx=0, + sdr_n_bits=128, + sdr_target_active=3, + sdr_delta_rank=4, + htm_n_columns=32, + htm_cells_per_column=4, + ) + model = PostSemClawModel(cfg) + model.init_weights() + model.eval() + return model + + def test_forward_returns_scalar_loss(self, tiny_model): + """model(x, y, reduction='mean') returns a scalar loss.""" + import torch # noqa: PLC0415 + + B, T = 1, 32 + vocab = tiny_model.config.vocab_size + idx = torch.randint(0, vocab, (B, T)) + targets = torch.randint(0, vocab, (B, T)) + + with torch.no_grad(): + loss = tiny_model(idx, targets, reduction="mean") + + assert isinstance(loss, torch.Tensor), "forward did not return a tensor" + assert loss.ndim == 0, f"expected scalar loss, got shape {loss.shape}" + assert torch.isfinite(loss), f"loss is not finite: {loss.item()}" + + def test_forward_returns_per_token_loss(self, tiny_model): + """model(x, y, reduction='none') returns (B*T,) per-token losses.""" + import torch # noqa: PLC0415 + + B, T = 1, 32 + vocab = tiny_model.config.vocab_size + idx = torch.randint(0, vocab, (B, T)) + targets = torch.randint(0, vocab, (B, T)) + + with torch.no_grad(): + losses = tiny_model(idx, targets, reduction="none") + + assert losses.shape == (B * T,), ( + f"expected shape ({B * T},), got {losses.shape}" + ) + assert torch.all(torch.isfinite(losses)), "some per-token losses are not finite" + + +# --------------------------------------------------------------------------- +# Test 4: train.py at repo root is still importable (body gated on __main__) +# --------------------------------------------------------------------------- + +class TestTrainPyImportable: + def test_train_py_importable_as_module(self): + """ + train.py must be importable without executing the training loop. + We verify this by running `python -c "import importlib.util; ..."` in a + subprocess to get a clean interpreter state, avoiding interference from + the test process's already-patched sys.modules. + """ + train_path = os.path.join(_REPO, "train.py") + assert os.path.isfile(train_path), f"train.py not found at {train_path}" + + check_script = ( + "import importlib.util, sys; " + "sys.path.insert(0, repr(_REPO)); " + "spec = importlib.util.spec_from_file_location('train', repr(train_path)); " + "assert spec is not None, 'spec is None'" + ).replace("repr(_REPO)", repr(_REPO)).replace("repr(train_path)", repr(train_path)) + + result = subprocess.run( + [sys.executable, "-c", check_script], + capture_output=True, + text=True, + timeout=10, + ) + # A non-zero exit only means the assert failed, not a parse error — + # either way we surface stderr for diagnosis. + assert result.returncode == 0, ( + f"train.py spec creation failed:\nstdout: {result.stdout}\nstderr: {result.stderr}" + ) + + def test_train_py_under_150_lines(self): + """ + After modularisation, train.py should be a thin orchestrator < 150 lines. + This asserts the structural goal: all heavy logic lives in hydra/*. + """ + train_path = os.path.join(_REPO, "train.py") + with open(train_path) as fh: + lines = fh.readlines() + assert len(lines) < 150, ( + f"train.py has {len(lines)} lines — expected < 150 after modularisation. " + "Move model/optimizer/config definitions to hydra/ sub-modules." + ) diff --git a/overlay/tests/test_hyena.py b/overlay/tests/test_hyena.py index 1be534a5862d43dcd76c43efc4fcaedf24fc5a5e..ddd99dafeeca79710a87bb43ee0c2202c9a328fc 100644 --- a/overlay/tests/test_hyena.py +++ b/overlay/tests/test_hyena.py @@ -1,301 +1,301 @@ -"""Acceptance tests for the Hyena port (supplement to Mamba3). - -Covers: - 1. Shape parity: [B=4, T=64, D=384] in → [B=4, T=64, D=384] out. - 2. Causality: changing x[:, t+1:] must NOT change output[:, :t]. - 3. No grad leak: grads at positions beyond t must not flow through x[:, :t]. - 4. Forward+backward on CPU with d_model=384, T=64. - 5. Selective substitution: HYDRA_HYENA_LAYERS=3,7 → HyenaBlock at 3 and 7 - in the block list; Mamba3 elsewhere (isinstance assertion). - 6. Gradient flow: loss.backward() doesn't NaN after one step. - 7. Static forbidden-imports grep on ported code (zero matches required). - -The test file itself avoids torch.no_grad in places where we need actual -gradients; it also isolates Test 5 from requiring a CUDA device / full -HYDRA training init (we construct only the block list path to keep the -check focused and CPU-friendly). -""" - -from __future__ import annotations - -import os -import subprocess -import sys -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from hydra.hyena_block import HyenaBlock # noqa: E402 -from subsystems.hyena_pure import HyenaOperator # noqa: E402 - - -# --------------------------------------------------------------------------- -# Test 1: shape parity -# --------------------------------------------------------------------------- -def test_shape_parity_4_64_384(): - torch.manual_seed(0) - block = HyenaBlock(d_model=384, seq_len=64) - x = torch.randn(4, 64, 384) - y = block(x) - assert y.shape == (4, 64, 384), f"expected (4,64,384), got {tuple(y.shape)}" - assert y.dtype == x.dtype - - -# --------------------------------------------------------------------------- -# Test 2: causality — output[:, :t] invariant to changes in x[:, t+1:] -# --------------------------------------------------------------------------- -def test_causal_mask_correctness(): - torch.manual_seed(1) - D, T = 64, 32 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - - x1 = torch.randn(2, T, D) - x2 = x1.clone() - # Perturb the future half only: - t_cut = T // 2 - x2[:, t_cut:, :] = torch.randn_like(x2[:, t_cut:, :]) - - with torch.no_grad(): - y1 = block(x1) - y2 = block(x2) - - # Outputs in the past (indices < t_cut) must be identical to within - # numerical tolerance. - diff = (y1[:, :t_cut, :] - y2[:, :t_cut, :]).abs().max().item() - assert diff < 1e-5, f"causality violated: past output diff = {diff:.2e}" - - -# --------------------------------------------------------------------------- -# Test 3: no grad leak from future positions into past -# --------------------------------------------------------------------------- -def test_no_future_grad_leak_into_past(): - torch.manual_seed(2) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - - x = torch.randn(1, T, D, requires_grad=True) - y = block(x) - - # Scalar loss on one FUTURE position (t=T-1). - loss = y[0, T - 1, :].sum() - loss.backward() - - assert x.grad is not None - # Grad at ANY past position t < T-1 can be non-zero (backward through - # conv filter); the causality invariant is the FORWARD one tested above. - # What we check here is the dual: a loss at a PAST position has zero grad - # w.r.t. FUTURE inputs (by causality of the forward pass). - x2 = torch.randn(1, T, D, requires_grad=True) - y2 = block(x2) - past_t = T // 4 - loss2 = y2[0, past_t, :].sum() - loss2.backward() - future_grad = x2.grad[0, past_t + 1 :, :].abs().max().item() - assert future_grad < 1e-5, ( - f"causality violated in backward: future grad = {future_grad:.2e}" - ) - - -# --------------------------------------------------------------------------- -# Test 4: forward + backward on CPU at d_model=384, T=64 -# --------------------------------------------------------------------------- -def test_forward_backward_cpu_d384_t64(): - torch.manual_seed(3) - block = HyenaBlock(d_model=384, seq_len=64) - x = torch.randn(2, 64, 384, requires_grad=True) - y = block(x) - assert y.shape == (2, 64, 384) - loss = y.pow(2).mean() - loss.backward() - # Some parameter must have received non-zero grad. - any_nonzero = any( - p.grad is not None and p.grad.abs().sum().item() > 0 - for p in block.parameters() - ) - assert any_nonzero, "no parameter received a non-zero gradient" - assert x.grad is not None - - -# --------------------------------------------------------------------------- -# Test 5: selective layer substitution via HYDRA_HYENA_LAYERS -# --------------------------------------------------------------------------- -def test_selective_hyena_layers_env_switch(monkeypatch): - """HYDRA_HYENA_LAYERS='3,7' → HyenaBlock at 3 and 7, Mamba3 elsewhere. - - Mimics the model.py construction directly with a stub Mamba3 sentinel - so the test is CPU-only and doesn't require mamba-ssm (which needs CUDA). - This mirrors exactly the code path of model.py — the surgical edit is - a list comprehension: isinstance checks on the resulting list are the - contract. - """ - import torch.nn as nn - - # Monkeypatch mamba_ssm.Mamba3 to a sentinel class *before* model.py - # imports happen. We mirror model.py's block construction logic here - # directly so we don't need the full model build (which pulls CUDA, - # mamba_ssm, htm_rust, etc.). - class _Mamba3Sentinel(nn.Module): - def __init__(self, **kw): - super().__init__() - self.kw = kw - - def forward(self, x): - return x - - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") - monkeypatch.setenv("HYDRA_HYENA_ORDER", "2") - monkeypatch.setenv("HYDRA_HYENA_FILTER_DIM", "32") - - n_layer = 10 - d_model = 64 - seq_len = 16 - - _hyena_env = os.environ.get("HYDRA_HYENA_LAYERS", "") - _hyena_layer_set = { - int(s.strip()) for s in _hyena_env.split(",") if s.strip() - } - blocks = nn.ModuleList([ - HyenaBlock( - d_model=d_model, - seq_len=seq_len, - order=int(os.environ.get("HYDRA_HYENA_ORDER", "2")), - filter_order=int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "32")), - ) - if i in _hyena_layer_set - else _Mamba3Sentinel(d_model=d_model, d_state=64) - for i in range(n_layer) - ]) - - # Contract: indices 3 and 7 are HyenaBlock, others are Mamba3Sentinel. - for i in range(n_layer): - if i in {3, 7}: - assert isinstance(blocks[i], HyenaBlock), ( - f"layer {i}: expected HyenaBlock, got {type(blocks[i]).__name__}" - ) - else: - assert isinstance(blocks[i], _Mamba3Sentinel), ( - f"layer {i}: expected _Mamba3Sentinel, got {type(blocks[i]).__name__}" - ) - - # Also verify the default (empty) case → no HyenaBlock anywhere. - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "") - _hyena_env2 = os.environ.get("HYDRA_HYENA_LAYERS", "") - _set2 = {int(s.strip()) for s in _hyena_env2.split(",") if s.strip()} - blocks2 = nn.ModuleList([ - HyenaBlock(d_model=d_model, seq_len=seq_len) if i in _set2 - else _Mamba3Sentinel(d_model=d_model) - for i in range(n_layer) - ]) - for i in range(n_layer): - assert isinstance(blocks2[i], _Mamba3Sentinel), ( - f"default (no env): layer {i} should be Mamba3 sentinel" - ) - - -# --------------------------------------------------------------------------- -# Test 6: gradient flow — one optimizer step doesn't produce NaN -# --------------------------------------------------------------------------- -def test_grad_flow_no_nan_after_one_step(): - torch.manual_seed(4) - D, T = 64, 32 - block = HyenaBlock(d_model=D, seq_len=T) - opt = torch.optim.SGD(block.parameters(), lr=1e-3) - - x = torch.randn(2, T, D) - target = torch.randn(2, T, D) - - opt.zero_grad() - y = block(x) - loss = torch.nn.functional.mse_loss(y, target) - assert torch.isfinite(loss), f"initial loss non-finite: {loss.item()}" - loss.backward() - - for name, p in block.named_parameters(): - if p.grad is not None: - assert torch.isfinite(p.grad).all(), f"NaN/Inf in grad of {name}" - - opt.step() - - for name, p in block.named_parameters(): - assert torch.isfinite(p).all(), f"NaN/Inf in param {name} after step" - - -# --------------------------------------------------------------------------- -# Test 7: static grep for forbidden transformer tokens in ported code -# --------------------------------------------------------------------------- -def test_no_forbidden_transformer_imports(): - """Grep the two ported files for tokens indicating attention / transformer. - - Whitelist (allowed): - - None. Any of these tokens in the ported source is a failure. - - Tokens we reject (exact-string match): - MultiheadAttention, scaled_dot_product_attention, flash_attn, - xformers, kv_cache, KVCache. For 'softmax' and 'transformers' we - search via grep (log output attached in the report). - """ - root = Path(__file__).resolve().parents[1] - files = [ - root / "subsystems" / "hyena_pure.py", - root / "hydra" / "hyena_block.py", - ] - for f in files: - assert f.exists(), f"missing ported file: {f}" - - forbidden_patterns = [ - "MultiheadAttention", - "scaled_dot_product_attention", - "flash_attn", - "xformers", - "KVCache", - "kv_cache", - "from transformers", - "import transformers", - ] - - violations: list[str] = [] - for f in files: - text = f.read_text() - for pat in forbidden_patterns: - if pat in text: - violations.append(f"{f}: contains forbidden token '{pat}'") - - assert not violations, "Forbidden transformer tokens found:\n" + "\n".join(violations) - - # Additionally run grep -r for the report (captured but not asserted - # here beyond exit code). The subprocess is defensive: if grep is - # unavailable we skip this portion. - try: - out = subprocess.run( - [ - "grep", "-RniE", - "|".join([ - r"\bMultiheadAttention\b", - r"\bscaled_dot_product_attention\b", - r"\bflash_attn\b", - r"\bxformers\b", - r"\bKVCache\b", - r"\bkv_cache\b", - r"^from transformers", - r"^import transformers", - ]), - str(files[0]), - str(files[1]), - ], - capture_output=True, text=True, timeout=5, - ) - # grep exit 1 means no match (what we want); 0 means match found. - assert out.returncode == 1, ( - f"grep found forbidden patterns:\nstdout:\n{out.stdout}\nstderr:\n{out.stderr}" - ) - except FileNotFoundError: - pytest.skip("grep not available; regex check skipped (inline check passed)") - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Acceptance tests for the Hyena port (supplement to Mamba3). + +Covers: + 1. Shape parity: [B=4, T=64, D=384] in → [B=4, T=64, D=384] out. + 2. Causality: changing x[:, t+1:] must NOT change output[:, :t]. + 3. No grad leak: grads at positions beyond t must not flow through x[:, :t]. + 4. Forward+backward on CPU with d_model=384, T=64. + 5. Selective substitution: HYDRA_HYENA_LAYERS=3,7 → HyenaBlock at 3 and 7 + in the block list; Mamba3 elsewhere (isinstance assertion). + 6. Gradient flow: loss.backward() doesn't NaN after one step. + 7. Static forbidden-imports grep on ported code (zero matches required). + +The test file itself avoids torch.no_grad in places where we need actual +gradients; it also isolates Test 5 from requiring a CUDA device / full +HYDRA training init (we construct only the block list path to keep the +check focused and CPU-friendly). +""" + +from __future__ import annotations + +import os +import subprocess +import sys +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hydra.hyena_block import HyenaBlock # noqa: E402 +from subsystems.hyena_pure import HyenaOperator # noqa: E402 + + +# --------------------------------------------------------------------------- +# Test 1: shape parity +# --------------------------------------------------------------------------- +def test_shape_parity_4_64_384(): + torch.manual_seed(0) + block = HyenaBlock(d_model=384, seq_len=64) + x = torch.randn(4, 64, 384) + y = block(x) + assert y.shape == (4, 64, 384), f"expected (4,64,384), got {tuple(y.shape)}" + assert y.dtype == x.dtype + + +# --------------------------------------------------------------------------- +# Test 2: causality — output[:, :t] invariant to changes in x[:, t+1:] +# --------------------------------------------------------------------------- +def test_causal_mask_correctness(): + torch.manual_seed(1) + D, T = 64, 32 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + + x1 = torch.randn(2, T, D) + x2 = x1.clone() + # Perturb the future half only: + t_cut = T // 2 + x2[:, t_cut:, :] = torch.randn_like(x2[:, t_cut:, :]) + + with torch.no_grad(): + y1 = block(x1) + y2 = block(x2) + + # Outputs in the past (indices < t_cut) must be identical to within + # numerical tolerance. + diff = (y1[:, :t_cut, :] - y2[:, :t_cut, :]).abs().max().item() + assert diff < 1e-5, f"causality violated: past output diff = {diff:.2e}" + + +# --------------------------------------------------------------------------- +# Test 3: no grad leak from future positions into past +# --------------------------------------------------------------------------- +def test_no_future_grad_leak_into_past(): + torch.manual_seed(2) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + + x = torch.randn(1, T, D, requires_grad=True) + y = block(x) + + # Scalar loss on one FUTURE position (t=T-1). + loss = y[0, T - 1, :].sum() + loss.backward() + + assert x.grad is not None + # Grad at ANY past position t < T-1 can be non-zero (backward through + # conv filter); the causality invariant is the FORWARD one tested above. + # What we check here is the dual: a loss at a PAST position has zero grad + # w.r.t. FUTURE inputs (by causality of the forward pass). + x2 = torch.randn(1, T, D, requires_grad=True) + y2 = block(x2) + past_t = T // 4 + loss2 = y2[0, past_t, :].sum() + loss2.backward() + future_grad = x2.grad[0, past_t + 1 :, :].abs().max().item() + assert future_grad < 1e-5, ( + f"causality violated in backward: future grad = {future_grad:.2e}" + ) + + +# --------------------------------------------------------------------------- +# Test 4: forward + backward on CPU at d_model=384, T=64 +# --------------------------------------------------------------------------- +def test_forward_backward_cpu_d384_t64(): + torch.manual_seed(3) + block = HyenaBlock(d_model=384, seq_len=64) + x = torch.randn(2, 64, 384, requires_grad=True) + y = block(x) + assert y.shape == (2, 64, 384) + loss = y.pow(2).mean() + loss.backward() + # Some parameter must have received non-zero grad. + any_nonzero = any( + p.grad is not None and p.grad.abs().sum().item() > 0 + for p in block.parameters() + ) + assert any_nonzero, "no parameter received a non-zero gradient" + assert x.grad is not None + + +# --------------------------------------------------------------------------- +# Test 5: selective layer substitution via HYDRA_HYENA_LAYERS +# --------------------------------------------------------------------------- +def test_selective_hyena_layers_env_switch(monkeypatch): + """HYDRA_HYENA_LAYERS='3,7' → HyenaBlock at 3 and 7, Mamba3 elsewhere. + + Mimics the model.py construction directly with a stub Mamba3 sentinel + so the test is CPU-only and doesn't require mamba-ssm (which needs CUDA). + This mirrors exactly the code path of model.py — the surgical edit is + a list comprehension: isinstance checks on the resulting list are the + contract. + """ + import torch.nn as nn + + # Monkeypatch mamba_ssm.Mamba3 to a sentinel class *before* model.py + # imports happen. We mirror model.py's block construction logic here + # directly so we don't need the full model build (which pulls CUDA, + # mamba_ssm, htm_rust, etc.). + class _Mamba3Sentinel(nn.Module): + def __init__(self, **kw): + super().__init__() + self.kw = kw + + def forward(self, x): + return x + + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "3,7") + monkeypatch.setenv("HYDRA_HYENA_ORDER", "2") + monkeypatch.setenv("HYDRA_HYENA_FILTER_DIM", "32") + + n_layer = 10 + d_model = 64 + seq_len = 16 + + _hyena_env = os.environ.get("HYDRA_HYENA_LAYERS", "") + _hyena_layer_set = { + int(s.strip()) for s in _hyena_env.split(",") if s.strip() + } + blocks = nn.ModuleList([ + HyenaBlock( + d_model=d_model, + seq_len=seq_len, + order=int(os.environ.get("HYDRA_HYENA_ORDER", "2")), + filter_order=int(os.environ.get("HYDRA_HYENA_FILTER_DIM", "32")), + ) + if i in _hyena_layer_set + else _Mamba3Sentinel(d_model=d_model, d_state=64) + for i in range(n_layer) + ]) + + # Contract: indices 3 and 7 are HyenaBlock, others are Mamba3Sentinel. + for i in range(n_layer): + if i in {3, 7}: + assert isinstance(blocks[i], HyenaBlock), ( + f"layer {i}: expected HyenaBlock, got {type(blocks[i]).__name__}" + ) + else: + assert isinstance(blocks[i], _Mamba3Sentinel), ( + f"layer {i}: expected _Mamba3Sentinel, got {type(blocks[i]).__name__}" + ) + + # Also verify the default (empty) case → no HyenaBlock anywhere. + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "") + _hyena_env2 = os.environ.get("HYDRA_HYENA_LAYERS", "") + _set2 = {int(s.strip()) for s in _hyena_env2.split(",") if s.strip()} + blocks2 = nn.ModuleList([ + HyenaBlock(d_model=d_model, seq_len=seq_len) if i in _set2 + else _Mamba3Sentinel(d_model=d_model) + for i in range(n_layer) + ]) + for i in range(n_layer): + assert isinstance(blocks2[i], _Mamba3Sentinel), ( + f"default (no env): layer {i} should be Mamba3 sentinel" + ) + + +# --------------------------------------------------------------------------- +# Test 6: gradient flow — one optimizer step doesn't produce NaN +# --------------------------------------------------------------------------- +def test_grad_flow_no_nan_after_one_step(): + torch.manual_seed(4) + D, T = 64, 32 + block = HyenaBlock(d_model=D, seq_len=T) + opt = torch.optim.SGD(block.parameters(), lr=1e-3) + + x = torch.randn(2, T, D) + target = torch.randn(2, T, D) + + opt.zero_grad() + y = block(x) + loss = torch.nn.functional.mse_loss(y, target) + assert torch.isfinite(loss), f"initial loss non-finite: {loss.item()}" + loss.backward() + + for name, p in block.named_parameters(): + if p.grad is not None: + assert torch.isfinite(p.grad).all(), f"NaN/Inf in grad of {name}" + + opt.step() + + for name, p in block.named_parameters(): + assert torch.isfinite(p).all(), f"NaN/Inf in param {name} after step" + + +# --------------------------------------------------------------------------- +# Test 7: static grep for forbidden transformer tokens in ported code +# --------------------------------------------------------------------------- +def test_no_forbidden_transformer_imports(): + """Grep the two ported files for tokens indicating attention / transformer. + + Whitelist (allowed): + - None. Any of these tokens in the ported source is a failure. + + Tokens we reject (exact-string match): + MultiheadAttention, scaled_dot_product_attention, flash_attn, + xformers, kv_cache, KVCache. For 'softmax' and 'transformers' we + search via grep (log output attached in the report). + """ + root = Path(__file__).resolve().parents[1] + files = [ + root / "subsystems" / "hyena_pure.py", + root / "hydra" / "hyena_block.py", + ] + for f in files: + assert f.exists(), f"missing ported file: {f}" + + forbidden_patterns = [ + "MultiheadAttention", + "scaled_dot_product_attention", + "flash_attn", + "xformers", + "KVCache", + "kv_cache", + "from transformers", + "import transformers", + ] + + violations: list[str] = [] + for f in files: + text = f.read_text() + for pat in forbidden_patterns: + if pat in text: + violations.append(f"{f}: contains forbidden token '{pat}'") + + assert not violations, "Forbidden transformer tokens found:\n" + "\n".join(violations) + + # Additionally run grep -r for the report (captured but not asserted + # here beyond exit code). The subprocess is defensive: if grep is + # unavailable we skip this portion. + try: + out = subprocess.run( + [ + "grep", "-RniE", + "|".join([ + r"\bMultiheadAttention\b", + r"\bscaled_dot_product_attention\b", + r"\bflash_attn\b", + r"\bxformers\b", + r"\bKVCache\b", + r"\bkv_cache\b", + r"^from transformers", + r"^import transformers", + ]), + str(files[0]), + str(files[1]), + ], + capture_output=True, text=True, timeout=5, + ) + # grep exit 1 means no match (what we want); 0 means match found. + assert out.returncode == 1, ( + f"grep found forbidden patterns:\nstdout:\n{out.stdout}\nstderr:\n{out.stderr}" + ) + except FileNotFoundError: + pytest.skip("grep not available; regex check skipped (inline check passed)") + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_hyena_filter_cache.py b/overlay/tests/test_hyena_filter_cache.py index 81d74665eb10319215a2957c72e359b5dcfd828f..e7b63679e65f51e82c769deabd421cdee1c95a6e 100644 --- a/overlay/tests/test_hyena_filter_cache.py +++ b/overlay/tests/test_hyena_filter_cache.py @@ -1,215 +1,215 @@ -"""Filter-rfft cache tests for HyenaOperator. - -The cache is gated by HYDRA_HYENA_FILTER_CACHE=1. When enabled, within a -single version epoch (between calls to `invalidate_filter_cache()`), the -filter rfft is materialized once and re-used across forwards. - -Correctness requirement: outputs must be **bit-identical** to the uncached -path in single-step isolation (we accept 0 tolerance since the math is the -same rfft of the same tensor). - -Caching impl lives in: - * subsystems/hyena_pure.py :: HyenaFilter.get_cached_kf - * subsystems/hyena_pure.py :: HyenaOperator.forward (k_f_per_order hoist) - * subsystems/hyena_pure.py :: _fftconv_filter_rfft_count (test hook) - * hydra/model.py :: PostSemClawModel.invalidate_hyena_caches - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_hyena_filter_cache.py -v -""" - -from __future__ import annotations - -import sys -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from hydra.hyena_block import HyenaBlock # noqa: E402 -from subsystems import hyena_pure # noqa: E402 - - -def _reset_rfft_counter(): - hyena_pure._fftconv_filter_rfft_count = 0 - - -def _rfft_count() -> int: - return hyena_pure._fftconv_filter_rfft_count - - -def test_cache_skips_rfft_within_same_version(monkeypatch): - """Second forward without version bump must not recompute filter rfft. - - With cache enabled and no invalidate call, the reshaped k_f is reused - and `fftconv_ref` is invoked with `k_f` != None → the filter-rfft - counter stays flat. - """ - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") - - torch.manual_seed(0) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - - x = torch.randn(2, T, D) - - # Warm the cache. - _reset_rfft_counter() - with torch.no_grad(): - _ = block(x) - first_count = _rfft_count() - assert first_count >= 0, "counter monotonicity broken" - - # Second forward in the same version — cache should serve everything. - _reset_rfft_counter() - with torch.no_grad(): - _ = block(x) - assert _rfft_count() == 0, ( - f"expected 0 filter rfft calls on cached path, got {_rfft_count()}" - ) - - -def test_invalidate_forces_recompute(monkeypatch): - """After invalidate_filter_cache(), the next forward must recompute.""" - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") - - torch.manual_seed(1) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - - x = torch.randn(1, T, D) - - # Warm + cached call. - with torch.no_grad(): - _ = block(x) - _reset_rfft_counter() - _ = block(x) - assert _rfft_count() == 0, "expected 0 on cached call" - - # Invalidate (simulates post-optimizer-step bookkeeping). - block.operator.invalidate_filter_cache() - - _reset_rfft_counter() - with torch.no_grad(): - _ = block(x) - assert _rfft_count() >= 1, ( - f"expected at least 1 filter rfft call after invalidation, got {_rfft_count()}" - ) - - -def test_cached_output_bit_identical_to_uncached(monkeypatch): - """Enabling the cache must not change the forward numerically. - - We assert strict equality (atol=0) since cache on/off differ only in - WHICH rfft call produced the spectrum — same input tensor, same FFT - backend, same fp dtype → identical bits. - """ - torch.manual_seed(2) - D, T = 32, 16 - - # Build once on a fresh env (no cache), run. - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "0") - block_a = HyenaBlock(d_model=D, seq_len=T) - block_a.eval() - x = torch.randn(2, T, D) - with torch.no_grad(): - y_nocache = block_a(x) - - # Build an identical block with the cache ON and copy weights. - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") - block_b = HyenaBlock(d_model=D, seq_len=T) - block_b.load_state_dict(block_a.state_dict()) - block_b.eval() - with torch.no_grad(): - y_cache_first = block_b(x) - y_cache_second = block_b(x) - - # Uncached vs cached must match bit-for-bit for both calls. - diff_first = (y_nocache - y_cache_first).abs().max().item() - diff_second = (y_nocache - y_cache_second).abs().max().item() - assert diff_first <= 1e-6, f"cache changed forward output: |Δ| = {diff_first:.3e}" - assert diff_second <= 1e-6, f"cache drift on repeat: |Δ| = {diff_second:.3e}" - - -def test_cache_disabled_by_default(monkeypatch): - """With env var unset, every forward computes the filter rfft fresh.""" - monkeypatch.delenv("HYDRA_HYENA_FILTER_CACHE", raising=False) - - torch.manual_seed(3) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - - x = torch.randn(1, T, D) - with torch.no_grad(): - _ = block(x) # warm - _reset_rfft_counter() - _ = block(x) - # Default = cache off → at least one rfft per forward. - assert _rfft_count() >= 1, ( - f"default (no env) should compute filter rfft; got {_rfft_count()}" - ) - - -def test_cache_env_flag_opt_in(monkeypatch): - """Explicit HYDRA_HYENA_FILTER_CACHE=0 keeps the cache off.""" - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "0") - - torch.manual_seed(4) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.eval() - assert block.operator._use_filter_cache is False - - x = torch.randn(1, T, D) - with torch.no_grad(): - _ = block(x) - _reset_rfft_counter() - _ = block(x) - assert _rfft_count() >= 1 - - -def test_grad_accum_no_backward_twice_error(monkeypatch): - """Cache must not break two successive forward+backward passes. - - This is the exact grad-accumulation pattern in the training loop: - for i in range(accum_steps): - loss_i = model(x_i) / accum_steps - loss_i.backward() # releases the graph - optimizer.step() - model.invalidate_hyena_caches() - - Under PyTorch's autograd, a cached tensor in the graph would cause - `RuntimeError: Trying to backward through the graph a second time`. - We require the cache implementation to be SAFE under grad-enabled forwards - (i.e. it silently bypasses the cache rather than corrupting autograd). - """ - monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") - - torch.manual_seed(5) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - accum_steps = 3 - for i in range(accum_steps): - x = torch.randn(1, T, D, requires_grad=False) - y = block(x) - loss = (y.pow(2).mean()) / accum_steps - loss.backward() - - # Sanity: every Hyena param received a finite gradient across the - # accum_steps backward calls. - for name, p in block.named_parameters(): - if p.requires_grad: - assert p.grad is not None, f"{name} has no grad after {accum_steps} backwards" - assert torch.isfinite(p.grad).all(), f"{name} grad has NaN/Inf" - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Filter-rfft cache tests for HyenaOperator. + +The cache is gated by HYDRA_HYENA_FILTER_CACHE=1. When enabled, within a +single version epoch (between calls to `invalidate_filter_cache()`), the +filter rfft is materialized once and re-used across forwards. + +Correctness requirement: outputs must be **bit-identical** to the uncached +path in single-step isolation (we accept 0 tolerance since the math is the +same rfft of the same tensor). + +Caching impl lives in: + * subsystems/hyena_pure.py :: HyenaFilter.get_cached_kf + * subsystems/hyena_pure.py :: HyenaOperator.forward (k_f_per_order hoist) + * subsystems/hyena_pure.py :: _fftconv_filter_rfft_count (test hook) + * hydra/model.py :: PostSemClawModel.invalidate_hyena_caches + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_hyena_filter_cache.py -v +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hydra.hyena_block import HyenaBlock # noqa: E402 +from subsystems import hyena_pure # noqa: E402 + + +def _reset_rfft_counter(): + hyena_pure._fftconv_filter_rfft_count = 0 + + +def _rfft_count() -> int: + return hyena_pure._fftconv_filter_rfft_count + + +def test_cache_skips_rfft_within_same_version(monkeypatch): + """Second forward without version bump must not recompute filter rfft. + + With cache enabled and no invalidate call, the reshaped k_f is reused + and `fftconv_ref` is invoked with `k_f` != None → the filter-rfft + counter stays flat. + """ + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") + + torch.manual_seed(0) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + + x = torch.randn(2, T, D) + + # Warm the cache. + _reset_rfft_counter() + with torch.no_grad(): + _ = block(x) + first_count = _rfft_count() + assert first_count >= 0, "counter monotonicity broken" + + # Second forward in the same version — cache should serve everything. + _reset_rfft_counter() + with torch.no_grad(): + _ = block(x) + assert _rfft_count() == 0, ( + f"expected 0 filter rfft calls on cached path, got {_rfft_count()}" + ) + + +def test_invalidate_forces_recompute(monkeypatch): + """After invalidate_filter_cache(), the next forward must recompute.""" + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") + + torch.manual_seed(1) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + + x = torch.randn(1, T, D) + + # Warm + cached call. + with torch.no_grad(): + _ = block(x) + _reset_rfft_counter() + _ = block(x) + assert _rfft_count() == 0, "expected 0 on cached call" + + # Invalidate (simulates post-optimizer-step bookkeeping). + block.operator.invalidate_filter_cache() + + _reset_rfft_counter() + with torch.no_grad(): + _ = block(x) + assert _rfft_count() >= 1, ( + f"expected at least 1 filter rfft call after invalidation, got {_rfft_count()}" + ) + + +def test_cached_output_bit_identical_to_uncached(monkeypatch): + """Enabling the cache must not change the forward numerically. + + We assert strict equality (atol=0) since cache on/off differ only in + WHICH rfft call produced the spectrum — same input tensor, same FFT + backend, same fp dtype → identical bits. + """ + torch.manual_seed(2) + D, T = 32, 16 + + # Build once on a fresh env (no cache), run. + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "0") + block_a = HyenaBlock(d_model=D, seq_len=T) + block_a.eval() + x = torch.randn(2, T, D) + with torch.no_grad(): + y_nocache = block_a(x) + + # Build an identical block with the cache ON and copy weights. + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") + block_b = HyenaBlock(d_model=D, seq_len=T) + block_b.load_state_dict(block_a.state_dict()) + block_b.eval() + with torch.no_grad(): + y_cache_first = block_b(x) + y_cache_second = block_b(x) + + # Uncached vs cached must match bit-for-bit for both calls. + diff_first = (y_nocache - y_cache_first).abs().max().item() + diff_second = (y_nocache - y_cache_second).abs().max().item() + assert diff_first <= 1e-6, f"cache changed forward output: |Δ| = {diff_first:.3e}" + assert diff_second <= 1e-6, f"cache drift on repeat: |Δ| = {diff_second:.3e}" + + +def test_cache_disabled_by_default(monkeypatch): + """With env var unset, every forward computes the filter rfft fresh.""" + monkeypatch.delenv("HYDRA_HYENA_FILTER_CACHE", raising=False) + + torch.manual_seed(3) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + + x = torch.randn(1, T, D) + with torch.no_grad(): + _ = block(x) # warm + _reset_rfft_counter() + _ = block(x) + # Default = cache off → at least one rfft per forward. + assert _rfft_count() >= 1, ( + f"default (no env) should compute filter rfft; got {_rfft_count()}" + ) + + +def test_cache_env_flag_opt_in(monkeypatch): + """Explicit HYDRA_HYENA_FILTER_CACHE=0 keeps the cache off.""" + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "0") + + torch.manual_seed(4) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.eval() + assert block.operator._use_filter_cache is False + + x = torch.randn(1, T, D) + with torch.no_grad(): + _ = block(x) + _reset_rfft_counter() + _ = block(x) + assert _rfft_count() >= 1 + + +def test_grad_accum_no_backward_twice_error(monkeypatch): + """Cache must not break two successive forward+backward passes. + + This is the exact grad-accumulation pattern in the training loop: + for i in range(accum_steps): + loss_i = model(x_i) / accum_steps + loss_i.backward() # releases the graph + optimizer.step() + model.invalidate_hyena_caches() + + Under PyTorch's autograd, a cached tensor in the graph would cause + `RuntimeError: Trying to backward through the graph a second time`. + We require the cache implementation to be SAFE under grad-enabled forwards + (i.e. it silently bypasses the cache rather than corrupting autograd). + """ + monkeypatch.setenv("HYDRA_HYENA_FILTER_CACHE", "1") + + torch.manual_seed(5) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + accum_steps = 3 + for i in range(accum_steps): + x = torch.randn(1, T, D, requires_grad=False) + y = block(x) + loss = (y.pow(2).mean()) / accum_steps + loss.backward() + + # Sanity: every Hyena param received a finite gradient across the + # accum_steps backward calls. + for name, p in block.named_parameters(): + if p.requires_grad: + assert p.grad is not None, f"{name} has no grad after {accum_steps} backwards" + assert torch.isfinite(p.grad).all(), f"{name} grad has NaN/Inf" + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_hyena_train_cache.py b/overlay/tests/test_hyena_train_cache.py index ec18b275588f588b962c23304e3c16cdd7455441..2f56ad30720fc6b1d8351afda62ac8bdca07f569 100644 --- a/overlay/tests/test_hyena_train_cache.py +++ b/overlay/tests/test_hyena_train_cache.py @@ -1,335 +1,335 @@ -"""Training-safe filter cache for HyenaOperator. - -**What this validates:** -When `HYDRA_HYENA_TRAIN_CACHE=1`, the filter MLP must: - 1. Run EXACTLY ONCE per optimizer step, not once per micro-batch. - 2. Produce gradients on its params that match the uncached path to within - bf16 tolerance (we use fp32 CPU tensors here, so atol should be tight). - 3. Not trip `RuntimeError: Trying to backward through the graph a second time` - under the grad-accum pattern. - -**Design under test:** -`HyenaFilter.get_or_build_train_cache(L, fft_size)` returns a LEAF tensor -`k_leaf` whose grad accumulates across micro-batches. After all micro-batch -backwards, `flush_pending_filter_grads()` does one -`torch.autograd.backward(_k_graph, _k_leaf.grad)` to populate the filter -MLP params' `.grad`. Then `invalidate_cache()` resets state for the next -step. - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_hyena_train_cache.py -v -""" - -from __future__ import annotations - -import sys -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from hydra.hyena_block import HyenaBlock # noqa: E402 -from subsystems import hyena_pure # noqa: E402 - - -def _reset_rfft_counter(): - hyena_pure._fftconv_filter_rfft_count = 0 - - -def _rfft_count() -> int: - return hyena_pure._fftconv_filter_rfft_count - - -def test_train_cache_runs_filter_mlp_once_per_step(monkeypatch): - """With HYDRA_HYENA_TRAIN_CACHE=1, the IMPLICIT FILTER MLP runs exactly - once across N accum micro-batches, not once per micro-batch. - - We can't distinguish MLP forwards via the rfft counter alone (rfft also - fires for `k_f` per micro-batch for graph-safety reasons, see - `HyenaFilter.get_or_build_train_cache` docstring). We instead patch the - `implicit_filter` Sequential's forward with a counting proxy and verify - it ran once. - """ - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - torch.manual_seed(0) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - assert block.operator._use_train_cache is True - - # Count MLP forwards. - orig_forward = block.operator.filter_fn.implicit_filter.forward - n_calls = {"count": 0} - - def counting_forward(*args, **kwargs): - n_calls["count"] += 1 - return orig_forward(*args, **kwargs) - - block.operator.filter_fn.implicit_filter.forward = counting_forward - - accum = 3 - for _ in range(accum): - x = torch.randn(1, T, D) - y = block(x) - loss = y.pow(2).mean() / accum - loss.backward() - - # EXACTLY 1 MLP forward total, not 3. - assert n_calls["count"] == 1, ( - f"expected exactly 1 filter MLP forward under train-cache across " - f"{accum} micro-batches, got {n_calls['count']}" - ) - - -def test_train_cache_no_backward_twice_error(monkeypatch): - """Three micro-batches with train-cache on must NOT raise - 'Trying to backward through the graph a second time'. - - This is the core correctness guarantee. Without the fix, this test - reliably reproduces the runtime error. - """ - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - torch.manual_seed(1) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - accum = 4 - # This must not raise. - for _ in range(accum): - x = torch.randn(1, T, D) - y = block(x) - loss = y.pow(2).mean() / accum - loss.backward() - - # After all micro-batches, k_leaf.grad must be non-None (grad accumulated). - k_leaf = block.operator.filter_fn._k_leaf - assert k_leaf is not None, "train-cache failed to populate _k_leaf" - assert k_leaf.grad is not None, "no accumulated gradient on _k_leaf" - assert torch.isfinite(k_leaf.grad).all(), "k_leaf.grad has NaN/Inf" - - -def test_train_cache_flush_populates_filter_params(monkeypatch): - """After flush, the filter MLP params must have non-zero, finite grads.""" - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - torch.manual_seed(2) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - # Zero-init params' grads. - for p in block.parameters(): - p.grad = None - - # Run 3 accum micro-batches. - for _ in range(3): - x = torch.randn(1, T, D) - y = block(x) - loss = y.pow(2).mean() / 3 - loss.backward() - - # Before flush, filter MLP params should have NO grad (the backward chain - # was cut at k_leaf). Only params downstream of k_leaf (short_filter, - # in_proj, out_proj) should have grads. - # NOTE: the filter's `bias` is actually used AFTER the leaf stash (see - # HyenaOperator.forward: bias comes from filter_fn.bias directly, not from - # the cached k_leaf) so `bias.grad` WILL be populated by the direct path. - for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): - if p.requires_grad: - assert p.grad is None or p.grad.abs().max() == 0, ( - f"implicit_filter.{name} has grad before flush — the leaf " - f"cache didn't actually cut the graph" - ) - - # Flush: this invokes torch.autograd.backward(_k_graph, _k_leaf.grad). - block.operator.flush_pending_filter_grads() - - # Now implicit_filter params must have real grads. - for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): - if p.requires_grad: - assert p.grad is not None, f"implicit_filter.{name} has no grad after flush" - assert torch.isfinite(p.grad).all(), f"implicit_filter.{name} grad NaN/Inf" - # With 3 random micro-batches and dL/dy = 2*y/(B*T*D*3), the - # propagated grad MUST be non-zero for every param that's - # reachable from the filter output. - assert p.grad.abs().max() > 0, ( - f"implicit_filter.{name}.grad is all zero — flush didn't " - f"push the k_leaf.grad back" - ) - - -def test_train_cache_gradient_matches_uncached(monkeypatch): - """Parameter gradients under train-cache must numerically match - the uncached path within tolerance. - - We construct two identical blocks, run the same 3 micro-batches on each, - flush train-cache, then compare .grad on every param. - """ - torch.manual_seed(3) - D, T = 32, 16 - - # Block A: no cache (baseline). - block_a = HyenaBlock(d_model=D, seq_len=T) - block_a.train() - # Block B: train-cache on, same weights. - # Note: monkeypatch.setenv only affects env reads AT CONSTRUCTION; the - # block reads the flag in __init__. So we set before constructing B. - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - block_b = HyenaBlock(d_model=D, seq_len=T) - block_b.load_state_dict(block_a.state_dict()) - block_b.train() - # Verify the flag actually took effect. - assert block_b.operator._use_train_cache is True - assert block_a.operator._use_train_cache is False - - # Same 3 micro-batches. - xs = [torch.randn(1, T, D) for _ in range(3)] - - for block, label in ((block_a, "a"), (block_b, "b")): - for p in block.parameters(): - p.grad = None - for x in xs: - y = block(x) - loss = y.pow(2).mean() / len(xs) - loss.backward() - - # Flush train-cache (block_b only). - block_b.operator.flush_pending_filter_grads() - - # Compare grads. - state_a = dict(block_a.named_parameters()) - state_b = dict(block_b.named_parameters()) - max_abs_diff = 0.0 - max_diff_name = "" - for name, p_a in state_a.items(): - p_b = state_b[name] - if p_a.grad is None: - assert p_b.grad is None or p_b.grad.abs().max() == 0, ( - f"{name}: A has no grad, B has nonzero grad" - ) - continue - assert p_b.grad is not None, f"{name}: A has grad, B has none" - diff = (p_a.grad - p_b.grad).abs().max().item() - if diff > max_abs_diff: - max_abs_diff = diff - max_diff_name = name - - # Tight tolerance: the two paths do the SAME math in fp32 CPU, just the - # cached path defers the backward. Expected diff ≈ 0 modulo FP noise. - assert max_abs_diff < 1e-4, ( - f"grad mismatch between cached and uncached paths: " - f"max |Δgrad| = {max_abs_diff:.3e} on {max_diff_name!r}" - ) - - -def test_train_cache_invalidate_resets_state(monkeypatch): - """After invalidate_cache(), the next step rebuilds k_graph fresh. - - Simulates the post-optimizer.step() lifecycle. - """ - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - torch.manual_seed(4) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - # Step 1: 2 micro-batches, flush, invalidate. - for _ in range(2): - y = block(torch.randn(1, T, D)) - (y.pow(2).mean() / 2).backward() - assert block.operator.filter_fn._k_graph is not None - block.operator.flush_pending_filter_grads() - block.operator.invalidate_filter_cache() - assert block.operator.filter_fn._k_graph is None - assert block.operator.filter_fn._k_leaf is None - - # Zero filter params' grads (simulating optimizer.zero_grad()) - for p in block.parameters(): - p.grad = None - - # Step 2: must work the same (not use stale state). - for _ in range(2): - y = block(torch.randn(1, T, D)) - (y.pow(2).mean() / 2).backward() - assert block.operator.filter_fn._k_graph is not None, ( - "second step failed to rebuild k_graph" - ) - block.operator.flush_pending_filter_grads() - # All filter MLP params got grad again. - for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): - if p.requires_grad: - assert p.grad is not None, f"step 2: {name} has no grad" - - -def test_train_cache_disabled_by_default(monkeypatch): - """Unset env var → train-cache OFF → filter runs per micro-batch as before.""" - monkeypatch.delenv("HYDRA_HYENA_TRAIN_CACHE", raising=False) - - torch.manual_seed(5) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - assert block.operator._use_train_cache is False - - -def test_train_cache_forward_output_matches_uncached(monkeypatch): - """Cached vs uncached forward outputs must match numerically.""" - torch.manual_seed(6) - D, T = 32, 16 - - # Uncached. - block_a = HyenaBlock(d_model=D, seq_len=T) - block_a.eval() - - # Cached copy. - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - block_b = HyenaBlock(d_model=D, seq_len=T) - block_b.load_state_dict(block_a.state_dict()) - block_b.train() # train-cache only activates under grad_enabled - - x = torch.randn(1, T, D) - y_a = block_a(x) # uncached path (no grad → eval mode anyway) - y_b = block_b(x) # cached path - - max_diff = (y_a - y_b).abs().max().item() - assert max_diff < 1e-5, f"forward drift under train-cache: {max_diff:.3e}" - - -def test_flush_is_no_op_on_second_call(monkeypatch): - """Idempotent flush: second call in the same step must not crash.""" - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - torch.manual_seed(7) - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - y = block(torch.randn(1, T, D)) - y.pow(2).mean().backward() - - # First flush: real work. - block.operator.flush_pending_filter_grads() - # Second flush: must silently no-op. - block.operator.flush_pending_filter_grads() - - -def test_flush_is_no_op_when_no_forward(monkeypatch): - """If no Hyena forward ran this step, flush is a safe no-op.""" - monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") - - D, T = 32, 16 - block = HyenaBlock(d_model=D, seq_len=T) - block.train() - - # No forward called. Flush should just return. - block.operator.flush_pending_filter_grads() - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Training-safe filter cache for HyenaOperator. + +**What this validates:** +When `HYDRA_HYENA_TRAIN_CACHE=1`, the filter MLP must: + 1. Run EXACTLY ONCE per optimizer step, not once per micro-batch. + 2. Produce gradients on its params that match the uncached path to within + bf16 tolerance (we use fp32 CPU tensors here, so atol should be tight). + 3. Not trip `RuntimeError: Trying to backward through the graph a second time` + under the grad-accum pattern. + +**Design under test:** +`HyenaFilter.get_or_build_train_cache(L, fft_size)` returns a LEAF tensor +`k_leaf` whose grad accumulates across micro-batches. After all micro-batch +backwards, `flush_pending_filter_grads()` does one +`torch.autograd.backward(_k_graph, _k_leaf.grad)` to populate the filter +MLP params' `.grad`. Then `invalidate_cache()` resets state for the next +step. + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_hyena_train_cache.py -v +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hydra.hyena_block import HyenaBlock # noqa: E402 +from subsystems import hyena_pure # noqa: E402 + + +def _reset_rfft_counter(): + hyena_pure._fftconv_filter_rfft_count = 0 + + +def _rfft_count() -> int: + return hyena_pure._fftconv_filter_rfft_count + + +def test_train_cache_runs_filter_mlp_once_per_step(monkeypatch): + """With HYDRA_HYENA_TRAIN_CACHE=1, the IMPLICIT FILTER MLP runs exactly + once across N accum micro-batches, not once per micro-batch. + + We can't distinguish MLP forwards via the rfft counter alone (rfft also + fires for `k_f` per micro-batch for graph-safety reasons, see + `HyenaFilter.get_or_build_train_cache` docstring). We instead patch the + `implicit_filter` Sequential's forward with a counting proxy and verify + it ran once. + """ + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + torch.manual_seed(0) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + assert block.operator._use_train_cache is True + + # Count MLP forwards. + orig_forward = block.operator.filter_fn.implicit_filter.forward + n_calls = {"count": 0} + + def counting_forward(*args, **kwargs): + n_calls["count"] += 1 + return orig_forward(*args, **kwargs) + + block.operator.filter_fn.implicit_filter.forward = counting_forward + + accum = 3 + for _ in range(accum): + x = torch.randn(1, T, D) + y = block(x) + loss = y.pow(2).mean() / accum + loss.backward() + + # EXACTLY 1 MLP forward total, not 3. + assert n_calls["count"] == 1, ( + f"expected exactly 1 filter MLP forward under train-cache across " + f"{accum} micro-batches, got {n_calls['count']}" + ) + + +def test_train_cache_no_backward_twice_error(monkeypatch): + """Three micro-batches with train-cache on must NOT raise + 'Trying to backward through the graph a second time'. + + This is the core correctness guarantee. Without the fix, this test + reliably reproduces the runtime error. + """ + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + torch.manual_seed(1) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + accum = 4 + # This must not raise. + for _ in range(accum): + x = torch.randn(1, T, D) + y = block(x) + loss = y.pow(2).mean() / accum + loss.backward() + + # After all micro-batches, k_leaf.grad must be non-None (grad accumulated). + k_leaf = block.operator.filter_fn._k_leaf + assert k_leaf is not None, "train-cache failed to populate _k_leaf" + assert k_leaf.grad is not None, "no accumulated gradient on _k_leaf" + assert torch.isfinite(k_leaf.grad).all(), "k_leaf.grad has NaN/Inf" + + +def test_train_cache_flush_populates_filter_params(monkeypatch): + """After flush, the filter MLP params must have non-zero, finite grads.""" + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + torch.manual_seed(2) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + # Zero-init params' grads. + for p in block.parameters(): + p.grad = None + + # Run 3 accum micro-batches. + for _ in range(3): + x = torch.randn(1, T, D) + y = block(x) + loss = y.pow(2).mean() / 3 + loss.backward() + + # Before flush, filter MLP params should have NO grad (the backward chain + # was cut at k_leaf). Only params downstream of k_leaf (short_filter, + # in_proj, out_proj) should have grads. + # NOTE: the filter's `bias` is actually used AFTER the leaf stash (see + # HyenaOperator.forward: bias comes from filter_fn.bias directly, not from + # the cached k_leaf) so `bias.grad` WILL be populated by the direct path. + for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): + if p.requires_grad: + assert p.grad is None or p.grad.abs().max() == 0, ( + f"implicit_filter.{name} has grad before flush — the leaf " + f"cache didn't actually cut the graph" + ) + + # Flush: this invokes torch.autograd.backward(_k_graph, _k_leaf.grad). + block.operator.flush_pending_filter_grads() + + # Now implicit_filter params must have real grads. + for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): + if p.requires_grad: + assert p.grad is not None, f"implicit_filter.{name} has no grad after flush" + assert torch.isfinite(p.grad).all(), f"implicit_filter.{name} grad NaN/Inf" + # With 3 random micro-batches and dL/dy = 2*y/(B*T*D*3), the + # propagated grad MUST be non-zero for every param that's + # reachable from the filter output. + assert p.grad.abs().max() > 0, ( + f"implicit_filter.{name}.grad is all zero — flush didn't " + f"push the k_leaf.grad back" + ) + + +def test_train_cache_gradient_matches_uncached(monkeypatch): + """Parameter gradients under train-cache must numerically match + the uncached path within tolerance. + + We construct two identical blocks, run the same 3 micro-batches on each, + flush train-cache, then compare .grad on every param. + """ + torch.manual_seed(3) + D, T = 32, 16 + + # Block A: no cache (baseline). + block_a = HyenaBlock(d_model=D, seq_len=T) + block_a.train() + # Block B: train-cache on, same weights. + # Note: monkeypatch.setenv only affects env reads AT CONSTRUCTION; the + # block reads the flag in __init__. So we set before constructing B. + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + block_b = HyenaBlock(d_model=D, seq_len=T) + block_b.load_state_dict(block_a.state_dict()) + block_b.train() + # Verify the flag actually took effect. + assert block_b.operator._use_train_cache is True + assert block_a.operator._use_train_cache is False + + # Same 3 micro-batches. + xs = [torch.randn(1, T, D) for _ in range(3)] + + for block, label in ((block_a, "a"), (block_b, "b")): + for p in block.parameters(): + p.grad = None + for x in xs: + y = block(x) + loss = y.pow(2).mean() / len(xs) + loss.backward() + + # Flush train-cache (block_b only). + block_b.operator.flush_pending_filter_grads() + + # Compare grads. + state_a = dict(block_a.named_parameters()) + state_b = dict(block_b.named_parameters()) + max_abs_diff = 0.0 + max_diff_name = "" + for name, p_a in state_a.items(): + p_b = state_b[name] + if p_a.grad is None: + assert p_b.grad is None or p_b.grad.abs().max() == 0, ( + f"{name}: A has no grad, B has nonzero grad" + ) + continue + assert p_b.grad is not None, f"{name}: A has grad, B has none" + diff = (p_a.grad - p_b.grad).abs().max().item() + if diff > max_abs_diff: + max_abs_diff = diff + max_diff_name = name + + # Tight tolerance: the two paths do the SAME math in fp32 CPU, just the + # cached path defers the backward. Expected diff ≈ 0 modulo FP noise. + assert max_abs_diff < 1e-4, ( + f"grad mismatch between cached and uncached paths: " + f"max |Δgrad| = {max_abs_diff:.3e} on {max_diff_name!r}" + ) + + +def test_train_cache_invalidate_resets_state(monkeypatch): + """After invalidate_cache(), the next step rebuilds k_graph fresh. + + Simulates the post-optimizer.step() lifecycle. + """ + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + torch.manual_seed(4) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + # Step 1: 2 micro-batches, flush, invalidate. + for _ in range(2): + y = block(torch.randn(1, T, D)) + (y.pow(2).mean() / 2).backward() + assert block.operator.filter_fn._k_graph is not None + block.operator.flush_pending_filter_grads() + block.operator.invalidate_filter_cache() + assert block.operator.filter_fn._k_graph is None + assert block.operator.filter_fn._k_leaf is None + + # Zero filter params' grads (simulating optimizer.zero_grad()) + for p in block.parameters(): + p.grad = None + + # Step 2: must work the same (not use stale state). + for _ in range(2): + y = block(torch.randn(1, T, D)) + (y.pow(2).mean() / 2).backward() + assert block.operator.filter_fn._k_graph is not None, ( + "second step failed to rebuild k_graph" + ) + block.operator.flush_pending_filter_grads() + # All filter MLP params got grad again. + for name, p in block.operator.filter_fn.implicit_filter.named_parameters(): + if p.requires_grad: + assert p.grad is not None, f"step 2: {name} has no grad" + + +def test_train_cache_disabled_by_default(monkeypatch): + """Unset env var → train-cache OFF → filter runs per micro-batch as before.""" + monkeypatch.delenv("HYDRA_HYENA_TRAIN_CACHE", raising=False) + + torch.manual_seed(5) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + assert block.operator._use_train_cache is False + + +def test_train_cache_forward_output_matches_uncached(monkeypatch): + """Cached vs uncached forward outputs must match numerically.""" + torch.manual_seed(6) + D, T = 32, 16 + + # Uncached. + block_a = HyenaBlock(d_model=D, seq_len=T) + block_a.eval() + + # Cached copy. + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + block_b = HyenaBlock(d_model=D, seq_len=T) + block_b.load_state_dict(block_a.state_dict()) + block_b.train() # train-cache only activates under grad_enabled + + x = torch.randn(1, T, D) + y_a = block_a(x) # uncached path (no grad → eval mode anyway) + y_b = block_b(x) # cached path + + max_diff = (y_a - y_b).abs().max().item() + assert max_diff < 1e-5, f"forward drift under train-cache: {max_diff:.3e}" + + +def test_flush_is_no_op_on_second_call(monkeypatch): + """Idempotent flush: second call in the same step must not crash.""" + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + torch.manual_seed(7) + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + y = block(torch.randn(1, T, D)) + y.pow(2).mean().backward() + + # First flush: real work. + block.operator.flush_pending_filter_grads() + # Second flush: must silently no-op. + block.operator.flush_pending_filter_grads() + + +def test_flush_is_no_op_when_no_forward(monkeypatch): + """If no Hyena forward ran this step, flush is a safe no-op.""" + monkeypatch.setenv("HYDRA_HYENA_TRAIN_CACHE", "1") + + D, T = 32, 16 + block = HyenaBlock(d_model=D, seq_len=T) + block.train() + + # No forward called. Flush should just return. + block.operator.flush_pending_filter_grads() + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_kernels.py b/overlay/tests/test_kernels.py index 76fc5dc8ad62911ce7ae1fcf5b16936ea473fca0..fc1287a68ae850bd34f74c9b45c79d5a39e2129b 100644 --- a/overlay/tests/test_kernels.py +++ b/overlay/tests/test_kernels.py @@ -1,141 +1,141 @@ -"""Tests for kernel stubs. - -Verifies that: - 1. Every kernel stub file exists on disk. - 2. Python stub files contain a module-level docstring. - 3. Python stub files do NOT define a callable with that name - (they are stubs — Phase 2 will implement them). - -Run: - uv run pytest tests/test_kernels.py -v -""" -import os -import pytest - -import sys -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -KERNEL_DIR = os.path.join(_REPO, "kernels") - -# --------------------------------------------------------------------------- -# Existence checks — one per stub file -# --------------------------------------------------------------------------- - -_ALL_STUBS = [ - ("triton", "ssd_exp_trap.py"), - ("triton", "sinkhorn_fused.py"), - ("triton", "bcnorm_fused.py"), - ("triton", "oja_update.py"), - ("tilelang", "ssd_mimo_prefill.py"), - ("tilelang", "mhc_kernels.py"), - ("cuda", "hash_kernel.cu"), - ("cuda", "decode_kernels.cu"), -] - -_PYTHON_STUBS = [ - ("triton", "ssd_exp_trap.py"), - ("triton", "sinkhorn_fused.py"), - ("triton", "bcnorm_fused.py"), - ("triton", "oja_update.py"), - ("tilelang", "ssd_mimo_prefill.py"), - ("tilelang", "mhc_kernels.py"), -] - -_CUDA_STUBS = [ - ("cuda", "hash_kernel.cu"), - ("cuda", "decode_kernels.cu"), -] - - -@pytest.mark.parametrize("subdir,filename", _ALL_STUBS) -def test_kernel_stub_exists(subdir: str, filename: str) -> None: - """Each kernel stub file must exist on disk.""" - path = os.path.join(KERNEL_DIR, subdir, filename) - assert os.path.exists(path), ( - f"Missing kernel stub: kernels/{subdir}/{filename}\n" - f"(Full path: {path})" - ) - - -@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) -def test_python_stub_has_docstring(subdir: str, filename: str) -> None: - """Python kernel stubs must have a module-level docstring.""" - path = os.path.join(KERNEL_DIR, subdir, filename) - with open(path) as fh: - content = fh.read() - assert '"""' in content or "'''" in content, ( - f"No docstring found in kernels/{subdir}/{filename}" - ) - - -@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) -def test_python_stub_is_non_empty(subdir: str, filename: str) -> None: - """Python stub files must contain at least some text (not empty).""" - path = os.path.join(KERNEL_DIR, subdir, filename) - assert os.path.getsize(path) > 0, ( - f"kernels/{subdir}/{filename} is empty" - ) - - -@pytest.mark.parametrize("subdir,filename", _CUDA_STUBS) -def test_cuda_stub_has_comment(subdir: str, filename: str) -> None: - """CUDA stub files must contain a comment describing their purpose.""" - path = os.path.join(KERNEL_DIR, subdir, filename) - with open(path) as fh: - content = fh.read() - assert "/*" in content or "//" in content, ( - f"No comment found in kernels/{subdir}/{filename}" - ) - - -def test_kernel_dir_structure() -> None: - """kernels/ directory contains triton/, tilelang/, and cuda/ subdirectories.""" - for subdir in ("triton", "tilelang", "cuda"): - path = os.path.join(KERNEL_DIR, subdir) - assert os.path.isdir(path), f"Missing kernels/{subdir}/ directory" - - -def test_triton_stub_count() -> None: - """kernels/triton/ contains exactly the expected number of stubs.""" - triton_dir = os.path.join(KERNEL_DIR, "triton") - py_files = [f for f in os.listdir(triton_dir) if f.endswith(".py")] - expected = {name for _, name in _PYTHON_STUBS if _ == "triton"} - assert expected.issubset(set(py_files)), ( - f"Missing triton stubs: {expected - set(py_files)}" - ) - - -def test_tilelang_stub_count() -> None: - """kernels/tilelang/ contains exactly the expected number of stubs.""" - tilelang_dir = os.path.join(KERNEL_DIR, "tilelang") - py_files = [f for f in os.listdir(tilelang_dir) if f.endswith(".py")] - expected = {name for _, name in _PYTHON_STUBS if _ == "tilelang"} - assert expected.issubset(set(py_files)), ( - f"Missing tilelang stubs: {expected - set(py_files)}" - ) - - -def test_cuda_stub_count() -> None: - """kernels/cuda/ contains exactly the expected number of stubs.""" - cuda_dir = os.path.join(KERNEL_DIR, "cuda") - cu_files = [f for f in os.listdir(cuda_dir) if f.endswith(".cu")] - expected = {name for _, name in _CUDA_STUBS} - assert expected.issubset(set(cu_files)), ( - f"Missing CUDA stubs: {expected - set(cu_files)}" - ) - - -# --------------------------------------------------------------------------- -# Content-quality checks for Python stubs -# --------------------------------------------------------------------------- - -@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) -def test_stub_mentions_phase(subdir: str, filename: str) -> None: - """Python stubs should document which Phase will implement them.""" - path = os.path.join(KERNEL_DIR, subdir, filename) - with open(path) as fh: - content = fh.read() - assert "Phase" in content, ( - f"kernels/{subdir}/{filename} should mention 'Phase 1' or 'Phase 2' in its docs" - ) +"""Tests for kernel stubs. + +Verifies that: + 1. Every kernel stub file exists on disk. + 2. Python stub files contain a module-level docstring. + 3. Python stub files do NOT define a callable with that name + (they are stubs — Phase 2 will implement them). + +Run: + uv run pytest tests/test_kernels.py -v +""" +import os +import pytest + +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +KERNEL_DIR = os.path.join(_REPO, "kernels") + +# --------------------------------------------------------------------------- +# Existence checks — one per stub file +# --------------------------------------------------------------------------- + +_ALL_STUBS = [ + ("triton", "ssd_exp_trap.py"), + ("triton", "sinkhorn_fused.py"), + ("triton", "bcnorm_fused.py"), + ("triton", "oja_update.py"), + ("tilelang", "ssd_mimo_prefill.py"), + ("tilelang", "mhc_kernels.py"), + ("cuda", "hash_kernel.cu"), + ("cuda", "decode_kernels.cu"), +] + +_PYTHON_STUBS = [ + ("triton", "ssd_exp_trap.py"), + ("triton", "sinkhorn_fused.py"), + ("triton", "bcnorm_fused.py"), + ("triton", "oja_update.py"), + ("tilelang", "ssd_mimo_prefill.py"), + ("tilelang", "mhc_kernels.py"), +] + +_CUDA_STUBS = [ + ("cuda", "hash_kernel.cu"), + ("cuda", "decode_kernels.cu"), +] + + +@pytest.mark.parametrize("subdir,filename", _ALL_STUBS) +def test_kernel_stub_exists(subdir: str, filename: str) -> None: + """Each kernel stub file must exist on disk.""" + path = os.path.join(KERNEL_DIR, subdir, filename) + assert os.path.exists(path), ( + f"Missing kernel stub: kernels/{subdir}/{filename}\n" + f"(Full path: {path})" + ) + + +@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) +def test_python_stub_has_docstring(subdir: str, filename: str) -> None: + """Python kernel stubs must have a module-level docstring.""" + path = os.path.join(KERNEL_DIR, subdir, filename) + with open(path) as fh: + content = fh.read() + assert '"""' in content or "'''" in content, ( + f"No docstring found in kernels/{subdir}/{filename}" + ) + + +@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) +def test_python_stub_is_non_empty(subdir: str, filename: str) -> None: + """Python stub files must contain at least some text (not empty).""" + path = os.path.join(KERNEL_DIR, subdir, filename) + assert os.path.getsize(path) > 0, ( + f"kernels/{subdir}/{filename} is empty" + ) + + +@pytest.mark.parametrize("subdir,filename", _CUDA_STUBS) +def test_cuda_stub_has_comment(subdir: str, filename: str) -> None: + """CUDA stub files must contain a comment describing their purpose.""" + path = os.path.join(KERNEL_DIR, subdir, filename) + with open(path) as fh: + content = fh.read() + assert "/*" in content or "//" in content, ( + f"No comment found in kernels/{subdir}/{filename}" + ) + + +def test_kernel_dir_structure() -> None: + """kernels/ directory contains triton/, tilelang/, and cuda/ subdirectories.""" + for subdir in ("triton", "tilelang", "cuda"): + path = os.path.join(KERNEL_DIR, subdir) + assert os.path.isdir(path), f"Missing kernels/{subdir}/ directory" + + +def test_triton_stub_count() -> None: + """kernels/triton/ contains exactly the expected number of stubs.""" + triton_dir = os.path.join(KERNEL_DIR, "triton") + py_files = [f for f in os.listdir(triton_dir) if f.endswith(".py")] + expected = {name for _, name in _PYTHON_STUBS if _ == "triton"} + assert expected.issubset(set(py_files)), ( + f"Missing triton stubs: {expected - set(py_files)}" + ) + + +def test_tilelang_stub_count() -> None: + """kernels/tilelang/ contains exactly the expected number of stubs.""" + tilelang_dir = os.path.join(KERNEL_DIR, "tilelang") + py_files = [f for f in os.listdir(tilelang_dir) if f.endswith(".py")] + expected = {name for _, name in _PYTHON_STUBS if _ == "tilelang"} + assert expected.issubset(set(py_files)), ( + f"Missing tilelang stubs: {expected - set(py_files)}" + ) + + +def test_cuda_stub_count() -> None: + """kernels/cuda/ contains exactly the expected number of stubs.""" + cuda_dir = os.path.join(KERNEL_DIR, "cuda") + cu_files = [f for f in os.listdir(cuda_dir) if f.endswith(".cu")] + expected = {name for _, name in _CUDA_STUBS} + assert expected.issubset(set(cu_files)), ( + f"Missing CUDA stubs: {expected - set(cu_files)}" + ) + + +# --------------------------------------------------------------------------- +# Content-quality checks for Python stubs +# --------------------------------------------------------------------------- + +@pytest.mark.parametrize("subdir,filename", _PYTHON_STUBS) +def test_stub_mentions_phase(subdir: str, filename: str) -> None: + """Python stubs should document which Phase will implement them.""" + path = os.path.join(KERNEL_DIR, subdir, filename) + with open(path) as fh: + content = fh.read() + assert "Phase" in content, ( + f"kernels/{subdir}/{filename} should mention 'Phase 1' or 'Phase 2' in its docs" + ) diff --git a/overlay/tests/test_learnability.py b/overlay/tests/test_learnability.py index d0d2da1d56ad4173e8420d44a5aca6fe0116d486..be4833570ebd21e253db442d3e8d46296fb0b0bb 100644 --- a/overlay/tests/test_learnability.py +++ b/overlay/tests/test_learnability.py @@ -1,550 +1,550 @@ -"""Unit tests for the 7 HYDRA learnability improvements. - -Each feature gets isolated tests that exercise the minimal code path without -requiring a full model forward. Where the feature is an env-var gate on the -model, we construct a ``PostSemClawModel`` with ``sdr_n_bits`` matching the -shipping retina (65536 × 16384) but all other dims shrunk so the model is -tiny on CPU. For pure-math features (entropy penalty, MTP loss computation, -doc-sep mask transform) we test the math directly on synthetic tensors so -the test doesn't depend on the retina at all. - -Features covered: - 1. Multi-Token Prediction (HYDRA_MTP_K) - 2. EMA of weights (HYDRA_USE_EMA, HYDRA_EMA_DECAY) - 3. Gradient checkpointing (HYDRA_GRAD_CKPT) - 4. Doc-separator masking (HYDRA_DOC_SEP_MASK) - 5. HTM stop-grad (HYDRA_HTM_STOP_GRAD) - 6. Entropy penalty (HYDRA_ENTROPY_PENALTY) - 7. Curriculum short→long (HYDRA_CURRICULUM_SHORT_STEPS) - -All tests run on CPU (forced via ``torch.set_default_device('cpu')`` at the -module start) so they coexist with the running production training on the -GPU. -""" - -from __future__ import annotations - -import importlib -import os -import sys -from pathlib import Path - -import pytest - -_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -if _REPO not in sys.path: - sys.path.insert(0, _REPO) - - -# --------------------------------------------------------------------------- -# Graceful skip if hydra/ package isn't present (same guard as the existing -# test_hydra_modular.py uses). -# --------------------------------------------------------------------------- - -if not os.path.isfile(os.path.join(_REPO, "hydra", "__init__.py")): - pytest.skip( - "hydra/ package not found — cannot run learnability tests.", - allow_module_level=True, - ) - - -# --------------------------------------------------------------------------- -# Fixture: a minimal model on CPU that uses the shipping retina shape -# (65536, 16384) so SemanticFoldingSDR loads without resizing. We shrink all -# other dims to stay tiny. -# --------------------------------------------------------------------------- - -def _retina_present() -> bool: - p = Path(os.path.expanduser("~/.cache/autoresearch/retina.npz")) - return p.exists() - - -@pytest.fixture(scope="module") -def tiny_cfg(): - """Tiny ``PostSemClawConfig`` sized to the shipping retina.""" - from hydra.config import PostSemClawConfig - return PostSemClawConfig( - sequence_len=32, - vocab_size=65536, # matches shipping retina - n_layer=1, - d_model=32, - d_state=8, - headdim=16, - n_heads=2, - expand=2, - engram_n_columns=16, - engram_key_dim=8, - engram_layer_idx=0, - sdr_n_bits=16384, # matches shipping retina - sdr_target_active=327, # matches shipping retina - sdr_delta_rank=4, - htm_n_columns=32, - htm_cells_per_column=4, - ) - - -@pytest.fixture(scope="function") -def clean_env(monkeypatch): - """Clear all learnability env vars before a test, so defaults apply.""" - for k in ( - "HYDRA_MTP_K", - "HYDRA_USE_EMA", - "HYDRA_EMA_DECAY", - "HYDRA_GRAD_CKPT", - "HYDRA_DOC_SEP_MASK", - "HYDRA_HTM_STOP_GRAD", - "HYDRA_ENTROPY_PENALTY", - "HYDRA_CURRICULUM_SHORT_STEPS", - "HYDRA_CURRICULUM_SHORT_SEQ_LEN", - ): - monkeypatch.delenv(k, raising=False) - - -# --------------------------------------------------------------------------- -# Feature 1: Multi-Token Prediction (MTP) -# --------------------------------------------------------------------------- - -class TestMTP: - """K extra heads predict t+1..t+K, all weight-tied to lm_head. - - Verified aspects: - * env var wires through to model attribute - * loss with K=4 differs from K=1 on the same deterministic inputs (extra CEs) - * K=1 leaves loss unchanged from baseline - * MTP loss math on synthetic tensors is invariant to sharing the lm_head - """ - - def test_env_flag_sets_mtp_k(self, monkeypatch, clean_env): - """``HYDRA_MTP_K=4`` → ``model._mtp_k == 4``. Pure attribute check, - no forward pass so no retina needed.""" - monkeypatch.setenv("HYDRA_MTP_K", "4") - # Re-import the config and model modules so the env var is re-read. - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - # We can't reload the model module (it will try to import mamba_ssm); - # instead, just check the config constant reflects the env var. - assert _cfg_mod.MTP_K == 4 - - def test_mtp_k_defaults_off(self, monkeypatch, clean_env): - """With no env var, MTP_K defaults to 1 (standard next-token).""" - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.MTP_K == 1 - - def test_mtp_loss_math_synthetic(self): - """Verify the MTP math: shift=k-1 pairs (hidden[:T-shift], targets[shift:]) - and averages K CEs. Done on synthetic tensors without the full model.""" - import torch - import torch.nn.functional as F - torch.manual_seed(0) - B, T, d, V = 1, 16, 8, 32 - K = 4 - # Fake hidden states + tied head weight. - h = torch.randn(B, T, d) - w = torch.randn(V, d) - targets = torch.randint(0, V, (B, T)) - - # Build the K CE losses manually, matching hydra/model.py lines 721-763. - primary = F.cross_entropy( - F.linear(h, w).reshape(-1, V).float(), - targets.reshape(-1), - ignore_index=-1, - ) - mtp_terms = 0 - extras_sum = torch.tensor(0.0) - for k in range(2, K + 1): - shift = k - 1 - if T <= shift: - continue - h_k = h[:, : T - shift, :] - t_k = targets[:, shift:] - logits_k = F.linear(h_k, w).reshape(-1, V).float() - extras_sum = extras_sum + F.cross_entropy( - logits_k, t_k.reshape(-1), ignore_index=-1, - ) - mtp_terms += 1 - combined = (primary + extras_sum) / (mtp_terms + 1) - # The combined loss must be a valid scalar; extras contribute non-zero - # values since random logits rarely match random targets. - assert combined.ndim == 0 - assert torch.isfinite(combined) - assert mtp_terms == K - 1 - # Combined is a weighted average of primary + K-1 extras. Since all - # CEs are >0 and close to log(V), combined is O(log V). - import math - assert 0.5 < combined.item() < 2.5 * math.log(V) - - @pytest.mark.skipif(not _retina_present(), reason="retina.npz absent") - def test_model_forward_mtp_differs_from_baseline(self, tiny_cfg, monkeypatch, clean_env): - """Smoke: full model forward with MTP_K=4 returns a different (generally - larger magnitude) loss than MTP_K=1 under the same seed/inputs.""" - import torch - torch.manual_seed(42) - from hydra.model import PostSemClawModel - - # Baseline - monkeypatch.setenv("HYDRA_MTP_K", "1") - with torch.device("meta"): - m1 = PostSemClawModel(tiny_cfg) - m1.to_empty(device="cpu") - m1.init_weights() - m1.train() # MTP only fires in train mode - assert m1._mtp_k == 1 - - monkeypatch.setenv("HYDRA_MTP_K", "4") - with torch.device("meta"): - m4 = PostSemClawModel(tiny_cfg) - m4.to_empty(device="cpu") - m4.init_weights() - m4.train() - assert m4._mtp_k == 4 - # The two models have different random state - we're just asserting - # the MTP wiring holds (attribute + training-mode gate). The per-value - # loss difference can be validated at integration time. - - -# --------------------------------------------------------------------------- -# Feature 2: EMA of weights -# --------------------------------------------------------------------------- - -class TestEMA: - """``torch.optim.swa_utils.AveragedModel`` with decay=0.999 shadows the - trained params. Save hook writes ``latest_ema.pt`` alongside ``latest.pt``. - """ - - def test_env_flag_parses(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_USE_EMA", "1") - monkeypatch.setenv("HYDRA_EMA_DECAY", "0.995") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.USE_EMA is True - assert _cfg_mod.EMA_DECAY == pytest.approx(0.995) - - def test_ema_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.USE_EMA is False - assert _cfg_mod.EMA_DECAY == pytest.approx(0.999) - - def test_ema_averaging_converges_to_target(self): - """Smoke test: on a tiny linear layer, after 100 update steps with - decay=0.9 where params are held constant, the EMA weights converge to - the underlying weight.""" - import torch - import torch.nn as nn - from torch.optim.swa_utils import AveragedModel, get_ema_multi_avg_fn - - torch.manual_seed(0) - model = nn.Linear(4, 4, bias=False) - target = torch.zeros_like(model.weight) - target += 3.14 - # Freeze model at the target value; EMA should track it. - with torch.no_grad(): - model.weight.copy_(target) - ema = AveragedModel(model, multi_avg_fn=get_ema_multi_avg_fn(0.9)) - for _ in range(100): - ema.update_parameters(model) - # The EMA weight must be within 1% of the fixed target. - diff = (ema.module.weight - target).abs().max().item() - assert diff < 0.04, f"EMA did not converge: max diff={diff}" - - -# --------------------------------------------------------------------------- -# Feature 3: Gradient checkpointing -# --------------------------------------------------------------------------- - -class TestGradCheckpointing: - def test_env_flag_sets_attr(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_GRAD_CKPT", "1") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.GRAD_CKPT is True - - def test_grad_ckpt_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.GRAD_CKPT is False - - def test_checkpoint_api_available(self): - """``torch.utils.checkpoint.checkpoint`` must exist with the - ``use_reentrant`` kwarg the model passes.""" - import inspect - import torch.utils.checkpoint as ckpt - assert callable(ckpt.checkpoint) - sig = inspect.signature(ckpt.checkpoint) - assert "use_reentrant" in sig.parameters - - def test_checkpoint_preserves_output(self): - """Running a function via checkpoint(fn, x, use_reentrant=False) - yields the same output as fn(x) and a real backward gradient.""" - import torch - import torch.utils.checkpoint as _ckpt - - def fn(z): - return (z * 2.0 + 1.0).sum() - - x = torch.randn(3, 4, requires_grad=True) - y1 = fn(x) - x2 = x.detach().clone().requires_grad_(True) - y2 = _ckpt.checkpoint(fn, x2, use_reentrant=False) - assert torch.allclose(y1, y2) - y2.backward() - assert x2.grad is not None - assert torch.allclose(x2.grad, torch.full_like(x2, 2.0)) - - -# --------------------------------------------------------------------------- -# Feature 4: Doc-separator masking -# --------------------------------------------------------------------------- - -class TestDocSepMask: - def test_env_flag_sets_attr(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_DOC_SEP_MASK", "1") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.DOC_SEP_MASK is True - - def test_doc_sep_mask_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.DOC_SEP_MASK is False - - def test_mask_transform_replaces_bos_with_neg_one(self): - """Verify the ``torch.where(targets == bos, -1, targets)`` transform - used at hydra/model.py:596-601.""" - import torch - bos = 7 - targets = torch.tensor([[3, 7, 5, 7, 2]]) - masked = torch.where( - targets == bos, - torch.full_like(targets, -1), - targets, - ) - assert masked.tolist() == [[3, -1, 5, -1, 2]] - - def test_cross_entropy_ignores_masked_targets(self): - """``F.cross_entropy(..., ignore_index=-1)`` skips -1 positions. - We feed synthetic logits + a half-masked target sequence and verify - the resulting loss equals the loss on the un-masked positions alone. - """ - import torch - import torch.nn.functional as F - - torch.manual_seed(3) - B, T, V = 1, 8, 16 - logits = torch.randn(B * T, V) - targets = torch.randint(0, V, (B * T,)) - # Mask every other position. - masked_targets = targets.clone() - masked_targets[::2] = -1 - loss_masked = F.cross_entropy(logits, masked_targets, ignore_index=-1, reduction="mean") - # Reference: mean over only the unmasked positions. - keep = masked_targets != -1 - loss_ref = F.cross_entropy( - logits[keep], targets[keep], reduction="mean", - ) - assert torch.allclose(loss_masked, loss_ref, atol=1e-6) - - def test_dataloader_packs_bos_between_docs(self): - """Confirm ``prepare_nemotron.make_dataloader`` prepends BOS to every - doc during tokenization (line 378). Read the source to assert the - ``prepend=bos_token`` kwarg is passed — this is a structural test so - we don't need to actually stream from HF.""" - src = Path(_REPO, "prepare_nemotron.py").read_text() - # The intended semantics: tokenizer.encode(doc_batch, prepend=bos_token) - assert "prepend=bos_token" in src, ( - "prepare_nemotron.py must prepend BOS to every document for " - "doc-separator masking to work." - ) - - -# --------------------------------------------------------------------------- -# Feature 5: HTM stop-grad -# --------------------------------------------------------------------------- - -class TestHTMStopGrad: - def test_env_flag_sets_attr(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_HTM_STOP_GRAD", "1") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.HTM_STOP_GRAD is True - - def test_htm_stop_grad_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.HTM_STOP_GRAD is False - - def test_detach_breaks_autograd(self): - """``.detach()`` returns a tensor that has no backward path to the - source. This is the operation applied to HTM output at model.py:495. - The key properties: - 1. ``z.requires_grad`` is False - 2. ``z.grad_fn`` is None - 3. A downstream op that mixes z with a grad-bearing tensor w does - not route any gradient into x (verified by w.grad alone being - populated, x.grad remaining None). - """ - import torch - x = torch.randn(3, 4, requires_grad=True) - y = x * 2.0 - z = y.detach() - assert not z.requires_grad - assert z.grad_fn is None - # Mix z into a downstream op with a grad-bearing second tensor so - # the backward call itself is valid; verify grad only flows through w. - w = torch.randn(3, 4, requires_grad=True) - (z * w).sum().backward() - assert x.grad is None, ( - "x.grad should be None because z.detach() severed the graph." - ) - assert w.grad is not None - - -# --------------------------------------------------------------------------- -# Feature 6: Output entropy penalty -# --------------------------------------------------------------------------- - -class TestEntropyPenalty: - def test_env_flag_sets_attr(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_ENTROPY_PENALTY", "0.01") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.01) - - def test_entropy_penalty_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.0) - - def test_entropy_uniform_is_max(self): - """Entropy of a uniform distribution equals log(V). Peaked - distributions have lower entropy. ``-lambda * H(p)`` is thus more - negative for uniform and less negative for peaked — penalizing - peaked distributions = encouraging diversity. - """ - import math - import torch - import torch.nn.functional as F - - V = 16 - uniform_logits = torch.zeros(V) - peaked_logits = torch.zeros(V) - peaked_logits[0] = 100.0 # extreme peak at token 0 - - def entropy(log_probs): - probs = log_probs.exp() - return -(probs * log_probs).sum() - - H_uniform = entropy(F.log_softmax(uniform_logits, dim=-1)) - H_peaked = entropy(F.log_softmax(peaked_logits, dim=-1)) - assert H_uniform > H_peaked - assert H_uniform.item() == pytest.approx(math.log(V), rel=1e-4) - assert H_peaked.item() < 0.01 # essentially zero - - def test_entropy_term_sign_on_loss(self): - """Adding ``-lambda*H(p)`` to the CE loss penalizes peaked - distributions. Start from a base loss and apply the penalty formula - (model.py:789); verify the combined scalar is smaller when the logits - are more uniform (higher H).""" - import torch - import torch.nn.functional as F - - V = 16 - lam = 0.5 - uniform = torch.zeros(V) - peaked = torch.zeros(V) - peaked[0] = 100.0 - base_loss = torch.tensor(2.0) - - def combine(logits): - lp = F.log_softmax(logits, dim=-1) - H = -(lp.exp() * lp).sum() - return base_loss - lam * H - - # With λ>0, combined loss = base - λ*H. The HIGHER H (uniform) thus - # produces a LOWER combined loss — i.e. optimizer is encouraged to - # keep H high (= encourage diverse, high-entropy outputs). - assert combine(uniform) < combine(peaked) - - -# --------------------------------------------------------------------------- -# Feature 7: Curriculum short→long -# --------------------------------------------------------------------------- - -class TestCurriculum: - def test_env_flags_parse(self, monkeypatch, clean_env): - monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_STEPS", "2000") - monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256") - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.CURRICULUM_SHORT_STEPS == 2000 - assert _cfg_mod.CURRICULUM_SHORT_SEQ_LEN == 256 - - def test_curriculum_defaults_off(self, monkeypatch, clean_env): - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - # Defaults mean no curriculum — 0 steps disables. - assert _cfg_mod.CURRICULUM_SHORT_STEPS == 0 - - def test_curriculum_activation_condition(self): - """Replicate the training.py:258 condition: curriculum is only - active when SHORT_STEPS > 0 AND SHORT_SEQ_LEN < MAX_SEQ_LEN.""" - MAX_SEQ_LEN = 512 - # Active case - assert (2000 > 0) and (256 < MAX_SEQ_LEN) - # Inactive because steps=0 - assert not ((0 > 0) and (256 < MAX_SEQ_LEN)) - # Inactive because short seq_len >= MAX - assert not ((2000 > 0) and (512 < MAX_SEQ_LEN)) - assert not ((2000 > 0) and (1024 < MAX_SEQ_LEN)) - - def test_curriculum_transition_logic(self): - """Simulate the step counter reaching SHORT_STEPS → seq_len flips. - Mirrors training.py:329-340.""" - SHORT_STEPS = 5 - SHORT_SEQ_LEN = 64 - MAX_SEQ_LEN = 256 - active = (SHORT_STEPS > 0) and (SHORT_SEQ_LEN < MAX_SEQ_LEN) - current = SHORT_SEQ_LEN if active else MAX_SEQ_LEN - for step in range(10): - if active and step + 1 >= SHORT_STEPS: - current = MAX_SEQ_LEN - active = False - if step < SHORT_STEPS - 1: - assert current == SHORT_SEQ_LEN - else: - assert current == MAX_SEQ_LEN - # Flag must have been flipped exactly once. - assert active is False - assert current == MAX_SEQ_LEN - - -# --------------------------------------------------------------------------- -# Integration: all 7 flags coexist in the config module without errors. -# --------------------------------------------------------------------------- - -class TestAllFeaturesIntegration: - def test_all_env_vars_exposed_in_config(self, monkeypatch, clean_env): - """With every flag set, the config module imports cleanly and - exposes all 7 knobs at module level.""" - monkeypatch.setenv("HYDRA_MTP_K", "4") - monkeypatch.setenv("HYDRA_USE_EMA", "1") - monkeypatch.setenv("HYDRA_EMA_DECAY", "0.995") - monkeypatch.setenv("HYDRA_GRAD_CKPT", "1") - monkeypatch.setenv("HYDRA_DOC_SEP_MASK", "1") - monkeypatch.setenv("HYDRA_HTM_STOP_GRAD", "1") - monkeypatch.setenv("HYDRA_ENTROPY_PENALTY", "0.01") - monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_STEPS", "2000") - monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256") - - from hydra import config as _cfg_mod - importlib.reload(_cfg_mod) - assert _cfg_mod.MTP_K == 4 - assert _cfg_mod.USE_EMA is True - assert _cfg_mod.EMA_DECAY == pytest.approx(0.995) - assert _cfg_mod.GRAD_CKPT is True - assert _cfg_mod.DOC_SEP_MASK is True - assert _cfg_mod.HTM_STOP_GRAD is True - assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.01) - assert _cfg_mod.CURRICULUM_SHORT_STEPS == 2000 - assert _cfg_mod.CURRICULUM_SHORT_SEQ_LEN == 256 +"""Unit tests for the 7 HYDRA learnability improvements. + +Each feature gets isolated tests that exercise the minimal code path without +requiring a full model forward. Where the feature is an env-var gate on the +model, we construct a ``PostSemClawModel`` with ``sdr_n_bits`` matching the +shipping retina (65536 × 16384) but all other dims shrunk so the model is +tiny on CPU. For pure-math features (entropy penalty, MTP loss computation, +doc-sep mask transform) we test the math directly on synthetic tensors so +the test doesn't depend on the retina at all. + +Features covered: + 1. Multi-Token Prediction (HYDRA_MTP_K) + 2. EMA of weights (HYDRA_USE_EMA, HYDRA_EMA_DECAY) + 3. Gradient checkpointing (HYDRA_GRAD_CKPT) + 4. Doc-separator masking (HYDRA_DOC_SEP_MASK) + 5. HTM stop-grad (HYDRA_HTM_STOP_GRAD) + 6. Entropy penalty (HYDRA_ENTROPY_PENALTY) + 7. Curriculum short→long (HYDRA_CURRICULUM_SHORT_STEPS) + +All tests run on CPU (forced via ``torch.set_default_device('cpu')`` at the +module start) so they coexist with the running production training on the +GPU. +""" + +from __future__ import annotations + +import importlib +import os +import sys +from pathlib import Path + +import pytest + +_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _REPO not in sys.path: + sys.path.insert(0, _REPO) + + +# --------------------------------------------------------------------------- +# Graceful skip if hydra/ package isn't present (same guard as the existing +# test_hydra_modular.py uses). +# --------------------------------------------------------------------------- + +if not os.path.isfile(os.path.join(_REPO, "hydra", "__init__.py")): + pytest.skip( + "hydra/ package not found — cannot run learnability tests.", + allow_module_level=True, + ) + + +# --------------------------------------------------------------------------- +# Fixture: a minimal model on CPU that uses the shipping retina shape +# (65536, 16384) so SemanticFoldingSDR loads without resizing. We shrink all +# other dims to stay tiny. +# --------------------------------------------------------------------------- + +def _retina_present() -> bool: + p = Path(os.path.expanduser("~/.cache/autoresearch/retina.npz")) + return p.exists() + + +@pytest.fixture(scope="module") +def tiny_cfg(): + """Tiny ``PostSemClawConfig`` sized to the shipping retina.""" + from hydra.config import PostSemClawConfig + return PostSemClawConfig( + sequence_len=32, + vocab_size=65536, # matches shipping retina + n_layer=1, + d_model=32, + d_state=8, + headdim=16, + n_heads=2, + expand=2, + engram_n_columns=16, + engram_key_dim=8, + engram_layer_idx=0, + sdr_n_bits=16384, # matches shipping retina + sdr_target_active=327, # matches shipping retina + sdr_delta_rank=4, + htm_n_columns=32, + htm_cells_per_column=4, + ) + + +@pytest.fixture(scope="function") +def clean_env(monkeypatch): + """Clear all learnability env vars before a test, so defaults apply.""" + for k in ( + "HYDRA_MTP_K", + "HYDRA_USE_EMA", + "HYDRA_EMA_DECAY", + "HYDRA_GRAD_CKPT", + "HYDRA_DOC_SEP_MASK", + "HYDRA_HTM_STOP_GRAD", + "HYDRA_ENTROPY_PENALTY", + "HYDRA_CURRICULUM_SHORT_STEPS", + "HYDRA_CURRICULUM_SHORT_SEQ_LEN", + ): + monkeypatch.delenv(k, raising=False) + + +# --------------------------------------------------------------------------- +# Feature 1: Multi-Token Prediction (MTP) +# --------------------------------------------------------------------------- + +class TestMTP: + """K extra heads predict t+1..t+K, all weight-tied to lm_head. + + Verified aspects: + * env var wires through to model attribute + * loss with K=4 differs from K=1 on the same deterministic inputs (extra CEs) + * K=1 leaves loss unchanged from baseline + * MTP loss math on synthetic tensors is invariant to sharing the lm_head + """ + + def test_env_flag_sets_mtp_k(self, monkeypatch, clean_env): + """``HYDRA_MTP_K=4`` → ``model._mtp_k == 4``. Pure attribute check, + no forward pass so no retina needed.""" + monkeypatch.setenv("HYDRA_MTP_K", "4") + # Re-import the config and model modules so the env var is re-read. + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + # We can't reload the model module (it will try to import mamba_ssm); + # instead, just check the config constant reflects the env var. + assert _cfg_mod.MTP_K == 4 + + def test_mtp_k_defaults_off(self, monkeypatch, clean_env): + """With no env var, MTP_K defaults to 1 (standard next-token).""" + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.MTP_K == 1 + + def test_mtp_loss_math_synthetic(self): + """Verify the MTP math: shift=k-1 pairs (hidden[:T-shift], targets[shift:]) + and averages K CEs. Done on synthetic tensors without the full model.""" + import torch + import torch.nn.functional as F + torch.manual_seed(0) + B, T, d, V = 1, 16, 8, 32 + K = 4 + # Fake hidden states + tied head weight. + h = torch.randn(B, T, d) + w = torch.randn(V, d) + targets = torch.randint(0, V, (B, T)) + + # Build the K CE losses manually, matching hydra/model.py lines 721-763. + primary = F.cross_entropy( + F.linear(h, w).reshape(-1, V).float(), + targets.reshape(-1), + ignore_index=-1, + ) + mtp_terms = 0 + extras_sum = torch.tensor(0.0) + for k in range(2, K + 1): + shift = k - 1 + if T <= shift: + continue + h_k = h[:, : T - shift, :] + t_k = targets[:, shift:] + logits_k = F.linear(h_k, w).reshape(-1, V).float() + extras_sum = extras_sum + F.cross_entropy( + logits_k, t_k.reshape(-1), ignore_index=-1, + ) + mtp_terms += 1 + combined = (primary + extras_sum) / (mtp_terms + 1) + # The combined loss must be a valid scalar; extras contribute non-zero + # values since random logits rarely match random targets. + assert combined.ndim == 0 + assert torch.isfinite(combined) + assert mtp_terms == K - 1 + # Combined is a weighted average of primary + K-1 extras. Since all + # CEs are >0 and close to log(V), combined is O(log V). + import math + assert 0.5 < combined.item() < 2.5 * math.log(V) + + @pytest.mark.skipif(not _retina_present(), reason="retina.npz absent") + def test_model_forward_mtp_differs_from_baseline(self, tiny_cfg, monkeypatch, clean_env): + """Smoke: full model forward with MTP_K=4 returns a different (generally + larger magnitude) loss than MTP_K=1 under the same seed/inputs.""" + import torch + torch.manual_seed(42) + from hydra.model import PostSemClawModel + + # Baseline + monkeypatch.setenv("HYDRA_MTP_K", "1") + with torch.device("meta"): + m1 = PostSemClawModel(tiny_cfg) + m1.to_empty(device="cpu") + m1.init_weights() + m1.train() # MTP only fires in train mode + assert m1._mtp_k == 1 + + monkeypatch.setenv("HYDRA_MTP_K", "4") + with torch.device("meta"): + m4 = PostSemClawModel(tiny_cfg) + m4.to_empty(device="cpu") + m4.init_weights() + m4.train() + assert m4._mtp_k == 4 + # The two models have different random state - we're just asserting + # the MTP wiring holds (attribute + training-mode gate). The per-value + # loss difference can be validated at integration time. + + +# --------------------------------------------------------------------------- +# Feature 2: EMA of weights +# --------------------------------------------------------------------------- + +class TestEMA: + """``torch.optim.swa_utils.AveragedModel`` with decay=0.999 shadows the + trained params. Save hook writes ``latest_ema.pt`` alongside ``latest.pt``. + """ + + def test_env_flag_parses(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_USE_EMA", "1") + monkeypatch.setenv("HYDRA_EMA_DECAY", "0.995") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.USE_EMA is True + assert _cfg_mod.EMA_DECAY == pytest.approx(0.995) + + def test_ema_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.USE_EMA is False + assert _cfg_mod.EMA_DECAY == pytest.approx(0.999) + + def test_ema_averaging_converges_to_target(self): + """Smoke test: on a tiny linear layer, after 100 update steps with + decay=0.9 where params are held constant, the EMA weights converge to + the underlying weight.""" + import torch + import torch.nn as nn + from torch.optim.swa_utils import AveragedModel, get_ema_multi_avg_fn + + torch.manual_seed(0) + model = nn.Linear(4, 4, bias=False) + target = torch.zeros_like(model.weight) + target += 3.14 + # Freeze model at the target value; EMA should track it. + with torch.no_grad(): + model.weight.copy_(target) + ema = AveragedModel(model, multi_avg_fn=get_ema_multi_avg_fn(0.9)) + for _ in range(100): + ema.update_parameters(model) + # The EMA weight must be within 1% of the fixed target. + diff = (ema.module.weight - target).abs().max().item() + assert diff < 0.04, f"EMA did not converge: max diff={diff}" + + +# --------------------------------------------------------------------------- +# Feature 3: Gradient checkpointing +# --------------------------------------------------------------------------- + +class TestGradCheckpointing: + def test_env_flag_sets_attr(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_GRAD_CKPT", "1") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.GRAD_CKPT is True + + def test_grad_ckpt_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.GRAD_CKPT is False + + def test_checkpoint_api_available(self): + """``torch.utils.checkpoint.checkpoint`` must exist with the + ``use_reentrant`` kwarg the model passes.""" + import inspect + import torch.utils.checkpoint as ckpt + assert callable(ckpt.checkpoint) + sig = inspect.signature(ckpt.checkpoint) + assert "use_reentrant" in sig.parameters + + def test_checkpoint_preserves_output(self): + """Running a function via checkpoint(fn, x, use_reentrant=False) + yields the same output as fn(x) and a real backward gradient.""" + import torch + import torch.utils.checkpoint as _ckpt + + def fn(z): + return (z * 2.0 + 1.0).sum() + + x = torch.randn(3, 4, requires_grad=True) + y1 = fn(x) + x2 = x.detach().clone().requires_grad_(True) + y2 = _ckpt.checkpoint(fn, x2, use_reentrant=False) + assert torch.allclose(y1, y2) + y2.backward() + assert x2.grad is not None + assert torch.allclose(x2.grad, torch.full_like(x2, 2.0)) + + +# --------------------------------------------------------------------------- +# Feature 4: Doc-separator masking +# --------------------------------------------------------------------------- + +class TestDocSepMask: + def test_env_flag_sets_attr(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_DOC_SEP_MASK", "1") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.DOC_SEP_MASK is True + + def test_doc_sep_mask_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.DOC_SEP_MASK is False + + def test_mask_transform_replaces_bos_with_neg_one(self): + """Verify the ``torch.where(targets == bos, -1, targets)`` transform + used at hydra/model.py:596-601.""" + import torch + bos = 7 + targets = torch.tensor([[3, 7, 5, 7, 2]]) + masked = torch.where( + targets == bos, + torch.full_like(targets, -1), + targets, + ) + assert masked.tolist() == [[3, -1, 5, -1, 2]] + + def test_cross_entropy_ignores_masked_targets(self): + """``F.cross_entropy(..., ignore_index=-1)`` skips -1 positions. + We feed synthetic logits + a half-masked target sequence and verify + the resulting loss equals the loss on the un-masked positions alone. + """ + import torch + import torch.nn.functional as F + + torch.manual_seed(3) + B, T, V = 1, 8, 16 + logits = torch.randn(B * T, V) + targets = torch.randint(0, V, (B * T,)) + # Mask every other position. + masked_targets = targets.clone() + masked_targets[::2] = -1 + loss_masked = F.cross_entropy(logits, masked_targets, ignore_index=-1, reduction="mean") + # Reference: mean over only the unmasked positions. + keep = masked_targets != -1 + loss_ref = F.cross_entropy( + logits[keep], targets[keep], reduction="mean", + ) + assert torch.allclose(loss_masked, loss_ref, atol=1e-6) + + def test_dataloader_packs_bos_between_docs(self): + """Confirm ``prepare_nemotron.make_dataloader`` prepends BOS to every + doc during tokenization (line 378). Read the source to assert the + ``prepend=bos_token`` kwarg is passed — this is a structural test so + we don't need to actually stream from HF.""" + src = Path(_REPO, "prepare_nemotron.py").read_text() + # The intended semantics: tokenizer.encode(doc_batch, prepend=bos_token) + assert "prepend=bos_token" in src, ( + "prepare_nemotron.py must prepend BOS to every document for " + "doc-separator masking to work." + ) + + +# --------------------------------------------------------------------------- +# Feature 5: HTM stop-grad +# --------------------------------------------------------------------------- + +class TestHTMStopGrad: + def test_env_flag_sets_attr(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_HTM_STOP_GRAD", "1") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.HTM_STOP_GRAD is True + + def test_htm_stop_grad_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.HTM_STOP_GRAD is False + + def test_detach_breaks_autograd(self): + """``.detach()`` returns a tensor that has no backward path to the + source. This is the operation applied to HTM output at model.py:495. + The key properties: + 1. ``z.requires_grad`` is False + 2. ``z.grad_fn`` is None + 3. A downstream op that mixes z with a grad-bearing tensor w does + not route any gradient into x (verified by w.grad alone being + populated, x.grad remaining None). + """ + import torch + x = torch.randn(3, 4, requires_grad=True) + y = x * 2.0 + z = y.detach() + assert not z.requires_grad + assert z.grad_fn is None + # Mix z into a downstream op with a grad-bearing second tensor so + # the backward call itself is valid; verify grad only flows through w. + w = torch.randn(3, 4, requires_grad=True) + (z * w).sum().backward() + assert x.grad is None, ( + "x.grad should be None because z.detach() severed the graph." + ) + assert w.grad is not None + + +# --------------------------------------------------------------------------- +# Feature 6: Output entropy penalty +# --------------------------------------------------------------------------- + +class TestEntropyPenalty: + def test_env_flag_sets_attr(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_ENTROPY_PENALTY", "0.01") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.01) + + def test_entropy_penalty_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.0) + + def test_entropy_uniform_is_max(self): + """Entropy of a uniform distribution equals log(V). Peaked + distributions have lower entropy. ``-lambda * H(p)`` is thus more + negative for uniform and less negative for peaked — penalizing + peaked distributions = encouraging diversity. + """ + import math + import torch + import torch.nn.functional as F + + V = 16 + uniform_logits = torch.zeros(V) + peaked_logits = torch.zeros(V) + peaked_logits[0] = 100.0 # extreme peak at token 0 + + def entropy(log_probs): + probs = log_probs.exp() + return -(probs * log_probs).sum() + + H_uniform = entropy(F.log_softmax(uniform_logits, dim=-1)) + H_peaked = entropy(F.log_softmax(peaked_logits, dim=-1)) + assert H_uniform > H_peaked + assert H_uniform.item() == pytest.approx(math.log(V), rel=1e-4) + assert H_peaked.item() < 0.01 # essentially zero + + def test_entropy_term_sign_on_loss(self): + """Adding ``-lambda*H(p)`` to the CE loss penalizes peaked + distributions. Start from a base loss and apply the penalty formula + (model.py:789); verify the combined scalar is smaller when the logits + are more uniform (higher H).""" + import torch + import torch.nn.functional as F + + V = 16 + lam = 0.5 + uniform = torch.zeros(V) + peaked = torch.zeros(V) + peaked[0] = 100.0 + base_loss = torch.tensor(2.0) + + def combine(logits): + lp = F.log_softmax(logits, dim=-1) + H = -(lp.exp() * lp).sum() + return base_loss - lam * H + + # With λ>0, combined loss = base - λ*H. The HIGHER H (uniform) thus + # produces a LOWER combined loss — i.e. optimizer is encouraged to + # keep H high (= encourage diverse, high-entropy outputs). + assert combine(uniform) < combine(peaked) + + +# --------------------------------------------------------------------------- +# Feature 7: Curriculum short→long +# --------------------------------------------------------------------------- + +class TestCurriculum: + def test_env_flags_parse(self, monkeypatch, clean_env): + monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_STEPS", "2000") + monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256") + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.CURRICULUM_SHORT_STEPS == 2000 + assert _cfg_mod.CURRICULUM_SHORT_SEQ_LEN == 256 + + def test_curriculum_defaults_off(self, monkeypatch, clean_env): + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + # Defaults mean no curriculum — 0 steps disables. + assert _cfg_mod.CURRICULUM_SHORT_STEPS == 0 + + def test_curriculum_activation_condition(self): + """Replicate the training.py:258 condition: curriculum is only + active when SHORT_STEPS > 0 AND SHORT_SEQ_LEN < MAX_SEQ_LEN.""" + MAX_SEQ_LEN = 512 + # Active case + assert (2000 > 0) and (256 < MAX_SEQ_LEN) + # Inactive because steps=0 + assert not ((0 > 0) and (256 < MAX_SEQ_LEN)) + # Inactive because short seq_len >= MAX + assert not ((2000 > 0) and (512 < MAX_SEQ_LEN)) + assert not ((2000 > 0) and (1024 < MAX_SEQ_LEN)) + + def test_curriculum_transition_logic(self): + """Simulate the step counter reaching SHORT_STEPS → seq_len flips. + Mirrors training.py:329-340.""" + SHORT_STEPS = 5 + SHORT_SEQ_LEN = 64 + MAX_SEQ_LEN = 256 + active = (SHORT_STEPS > 0) and (SHORT_SEQ_LEN < MAX_SEQ_LEN) + current = SHORT_SEQ_LEN if active else MAX_SEQ_LEN + for step in range(10): + if active and step + 1 >= SHORT_STEPS: + current = MAX_SEQ_LEN + active = False + if step < SHORT_STEPS - 1: + assert current == SHORT_SEQ_LEN + else: + assert current == MAX_SEQ_LEN + # Flag must have been flipped exactly once. + assert active is False + assert current == MAX_SEQ_LEN + + +# --------------------------------------------------------------------------- +# Integration: all 7 flags coexist in the config module without errors. +# --------------------------------------------------------------------------- + +class TestAllFeaturesIntegration: + def test_all_env_vars_exposed_in_config(self, monkeypatch, clean_env): + """With every flag set, the config module imports cleanly and + exposes all 7 knobs at module level.""" + monkeypatch.setenv("HYDRA_MTP_K", "4") + monkeypatch.setenv("HYDRA_USE_EMA", "1") + monkeypatch.setenv("HYDRA_EMA_DECAY", "0.995") + monkeypatch.setenv("HYDRA_GRAD_CKPT", "1") + monkeypatch.setenv("HYDRA_DOC_SEP_MASK", "1") + monkeypatch.setenv("HYDRA_HTM_STOP_GRAD", "1") + monkeypatch.setenv("HYDRA_ENTROPY_PENALTY", "0.01") + monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_STEPS", "2000") + monkeypatch.setenv("HYDRA_CURRICULUM_SHORT_SEQ_LEN", "256") + + from hydra import config as _cfg_mod + importlib.reload(_cfg_mod) + assert _cfg_mod.MTP_K == 4 + assert _cfg_mod.USE_EMA is True + assert _cfg_mod.EMA_DECAY == pytest.approx(0.995) + assert _cfg_mod.GRAD_CKPT is True + assert _cfg_mod.DOC_SEP_MASK is True + assert _cfg_mod.HTM_STOP_GRAD is True + assert _cfg_mod.ENTROPY_PENALTY == pytest.approx(0.01) + assert _cfg_mod.CURRICULUM_SHORT_STEPS == 2000 + assert _cfg_mod.CURRICULUM_SHORT_SEQ_LEN == 256 diff --git a/overlay/tests/test_muon_grad_accum.py b/overlay/tests/test_muon_grad_accum.py index 72ed0c42f8d781b77670ad6b8e8efa8b67f30a36..78696f4f0d656af9c02f3c3f5182973f3aad646b 100644 --- a/overlay/tests/test_muon_grad_accum.py +++ b/overlay/tests/test_muon_grad_accum.py @@ -1,303 +1,303 @@ -""" -Regression tests for gradient accumulation compatibility with Engram-style -in-place writes (index_add_/scatter operations) inside the autograd path. - -The "inplace op modified tensor needed for backward on micro-step 2" error -is reproduced by building a tiny model that: - 1. Has an Engram-like module that does .data.index_add_() under no_grad - AND reads from its memory buffer via an indexed gather that IS in the - autograd graph (grad flows through the read path). - 2. Wraps that in an mHC-style 2-stream doubly-stochastic residual. - 3. Accumulates gradients over multiple micro-steps by repeating - forward -> loss / grad_accum -> backward before calling optimizer.step(). - -The bug manifests only on micro-step >= 2 because the first backward stores -references to the activation tensors; the in-place write on the memory buffer -during the SECOND forward corrupts those saved tensors. - -Fix: any Hebbian write must be via `.data.index_add_()` (detached) so that -autograd's saved-tensor machinery never sees a version-counter increment on a -leaf that has requires_grad=True. - -Run: - cd /home/mikeb/work/feather - .venv/bin/pytest tests/test_muon_grad_accum.py -v -""" - -import sys -import os -import types -import pytest -import torch -import torch.nn as nn -import torch.nn.functional as F - -# --------------------------------------------------------------------------- -# Tiny self-contained model — no imports from train.py or hydra/ -# --------------------------------------------------------------------------- - -class TinyEngram(nn.Module): - """ - Minimal stand-in for GPUEngram. - - In-place write: self.memory.data.index_add_() under torch.no_grad(). - This means the memory Parameter has requires_grad=True (so the READ path - gets gradients) but the WRITE never touches the grad-tracked version of - memory — it goes through .data, bypassing the version counter. - - If instead we wrote to self.memory directly (without .data), the version - counter would be bumped and any saved references from a prior backward - would be invalidated, triggering the "inplace op modified a leaf Tensor - that requires grad" RuntimeError on micro-step 2. - """ - def __init__(self, d_model: int, n_columns: int = 32): - super().__init__() - self.n_columns = n_columns - self.memory = nn.Parameter(torch.zeros(n_columns, d_model)) - self.out_proj = nn.Linear(d_model, d_model, bias=False) - - def forward(self, x: torch.Tensor, token_ids: torch.Tensor) -> torch.Tensor: - """ - x: (B, T, d_model) - token_ids: (B, T) long - """ - # Hash token_ids to column indices - indices = token_ids % self.n_columns # (B, T) - - # --- AUTOGRAD READ PATH --- - # This gather IS in the autograd graph; gradients flow back to self.memory. - retrieved = self.memory[indices] # (B, T, d_model) - - # --- IN-PLACE HEBBIAN WRITE (must NOT corrupt autograd) --- - if self.training: - with torch.no_grad(): - flat_idx = indices.reshape(-1) # (B*T,) - flat_x = x.detach().reshape(-1, x.shape[-1]) # (B*T, d) - lr = 0.01 - # .data bypasses the version counter — safe across micro-steps - delta = lr * (flat_x - self.memory.data[flat_idx]) - self.memory.data.index_add_(0, flat_idx, delta) - - # Gate - gate = torch.sigmoid(self.out_proj(x)) - return x + gate * retrieved - - -class TinymHCResidual(nn.Module): - """ - Minimal doubly-stochastic 2-stream residual (mHC-like). - Uses a learnable scalar alpha to blend the two streams. - """ - def __init__(self, d_model: int): - super().__init__() - self.log_alpha = nn.Parameter(torch.zeros(1)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # Two streams: x itself and a scaled version - alpha = torch.sigmoid(self.log_alpha) - stream0 = alpha * x - stream1 = (1.0 - alpha) * x - # Sinkhorn-style doubly-stochastic merge (simplified: just add) - return stream0 + stream1 # trivially = x, but exercises the alpha grad path - - -class TinyModel(nn.Module): - """ - Tiny model exercising the same mechanism as the real training loop: - Embedding -> TinyEngram (in-place Hebbian write + grad-bearing read) - -> TinymHCResidual -> Linear -> CrossEntropy - """ - def __init__(self, vocab_size: int = 64, d_model: int = 32, n_columns: int = 16): - super().__init__() - self.embed = nn.Embedding(vocab_size, d_model) - self.engram = TinyEngram(d_model, n_columns) - self.mhc = TinymHCResidual(d_model) - self.norm = nn.LayerNorm(d_model) - self.head = nn.Linear(d_model, vocab_size, bias=False) - - def forward(self, idx: torch.Tensor, targets: torch.Tensor) -> torch.Tensor: - """ - idx: (B, T) long - targets: (B, T) long - Returns: scalar loss - """ - x = self.embed(idx) # (B, T, d_model) - x = self.engram(x, idx) # in-place Hebbian write + read - x = self.mhc(x) # 2-stream residual - x = self.norm(x) - logits = self.head(x) # (B, T, vocab_size) - return F.cross_entropy( - logits.view(-1, logits.size(-1)), - targets.reshape(-1), - ) - - -# --------------------------------------------------------------------------- -# Test 1: grad_accum regression — parametrised over accumulation counts -# --------------------------------------------------------------------------- - -@pytest.mark.parametrize("grad_accum", [1, 2, 4]) -def test_grad_accum_no_inplace_error(grad_accum: int): - """ - Verifies that accumulating gradients over `grad_accum` micro-steps succeeds - without RuntimeError for any accumulation count. - - With anomaly detection ON, PyTorch will raise the moment an in-place op - corrupts a saved tensor — even if the numerical result happens to be close. - This is the strongest available signal for the bug. - """ - torch.autograd.set_detect_anomaly(True) - try: - model = TinyModel(vocab_size=64, d_model=32, n_columns=16) - model.train() - optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) - - B, T = 2, 8 - vocab_size = 64 - - optimizer.zero_grad() - for micro_step in range(grad_accum): - idx = torch.randint(0, vocab_size, (B, T)) - targets = torch.randint(0, vocab_size, (B, T)) - # forward - loss = model(idx, targets) - # scale loss for accumulation - loss = loss / grad_accum - # backward — must NOT raise on micro_step >= 1 - loss.backward() - - optimizer.step() - except RuntimeError as exc: - # Re-raise with a clearer message so W1 can diagnose the exact failure. - raise AssertionError( - f"grad_accum={grad_accum}: RuntimeError during backward " - f"(likely inplace-op/version-counter bug): {exc}" - ) from exc - finally: - torch.autograd.set_detect_anomaly(False) - - -# --------------------------------------------------------------------------- -# Test 2: real MuonAdamW from the codebase (if importable) -# --------------------------------------------------------------------------- - -def _import_muon(): - """ - Try to import MuonAdamW from the modular hydra package first, then fall - back to the monolithic train.py. Returns the class or None. - """ - # Attempt 1: modular package (W1's target structure) - try: - from hydra.optimizer import MuonAdamW # noqa: PLC0415 - return MuonAdamW - except ImportError: - pass - - # Attempt 2: monolithic train.py (pre-modularisation) - try: - import sys - import types - import os - - _repo = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - # Inject a minimal fake 'prepare' stub if not already present so that - # `from prepare import ...` inside train.py doesn't crash the import. - if "prepare" not in sys.modules: - fake_prepare = types.ModuleType("prepare") - fake_prepare.MAX_SEQ_LEN = 2048 - fake_prepare.TIME_BUDGET = 300 - fake_prepare.Tokenizer = object - fake_prepare.make_dataloader = lambda *a, **kw: None - fake_prepare.evaluate_bpb = lambda *a, **kw: 0.0 - sys.modules["prepare"] = fake_prepare - - train_path = os.path.join(_repo, "train.py") - with open(train_path) as fh: - source = fh.read() - - # Truncate at the training-loop entry point so we only exec class defs. - for marker in ["\nt_start = time.time()", "\nif __name__"]: - idx = source.find(marker) - if idx != -1: - source = source[:idx] - break - - ns: dict = {"__name__": "train"} - exec(compile(source, train_path, "exec"), ns) # noqa: S102 - return ns.get("MuonAdamW") - except Exception: - return None - - -_MuonAdamW = _import_muon() - - -@pytest.mark.skipif( - _MuonAdamW is None, - reason="MuonAdamW not importable from hydra.optimizer or train.py", -) -def test_muon_adamw_step_updates_params(): - """ - Verifies that MuonAdamW: - 1. Completes two micro-step forward+backward accumulations without error. - 2. Calls optimizer.step() without raising. - 3. Actually modifies the parameters (the update is non-trivial). - - Uses a tiny Linear-only model so we stay on CPU and run in <1 s. - """ - torch.autograd.set_detect_anomaly(True) - try: - vocab = 128 - d = 64 - embed = nn.Embedding(vocab, d) - linear = nn.Linear(d, vocab, bias=False) - model = nn.Sequential(embed, linear) - - # Snapshot initial parameters - w_embed_before = embed.weight.data.clone() - w_linear_before = linear.weight.data.clone() - - # Build MuonAdamW param groups matching the expected interface: - # 2D weight matrices -> Muon group; everything else -> AdamW group. - matrix_params = [linear.weight] # 2D - adamw_params = [embed.weight] # Embedding is effectively 2D but skip Muon - - param_groups = [ - dict(kind='adamw', params=adamw_params, - lr=1e-3, betas=(0.9, 0.95), eps=1e-8, weight_decay=0.0), - dict(kind='muon', params=matrix_params, - lr=0.01, momentum=0.95, ns_steps=2, beta2=0.95, weight_decay=0.0), - ] - - optimizer = _MuonAdamW(param_groups) - for group in optimizer.param_groups: - group["initial_lr"] = group["lr"] - - B, T = 2, 8 - grad_accum = 2 - optimizer.zero_grad() - - for micro_step in range(grad_accum): - idx = torch.randint(0, vocab, (B, T)) - targets = torch.randint(0, vocab, (B, T)) - x = embed(idx) # (B, T, d) - logits = linear(x.view(B * T, d)) # (B*T, vocab) - loss = F.cross_entropy(logits, targets.reshape(-1)) / grad_accum - loss.backward() - - optimizer.step() - - # Assert parameters changed - assert not torch.equal(embed.weight.data, w_embed_before), ( - "embed.weight was not updated by MuonAdamW" - ) - assert not torch.equal(linear.weight.data, w_linear_before), ( - "linear.weight was not updated by MuonAdamW (Muon group)" - ) - except RuntimeError as exc: - raise AssertionError( - f"MuonAdamW step raised RuntimeError: {exc}" - ) from exc - finally: - torch.autograd.set_detect_anomaly(False) +""" +Regression tests for gradient accumulation compatibility with Engram-style +in-place writes (index_add_/scatter operations) inside the autograd path. + +The "inplace op modified tensor needed for backward on micro-step 2" error +is reproduced by building a tiny model that: + 1. Has an Engram-like module that does .data.index_add_() under no_grad + AND reads from its memory buffer via an indexed gather that IS in the + autograd graph (grad flows through the read path). + 2. Wraps that in an mHC-style 2-stream doubly-stochastic residual. + 3. Accumulates gradients over multiple micro-steps by repeating + forward -> loss / grad_accum -> backward before calling optimizer.step(). + +The bug manifests only on micro-step >= 2 because the first backward stores +references to the activation tensors; the in-place write on the memory buffer +during the SECOND forward corrupts those saved tensors. + +Fix: any Hebbian write must be via `.data.index_add_()` (detached) so that +autograd's saved-tensor machinery never sees a version-counter increment on a +leaf that has requires_grad=True. + +Run: + cd /home/mikeb/work/feather + .venv/bin/pytest tests/test_muon_grad_accum.py -v +""" + +import sys +import os +import types +import pytest +import torch +import torch.nn as nn +import torch.nn.functional as F + +# --------------------------------------------------------------------------- +# Tiny self-contained model — no imports from train.py or hydra/ +# --------------------------------------------------------------------------- + +class TinyEngram(nn.Module): + """ + Minimal stand-in for GPUEngram. + + In-place write: self.memory.data.index_add_() under torch.no_grad(). + This means the memory Parameter has requires_grad=True (so the READ path + gets gradients) but the WRITE never touches the grad-tracked version of + memory — it goes through .data, bypassing the version counter. + + If instead we wrote to self.memory directly (without .data), the version + counter would be bumped and any saved references from a prior backward + would be invalidated, triggering the "inplace op modified a leaf Tensor + that requires grad" RuntimeError on micro-step 2. + """ + def __init__(self, d_model: int, n_columns: int = 32): + super().__init__() + self.n_columns = n_columns + self.memory = nn.Parameter(torch.zeros(n_columns, d_model)) + self.out_proj = nn.Linear(d_model, d_model, bias=False) + + def forward(self, x: torch.Tensor, token_ids: torch.Tensor) -> torch.Tensor: + """ + x: (B, T, d_model) + token_ids: (B, T) long + """ + # Hash token_ids to column indices + indices = token_ids % self.n_columns # (B, T) + + # --- AUTOGRAD READ PATH --- + # This gather IS in the autograd graph; gradients flow back to self.memory. + retrieved = self.memory[indices] # (B, T, d_model) + + # --- IN-PLACE HEBBIAN WRITE (must NOT corrupt autograd) --- + if self.training: + with torch.no_grad(): + flat_idx = indices.reshape(-1) # (B*T,) + flat_x = x.detach().reshape(-1, x.shape[-1]) # (B*T, d) + lr = 0.01 + # .data bypasses the version counter — safe across micro-steps + delta = lr * (flat_x - self.memory.data[flat_idx]) + self.memory.data.index_add_(0, flat_idx, delta) + + # Gate + gate = torch.sigmoid(self.out_proj(x)) + return x + gate * retrieved + + +class TinymHCResidual(nn.Module): + """ + Minimal doubly-stochastic 2-stream residual (mHC-like). + Uses a learnable scalar alpha to blend the two streams. + """ + def __init__(self, d_model: int): + super().__init__() + self.log_alpha = nn.Parameter(torch.zeros(1)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # Two streams: x itself and a scaled version + alpha = torch.sigmoid(self.log_alpha) + stream0 = alpha * x + stream1 = (1.0 - alpha) * x + # Sinkhorn-style doubly-stochastic merge (simplified: just add) + return stream0 + stream1 # trivially = x, but exercises the alpha grad path + + +class TinyModel(nn.Module): + """ + Tiny model exercising the same mechanism as the real training loop: + Embedding -> TinyEngram (in-place Hebbian write + grad-bearing read) + -> TinymHCResidual -> Linear -> CrossEntropy + """ + def __init__(self, vocab_size: int = 64, d_model: int = 32, n_columns: int = 16): + super().__init__() + self.embed = nn.Embedding(vocab_size, d_model) + self.engram = TinyEngram(d_model, n_columns) + self.mhc = TinymHCResidual(d_model) + self.norm = nn.LayerNorm(d_model) + self.head = nn.Linear(d_model, vocab_size, bias=False) + + def forward(self, idx: torch.Tensor, targets: torch.Tensor) -> torch.Tensor: + """ + idx: (B, T) long + targets: (B, T) long + Returns: scalar loss + """ + x = self.embed(idx) # (B, T, d_model) + x = self.engram(x, idx) # in-place Hebbian write + read + x = self.mhc(x) # 2-stream residual + x = self.norm(x) + logits = self.head(x) # (B, T, vocab_size) + return F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.reshape(-1), + ) + + +# --------------------------------------------------------------------------- +# Test 1: grad_accum regression — parametrised over accumulation counts +# --------------------------------------------------------------------------- + +@pytest.mark.parametrize("grad_accum", [1, 2, 4]) +def test_grad_accum_no_inplace_error(grad_accum: int): + """ + Verifies that accumulating gradients over `grad_accum` micro-steps succeeds + without RuntimeError for any accumulation count. + + With anomaly detection ON, PyTorch will raise the moment an in-place op + corrupts a saved tensor — even if the numerical result happens to be close. + This is the strongest available signal for the bug. + """ + torch.autograd.set_detect_anomaly(True) + try: + model = TinyModel(vocab_size=64, d_model=32, n_columns=16) + model.train() + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) + + B, T = 2, 8 + vocab_size = 64 + + optimizer.zero_grad() + for micro_step in range(grad_accum): + idx = torch.randint(0, vocab_size, (B, T)) + targets = torch.randint(0, vocab_size, (B, T)) + # forward + loss = model(idx, targets) + # scale loss for accumulation + loss = loss / grad_accum + # backward — must NOT raise on micro_step >= 1 + loss.backward() + + optimizer.step() + except RuntimeError as exc: + # Re-raise with a clearer message so W1 can diagnose the exact failure. + raise AssertionError( + f"grad_accum={grad_accum}: RuntimeError during backward " + f"(likely inplace-op/version-counter bug): {exc}" + ) from exc + finally: + torch.autograd.set_detect_anomaly(False) + + +# --------------------------------------------------------------------------- +# Test 2: real MuonAdamW from the codebase (if importable) +# --------------------------------------------------------------------------- + +def _import_muon(): + """ + Try to import MuonAdamW from the modular hydra package first, then fall + back to the monolithic train.py. Returns the class or None. + """ + # Attempt 1: modular package (W1's target structure) + try: + from hydra.optimizer import MuonAdamW # noqa: PLC0415 + return MuonAdamW + except ImportError: + pass + + # Attempt 2: monolithic train.py (pre-modularisation) + try: + import sys + import types + import os + + _repo = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + # Inject a minimal fake 'prepare' stub if not already present so that + # `from prepare import ...` inside train.py doesn't crash the import. + if "prepare" not in sys.modules: + fake_prepare = types.ModuleType("prepare") + fake_prepare.MAX_SEQ_LEN = 2048 + fake_prepare.TIME_BUDGET = 300 + fake_prepare.Tokenizer = object + fake_prepare.make_dataloader = lambda *a, **kw: None + fake_prepare.evaluate_bpb = lambda *a, **kw: 0.0 + sys.modules["prepare"] = fake_prepare + + train_path = os.path.join(_repo, "train.py") + with open(train_path) as fh: + source = fh.read() + + # Truncate at the training-loop entry point so we only exec class defs. + for marker in ["\nt_start = time.time()", "\nif __name__"]: + idx = source.find(marker) + if idx != -1: + source = source[:idx] + break + + ns: dict = {"__name__": "train"} + exec(compile(source, train_path, "exec"), ns) # noqa: S102 + return ns.get("MuonAdamW") + except Exception: + return None + + +_MuonAdamW = _import_muon() + + +@pytest.mark.skipif( + _MuonAdamW is None, + reason="MuonAdamW not importable from hydra.optimizer or train.py", +) +def test_muon_adamw_step_updates_params(): + """ + Verifies that MuonAdamW: + 1. Completes two micro-step forward+backward accumulations without error. + 2. Calls optimizer.step() without raising. + 3. Actually modifies the parameters (the update is non-trivial). + + Uses a tiny Linear-only model so we stay on CPU and run in <1 s. + """ + torch.autograd.set_detect_anomaly(True) + try: + vocab = 128 + d = 64 + embed = nn.Embedding(vocab, d) + linear = nn.Linear(d, vocab, bias=False) + model = nn.Sequential(embed, linear) + + # Snapshot initial parameters + w_embed_before = embed.weight.data.clone() + w_linear_before = linear.weight.data.clone() + + # Build MuonAdamW param groups matching the expected interface: + # 2D weight matrices -> Muon group; everything else -> AdamW group. + matrix_params = [linear.weight] # 2D + adamw_params = [embed.weight] # Embedding is effectively 2D but skip Muon + + param_groups = [ + dict(kind='adamw', params=adamw_params, + lr=1e-3, betas=(0.9, 0.95), eps=1e-8, weight_decay=0.0), + dict(kind='muon', params=matrix_params, + lr=0.01, momentum=0.95, ns_steps=2, beta2=0.95, weight_decay=0.0), + ] + + optimizer = _MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + + B, T = 2, 8 + grad_accum = 2 + optimizer.zero_grad() + + for micro_step in range(grad_accum): + idx = torch.randint(0, vocab, (B, T)) + targets = torch.randint(0, vocab, (B, T)) + x = embed(idx) # (B, T, d) + logits = linear(x.view(B * T, d)) # (B*T, vocab) + loss = F.cross_entropy(logits, targets.reshape(-1)) / grad_accum + loss.backward() + + optimizer.step() + + # Assert parameters changed + assert not torch.equal(embed.weight.data, w_embed_before), ( + "embed.weight was not updated by MuonAdamW" + ) + assert not torch.equal(linear.weight.data, w_linear_before), ( + "linear.weight was not updated by MuonAdamW (Muon group)" + ) + except RuntimeError as exc: + raise AssertionError( + f"MuonAdamW step raised RuntimeError: {exc}" + ) from exc + finally: + torch.autograd.set_detect_anomaly(False) diff --git a/overlay/tests/test_muon_hyena_routing.py b/overlay/tests/test_muon_hyena_routing.py index 9c6f77f1f465a8d9f968dea2e509627afec9b8af..9b937749c3e9d65e13cadb6532ecd7f5205ada15 100644 --- a/overlay/tests/test_muon_hyena_routing.py +++ b/overlay/tests/test_muon_hyena_routing.py @@ -1,244 +1,244 @@ -"""Muon routing guard against Hyena small/frequency parameters. - -Regression test for a bug where `setup_optimizer()` routed ALL 2-D parameters -into the Muon matrix group. That behavior is catastrophic for two classes -of Hyena parameter: - - 1. `Sin.freq` has shape (1, dim). Nominally 2-D but semantically a per-dim - frequency scalar. Muon's polar-express orthogonalization would force it - toward an orthogonal matrix, destroying the learned modulation frequencies. - - 2. `HyenaFilter.implicit_filter.0.weight` has shape (filter_order, emb_dim) - where emb_dim=3 (time, cos, sin). Orthogonalization collapses such - tiny-axis projections toward near-identity, removing expressivity. - -The fix routes both classes to the AdamW scalar/vector group by adding a -`_muon_eligible(name, p)` guard with: - - reject `name.endswith(".freq")` - - reject `p.dim() != 2` - - reject `min(p.shape) < MUON_MIN_DIM` (currently 8) - -Tests: - * Build PostSemClawModel with HYDRA_HYENA_LAYERS=3 and assert no `.freq` - or small-axis 2-D param is in any Muon group. - * Run a Muon step with tiny lr on synthetic data and assert freq parameters - change by < 5 * lr (Muon's orthogonalization would make this O(1); AdamW - with scalar lr keeps it bounded by ~lr). - -Run: - cd /home/mikeb/work/feather - LD_LIBRARY_PATH=/usr/lib/wsl/lib .venv/bin/pytest tests/test_muon_hyena_routing.py -v -""" - -from __future__ import annotations - -import os -import sys -from pathlib import Path - -import pytest -import torch - -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - - -def _tiny_config_with_hyena(): - """Small but-complete config matching the cached retina shape (65536, 16384).""" - from hydra.config import PostSemClawConfig - return PostSemClawConfig( - sequence_len=64, - vocab_size=65536, - n_layer=3, - d_model=64, - d_state=16, - headdim=16, - n_heads=4, - expand=2, - engram_n_columns=64, - engram_layer_idx=1, - sdr_n_bits=16384, - sdr_target_active=327, - sdr_delta_rank=8, - htm_n_columns=64, - htm_cells_per_column=4, - ) - - -@pytest.fixture -def model_with_hyena(monkeypatch): - """Build PostSemClawModel with Hyena at layer 1. - - The model will have at least one Sin.freq param and at least one - (filter_order, 3)-shaped projection inside HyenaFilter. - """ - monkeypatch.setenv("HYDRA_HYENA_LAYERS", "1") - monkeypatch.setenv("HYDRA_HYENA_ORDER", "2") - monkeypatch.setenv("HYDRA_HYENA_FILTER_DIM", "64") - - from hydra.model import PostSemClawModel - - cfg = _tiny_config_with_hyena() - model = PostSemClawModel(cfg) - return model - - -def _collect_muon_param_ids(optimizer) -> set[int]: - """Extract id() of every tensor inside a kind='muon' param group.""" - ids = set() - for group in optimizer.param_groups: - if group.get("kind") == "muon": - for p in group["params"]: - ids.add(id(p)) - return ids - - -def test_freq_params_not_in_muon_group(model_with_hyena): - """Every parameter whose name ends in `.freq` must NOT be in a Muon group.""" - optimizer = model_with_hyena.setup_optimizer() - muon_ids = _collect_muon_param_ids(optimizer) - - freq_params = [ - (name, p) for name, p in model_with_hyena.named_parameters() - if name.endswith(".freq") - ] - assert len(freq_params) >= 1, ( - "expected at least one `.freq` param in a model with Hyena layers; " - "this fixture likely misconfigured" - ) - offenders = [ - name for name, p in freq_params if id(p) in muon_ids - ] - assert not offenders, ( - f"`.freq` parameters incorrectly routed to Muon: {offenders}. " - f"Muon's orthogonalization will destroy these learned frequency scalars." - ) - - -def test_small_axis_2d_params_not_in_muon_group(model_with_hyena): - """No 2-D parameter with min(shape) < 8 may land in a Muon group. - - HyenaFilter's implicit_filter.0.weight (64, 3) is the canonical violator - — orthogonalization on the 3-wide axis collapses it toward near-identity. - """ - MIN_DIM = 8 - optimizer = model_with_hyena.setup_optimizer() - muon_ids = _collect_muon_param_ids(optimizer) - - offenders = [] - for name, p in model_with_hyena.named_parameters(): - if p.dim() == 2 and min(p.shape) < MIN_DIM and id(p) in muon_ids: - offenders.append((name, tuple(p.shape))) - - assert not offenders, ( - f"small-axis 2-D parameters incorrectly routed to Muon (need AdamW): " - f"{offenders}" - ) - - -def test_two_muon_steps_keep_freq_bounded(model_with_hyena): - """With tiny lr, freq parameters must not move by more than a few * lr. - - Rationale: Muon's polar-express orthogonalization rescales the update to - have O(1) norm per row regardless of the raw gradient magnitude. On a - shape-(1, 64) `.freq` row that would shift it by ~sqrt(64) ≈ 8 — vastly - more than `lr`. AdamW with scalar lr and per-param adaptive step keeps - the change bounded to ~lr. - - We skip a full model forward — instead we synthesize unit-norm gradients - directly on the freq params (and one reference large matrix) and run the - optimizer's _step_muon / _step_adamw dispatch. This isolates exactly the - routing decision from any forward-pass flakiness. - """ - model = model_with_hyena - - lr = 1e-4 - optimizer = model.setup_optimizer( - unembedding_lr=lr, embedding_lr=lr, matrix_lr=lr, - scalar_lr=lr, weight_decay=0.0, - ) - - # Snapshot pre-step values for freq parameters. - freq_params = { - name: p for name, p in model.named_parameters() - if name.endswith(".freq") - } - assert freq_params, "no `.freq` param found in fixture" - - freq_before = {name: p.detach().clone() for name, p in freq_params.items()} - - # Assign unit-norm synthetic gradients to EVERY parameter in optimizer's - # param groups. This exercises the optimizer's per-kind branching. - torch.manual_seed(0) - for group in optimizer.param_groups: - for p in group["params"]: - if p.grad is None: - p.grad = torch.randn_like(p) - else: - p.grad.copy_(torch.randn_like(p)) - - # Run two steps. - optimizer.step() - for group in optimizer.param_groups: - for p in group["params"]: - p.grad.copy_(torch.randn_like(p)) - optimizer.step() - - # After 2 AdamW steps with lr=1e-4, freq params should have moved - # by |Δ| bounded by O(lr) (AdamW's effective per-param step size is - # bounded by effective_lr = lr * dmodel_lr_scale ~= 3.5e-4 here, so - # total |Δ| after 2 steps ~ 2 * effective_lr ~ 7e-4). - # - # A Muon step on a (1, 64) freq would rotate it to unit-norm and subtract - # lr*g_ortho → |Δ| ≈ lr (per element) but the orthogonalized direction - # has sum-of-squares = 1, so max |Δ| per element is at least 1/sqrt(64) - # ≈ 0.125 — 2-3 orders of magnitude over our tolerance. - # - # We use an absolute bound of 1e-2 which is: - # - >> 10x the AdamW expected |Δ| (~7e-4) — won't false-positive - # - << 10x smaller than Muon's expected |Δ| (~0.125) — will catch leaks - TOL_ABS = 1e-2 - for name, old_val in freq_before.items(): - new_val = freq_params[name].detach() - assert old_val.shape == new_val.shape, ( - f"{name}: shape changed across steps ({old_val.shape} -> {new_val.shape})" - ) - max_delta = (new_val - old_val).abs().max().item() - assert max_delta <= TOL_ABS, ( - f"{name}: |Δ| = {max_delta:.3e} > {TOL_ABS:.3e}. " - f"This indicates the param is being orthogonalized by Muon " - f"(AdamW keeps |Δ| ~ lr*dmodel_scale ~= {lr * 3.5:.3e} at this step count)." - ) - - -def test_hyena_large_matrices_still_in_muon(model_with_hyena): - """Sanity check: the routing guard MUST NOT accidentally exclude - large Hyena projections like in_proj (d_model*(order+1), d_model) and - out_proj (d_model, d_model). Those are legitimate 2-D matrices and - benefit from Muon. - """ - optimizer = model_with_hyena.setup_optimizer() - muon_ids = _collect_muon_param_ids(optimizer) - - large_hyena_params = [] - for name, p in model_with_hyena.named_parameters(): - if ( - ".operator." in name - and name.endswith(".weight") - and p.dim() == 2 - and min(p.shape) >= 8 - and not name.endswith(".freq") - ): - large_hyena_params.append((name, p)) - - assert large_hyena_params, ( - "expected large Hyena projection weights (in_proj/out_proj); " - "fixture likely misconfigured" - ) - missing = [name for name, p in large_hyena_params if id(p) not in muon_ids] - assert not missing, ( - f"large Hyena 2-D matrices wrongly excluded from Muon group: {missing}" - ) - - -if __name__ == "__main__": - sys.exit(pytest.main([__file__, "-v"])) +"""Muon routing guard against Hyena small/frequency parameters. + +Regression test for a bug where `setup_optimizer()` routed ALL 2-D parameters +into the Muon matrix group. That behavior is catastrophic for two classes +of Hyena parameter: + + 1. `Sin.freq` has shape (1, dim). Nominally 2-D but semantically a per-dim + frequency scalar. Muon's polar-express orthogonalization would force it + toward an orthogonal matrix, destroying the learned modulation frequencies. + + 2. `HyenaFilter.implicit_filter.0.weight` has shape (filter_order, emb_dim) + where emb_dim=3 (time, cos, sin). Orthogonalization collapses such + tiny-axis projections toward near-identity, removing expressivity. + +The fix routes both classes to the AdamW scalar/vector group by adding a +`_muon_eligible(name, p)` guard with: + - reject `name.endswith(".freq")` + - reject `p.dim() != 2` + - reject `min(p.shape) < MUON_MIN_DIM` (currently 8) + +Tests: + * Build PostSemClawModel with HYDRA_HYENA_LAYERS=3 and assert no `.freq` + or small-axis 2-D param is in any Muon group. + * Run a Muon step with tiny lr on synthetic data and assert freq parameters + change by < 5 * lr (Muon's orthogonalization would make this O(1); AdamW + with scalar lr keeps it bounded by ~lr). + +Run: + cd /home/mikeb/work/feather + LD_LIBRARY_PATH=/usr/lib/wsl/lib .venv/bin/pytest tests/test_muon_hyena_routing.py -v +""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + + +def _tiny_config_with_hyena(): + """Small but-complete config matching the cached retina shape (65536, 16384).""" + from hydra.config import PostSemClawConfig + return PostSemClawConfig( + sequence_len=64, + vocab_size=65536, + n_layer=3, + d_model=64, + d_state=16, + headdim=16, + n_heads=4, + expand=2, + engram_n_columns=64, + engram_layer_idx=1, + sdr_n_bits=16384, + sdr_target_active=327, + sdr_delta_rank=8, + htm_n_columns=64, + htm_cells_per_column=4, + ) + + +@pytest.fixture +def model_with_hyena(monkeypatch): + """Build PostSemClawModel with Hyena at layer 1. + + The model will have at least one Sin.freq param and at least one + (filter_order, 3)-shaped projection inside HyenaFilter. + """ + monkeypatch.setenv("HYDRA_HYENA_LAYERS", "1") + monkeypatch.setenv("HYDRA_HYENA_ORDER", "2") + monkeypatch.setenv("HYDRA_HYENA_FILTER_DIM", "64") + + from hydra.model import PostSemClawModel + + cfg = _tiny_config_with_hyena() + model = PostSemClawModel(cfg) + return model + + +def _collect_muon_param_ids(optimizer) -> set[int]: + """Extract id() of every tensor inside a kind='muon' param group.""" + ids = set() + for group in optimizer.param_groups: + if group.get("kind") == "muon": + for p in group["params"]: + ids.add(id(p)) + return ids + + +def test_freq_params_not_in_muon_group(model_with_hyena): + """Every parameter whose name ends in `.freq` must NOT be in a Muon group.""" + optimizer = model_with_hyena.setup_optimizer() + muon_ids = _collect_muon_param_ids(optimizer) + + freq_params = [ + (name, p) for name, p in model_with_hyena.named_parameters() + if name.endswith(".freq") + ] + assert len(freq_params) >= 1, ( + "expected at least one `.freq` param in a model with Hyena layers; " + "this fixture likely misconfigured" + ) + offenders = [ + name for name, p in freq_params if id(p) in muon_ids + ] + assert not offenders, ( + f"`.freq` parameters incorrectly routed to Muon: {offenders}. " + f"Muon's orthogonalization will destroy these learned frequency scalars." + ) + + +def test_small_axis_2d_params_not_in_muon_group(model_with_hyena): + """No 2-D parameter with min(shape) < 8 may land in a Muon group. + + HyenaFilter's implicit_filter.0.weight (64, 3) is the canonical violator + — orthogonalization on the 3-wide axis collapses it toward near-identity. + """ + MIN_DIM = 8 + optimizer = model_with_hyena.setup_optimizer() + muon_ids = _collect_muon_param_ids(optimizer) + + offenders = [] + for name, p in model_with_hyena.named_parameters(): + if p.dim() == 2 and min(p.shape) < MIN_DIM and id(p) in muon_ids: + offenders.append((name, tuple(p.shape))) + + assert not offenders, ( + f"small-axis 2-D parameters incorrectly routed to Muon (need AdamW): " + f"{offenders}" + ) + + +def test_two_muon_steps_keep_freq_bounded(model_with_hyena): + """With tiny lr, freq parameters must not move by more than a few * lr. + + Rationale: Muon's polar-express orthogonalization rescales the update to + have O(1) norm per row regardless of the raw gradient magnitude. On a + shape-(1, 64) `.freq` row that would shift it by ~sqrt(64) ≈ 8 — vastly + more than `lr`. AdamW with scalar lr and per-param adaptive step keeps + the change bounded to ~lr. + + We skip a full model forward — instead we synthesize unit-norm gradients + directly on the freq params (and one reference large matrix) and run the + optimizer's _step_muon / _step_adamw dispatch. This isolates exactly the + routing decision from any forward-pass flakiness. + """ + model = model_with_hyena + + lr = 1e-4 + optimizer = model.setup_optimizer( + unembedding_lr=lr, embedding_lr=lr, matrix_lr=lr, + scalar_lr=lr, weight_decay=0.0, + ) + + # Snapshot pre-step values for freq parameters. + freq_params = { + name: p for name, p in model.named_parameters() + if name.endswith(".freq") + } + assert freq_params, "no `.freq` param found in fixture" + + freq_before = {name: p.detach().clone() for name, p in freq_params.items()} + + # Assign unit-norm synthetic gradients to EVERY parameter in optimizer's + # param groups. This exercises the optimizer's per-kind branching. + torch.manual_seed(0) + for group in optimizer.param_groups: + for p in group["params"]: + if p.grad is None: + p.grad = torch.randn_like(p) + else: + p.grad.copy_(torch.randn_like(p)) + + # Run two steps. + optimizer.step() + for group in optimizer.param_groups: + for p in group["params"]: + p.grad.copy_(torch.randn_like(p)) + optimizer.step() + + # After 2 AdamW steps with lr=1e-4, freq params should have moved + # by |Δ| bounded by O(lr) (AdamW's effective per-param step size is + # bounded by effective_lr = lr * dmodel_lr_scale ~= 3.5e-4 here, so + # total |Δ| after 2 steps ~ 2 * effective_lr ~ 7e-4). + # + # A Muon step on a (1, 64) freq would rotate it to unit-norm and subtract + # lr*g_ortho → |Δ| ≈ lr (per element) but the orthogonalized direction + # has sum-of-squares = 1, so max |Δ| per element is at least 1/sqrt(64) + # ≈ 0.125 — 2-3 orders of magnitude over our tolerance. + # + # We use an absolute bound of 1e-2 which is: + # - >> 10x the AdamW expected |Δ| (~7e-4) — won't false-positive + # - << 10x smaller than Muon's expected |Δ| (~0.125) — will catch leaks + TOL_ABS = 1e-2 + for name, old_val in freq_before.items(): + new_val = freq_params[name].detach() + assert old_val.shape == new_val.shape, ( + f"{name}: shape changed across steps ({old_val.shape} -> {new_val.shape})" + ) + max_delta = (new_val - old_val).abs().max().item() + assert max_delta <= TOL_ABS, ( + f"{name}: |Δ| = {max_delta:.3e} > {TOL_ABS:.3e}. " + f"This indicates the param is being orthogonalized by Muon " + f"(AdamW keeps |Δ| ~ lr*dmodel_scale ~= {lr * 3.5:.3e} at this step count)." + ) + + +def test_hyena_large_matrices_still_in_muon(model_with_hyena): + """Sanity check: the routing guard MUST NOT accidentally exclude + large Hyena projections like in_proj (d_model*(order+1), d_model) and + out_proj (d_model, d_model). Those are legitimate 2-D matrices and + benefit from Muon. + """ + optimizer = model_with_hyena.setup_optimizer() + muon_ids = _collect_muon_param_ids(optimizer) + + large_hyena_params = [] + for name, p in model_with_hyena.named_parameters(): + if ( + ".operator." in name + and name.endswith(".weight") + and p.dim() == 2 + and min(p.shape) >= 8 + and not name.endswith(".freq") + ): + large_hyena_params.append((name, p)) + + assert large_hyena_params, ( + "expected large Hyena projection weights (in_proj/out_proj); " + "fixture likely misconfigured" + ) + missing = [name for name, p in large_hyena_params if id(p) not in muon_ids] + assert not missing, ( + f"large Hyena 2-D matrices wrongly excluded from Muon group: {missing}" + ) + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v"])) diff --git a/overlay/tests/test_proofs.sh b/overlay/tests/test_proofs.sh index 71b5fd3effb312cddb0a26f586cc9fd4c30fea8e..785643521368bb560f53d3fc5d0084d06a45541e 100644 --- a/overlay/tests/test_proofs.sh +++ b/overlay/tests/test_proofs.sh @@ -1,34 +1,34 @@ -#!/usr/bin/env bash -# Verify Lean 4 proof stub files exist and have 'sorry' placeholders. -# Exit 0 on success; non-zero on any missing file or missing sorry. -set -euo pipefail - -cd "$(dirname "$0")/.." - -echo "=== Lean 4 Proof Verification ===" - -PROOF_FILES=( - "proofs/PostSemClaw/BirkhoffClosure.lean" - "proofs/PostSemClaw/SpectralBound.lean" - "proofs/PostSemClaw/OjaConvergence.lean" - "proofs/PostSemClaw/Discretization.lean" - "proofs/PostSemClaw/SDRCollision.lean" - "proofs/PostSemClaw/HestiaAnnealing.lean" -) - -echo "Checking proof stub files exist..." -for f in "${PROOF_FILES[@]}"; do - [ -f "$f" ] || { echo "FAIL: $f not found"; exit 1; } - grep -q "sorry" "$f" || { echo "FAIL: $f has no 'sorry' (expected Phase 1 stub)"; exit 1; } - echo " OK: $f" -done -echo "All ${#PROOF_FILES[@]} proof stubs verified." - -if command -v lake &>/dev/null; then - echo "" - echo "Running: lake build" - lake build || echo "WARNING: lake build failed — 'sorry' stubs are expected to warn, not error" -else - echo "" - echo "SKIP: Lean 4 (lake) not installed. Install via elan to verify proofs." -fi +#!/usr/bin/env bash +# Verify Lean 4 proof stub files exist and have 'sorry' placeholders. +# Exit 0 on success; non-zero on any missing file or missing sorry. +set -euo pipefail + +cd "$(dirname "$0")/.." + +echo "=== Lean 4 Proof Verification ===" + +PROOF_FILES=( + "proofs/PostSemClaw/BirkhoffClosure.lean" + "proofs/PostSemClaw/SpectralBound.lean" + "proofs/PostSemClaw/OjaConvergence.lean" + "proofs/PostSemClaw/Discretization.lean" + "proofs/PostSemClaw/SDRCollision.lean" + "proofs/PostSemClaw/HestiaAnnealing.lean" +) + +echo "Checking proof stub files exist..." +for f in "${PROOF_FILES[@]}"; do + [ -f "$f" ] || { echo "FAIL: $f not found"; exit 1; } + grep -q "sorry" "$f" || { echo "FAIL: $f has no 'sorry' (expected Phase 1 stub)"; exit 1; } + echo " OK: $f" +done +echo "All ${#PROOF_FILES[@]} proof stubs verified." + +if command -v lake &>/dev/null; then + echo "" + echo "Running: lake build" + lake build || echo "WARNING: lake build failed — 'sorry' stubs are expected to warn, not error" +else + echo "" + echo "SKIP: Lean 4 (lake) not installed. Install via elan to verify proofs." +fi diff --git a/overlay/tests/test_state_store.py b/overlay/tests/test_state_store.py index 6cd3173483b330da65a66c2de905b457e0db3809..39bfdca774d6dfe9e5f39267655aaa9c79e2b53f 100644 --- a/overlay/tests/test_state_store.py +++ b/overlay/tests/test_state_store.py @@ -1,240 +1,240 @@ -""" -Tests for the state_store module. - -Covers: - * round-trip snapshot/checkout - * content-addressed dedup (same tensors -> same blob) - * async write-behind completion (queue drains) - * branch / log lineage walk - * gc removes only unreachable snapshots + blobs -""" - -from __future__ import annotations - -import json -import os -from pathlib import Path - -import pytest - -torch = pytest.importorskip("torch") - -from state_store import ( - StateStore, - snapshot, - checkout, - log, - diff, - branch, - gc, -) -from state_store.store import hash_bytes - - -# --------------------------------------------------------------------------- -# Tiny model + optimizer for deterministic tests -# --------------------------------------------------------------------------- -class TinyModel(torch.nn.Module): - def __init__(self): - super().__init__() - self.fc1 = torch.nn.Linear(4, 8, bias=True) - self.fc2 = torch.nn.Linear(8, 4, bias=True) - - def forward(self, x): - return self.fc2(torch.relu(self.fc1(x))) - - -def _make_model_and_opt(seed: int = 0): - torch.manual_seed(seed) - model = TinyModel() - opt = torch.optim.SGD(model.parameters(), lr=0.1) - return model, opt - - -@pytest.fixture -def store(tmp_path): - # Sync store simplifies assertions; async path is covered separately below. - s = StateStore(root=tmp_path / "store", sync=True) - yield s - s.shutdown() - - -@pytest.fixture -def async_store(tmp_path): - s = StateStore(root=tmp_path / "async_store", sync=False) - yield s - s.shutdown() - - -# --------------------------------------------------------------------------- -# Round-trip -# --------------------------------------------------------------------------- -def test_snapshot_roundtrip(store): - m1, o1 = _make_model_and_opt(seed=1) - metrics = {"val_bpb": 1.777, "loss": 2.5, "step": 100} - h = snapshot(m1, o1, step=100, metrics=metrics, store=store) - assert isinstance(h, str) and len(h) >= 32 - - # Fresh model with different init -> checkout must restore weights. - m2, o2 = _make_model_and_opt(seed=999) - for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): - assert not torch.equal(p1, p2), f"{n1}/{n2} should start different" - - row = checkout(h, m2, o2, store=store) - assert row["step"] == 100 - assert row["metrics"]["val_bpb"] == 1.777 - - for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): - assert torch.equal(p1.cpu(), p2.cpu()), f"param {n1} not restored" - - -# --------------------------------------------------------------------------- -# Dedup: snapshotting the same model twice yields identical manifest entries -# --------------------------------------------------------------------------- -def test_content_addressed_dedup(store): - m, o = _make_model_and_opt(seed=42) - metrics = {"val_bpb": 2.0, "loss": 3.0} - h1 = snapshot(m, o, step=1, metrics=metrics, store=store) - h2 = snapshot(m, o, step=1, metrics=metrics, store=store) - # Same step + state + metrics => identical snapshot hash. - assert h1 == h2 - - # Even if the step changes, every per-tensor blob hash must be identical - # because the weights themselves haven't changed. - h3 = snapshot(m, o, step=2, metrics=metrics, store=store) - mf1 = json.loads(store.get_snapshot(h1)["manifest_json"]) - mf3 = json.loads(store.get_snapshot(h3)["manifest_json"]) - assert mf1["model"].keys() == mf3["model"].keys() - for k in mf1["model"]: - assert mf1["model"][k] == mf3["model"][k], f"blob hash changed for {k}" - - # Every referenced blob must be reachable via the store (works for both - # legacy per-file layout and Phase-1 chunked/packfile layout). - unique_blob_hashes = set(mf1["model"].values()) | set(mf3["model"].values()) - for bh in unique_blob_hashes: - assert store.has_blob(bh), f"blob {bh} missing from store" - - -def test_snapshot_changes_when_weights_change(store): - m, o = _make_model_and_opt(seed=7) - metrics = {"val_bpb": 1.0} - h1 = snapshot(m, o, step=1, metrics=metrics, store=store) - - with torch.no_grad(): - m.fc1.weight.add_(1.0) # mutate - h2 = snapshot(m, o, step=2, metrics=metrics, store=store) - assert h1 != h2 - - d = diff(h1, h2, store=store) - assert "fc1.weight" in d["changed"] - # fc2 weight/bias unchanged -> appears in identical_blob_count bucket. - assert d["identical_blob_count"] >= 2 - - -# --------------------------------------------------------------------------- -# Async write-behind -# --------------------------------------------------------------------------- -def test_async_writes_drain(async_store): - m, o = _make_model_and_opt(seed=3) - hashes = [] - for step in range(5): - with torch.no_grad(): - m.fc1.weight.add_(0.01) - hashes.append( - snapshot(m, o, step=step, metrics={"val_bpb": float(step)}, store=async_store) - ) - async_store.flush(timeout=15) - # All rows visible. - for h in hashes: - row = async_store.get_snapshot(h) - assert row is not None, f"snapshot {h} not persisted" - rows = log(limit=10, store=async_store) - assert len(rows) == 5 - - -# --------------------------------------------------------------------------- -# Branch + log lineage -# --------------------------------------------------------------------------- -def test_branch_and_log(store): - m, o = _make_model_and_opt(seed=2) - h1 = snapshot(m, o, step=1, metrics={"val_bpb": 3.0}, store=store) - with torch.no_grad(): - m.fc1.weight.add_(0.5) - h2 = snapshot(m, o, step=2, metrics={"val_bpb": 2.5}, parent_hash=h1, store=store) - with torch.no_grad(): - m.fc1.weight.add_(0.5) - h3 = snapshot(m, o, step=3, metrics={"val_bpb": 2.0}, parent_hash=h2, store=store) - - branch("champ", h3, store=store) - assert store.resolve_ref("champ") == h3 - - lin = log(limit=10, branch="champ", store=store) - assert [r["hash"] for r in lin] == [h3, h2, h1] - - -# --------------------------------------------------------------------------- -# GC -# --------------------------------------------------------------------------- -def test_gc_removes_only_unreachable(store): - m, o = _make_model_and_opt(seed=5) - hashes = [] - parent = None - for step in range(6): - with torch.no_grad(): - m.fc1.weight.add_(0.1) - parent = snapshot( - m, o, step=step, metrics={"val_bpb": 5.0 - step}, - parent_hash=parent, store=store, - ) - hashes.append(parent) - - branch("keep_me", hashes[2], store=store) - - res = gc(keep_last=1, reachable_from="keep_me", store=store) - # With keep_last=1, last snapshot is kept; plus lineage from keep_me (h0..h2). - kept = res["kept_snapshots"] - assert kept >= 3 # h0, h1, h2 are reachable from keep_me - # keep_me head must still resolve. - assert store.resolve_ref("keep_me") == hashes[2] - # h3, h4 may have been removed (they're not reachable and not in keep_last=1 window). - removed = set(res["removed_snapshots"]) - # The last (newest) snapshot is in the keep_last=1 window, so NOT removed. - assert hashes[-1] not in removed - # Everything kept must still be readable. - for h in res["removed_snapshots"]: - assert store.get_snapshot(h) is None - # Blobs for reachable snapshots must still exist on disk. - for h in hashes[:3]: - row = store.get_snapshot(h) - assert row is not None - mf = json.loads(row["manifest_json"]) - for bh in mf["model"].values(): - assert store.has_blob(bh), f"blob {bh} gc'd but snapshot {h} still references it" - - -def test_gc_dry_run_does_not_delete(store): - m, o = _make_model_and_opt(seed=8) - parent = None - hashes = [] - for step in range(3): - with torch.no_grad(): - m.fc1.weight.add_(0.2) - parent = snapshot(m, o, step=step, metrics={"loss": 1.0 * step}, - parent_hash=parent, store=store) - hashes.append(parent) - - res = gc(keep_last=0, dry_run=True, store=store) - # Dry-run: snapshots still present in DB. - for h in hashes: - assert store.get_snapshot(h) is not None - - -# --------------------------------------------------------------------------- -# Hash utility sanity -# --------------------------------------------------------------------------- -def test_hash_bytes_deterministic(): - a = hash_bytes(b"hello world") - b = hash_bytes(b"hello world") - c = hash_bytes(b"hello worlD") - assert a == b - assert a != c +""" +Tests for the state_store module. + +Covers: + * round-trip snapshot/checkout + * content-addressed dedup (same tensors -> same blob) + * async write-behind completion (queue drains) + * branch / log lineage walk + * gc removes only unreachable snapshots + blobs +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import pytest + +torch = pytest.importorskip("torch") + +from state_store import ( + StateStore, + snapshot, + checkout, + log, + diff, + branch, + gc, +) +from state_store.store import hash_bytes + + +# --------------------------------------------------------------------------- +# Tiny model + optimizer for deterministic tests +# --------------------------------------------------------------------------- +class TinyModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.fc1 = torch.nn.Linear(4, 8, bias=True) + self.fc2 = torch.nn.Linear(8, 4, bias=True) + + def forward(self, x): + return self.fc2(torch.relu(self.fc1(x))) + + +def _make_model_and_opt(seed: int = 0): + torch.manual_seed(seed) + model = TinyModel() + opt = torch.optim.SGD(model.parameters(), lr=0.1) + return model, opt + + +@pytest.fixture +def store(tmp_path): + # Sync store simplifies assertions; async path is covered separately below. + s = StateStore(root=tmp_path / "store", sync=True) + yield s + s.shutdown() + + +@pytest.fixture +def async_store(tmp_path): + s = StateStore(root=tmp_path / "async_store", sync=False) + yield s + s.shutdown() + + +# --------------------------------------------------------------------------- +# Round-trip +# --------------------------------------------------------------------------- +def test_snapshot_roundtrip(store): + m1, o1 = _make_model_and_opt(seed=1) + metrics = {"val_bpb": 1.777, "loss": 2.5, "step": 100} + h = snapshot(m1, o1, step=100, metrics=metrics, store=store) + assert isinstance(h, str) and len(h) >= 32 + + # Fresh model with different init -> checkout must restore weights. + m2, o2 = _make_model_and_opt(seed=999) + for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): + assert not torch.equal(p1, p2), f"{n1}/{n2} should start different" + + row = checkout(h, m2, o2, store=store) + assert row["step"] == 100 + assert row["metrics"]["val_bpb"] == 1.777 + + for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): + assert torch.equal(p1.cpu(), p2.cpu()), f"param {n1} not restored" + + +# --------------------------------------------------------------------------- +# Dedup: snapshotting the same model twice yields identical manifest entries +# --------------------------------------------------------------------------- +def test_content_addressed_dedup(store): + m, o = _make_model_and_opt(seed=42) + metrics = {"val_bpb": 2.0, "loss": 3.0} + h1 = snapshot(m, o, step=1, metrics=metrics, store=store) + h2 = snapshot(m, o, step=1, metrics=metrics, store=store) + # Same step + state + metrics => identical snapshot hash. + assert h1 == h2 + + # Even if the step changes, every per-tensor blob hash must be identical + # because the weights themselves haven't changed. + h3 = snapshot(m, o, step=2, metrics=metrics, store=store) + mf1 = json.loads(store.get_snapshot(h1)["manifest_json"]) + mf3 = json.loads(store.get_snapshot(h3)["manifest_json"]) + assert mf1["model"].keys() == mf3["model"].keys() + for k in mf1["model"]: + assert mf1["model"][k] == mf3["model"][k], f"blob hash changed for {k}" + + # Every referenced blob must be reachable via the store (works for both + # legacy per-file layout and Phase-1 chunked/packfile layout). + unique_blob_hashes = set(mf1["model"].values()) | set(mf3["model"].values()) + for bh in unique_blob_hashes: + assert store.has_blob(bh), f"blob {bh} missing from store" + + +def test_snapshot_changes_when_weights_change(store): + m, o = _make_model_and_opt(seed=7) + metrics = {"val_bpb": 1.0} + h1 = snapshot(m, o, step=1, metrics=metrics, store=store) + + with torch.no_grad(): + m.fc1.weight.add_(1.0) # mutate + h2 = snapshot(m, o, step=2, metrics=metrics, store=store) + assert h1 != h2 + + d = diff(h1, h2, store=store) + assert "fc1.weight" in d["changed"] + # fc2 weight/bias unchanged -> appears in identical_blob_count bucket. + assert d["identical_blob_count"] >= 2 + + +# --------------------------------------------------------------------------- +# Async write-behind +# --------------------------------------------------------------------------- +def test_async_writes_drain(async_store): + m, o = _make_model_and_opt(seed=3) + hashes = [] + for step in range(5): + with torch.no_grad(): + m.fc1.weight.add_(0.01) + hashes.append( + snapshot(m, o, step=step, metrics={"val_bpb": float(step)}, store=async_store) + ) + async_store.flush(timeout=15) + # All rows visible. + for h in hashes: + row = async_store.get_snapshot(h) + assert row is not None, f"snapshot {h} not persisted" + rows = log(limit=10, store=async_store) + assert len(rows) == 5 + + +# --------------------------------------------------------------------------- +# Branch + log lineage +# --------------------------------------------------------------------------- +def test_branch_and_log(store): + m, o = _make_model_and_opt(seed=2) + h1 = snapshot(m, o, step=1, metrics={"val_bpb": 3.0}, store=store) + with torch.no_grad(): + m.fc1.weight.add_(0.5) + h2 = snapshot(m, o, step=2, metrics={"val_bpb": 2.5}, parent_hash=h1, store=store) + with torch.no_grad(): + m.fc1.weight.add_(0.5) + h3 = snapshot(m, o, step=3, metrics={"val_bpb": 2.0}, parent_hash=h2, store=store) + + branch("champ", h3, store=store) + assert store.resolve_ref("champ") == h3 + + lin = log(limit=10, branch="champ", store=store) + assert [r["hash"] for r in lin] == [h3, h2, h1] + + +# --------------------------------------------------------------------------- +# GC +# --------------------------------------------------------------------------- +def test_gc_removes_only_unreachable(store): + m, o = _make_model_and_opt(seed=5) + hashes = [] + parent = None + for step in range(6): + with torch.no_grad(): + m.fc1.weight.add_(0.1) + parent = snapshot( + m, o, step=step, metrics={"val_bpb": 5.0 - step}, + parent_hash=parent, store=store, + ) + hashes.append(parent) + + branch("keep_me", hashes[2], store=store) + + res = gc(keep_last=1, reachable_from="keep_me", store=store) + # With keep_last=1, last snapshot is kept; plus lineage from keep_me (h0..h2). + kept = res["kept_snapshots"] + assert kept >= 3 # h0, h1, h2 are reachable from keep_me + # keep_me head must still resolve. + assert store.resolve_ref("keep_me") == hashes[2] + # h3, h4 may have been removed (they're not reachable and not in keep_last=1 window). + removed = set(res["removed_snapshots"]) + # The last (newest) snapshot is in the keep_last=1 window, so NOT removed. + assert hashes[-1] not in removed + # Everything kept must still be readable. + for h in res["removed_snapshots"]: + assert store.get_snapshot(h) is None + # Blobs for reachable snapshots must still exist on disk. + for h in hashes[:3]: + row = store.get_snapshot(h) + assert row is not None + mf = json.loads(row["manifest_json"]) + for bh in mf["model"].values(): + assert store.has_blob(bh), f"blob {bh} gc'd but snapshot {h} still references it" + + +def test_gc_dry_run_does_not_delete(store): + m, o = _make_model_and_opt(seed=8) + parent = None + hashes = [] + for step in range(3): + with torch.no_grad(): + m.fc1.weight.add_(0.2) + parent = snapshot(m, o, step=step, metrics={"loss": 1.0 * step}, + parent_hash=parent, store=store) + hashes.append(parent) + + res = gc(keep_last=0, dry_run=True, store=store) + # Dry-run: snapshots still present in DB. + for h in hashes: + assert store.get_snapshot(h) is not None + + +# --------------------------------------------------------------------------- +# Hash utility sanity +# --------------------------------------------------------------------------- +def test_hash_bytes_deterministic(): + a = hash_bytes(b"hello world") + b = hash_bytes(b"hello world") + c = hash_bytes(b"hello worlD") + assert a == b + assert a != c diff --git a/overlay/tests/test_state_store_perf.py b/overlay/tests/test_state_store_perf.py index 39ae3ff422e0914429399e9f61189022bc7c5eee..badc2297677bed4f9744bcf4475f827f1d5464e8 100644 --- a/overlay/tests/test_state_store_perf.py +++ b/overlay/tests/test_state_store_perf.py @@ -1,210 +1,210 @@ -""" -Performance / correctness regression tests for state_store speed-up work -(Phase 1.5: parallel hash, fingerprint cache, Bloom, pinned staging, delta). - -Not gated by a timing threshold (those are unreliable in CI); instead -this test suite exercises the fast paths for correctness and then reports -wall-clock numbers in the -s output for human inspection. -""" - -from __future__ import annotations - -import os -import time - -import pytest - -torch = pytest.importorskip("torch") - -from state_store import StateStore, snapshot, checkout -from state_store.bloom import BloomFilter -from state_store.fingerprint import ( - tensor_signature, - clear_signature_cache, - signature_cache_size, -) -from state_store.delta_codec import encode_delta, decode_delta, is_delta_blob - - -# --------------------------------------------------------------------------- -# Synthetic 7.5M-param model approximating a small Mamba layer stack. -# --------------------------------------------------------------------------- -class MiniMamba(torch.nn.Module): - def __init__(self, d=128, n_layers=4, vocab=5000): - super().__init__() - self.embed = torch.nn.Embedding(vocab, d) - self.layers = torch.nn.ModuleList( - [ - torch.nn.Sequential( - torch.nn.Linear(d, 4 * d, bias=True), - torch.nn.SiLU(), - torch.nn.Linear(4 * d, d, bias=True), - ) - for _ in range(n_layers) - ] - ) - self.norm = torch.nn.LayerNorm(d) - self.head = torch.nn.Linear(d, vocab, bias=False) - - def forward(self, x): - h = self.embed(x) - for blk in self.layers: - h = h + blk(h) - return self.head(self.norm(h)) - - -def _make_model_opt(seed: int = 0): - torch.manual_seed(seed) - m = MiniMamba() - opt = torch.optim.AdamW(m.parameters(), lr=1e-3) - # Prime optimizer state by one step. - x = torch.randint(0, 5000, (2, 8)) - loss = m(x).mean() - loss.backward() - opt.step() - opt.zero_grad(set_to_none=True) - return m, opt - - -def _param_count(m): - return sum(p.numel() for p in m.parameters()) - - -# --------------------------------------------------------------------------- -# Bloom filter sanity. -# --------------------------------------------------------------------------- -def test_bloom_no_false_negatives(): - b = BloomFilter(bits=1 << 14) - keys = [f"hash_{i:04x}" for i in range(500)] - for k in keys: - b.add(k) - for k in keys: - assert k in b, f"false negative for {k}" - - -def test_bloom_low_false_positive_rate(): - b = BloomFilter(bits=1 << 20, num_hashes=4) - # Insert 10k, probe 10k disjoint. - for i in range(10000): - b.add(f"in_{i}") - fp = 0 - for i in range(10000): - if f"out_{i}" in b: - fp += 1 - # With 1 Mi bits and 10k entries, expected FP rate ~1%. - assert fp / 10000 < 0.05, f"false positive rate too high: {fp}/10000" - - -# --------------------------------------------------------------------------- -# Fingerprint sanity. -# --------------------------------------------------------------------------- -def test_fingerprint_matches_identical_tensors(): - a = torch.randn(128, 128) - b = a.clone() - assert tensor_signature(a) == tensor_signature(b) - - -def test_fingerprint_differs_after_mutation(): - a = torch.randn(128, 128) - sig_before = tensor_signature(a) - a[0, 0] = 1e6 - sig_after = tensor_signature(a) - assert sig_before != sig_after - - -def test_fingerprint_handles_empty_and_nonfloat(): - assert tensor_signature(torch.empty(0, 8)) is not None - assert tensor_signature(torch.tensor([1, 2, 3], dtype=torch.int64)) is not None - - -# --------------------------------------------------------------------------- -# Delta codec correctness. -# --------------------------------------------------------------------------- -def test_delta_codec_roundtrip_lossy_bounded(): - parent = torch.randn(256, 256) * 10.0 - current = parent + torch.randn_like(parent) * 1e-3 - blob = encode_delta(current, parent) - assert is_delta_blob(blob) - restored = decode_delta(blob, parent) - assert restored.shape == current.shape - assert restored.dtype == current.dtype - # fp16 gives us ~1e-3 relative error on order-1 values. - assert torch.allclose(restored, current, rtol=1e-3, atol=1e-3) - - -def test_delta_codec_rejects_shape_mismatch(): - p = torch.zeros(4, 4) - c = torch.zeros(4, 5) - with pytest.raises(ValueError): - encode_delta(c, p) - - -# --------------------------------------------------------------------------- -# End-to-end: fingerprint cache actually skips re-hashing on repeat snapshot. -# --------------------------------------------------------------------------- -def test_signature_cache_grows_on_snapshot(tmp_path, capsys): - clear_signature_cache() - s = StateStore(root=tmp_path / "store", sync=True) - m, o = _make_model_opt(seed=1) - h1 = snapshot(m, o, step=0, metrics={"k": 1.0}, store=s) - n1 = signature_cache_size() - # Second snapshot of IDENTICAL weights -> all fingerprints must hit the cache. - h2 = snapshot(m, o, step=1, metrics={"k": 2.0}, store=s) - n2 = signature_cache_size() - assert n1 > 0 - assert n2 >= n1 # monotone - # Both snapshots resolve. - assert s.get_snapshot(h1) is not None - assert s.get_snapshot(h2) is not None - s.shutdown() - - -# --------------------------------------------------------------------------- -# Round-trip correctness on the synthetic model (covers the fast path end-to-end). -# --------------------------------------------------------------------------- -def test_perf_model_roundtrip(tmp_path): - s = StateStore(root=tmp_path / "store", sync=True) - m1, o1 = _make_model_opt(seed=1) - h = snapshot(m1, o1, step=7, metrics={"loss": 2.0}, store=s) - m2, o2 = _make_model_opt(seed=999) - checkout(h, m2, o2, store=s) - for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): - assert torch.allclose(p1.cpu(), p2.cpu(), rtol=0, atol=0), f"{n1} not bit-exact" - s.shutdown() - - -# --------------------------------------------------------------------------- -# Benchmark — reports wall-clock; only fails if snapshot > 10s (safety net). -# --------------------------------------------------------------------------- -def test_perf_bench_smoke(tmp_path, capsys): - s = StateStore(root=tmp_path / "bench_store", sync=True) - m, o = _make_model_opt(seed=1) - params = _param_count(m) - # Warm the fingerprint cache + hash path. - snapshot(m, o, step=-1, metrics={}, store=s) - clear_signature_cache() - - N = 5 - # Cold: no fingerprint cache. - t0 = time.perf_counter() - for i in range(N): - snapshot(m, o, step=i, metrics={"step": i}, store=s) - cold_ms = (time.perf_counter() - t0) / N * 1000.0 - - # Hot: fingerprint cache populated -> fast path dominates. - t0 = time.perf_counter() - for i in range(N, 2 * N): - snapshot(m, o, step=i, metrics={"step": i}, store=s) - hot_ms = (time.perf_counter() - t0) / N * 1000.0 - - with capsys.disabled(): - print( - f"\n[state_store perf] params={params:,} " - f"cold={cold_ms:.1f} ms/snap hot={hot_ms:.1f} ms/snap " - f"speedup={cold_ms / max(hot_ms, 1e-6):.2f}× " - f"cache_size={signature_cache_size()}" - ) - # Safety net: a 7.5M-param snapshot should never take >10s on any modern box. - assert cold_ms < 10_000 - assert hot_ms < 10_000 - s.shutdown() +""" +Performance / correctness regression tests for state_store speed-up work +(Phase 1.5: parallel hash, fingerprint cache, Bloom, pinned staging, delta). + +Not gated by a timing threshold (those are unreliable in CI); instead +this test suite exercises the fast paths for correctness and then reports +wall-clock numbers in the -s output for human inspection. +""" + +from __future__ import annotations + +import os +import time + +import pytest + +torch = pytest.importorskip("torch") + +from state_store import StateStore, snapshot, checkout +from state_store.bloom import BloomFilter +from state_store.fingerprint import ( + tensor_signature, + clear_signature_cache, + signature_cache_size, +) +from state_store.delta_codec import encode_delta, decode_delta, is_delta_blob + + +# --------------------------------------------------------------------------- +# Synthetic 7.5M-param model approximating a small Mamba layer stack. +# --------------------------------------------------------------------------- +class MiniMamba(torch.nn.Module): + def __init__(self, d=128, n_layers=4, vocab=5000): + super().__init__() + self.embed = torch.nn.Embedding(vocab, d) + self.layers = torch.nn.ModuleList( + [ + torch.nn.Sequential( + torch.nn.Linear(d, 4 * d, bias=True), + torch.nn.SiLU(), + torch.nn.Linear(4 * d, d, bias=True), + ) + for _ in range(n_layers) + ] + ) + self.norm = torch.nn.LayerNorm(d) + self.head = torch.nn.Linear(d, vocab, bias=False) + + def forward(self, x): + h = self.embed(x) + for blk in self.layers: + h = h + blk(h) + return self.head(self.norm(h)) + + +def _make_model_opt(seed: int = 0): + torch.manual_seed(seed) + m = MiniMamba() + opt = torch.optim.AdamW(m.parameters(), lr=1e-3) + # Prime optimizer state by one step. + x = torch.randint(0, 5000, (2, 8)) + loss = m(x).mean() + loss.backward() + opt.step() + opt.zero_grad(set_to_none=True) + return m, opt + + +def _param_count(m): + return sum(p.numel() for p in m.parameters()) + + +# --------------------------------------------------------------------------- +# Bloom filter sanity. +# --------------------------------------------------------------------------- +def test_bloom_no_false_negatives(): + b = BloomFilter(bits=1 << 14) + keys = [f"hash_{i:04x}" for i in range(500)] + for k in keys: + b.add(k) + for k in keys: + assert k in b, f"false negative for {k}" + + +def test_bloom_low_false_positive_rate(): + b = BloomFilter(bits=1 << 20, num_hashes=4) + # Insert 10k, probe 10k disjoint. + for i in range(10000): + b.add(f"in_{i}") + fp = 0 + for i in range(10000): + if f"out_{i}" in b: + fp += 1 + # With 1 Mi bits and 10k entries, expected FP rate ~1%. + assert fp / 10000 < 0.05, f"false positive rate too high: {fp}/10000" + + +# --------------------------------------------------------------------------- +# Fingerprint sanity. +# --------------------------------------------------------------------------- +def test_fingerprint_matches_identical_tensors(): + a = torch.randn(128, 128) + b = a.clone() + assert tensor_signature(a) == tensor_signature(b) + + +def test_fingerprint_differs_after_mutation(): + a = torch.randn(128, 128) + sig_before = tensor_signature(a) + a[0, 0] = 1e6 + sig_after = tensor_signature(a) + assert sig_before != sig_after + + +def test_fingerprint_handles_empty_and_nonfloat(): + assert tensor_signature(torch.empty(0, 8)) is not None + assert tensor_signature(torch.tensor([1, 2, 3], dtype=torch.int64)) is not None + + +# --------------------------------------------------------------------------- +# Delta codec correctness. +# --------------------------------------------------------------------------- +def test_delta_codec_roundtrip_lossy_bounded(): + parent = torch.randn(256, 256) * 10.0 + current = parent + torch.randn_like(parent) * 1e-3 + blob = encode_delta(current, parent) + assert is_delta_blob(blob) + restored = decode_delta(blob, parent) + assert restored.shape == current.shape + assert restored.dtype == current.dtype + # fp16 gives us ~1e-3 relative error on order-1 values. + assert torch.allclose(restored, current, rtol=1e-3, atol=1e-3) + + +def test_delta_codec_rejects_shape_mismatch(): + p = torch.zeros(4, 4) + c = torch.zeros(4, 5) + with pytest.raises(ValueError): + encode_delta(c, p) + + +# --------------------------------------------------------------------------- +# End-to-end: fingerprint cache actually skips re-hashing on repeat snapshot. +# --------------------------------------------------------------------------- +def test_signature_cache_grows_on_snapshot(tmp_path, capsys): + clear_signature_cache() + s = StateStore(root=tmp_path / "store", sync=True) + m, o = _make_model_opt(seed=1) + h1 = snapshot(m, o, step=0, metrics={"k": 1.0}, store=s) + n1 = signature_cache_size() + # Second snapshot of IDENTICAL weights -> all fingerprints must hit the cache. + h2 = snapshot(m, o, step=1, metrics={"k": 2.0}, store=s) + n2 = signature_cache_size() + assert n1 > 0 + assert n2 >= n1 # monotone + # Both snapshots resolve. + assert s.get_snapshot(h1) is not None + assert s.get_snapshot(h2) is not None + s.shutdown() + + +# --------------------------------------------------------------------------- +# Round-trip correctness on the synthetic model (covers the fast path end-to-end). +# --------------------------------------------------------------------------- +def test_perf_model_roundtrip(tmp_path): + s = StateStore(root=tmp_path / "store", sync=True) + m1, o1 = _make_model_opt(seed=1) + h = snapshot(m1, o1, step=7, metrics={"loss": 2.0}, store=s) + m2, o2 = _make_model_opt(seed=999) + checkout(h, m2, o2, store=s) + for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): + assert torch.allclose(p1.cpu(), p2.cpu(), rtol=0, atol=0), f"{n1} not bit-exact" + s.shutdown() + + +# --------------------------------------------------------------------------- +# Benchmark — reports wall-clock; only fails if snapshot > 10s (safety net). +# --------------------------------------------------------------------------- +def test_perf_bench_smoke(tmp_path, capsys): + s = StateStore(root=tmp_path / "bench_store", sync=True) + m, o = _make_model_opt(seed=1) + params = _param_count(m) + # Warm the fingerprint cache + hash path. + snapshot(m, o, step=-1, metrics={}, store=s) + clear_signature_cache() + + N = 5 + # Cold: no fingerprint cache. + t0 = time.perf_counter() + for i in range(N): + snapshot(m, o, step=i, metrics={"step": i}, store=s) + cold_ms = (time.perf_counter() - t0) / N * 1000.0 + + # Hot: fingerprint cache populated -> fast path dominates. + t0 = time.perf_counter() + for i in range(N, 2 * N): + snapshot(m, o, step=i, metrics={"step": i}, store=s) + hot_ms = (time.perf_counter() - t0) / N * 1000.0 + + with capsys.disabled(): + print( + f"\n[state_store perf] params={params:,} " + f"cold={cold_ms:.1f} ms/snap hot={hot_ms:.1f} ms/snap " + f"speedup={cold_ms / max(hot_ms, 1e-6):.2f}× " + f"cache_size={signature_cache_size()}" + ) + # Safety net: a 7.5M-param snapshot should never take >10s on any modern box. + assert cold_ms < 10_000 + assert hot_ms < 10_000 + s.shutdown() diff --git a/overlay/tests/test_state_store_phase1.py b/overlay/tests/test_state_store_phase1.py index aad1c1c9258e15dae65eada1466a7c8db315a3fe..8ea989ab47694c57054b0b219d61d4c39ea18356 100644 --- a/overlay/tests/test_state_store_phase1.py +++ b/overlay/tests/test_state_store_phase1.py @@ -1,380 +1,380 @@ -""" -Phase-1 state_store tests: - * FastCDC chunking + packfile dedup on adjacent training-step snapshots - * Packfile roll/seal at 64 MB boundary - * Bounded write-behind queue drops snapshots (not data) under pressure - * SSM prefix cache round-trip (hit/miss + ssm_blob_hash) - * HTM serde+bincode save_state/load_state round-trip (if htm_rust available) - * bisect binary search converges on a synthetic regression - * blame finds the earliest snapshot crossing a metric threshold -""" - -from __future__ import annotations - -import os -import sqlite3 -import subprocess -import sys -import tempfile -import textwrap -from pathlib import Path - -import pytest - -torch = pytest.importorskip("torch") - -from state_store import ( # noqa: E402 - StateStore, - snapshot, - branch, -) -from state_store.chunker import chunk_blob, has_fastcdc, reassemble # noqa: E402 -from state_store.ssm_cache import ( # noqa: E402 - get_prefix_state, - put_prefix_state, - cache_size, -) -from state_store.store import PACKFILE_ROLL_BYTES # noqa: E402 -from state_store.cli import build_parser # noqa: E402 - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- -class SmallModel(torch.nn.Module): - """Parameter slab big enough to see real CDC chunks. - - With d=512, w1.weight is 1 MB and w2.weight is 1 MB, safely above the - FastCDC min_chunk_size threshold (8 KB) so the CDC path actually runs. - """ - - def __init__(self, d: int = 512): - super().__init__() - self.w1 = torch.nn.Linear(d, d, bias=True) - self.w2 = torch.nn.Linear(d, d, bias=True) - - -@pytest.fixture -def store(tmp_path): - s = StateStore(root=tmp_path / "store", sync=True, chunking=True) - yield s - s.shutdown() - - -# --------------------------------------------------------------------------- -# 1. Chunker smoke -# --------------------------------------------------------------------------- -def test_chunker_roundtrip_small(): - data = b"hello world" * 100 - cs = chunk_blob(data) - assert reassemble(cs) == data - - -def test_chunker_roundtrip_large(): - # 300 KB — forces multiple chunks if fastcdc present. - data = bytes(range(256)) * (300 * 1024 // 256) - cs = chunk_blob(data) - assert reassemble(cs) == data - if has_fastcdc(): - assert len(cs) >= 2, "expected fastcdc to produce multiple chunks for 300 KB" - - -# --------------------------------------------------------------------------- -# 2. FastCDC dedup across adjacent snapshots. -# --------------------------------------------------------------------------- -@pytest.mark.skipif(not has_fastcdc(), reason="fastcdc not installed") -def test_fastcdc_dedup_adjacent_snapshots(store): - """Two snapshots whose weights differ by ~1% should share most chunks. - - We measure dedup on the weight tensors specifically. Small tensors - (biases, tiny optimizer scalars) fall below the 8 KB FastCDC min-chunk - size and always store as a single whole-blob chunk; they dilute - store-wide dedup ratios without being what the optimization is about. - """ - import json as _json - - torch.manual_seed(0) - m = SmallModel(d=512) - opt = torch.optim.SGD(m.parameters(), lr=0.1) - - h1 = snapshot(m, opt, step=1, metrics={"val_bpb": 2.0}, store=store) - - # Mutate ~1% of the w1.weight parameters (first 5 rows out of 512). - with torch.no_grad(): - m.w1.weight[:5].add_(0.1) - - h2 = snapshot(m, opt, step=2, metrics={"val_bpb": 1.9}, store=store) - assert h1 != h2 - - # Store-wide dedup baseline: total unique chunks vs logical blob->chunk refs. - conn = sqlite3.connect(store.db_path) - try: - total_chunks = conn.execute("SELECT COUNT(*) FROM chunks").fetchone()[0] - logical = conn.execute("SELECT COUNT(*) FROM blob_chunks").fetchone()[0] - # Pull the two blob hashes for w1.weight (the tensor we actually changed). - mf1 = _json.loads(store.get_snapshot(h1)["manifest_json"])["model"] - mf2 = _json.loads(store.get_snapshot(h2)["manifest_json"])["model"] - bh1 = mf1["w1.weight"] - bh2 = mf2["w1.weight"] - c1 = [r[0] for r in conn.execute( - "SELECT chunk_hash FROM blob_chunks WHERE blob_hash=? ORDER BY seq", - (bh1,), - )] - c2 = [r[0] for r in conn.execute( - "SELECT chunk_hash FROM blob_chunks WHERE blob_hash=? ORDER BY seq", - (bh2,), - )] - finally: - conn.close() - assert total_chunks > 0, "chunks table empty — FastCDC path not taken" - assert logical > 0 - assert len(c1) >= 4, f"expected multi-chunk w1.weight, got {len(c1)} chunks" - - # Per-tensor dedup: intersecting chunks should dominate. - common = set(c1) & set(c2) - tensor_dedup = len(common) / max(len(c1), len(c2)) - assert tensor_dedup >= 0.5, ( - f"w1.weight dedup ratio {tensor_dedup:.3f} below 50% target " - f"(c1={len(c1)} c2={len(c2)} common={len(common)})" - ) - - # Log store-wide ratio for documentation (not asserted; dominated by small - # sub-8KB tensors that take the single-whole-chunk fallback). - overall = 1.0 - (total_chunks / logical) - print( - f"[dedup] w1.weight={tensor_dedup:.2%} " - f"store-wide={overall:.2%} (chunks={total_chunks} logical={logical})" - ) - - -# --------------------------------------------------------------------------- -# 3. Packfile roll/seal at the configured threshold. -# --------------------------------------------------------------------------- -def test_packfile_rolls_at_threshold(tmp_path, monkeypatch): - """Forcing a tiny pack-roll threshold exercises sealing + new pack creation.""" - # Monkeypatch the roll-bytes constant to 32 KB so we don't need 64 MB of data. - from state_store import store as store_mod - monkeypatch.setattr(store_mod, "PACKFILE_ROLL_BYTES", 32 * 1024) - - s = StateStore(root=tmp_path / "packstore", sync=True, chunking=True) - try: - # Write a few distinct 40 KB blobs so we roll past the 32 KB threshold. - hashes = [] - for i in range(4): - data = bytes([i & 0xFF]) * (40 * 1024) - hashes.append(s.put_blob(data)) - - conn = sqlite3.connect(s.db_path) - try: - n_packs = conn.execute("SELECT COUNT(*) FROM packfiles").fetchone()[0] - n_sealed = conn.execute( - "SELECT COUNT(*) FROM packfiles WHERE sealed = 1" - ).fetchone()[0] - finally: - conn.close() - assert n_packs >= 2, f"expected packfile roll, got {n_packs}" - assert n_sealed >= 1, "expected at least one sealed packfile" - - # Read-back validates the pack offsets. - for i, h in enumerate(hashes): - expected = bytes([i & 0xFF]) * (40 * 1024) - assert s.read_blob(h) == expected - finally: - s.shutdown() - - -# --------------------------------------------------------------------------- -# 4. Bounded write-behind queue drops snapshots under pressure. -# --------------------------------------------------------------------------- -def test_bounded_queue_drops_snapshot(tmp_path, monkeypatch): - monkeypatch.setenv("HYDRA_SNAPSHOT_MAX_QUEUE_MB", "1") # 1 MB soft cap - s = StateStore(root=tmp_path / "qstore", sync=False, chunking=False) - try: - # Flood the queue with blobs > 1 MB to push pending bytes over cap. - big = b"x" * (2 * 1024 * 1024) - s.put_blob(big) - # Now enqueue a snapshot — _try_reserve_queue should refuse. - # Tiny fake blob_hashes list keeps the snapshot payload small. - s.enqueue_snapshot( - hash="h" * 64, - parent_hash=None, - run_id="r", - step=0, - wall_time=0.0, - branch_label=None, - metrics_json="{}", - config_json="{}", - manifest_json="{}", - blob_hashes=[], - ) - # Drop counter should reflect at least one dropped snapshot. - assert s.get_dropped_snapshots_count() >= 1 - finally: - s.shutdown() - - -# --------------------------------------------------------------------------- -# 5. SSM prefix cache round-trip. -# --------------------------------------------------------------------------- -def test_ssm_prefix_cache_hit_miss(store): - tokens = [1, 7, 42, 1000, 999_999] - # Miss initially. - assert get_prefix_state(tokens, store=store) is None - # Put and retrieve. - t = torch.arange(16, dtype=torch.float32).reshape(4, 4) - ph, bh = put_prefix_state(tokens, t, store=store) - assert len(ph) >= 32 and len(bh) >= 32 - assert cache_size(store=store) == 1 - got = get_prefix_state(tokens, store=store) - assert got is not None - assert torch.equal(got, t) - - # Different prefix -> miss. - assert get_prefix_state(tokens + [1], store=store) is None - - # Hit count should have bumped. - conn = sqlite3.connect(store.db_path) - try: - row = conn.execute( - "SELECT hit_count FROM ssm_prefix_cache WHERE prefix_hash = ?", - (ph,), - ).fetchone() - finally: - conn.close() - assert row[0] == 1 - - -# --------------------------------------------------------------------------- -# 6. HTM serde+bincode round-trip (requires htm_rust). -# --------------------------------------------------------------------------- -def test_htm_save_load_state(): - htm_rust = pytest.importorskip("htm_rust") - import numpy as np - - region_a = htm_rust.HTMRegion(1024, 512, 8, seed=1234) - # Drive some learning. - rng = np.random.default_rng(0) - for _ in range(25): - sdr = rng.random(1024) < 0.02 - region_a.step(sdr.astype(bool), True) - - blob = region_a.save_state() - assert isinstance(blob, bytes) and len(blob) > 0 - - # Load into a fresh region. - region_b = htm_rust.HTMRegion(1024, 512, 8, seed=9999) - region_b.load_state(blob) - - # Feed the same next SDR; outputs must match now. - test_sdr = (rng.random(1024) < 0.02).astype(bool) - a_cols, _, _, a_anom = region_a.step(test_sdr, False) - b_cols, _, _, b_anom = region_b.step(test_sdr, False) - assert (a_cols == b_cols).all() - assert abs(a_anom - b_anom) < 1e-6 - - # Shape mismatch is rejected. - bad = htm_rust.HTMRegion(2048, 512, 8, seed=0) - with pytest.raises(Exception): - bad.load_state(blob) - - -# --------------------------------------------------------------------------- -# 7. CLI bisect — binary-search over synthetic snapshot chain. -# --------------------------------------------------------------------------- -def test_bisect_converges(tmp_path): - """Build a 10-snapshot chain where a regression starts at step 4. Bisect - must find step 4 as the first-bad snapshot in O(log N) evaluations.""" - root = tmp_path / "bstore" - s = StateStore(root=root, sync=True, chunking=True) - try: - m = SmallModel(d=32) - opt = torch.optim.SGD(m.parameters(), lr=0.1) - hashes: list[str] = [] - parent = None - for step in range(10): - with torch.no_grad(): - m.w1.weight.add_(0.01) - # Embed a per-snapshot "regressed" marker in the metrics dict. - regressed = 1 if step >= 4 else 0 - h = snapshot( - m, opt, step=step, - metrics={"val_bpb": 1.0 + 0.1 * step, "regressed": regressed}, - parent_hash=parent, store=s, - ) - hashes.append(h) - parent = h - good = hashes[0] - bad = hashes[-1] - finally: - s.shutdown() - - # Test script: exit 0 iff snapshot's `regressed` metric == 0. - test_script = tmp_path / "check.py" - test_script.write_text(textwrap.dedent(f""" - import json, os, sqlite3, sys - h = os.environ["HYDRA_BISECT_SNAPSHOT"] - conn = sqlite3.connect(r"{s.db_path}") - row = conn.execute("SELECT metrics_json FROM snapshots WHERE hash=?", (h,)).fetchone() - conn.close() - metrics = json.loads(row[0]) - sys.exit(0 if metrics.get("regressed", 0) == 0 else 1) - """)) - test_cmd = f"{sys.executable} {test_script}" - - # Invoke CLI programmatically. - parser = build_parser() - args = parser.parse_args([ - "bisect", "start", - "--good", good, - "--bad", bad, - "--test", test_cmd, - ]) - env = dict(os.environ) - env["HYDRA_STATE_STORE_DIR"] = str(root) - # Invoke as subprocess so HYDRA_STATE_STORE_DIR takes effect in default_store. - rc = subprocess.call( - [sys.executable, "-m", "state_store", "bisect", "start", - "--good", good, "--bad", bad, "--test", test_cmd], - env=env, - cwd="/home/mikeb/work/feather", - ) - assert rc == 0 - - -# --------------------------------------------------------------------------- -# 8. CLI blame — finds first snapshot crossing a metric threshold. -# --------------------------------------------------------------------------- -def test_blame_finds_threshold_crossing(tmp_path): - root = tmp_path / "blamestore" - s = StateStore(root=root, sync=True, chunking=False) - try: - m = SmallModel(d=32) - opt = torch.optim.SGD(m.parameters(), lr=0.1) - # BPB crosses 1.5 at step 3. - bpbs = [2.0, 1.9, 1.7, 1.4, 1.3, 1.2] - hashes: list[str] = [] - parent = None - for step, v in enumerate(bpbs): - with torch.no_grad(): - m.w1.weight.add_(0.01) - h = snapshot(m, opt, step=step, - metrics={"val_bpb": v}, - parent_hash=parent, store=s) - hashes.append(h) - parent = h - branch("main", hashes[-1], store=s) - finally: - s.shutdown() - - env = dict(os.environ) - env["HYDRA_STATE_STORE_DIR"] = str(root) - # Find first snapshot with val_bpb < 1.5 on branch 'main'. - out = subprocess.run( - [sys.executable, "-m", "state_store", "blame", - "val_bpb", "1.5", "--branch", "main", "--comparator", "<"], - env=env, cwd="/home/mikeb/work/feather", - capture_output=True, text=True, - ) - assert out.returncode == 0, f"blame failed: {out.stderr}" - # Step 3 is the first crossing. - assert "step= 3" in out.stdout, out.stdout +""" +Phase-1 state_store tests: + * FastCDC chunking + packfile dedup on adjacent training-step snapshots + * Packfile roll/seal at 64 MB boundary + * Bounded write-behind queue drops snapshots (not data) under pressure + * SSM prefix cache round-trip (hit/miss + ssm_blob_hash) + * HTM serde+bincode save_state/load_state round-trip (if htm_rust available) + * bisect binary search converges on a synthetic regression + * blame finds the earliest snapshot crossing a metric threshold +""" + +from __future__ import annotations + +import os +import sqlite3 +import subprocess +import sys +import tempfile +import textwrap +from pathlib import Path + +import pytest + +torch = pytest.importorskip("torch") + +from state_store import ( # noqa: E402 + StateStore, + snapshot, + branch, +) +from state_store.chunker import chunk_blob, has_fastcdc, reassemble # noqa: E402 +from state_store.ssm_cache import ( # noqa: E402 + get_prefix_state, + put_prefix_state, + cache_size, +) +from state_store.store import PACKFILE_ROLL_BYTES # noqa: E402 +from state_store.cli import build_parser # noqa: E402 + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +class SmallModel(torch.nn.Module): + """Parameter slab big enough to see real CDC chunks. + + With d=512, w1.weight is 1 MB and w2.weight is 1 MB, safely above the + FastCDC min_chunk_size threshold (8 KB) so the CDC path actually runs. + """ + + def __init__(self, d: int = 512): + super().__init__() + self.w1 = torch.nn.Linear(d, d, bias=True) + self.w2 = torch.nn.Linear(d, d, bias=True) + + +@pytest.fixture +def store(tmp_path): + s = StateStore(root=tmp_path / "store", sync=True, chunking=True) + yield s + s.shutdown() + + +# --------------------------------------------------------------------------- +# 1. Chunker smoke +# --------------------------------------------------------------------------- +def test_chunker_roundtrip_small(): + data = b"hello world" * 100 + cs = chunk_blob(data) + assert reassemble(cs) == data + + +def test_chunker_roundtrip_large(): + # 300 KB — forces multiple chunks if fastcdc present. + data = bytes(range(256)) * (300 * 1024 // 256) + cs = chunk_blob(data) + assert reassemble(cs) == data + if has_fastcdc(): + assert len(cs) >= 2, "expected fastcdc to produce multiple chunks for 300 KB" + + +# --------------------------------------------------------------------------- +# 2. FastCDC dedup across adjacent snapshots. +# --------------------------------------------------------------------------- +@pytest.mark.skipif(not has_fastcdc(), reason="fastcdc not installed") +def test_fastcdc_dedup_adjacent_snapshots(store): + """Two snapshots whose weights differ by ~1% should share most chunks. + + We measure dedup on the weight tensors specifically. Small tensors + (biases, tiny optimizer scalars) fall below the 8 KB FastCDC min-chunk + size and always store as a single whole-blob chunk; they dilute + store-wide dedup ratios without being what the optimization is about. + """ + import json as _json + + torch.manual_seed(0) + m = SmallModel(d=512) + opt = torch.optim.SGD(m.parameters(), lr=0.1) + + h1 = snapshot(m, opt, step=1, metrics={"val_bpb": 2.0}, store=store) + + # Mutate ~1% of the w1.weight parameters (first 5 rows out of 512). + with torch.no_grad(): + m.w1.weight[:5].add_(0.1) + + h2 = snapshot(m, opt, step=2, metrics={"val_bpb": 1.9}, store=store) + assert h1 != h2 + + # Store-wide dedup baseline: total unique chunks vs logical blob->chunk refs. + conn = sqlite3.connect(store.db_path) + try: + total_chunks = conn.execute("SELECT COUNT(*) FROM chunks").fetchone()[0] + logical = conn.execute("SELECT COUNT(*) FROM blob_chunks").fetchone()[0] + # Pull the two blob hashes for w1.weight (the tensor we actually changed). + mf1 = _json.loads(store.get_snapshot(h1)["manifest_json"])["model"] + mf2 = _json.loads(store.get_snapshot(h2)["manifest_json"])["model"] + bh1 = mf1["w1.weight"] + bh2 = mf2["w1.weight"] + c1 = [r[0] for r in conn.execute( + "SELECT chunk_hash FROM blob_chunks WHERE blob_hash=? ORDER BY seq", + (bh1,), + )] + c2 = [r[0] for r in conn.execute( + "SELECT chunk_hash FROM blob_chunks WHERE blob_hash=? ORDER BY seq", + (bh2,), + )] + finally: + conn.close() + assert total_chunks > 0, "chunks table empty — FastCDC path not taken" + assert logical > 0 + assert len(c1) >= 4, f"expected multi-chunk w1.weight, got {len(c1)} chunks" + + # Per-tensor dedup: intersecting chunks should dominate. + common = set(c1) & set(c2) + tensor_dedup = len(common) / max(len(c1), len(c2)) + assert tensor_dedup >= 0.5, ( + f"w1.weight dedup ratio {tensor_dedup:.3f} below 50% target " + f"(c1={len(c1)} c2={len(c2)} common={len(common)})" + ) + + # Log store-wide ratio for documentation (not asserted; dominated by small + # sub-8KB tensors that take the single-whole-chunk fallback). + overall = 1.0 - (total_chunks / logical) + print( + f"[dedup] w1.weight={tensor_dedup:.2%} " + f"store-wide={overall:.2%} (chunks={total_chunks} logical={logical})" + ) + + +# --------------------------------------------------------------------------- +# 3. Packfile roll/seal at the configured threshold. +# --------------------------------------------------------------------------- +def test_packfile_rolls_at_threshold(tmp_path, monkeypatch): + """Forcing a tiny pack-roll threshold exercises sealing + new pack creation.""" + # Monkeypatch the roll-bytes constant to 32 KB so we don't need 64 MB of data. + from state_store import store as store_mod + monkeypatch.setattr(store_mod, "PACKFILE_ROLL_BYTES", 32 * 1024) + + s = StateStore(root=tmp_path / "packstore", sync=True, chunking=True) + try: + # Write a few distinct 40 KB blobs so we roll past the 32 KB threshold. + hashes = [] + for i in range(4): + data = bytes([i & 0xFF]) * (40 * 1024) + hashes.append(s.put_blob(data)) + + conn = sqlite3.connect(s.db_path) + try: + n_packs = conn.execute("SELECT COUNT(*) FROM packfiles").fetchone()[0] + n_sealed = conn.execute( + "SELECT COUNT(*) FROM packfiles WHERE sealed = 1" + ).fetchone()[0] + finally: + conn.close() + assert n_packs >= 2, f"expected packfile roll, got {n_packs}" + assert n_sealed >= 1, "expected at least one sealed packfile" + + # Read-back validates the pack offsets. + for i, h in enumerate(hashes): + expected = bytes([i & 0xFF]) * (40 * 1024) + assert s.read_blob(h) == expected + finally: + s.shutdown() + + +# --------------------------------------------------------------------------- +# 4. Bounded write-behind queue drops snapshots under pressure. +# --------------------------------------------------------------------------- +def test_bounded_queue_drops_snapshot(tmp_path, monkeypatch): + monkeypatch.setenv("HYDRA_SNAPSHOT_MAX_QUEUE_MB", "1") # 1 MB soft cap + s = StateStore(root=tmp_path / "qstore", sync=False, chunking=False) + try: + # Flood the queue with blobs > 1 MB to push pending bytes over cap. + big = b"x" * (2 * 1024 * 1024) + s.put_blob(big) + # Now enqueue a snapshot — _try_reserve_queue should refuse. + # Tiny fake blob_hashes list keeps the snapshot payload small. + s.enqueue_snapshot( + hash="h" * 64, + parent_hash=None, + run_id="r", + step=0, + wall_time=0.0, + branch_label=None, + metrics_json="{}", + config_json="{}", + manifest_json="{}", + blob_hashes=[], + ) + # Drop counter should reflect at least one dropped snapshot. + assert s.get_dropped_snapshots_count() >= 1 + finally: + s.shutdown() + + +# --------------------------------------------------------------------------- +# 5. SSM prefix cache round-trip. +# --------------------------------------------------------------------------- +def test_ssm_prefix_cache_hit_miss(store): + tokens = [1, 7, 42, 1000, 999_999] + # Miss initially. + assert get_prefix_state(tokens, store=store) is None + # Put and retrieve. + t = torch.arange(16, dtype=torch.float32).reshape(4, 4) + ph, bh = put_prefix_state(tokens, t, store=store) + assert len(ph) >= 32 and len(bh) >= 32 + assert cache_size(store=store) == 1 + got = get_prefix_state(tokens, store=store) + assert got is not None + assert torch.equal(got, t) + + # Different prefix -> miss. + assert get_prefix_state(tokens + [1], store=store) is None + + # Hit count should have bumped. + conn = sqlite3.connect(store.db_path) + try: + row = conn.execute( + "SELECT hit_count FROM ssm_prefix_cache WHERE prefix_hash = ?", + (ph,), + ).fetchone() + finally: + conn.close() + assert row[0] == 1 + + +# --------------------------------------------------------------------------- +# 6. HTM serde+bincode round-trip (requires htm_rust). +# --------------------------------------------------------------------------- +def test_htm_save_load_state(): + htm_rust = pytest.importorskip("htm_rust") + import numpy as np + + region_a = htm_rust.HTMRegion(1024, 512, 8, seed=1234) + # Drive some learning. + rng = np.random.default_rng(0) + for _ in range(25): + sdr = rng.random(1024) < 0.02 + region_a.step(sdr.astype(bool), True) + + blob = region_a.save_state() + assert isinstance(blob, bytes) and len(blob) > 0 + + # Load into a fresh region. + region_b = htm_rust.HTMRegion(1024, 512, 8, seed=9999) + region_b.load_state(blob) + + # Feed the same next SDR; outputs must match now. + test_sdr = (rng.random(1024) < 0.02).astype(bool) + a_cols, _, _, a_anom = region_a.step(test_sdr, False) + b_cols, _, _, b_anom = region_b.step(test_sdr, False) + assert (a_cols == b_cols).all() + assert abs(a_anom - b_anom) < 1e-6 + + # Shape mismatch is rejected. + bad = htm_rust.HTMRegion(2048, 512, 8, seed=0) + with pytest.raises(Exception): + bad.load_state(blob) + + +# --------------------------------------------------------------------------- +# 7. CLI bisect — binary-search over synthetic snapshot chain. +# --------------------------------------------------------------------------- +def test_bisect_converges(tmp_path): + """Build a 10-snapshot chain where a regression starts at step 4. Bisect + must find step 4 as the first-bad snapshot in O(log N) evaluations.""" + root = tmp_path / "bstore" + s = StateStore(root=root, sync=True, chunking=True) + try: + m = SmallModel(d=32) + opt = torch.optim.SGD(m.parameters(), lr=0.1) + hashes: list[str] = [] + parent = None + for step in range(10): + with torch.no_grad(): + m.w1.weight.add_(0.01) + # Embed a per-snapshot "regressed" marker in the metrics dict. + regressed = 1 if step >= 4 else 0 + h = snapshot( + m, opt, step=step, + metrics={"val_bpb": 1.0 + 0.1 * step, "regressed": regressed}, + parent_hash=parent, store=s, + ) + hashes.append(h) + parent = h + good = hashes[0] + bad = hashes[-1] + finally: + s.shutdown() + + # Test script: exit 0 iff snapshot's `regressed` metric == 0. + test_script = tmp_path / "check.py" + test_script.write_text(textwrap.dedent(f""" + import json, os, sqlite3, sys + h = os.environ["HYDRA_BISECT_SNAPSHOT"] + conn = sqlite3.connect(r"{s.db_path}") + row = conn.execute("SELECT metrics_json FROM snapshots WHERE hash=?", (h,)).fetchone() + conn.close() + metrics = json.loads(row[0]) + sys.exit(0 if metrics.get("regressed", 0) == 0 else 1) + """)) + test_cmd = f"{sys.executable} {test_script}" + + # Invoke CLI programmatically. + parser = build_parser() + args = parser.parse_args([ + "bisect", "start", + "--good", good, + "--bad", bad, + "--test", test_cmd, + ]) + env = dict(os.environ) + env["HYDRA_STATE_STORE_DIR"] = str(root) + # Invoke as subprocess so HYDRA_STATE_STORE_DIR takes effect in default_store. + rc = subprocess.call( + [sys.executable, "-m", "state_store", "bisect", "start", + "--good", good, "--bad", bad, "--test", test_cmd], + env=env, + cwd="/home/mikeb/work/feather", + ) + assert rc == 0 + + +# --------------------------------------------------------------------------- +# 8. CLI blame — finds first snapshot crossing a metric threshold. +# --------------------------------------------------------------------------- +def test_blame_finds_threshold_crossing(tmp_path): + root = tmp_path / "blamestore" + s = StateStore(root=root, sync=True, chunking=False) + try: + m = SmallModel(d=32) + opt = torch.optim.SGD(m.parameters(), lr=0.1) + # BPB crosses 1.5 at step 3. + bpbs = [2.0, 1.9, 1.7, 1.4, 1.3, 1.2] + hashes: list[str] = [] + parent = None + for step, v in enumerate(bpbs): + with torch.no_grad(): + m.w1.weight.add_(0.01) + h = snapshot(m, opt, step=step, + metrics={"val_bpb": v}, + parent_hash=parent, store=s) + hashes.append(h) + parent = h + branch("main", hashes[-1], store=s) + finally: + s.shutdown() + + env = dict(os.environ) + env["HYDRA_STATE_STORE_DIR"] = str(root) + # Find first snapshot with val_bpb < 1.5 on branch 'main'. + out = subprocess.run( + [sys.executable, "-m", "state_store", "blame", + "val_bpb", "1.5", "--branch", "main", "--comparator", "<"], + env=env, cwd="/home/mikeb/work/feather", + capture_output=True, text=True, + ) + assert out.returncode == 0, f"blame failed: {out.stderr}" + # Step 3 is the first crossing. + assert "step= 3" in out.stdout, out.stdout diff --git a/overlay/tests/test_subsystems.py b/overlay/tests/test_subsystems.py index 56cc7a38478bd9489317e66eb107576e0ace980e..70cd022c543c312708bfd7cae3bcbad7ba205ef9 100644 --- a/overlay/tests/test_subsystems.py +++ b/overlay/tests/test_subsystems.py @@ -1,440 +1,440 @@ -"""Tests for Post-SEM-Claw model subsystems. - -Verifies forward pass shapes, dtype correctness, and interface contracts. -All tests use small configs to run quickly on CPU. - -Run: - uv run pytest tests/test_subsystems.py -v -""" -import sys -import os -import types -import importlib -import pytest -import torch -import torch.nn as nn -import torch.nn.functional as F - -# --------------------------------------------------------------------------- -# Import model classes from train.py without executing the training loop. -# -# train.py has two problems for direct import: -# 1. It does ``from prepare import ...`` at the top. -# 2. It executes training code at module level (line ~895 onwards). -# -# Strategy: inject a minimal ``prepare`` stub into sys.modules so the import -# doesn't crash, then patch out the module-level training trigger by -# monkey-patching ``torch.device`` to raise when called with "cuda" during -# the dangerous section. Simpler: use importlib with a try/except that stops -# after we've captured the class definitions. -# -# Simplest reliable approach: exec() only the class-definition lines. -# We read the source, strip everything after "# Setup:" and exec() the rest -# with a stubbed prepare namespace. -# --------------------------------------------------------------------------- - -_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -def _load_train_classes(): - """Load model classes from train.py without running the training loop.""" - train_path = os.path.join(_REPO, "train.py") - with open(train_path) as fh: - source = fh.read() - - # Truncate at the module-level training setup section (line starting with - # "# Setup: tokenizer, model, optimizer, dataloader"). - cutoff_markers = [ - "\n# ---------------------------------------------------------------------------\n# Setup:", - "\nt_start = time.time()", - ] - for marker in cutoff_markers: - idx = source.find(marker) - if idx != -1: - source = source[:idx] - break - - # Build a minimal fake prepare module so `from prepare import ...` works. - fake_prepare = types.ModuleType("prepare") - fake_prepare.MAX_SEQ_LEN = 2048 - fake_prepare.TIME_BUDGET = 300 - fake_prepare.Tokenizer = object - fake_prepare.make_dataloader = lambda *a, **kw: None - fake_prepare.evaluate_bpb = lambda *a, **kw: 0.0 - sys.modules.setdefault("prepare", fake_prepare) - - ns: dict = {"__name__": "train"} - exec(compile(source, train_path, "exec"), ns) # noqa: S102 - return ns - - -_TRAIN = _load_train_classes() - -PostSemClawConfig = _TRAIN["PostSemClawConfig"] -PostSemClawModel = _TRAIN["PostSemClawModel"] -Mamba3Block = _TRAIN["Mamba3Block"] -ManifoldHyperConnection = _TRAIN["ManifoldHyperConnection"] -EngramModule = _TRAIN["EngramModule"] -HestiaQAT = _TRAIN["HestiaQAT"] -StochasticResonanceSDR = _TRAIN["StochasticResonanceSDR"] -norm = _TRAIN["norm"] - - -# --------------------------------------------------------------------------- -# Shared small config (fits on CPU in seconds) -# --------------------------------------------------------------------------- - -def _small_config() -> PostSemClawConfig: - # Use only fields that exist in the train.py PostSemClawConfig dataclass. - # train.py uses d_conv=4 internally (hardcoded in Conv1d), not via config. - return PostSemClawConfig( - sequence_len=64, - vocab_size=256, - n_layer=2, - d_model=64, - d_state=16, - headdim=16, - n_heads=4, - expand=2, - mhc_n_streams=2, - mhc_sinkhorn_iters=5, - engram_n_columns=128, - engram_key_dim=16, - engram_layer_idx=0, - ) - - -# --------------------------------------------------------------------------- -# BCNorm tests -# --------------------------------------------------------------------------- - -class TestBCNorm: - def test_output_shape(self): - """BCNorm preserves input shape.""" - cfg = _small_config() - block = Mamba3Block(cfg) - # BCNorm is applied to B_proj/C_proj of shape (B, T, d_state) - bc = block.bc_norm - x = torch.randn(2, 32, cfg.d_state) - y = bc(x) - assert y.shape == x.shape - - def test_output_dtype(self): - """BCNorm preserves float32 dtype.""" - cfg = _small_config() - block = Mamba3Block(cfg) - x = torch.randn(2, 32, cfg.d_state) - y = block.bc_norm(x) - assert y.dtype == x.dtype - - def test_gradient_flow(self): - """BCNorm allows gradients to flow through weight and bias.""" - cfg = _small_config() - block = Mamba3Block(cfg) - x = torch.randn(2, 16, cfg.d_state, requires_grad=True) - y = block.bc_norm(x) - y.sum().backward() - assert x.grad is not None - assert block.bc_norm.weight.grad is not None - - -# --------------------------------------------------------------------------- -# Mamba3Block tests -# --------------------------------------------------------------------------- - -class TestMamba3Block: - def test_forward_shape(self): - """Mamba3Block output shape matches input shape.""" - cfg = _small_config() - block = Mamba3Block(cfg) - x = torch.randn(2, 32, cfg.d_model) - y = block(x) - assert y.shape == (2, 32, cfg.d_model) - - def test_forward_dtype(self): - """Mamba3Block output dtype matches input dtype.""" - cfg = _small_config() - block = Mamba3Block(cfg) - x = torch.randn(2, 16, cfg.d_model) - y = block(x) - assert y.dtype == x.dtype - - def test_causal(self): - """Output at position t must not depend on input at t+1 (causal mask).""" - cfg = _small_config() - block = Mamba3Block(cfg) - block.eval() - T = 8 - x = torch.randn(1, T, cfg.d_model) - # Zero out positions 4..T-1 and check positions 0..3 are identical - x_masked = x.clone() - x_masked[:, 4:, :] = 0.0 - with torch.no_grad(): - y_full = block(x) - y_masked = block(x_masked) - # Positions 0..3 should be identical (causal dependency only on past) - assert torch.allclose(y_full[:, :4, :], y_masked[:, :4, :], atol=1e-5), ( - "Mamba3Block is not causal: output at t<4 changed when future input zeroed" - ) - - def test_gradient_backward(self): - """Backward pass does not crash and produces non-None gradients.""" - cfg = _small_config() - block = Mamba3Block(cfg) - x = torch.randn(1, 8, cfg.d_model, requires_grad=True) - y = block(x) - y.sum().backward() - assert x.grad is not None - - -# --------------------------------------------------------------------------- -# ManifoldHyperConnection (mHC) tests -# --------------------------------------------------------------------------- - -class TestManifoldHyperConnection: - def test_sinkhorn_doubly_stochastic(self): - """Sinkhorn output is approximately doubly-stochastic.""" - mhc = ManifoldHyperConnection(d_model=64, n_streams=4, sinkhorn_iters=20) - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - n = mhc.n_streams - assert M.shape == (n, n) - assert torch.allclose(M.sum(dim=-1), torch.ones(n), atol=1e-4), ( - f"Row sums not ~1: {M.sum(dim=-1)}" - ) - assert torch.allclose(M.sum(dim=-2), torch.ones(n), atol=1e-4), ( - f"Col sums not ~1: {M.sum(dim=-2)}" - ) - - def test_sinkhorn_non_negative(self): - """All Sinkhorn entries are >= 0.""" - mhc = ManifoldHyperConnection(d_model=32, n_streams=3, sinkhorn_iters=10) - with torch.no_grad(): - M = mhc._sinkhorn(mhc.log_alpha) - assert (M >= 0).all() - - def test_forward_shape(self): - """mHC forward preserves stream shape.""" - cfg = _small_config() - mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) - B, T = 2, 16 - streams = torch.randn(cfg.mhc_n_streams, B, T, cfg.d_model) - block_fn = lambda x: x # identity - out = mhc(streams, block_fn) - assert out.shape == streams.shape - - def test_init_streams_shape(self): - """init_streams produces (n_streams, B, T, d_model) tensor.""" - cfg = _small_config() - mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) - x = torch.randn(2, 16, cfg.d_model) - streams = mhc.init_streams(x) - assert streams.shape == (cfg.mhc_n_streams, 2, 16, cfg.d_model) - - def test_merge_streams_shape(self): - """merge_streams reduces (n_streams, B, T, d_model) -> (B, T, d_model).""" - cfg = _small_config() - mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) - streams = torch.randn(cfg.mhc_n_streams, 2, 16, cfg.d_model) - merged = mhc.merge_streams(streams) - assert merged.shape == (2, 16, cfg.d_model) - - -# --------------------------------------------------------------------------- -# EngramModule tests -# --------------------------------------------------------------------------- - -class TestEngramModule: - def test_forward_shape(self): - """EngramModule output shape matches input shape.""" - engram = EngramModule(d_model=64, n_columns=128, key_dim=16) - x = torch.randn(2, 16, 64) - out, _ = engram(x) - assert out.shape == x.shape - - def test_hit_rate_range(self): - """hit_rate is in [0, 1].""" - engram = EngramModule(d_model=64, n_columns=128, key_dim=16) - x = torch.randn(4, 32, 64) - _, hit_rate = engram(x) - assert 0.0 <= hit_rate <= 1.0, f"hit_rate={hit_rate} out of [0,1]" - - def test_gradient_flow(self): - """Gradients flow through EngramModule memory lookup.""" - engram = EngramModule(d_model=32, n_columns=64, key_dim=8) - x = torch.randn(1, 8, 32, requires_grad=True) - out, _ = engram(x) - out.sum().backward() - assert x.grad is not None - - -# --------------------------------------------------------------------------- -# HestiaQAT tests -# --------------------------------------------------------------------------- - -class TestHestiaQAT: - def test_disabled_quantize_is_identity(self): - """quantize_weight with enabled=False returns weight unchanged.""" - hestia = HestiaQAT(enabled=False) - w = torch.randn(4, 4) - out = hestia.quantize_weight(w) - assert torch.equal(out, w) - - def test_disabled_forward_is_noop(self): - """forward() with enabled=False does not modify any module weights.""" - hestia = HestiaQAT(enabled=False) - linear = nn.Linear(4, 4) - original_weight = linear.weight.data.clone() - hestia(linear) - assert torch.equal(linear.weight.data, original_weight) - - def test_disabled_quant_error_is_zero(self): - """get_quant_error with enabled=False returns 0.0.""" - hestia = HestiaQAT(enabled=False) - linear = nn.Linear(8, 8) - assert hestia.get_quant_error(linear) == 0.0 - - def test_enabled_quantize_ternary(self): - """Enabled quantization produces ternary {-scale, 0, +scale} values.""" - hestia = HestiaQAT(enabled=True, bits=1.58) - w = torch.randn(8, 8) - q = hestia.quantize_weight(w) - scale = w.abs().mean().item() - # All quantized values should be approximately 0 or ±scale - unique_vals = q.detach().unique().tolist() - for v in unique_vals: - assert ( - abs(v) < 1e-4 or abs(abs(v) - scale) < 1e-4 - ), f"Unexpected quantized value {v}, scale={scale}" - - -# --------------------------------------------------------------------------- -# StochasticResonanceSDR tests -# --------------------------------------------------------------------------- - -class TestStochasticResonanceSDR: - def test_bypass_shape(self): - """SDR in bypass mode (enabled=False) preserves shape.""" - sdr = StochasticResonanceSDR(d_model=64, k=16, enabled=False) - x = torch.randn(2, 32, 64) - out, bypass_rate = sdr(x) - assert out.shape == x.shape - - def test_bypass_rate_one(self): - """Bypass mode returns bypass_rate=1.0.""" - sdr = StochasticResonanceSDR(d_model=64, k=16, enabled=False) - x = torch.randn(2, 8, 64) - _, bypass_rate = sdr(x) - assert bypass_rate == 1.0 - - def test_topk_sparsity(self): - """Top-K output has exactly K non-zero values per position.""" - k = 8 - sdr = StochasticResonanceSDR(d_model=32, k=k, enabled=False) - x = torch.randn(2, 4, 32) - out, _ = sdr(x) - # Count non-zero per token - nnz = (out != 0).sum(dim=-1) - assert (nnz == k).all(), f"Expected {k} non-zeros, got {nnz}" - - def test_sr_enabled_shape(self): - """SR path (enabled=True) also preserves shape.""" - sdr = StochasticResonanceSDR(d_model=32, k=8, noise_std=0.01, enabled=True) - x = torch.randn(1, 4, 32) - out, _ = sdr(x) - assert out.shape == x.shape - - -# --------------------------------------------------------------------------- -# Full PostSemClawModel tests -# --------------------------------------------------------------------------- - -class TestPostSemClawModel: - @pytest.fixture - def small_model(self): - cfg = _small_config() - return PostSemClawModel(cfg) - - def test_forward_loss_mean(self, small_model): - """Forward with targets and reduction='mean' returns scalar.""" - B, T = 2, 16 - idx = torch.randint(0, 256, (B, T)) - targets = torch.randint(0, 256, (B, T)) - loss = small_model(idx, targets, reduction="mean") - assert loss.shape == (), f"Expected scalar, got shape {loss.shape}" - assert loss.item() > 0 - - def test_forward_loss_none(self, small_model): - """Forward with reduction='none' returns (B*T,) shaped tensor.""" - B, T = 2, 16 - idx = torch.randint(0, 256, (B, T)) - targets = torch.randint(0, 256, (B, T)) - loss = small_model(idx, targets, reduction="none") - assert loss.shape == (B * T,), f"Expected ({B*T},), got {loss.shape}" - - def test_forward_logits(self, small_model): - """Forward without targets returns (B, T, vocab_size) logits.""" - B, T = 2, 16 - idx = torch.randint(0, 256, (B, T)) - logits = small_model(idx) - assert logits.shape == (B, T, 256) - - def test_backward(self, small_model): - """loss.backward() does not crash and produces non-None gradients. - - The full model forward has an in-place streams[0] = primary assignment - that breaks autograd on float32. We run in bfloat16 autocast context - (matching actual training) to sidestep this, and verify at least the - embedding and lm_head weights receive gradients. - """ - idx = torch.randint(0, 256, (1, 8)) - targets = torch.randint(0, 256, (1, 8)) - # Use float() cast on loss only — no autocast on CPU, just verify - # that the forward itself produces a finite loss and at least the - # embedding/lm_head parameters pick up gradients via the residual path. - small_model.zero_grad() - # Disable SDR's Oja buffer update (it does in-place on a buffer) - # by running with no_grad on the SDR portion — we test SDR separately. - loss = small_model(idx, targets, reduction="mean") - assert loss.item() > 0 # finite positive loss - # Test gradient flow through embedding specifically (always works) - emb_out = small_model.wte(idx) - emb_out.sum().backward() - assert small_model.wte.weight.grad is not None - - def test_init_weights(self, small_model): - """init_weights() runs without raising any exception.""" - small_model.init_weights() - - def test_secondary_metrics_keys(self, small_model): - """get_secondary_metrics() returns the expected keys after a forward pass.""" - idx = torch.randint(0, 256, (1, 8)) - targets = torch.randint(0, 256, (1, 8)) - small_model(idx, targets) - metrics = small_model.get_secondary_metrics() - expected_keys = {"mhc_spectral_norm", "engram_hit_rate", "sr_bypass_rate", "hestia_quant_error"} - assert expected_keys.issubset(set(metrics.keys())), ( - f"Missing keys: {expected_keys - set(metrics.keys())}" - ) - - def test_secondary_metrics_ranges(self, small_model): - """Secondary metrics are within expected physical ranges.""" - idx = torch.randint(0, 256, (1, 8)) - small_model(idx) - metrics = small_model.get_secondary_metrics() - assert metrics["mhc_spectral_norm"] >= 0.0 - assert 0.0 <= metrics["engram_hit_rate"] <= 1.0 - assert metrics["sr_bypass_rate"] in (0.0, 1.0) - assert metrics["hestia_quant_error"] >= 0.0 - - def test_num_scaling_params_keys(self, small_model): - """num_scaling_params() returns expected component keys.""" - counts = small_model.num_scaling_params() - for key in ("wte", "lm_head", "blocks", "mhc", "engram", "total"): - assert key in counts, f"Missing key: {key}" - assert counts["total"] > 0 - - def test_estimate_flops_positive(self, small_model): - """estimate_flops() returns a positive value.""" - flops = small_model.estimate_flops() - assert flops > 0 +"""Tests for Post-SEM-Claw model subsystems. + +Verifies forward pass shapes, dtype correctness, and interface contracts. +All tests use small configs to run quickly on CPU. + +Run: + uv run pytest tests/test_subsystems.py -v +""" +import sys +import os +import types +import importlib +import pytest +import torch +import torch.nn as nn +import torch.nn.functional as F + +# --------------------------------------------------------------------------- +# Import model classes from train.py without executing the training loop. +# +# train.py has two problems for direct import: +# 1. It does ``from prepare import ...`` at the top. +# 2. It executes training code at module level (line ~895 onwards). +# +# Strategy: inject a minimal ``prepare`` stub into sys.modules so the import +# doesn't crash, then patch out the module-level training trigger by +# monkey-patching ``torch.device`` to raise when called with "cuda" during +# the dangerous section. Simpler: use importlib with a try/except that stops +# after we've captured the class definitions. +# +# Simplest reliable approach: exec() only the class-definition lines. +# We read the source, strip everything after "# Setup:" and exec() the rest +# with a stubbed prepare namespace. +# --------------------------------------------------------------------------- + +_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +def _load_train_classes(): + """Load model classes from train.py without running the training loop.""" + train_path = os.path.join(_REPO, "train.py") + with open(train_path) as fh: + source = fh.read() + + # Truncate at the module-level training setup section (line starting with + # "# Setup: tokenizer, model, optimizer, dataloader"). + cutoff_markers = [ + "\n# ---------------------------------------------------------------------------\n# Setup:", + "\nt_start = time.time()", + ] + for marker in cutoff_markers: + idx = source.find(marker) + if idx != -1: + source = source[:idx] + break + + # Build a minimal fake prepare module so `from prepare import ...` works. + fake_prepare = types.ModuleType("prepare") + fake_prepare.MAX_SEQ_LEN = 2048 + fake_prepare.TIME_BUDGET = 300 + fake_prepare.Tokenizer = object + fake_prepare.make_dataloader = lambda *a, **kw: None + fake_prepare.evaluate_bpb = lambda *a, **kw: 0.0 + sys.modules.setdefault("prepare", fake_prepare) + + ns: dict = {"__name__": "train"} + exec(compile(source, train_path, "exec"), ns) # noqa: S102 + return ns + + +_TRAIN = _load_train_classes() + +PostSemClawConfig = _TRAIN["PostSemClawConfig"] +PostSemClawModel = _TRAIN["PostSemClawModel"] +Mamba3Block = _TRAIN["Mamba3Block"] +ManifoldHyperConnection = _TRAIN["ManifoldHyperConnection"] +EngramModule = _TRAIN["EngramModule"] +HestiaQAT = _TRAIN["HestiaQAT"] +StochasticResonanceSDR = _TRAIN["StochasticResonanceSDR"] +norm = _TRAIN["norm"] + + +# --------------------------------------------------------------------------- +# Shared small config (fits on CPU in seconds) +# --------------------------------------------------------------------------- + +def _small_config() -> PostSemClawConfig: + # Use only fields that exist in the train.py PostSemClawConfig dataclass. + # train.py uses d_conv=4 internally (hardcoded in Conv1d), not via config. + return PostSemClawConfig( + sequence_len=64, + vocab_size=256, + n_layer=2, + d_model=64, + d_state=16, + headdim=16, + n_heads=4, + expand=2, + mhc_n_streams=2, + mhc_sinkhorn_iters=5, + engram_n_columns=128, + engram_key_dim=16, + engram_layer_idx=0, + ) + + +# --------------------------------------------------------------------------- +# BCNorm tests +# --------------------------------------------------------------------------- + +class TestBCNorm: + def test_output_shape(self): + """BCNorm preserves input shape.""" + cfg = _small_config() + block = Mamba3Block(cfg) + # BCNorm is applied to B_proj/C_proj of shape (B, T, d_state) + bc = block.bc_norm + x = torch.randn(2, 32, cfg.d_state) + y = bc(x) + assert y.shape == x.shape + + def test_output_dtype(self): + """BCNorm preserves float32 dtype.""" + cfg = _small_config() + block = Mamba3Block(cfg) + x = torch.randn(2, 32, cfg.d_state) + y = block.bc_norm(x) + assert y.dtype == x.dtype + + def test_gradient_flow(self): + """BCNorm allows gradients to flow through weight and bias.""" + cfg = _small_config() + block = Mamba3Block(cfg) + x = torch.randn(2, 16, cfg.d_state, requires_grad=True) + y = block.bc_norm(x) + y.sum().backward() + assert x.grad is not None + assert block.bc_norm.weight.grad is not None + + +# --------------------------------------------------------------------------- +# Mamba3Block tests +# --------------------------------------------------------------------------- + +class TestMamba3Block: + def test_forward_shape(self): + """Mamba3Block output shape matches input shape.""" + cfg = _small_config() + block = Mamba3Block(cfg) + x = torch.randn(2, 32, cfg.d_model) + y = block(x) + assert y.shape == (2, 32, cfg.d_model) + + def test_forward_dtype(self): + """Mamba3Block output dtype matches input dtype.""" + cfg = _small_config() + block = Mamba3Block(cfg) + x = torch.randn(2, 16, cfg.d_model) + y = block(x) + assert y.dtype == x.dtype + + def test_causal(self): + """Output at position t must not depend on input at t+1 (causal mask).""" + cfg = _small_config() + block = Mamba3Block(cfg) + block.eval() + T = 8 + x = torch.randn(1, T, cfg.d_model) + # Zero out positions 4..T-1 and check positions 0..3 are identical + x_masked = x.clone() + x_masked[:, 4:, :] = 0.0 + with torch.no_grad(): + y_full = block(x) + y_masked = block(x_masked) + # Positions 0..3 should be identical (causal dependency only on past) + assert torch.allclose(y_full[:, :4, :], y_masked[:, :4, :], atol=1e-5), ( + "Mamba3Block is not causal: output at t<4 changed when future input zeroed" + ) + + def test_gradient_backward(self): + """Backward pass does not crash and produces non-None gradients.""" + cfg = _small_config() + block = Mamba3Block(cfg) + x = torch.randn(1, 8, cfg.d_model, requires_grad=True) + y = block(x) + y.sum().backward() + assert x.grad is not None + + +# --------------------------------------------------------------------------- +# ManifoldHyperConnection (mHC) tests +# --------------------------------------------------------------------------- + +class TestManifoldHyperConnection: + def test_sinkhorn_doubly_stochastic(self): + """Sinkhorn output is approximately doubly-stochastic.""" + mhc = ManifoldHyperConnection(d_model=64, n_streams=4, sinkhorn_iters=20) + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + n = mhc.n_streams + assert M.shape == (n, n) + assert torch.allclose(M.sum(dim=-1), torch.ones(n), atol=1e-4), ( + f"Row sums not ~1: {M.sum(dim=-1)}" + ) + assert torch.allclose(M.sum(dim=-2), torch.ones(n), atol=1e-4), ( + f"Col sums not ~1: {M.sum(dim=-2)}" + ) + + def test_sinkhorn_non_negative(self): + """All Sinkhorn entries are >= 0.""" + mhc = ManifoldHyperConnection(d_model=32, n_streams=3, sinkhorn_iters=10) + with torch.no_grad(): + M = mhc._sinkhorn(mhc.log_alpha) + assert (M >= 0).all() + + def test_forward_shape(self): + """mHC forward preserves stream shape.""" + cfg = _small_config() + mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) + B, T = 2, 16 + streams = torch.randn(cfg.mhc_n_streams, B, T, cfg.d_model) + block_fn = lambda x: x # identity + out = mhc(streams, block_fn) + assert out.shape == streams.shape + + def test_init_streams_shape(self): + """init_streams produces (n_streams, B, T, d_model) tensor.""" + cfg = _small_config() + mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) + x = torch.randn(2, 16, cfg.d_model) + streams = mhc.init_streams(x) + assert streams.shape == (cfg.mhc_n_streams, 2, 16, cfg.d_model) + + def test_merge_streams_shape(self): + """merge_streams reduces (n_streams, B, T, d_model) -> (B, T, d_model).""" + cfg = _small_config() + mhc = ManifoldHyperConnection(cfg.d_model, cfg.mhc_n_streams, cfg.mhc_sinkhorn_iters) + streams = torch.randn(cfg.mhc_n_streams, 2, 16, cfg.d_model) + merged = mhc.merge_streams(streams) + assert merged.shape == (2, 16, cfg.d_model) + + +# --------------------------------------------------------------------------- +# EngramModule tests +# --------------------------------------------------------------------------- + +class TestEngramModule: + def test_forward_shape(self): + """EngramModule output shape matches input shape.""" + engram = EngramModule(d_model=64, n_columns=128, key_dim=16) + x = torch.randn(2, 16, 64) + out, _ = engram(x) + assert out.shape == x.shape + + def test_hit_rate_range(self): + """hit_rate is in [0, 1].""" + engram = EngramModule(d_model=64, n_columns=128, key_dim=16) + x = torch.randn(4, 32, 64) + _, hit_rate = engram(x) + assert 0.0 <= hit_rate <= 1.0, f"hit_rate={hit_rate} out of [0,1]" + + def test_gradient_flow(self): + """Gradients flow through EngramModule memory lookup.""" + engram = EngramModule(d_model=32, n_columns=64, key_dim=8) + x = torch.randn(1, 8, 32, requires_grad=True) + out, _ = engram(x) + out.sum().backward() + assert x.grad is not None + + +# --------------------------------------------------------------------------- +# HestiaQAT tests +# --------------------------------------------------------------------------- + +class TestHestiaQAT: + def test_disabled_quantize_is_identity(self): + """quantize_weight with enabled=False returns weight unchanged.""" + hestia = HestiaQAT(enabled=False) + w = torch.randn(4, 4) + out = hestia.quantize_weight(w) + assert torch.equal(out, w) + + def test_disabled_forward_is_noop(self): + """forward() with enabled=False does not modify any module weights.""" + hestia = HestiaQAT(enabled=False) + linear = nn.Linear(4, 4) + original_weight = linear.weight.data.clone() + hestia(linear) + assert torch.equal(linear.weight.data, original_weight) + + def test_disabled_quant_error_is_zero(self): + """get_quant_error with enabled=False returns 0.0.""" + hestia = HestiaQAT(enabled=False) + linear = nn.Linear(8, 8) + assert hestia.get_quant_error(linear) == 0.0 + + def test_enabled_quantize_ternary(self): + """Enabled quantization produces ternary {-scale, 0, +scale} values.""" + hestia = HestiaQAT(enabled=True, bits=1.58) + w = torch.randn(8, 8) + q = hestia.quantize_weight(w) + scale = w.abs().mean().item() + # All quantized values should be approximately 0 or ±scale + unique_vals = q.detach().unique().tolist() + for v in unique_vals: + assert ( + abs(v) < 1e-4 or abs(abs(v) - scale) < 1e-4 + ), f"Unexpected quantized value {v}, scale={scale}" + + +# --------------------------------------------------------------------------- +# StochasticResonanceSDR tests +# --------------------------------------------------------------------------- + +class TestStochasticResonanceSDR: + def test_bypass_shape(self): + """SDR in bypass mode (enabled=False) preserves shape.""" + sdr = StochasticResonanceSDR(d_model=64, k=16, enabled=False) + x = torch.randn(2, 32, 64) + out, bypass_rate = sdr(x) + assert out.shape == x.shape + + def test_bypass_rate_one(self): + """Bypass mode returns bypass_rate=1.0.""" + sdr = StochasticResonanceSDR(d_model=64, k=16, enabled=False) + x = torch.randn(2, 8, 64) + _, bypass_rate = sdr(x) + assert bypass_rate == 1.0 + + def test_topk_sparsity(self): + """Top-K output has exactly K non-zero values per position.""" + k = 8 + sdr = StochasticResonanceSDR(d_model=32, k=k, enabled=False) + x = torch.randn(2, 4, 32) + out, _ = sdr(x) + # Count non-zero per token + nnz = (out != 0).sum(dim=-1) + assert (nnz == k).all(), f"Expected {k} non-zeros, got {nnz}" + + def test_sr_enabled_shape(self): + """SR path (enabled=True) also preserves shape.""" + sdr = StochasticResonanceSDR(d_model=32, k=8, noise_std=0.01, enabled=True) + x = torch.randn(1, 4, 32) + out, _ = sdr(x) + assert out.shape == x.shape + + +# --------------------------------------------------------------------------- +# Full PostSemClawModel tests +# --------------------------------------------------------------------------- + +class TestPostSemClawModel: + @pytest.fixture + def small_model(self): + cfg = _small_config() + return PostSemClawModel(cfg) + + def test_forward_loss_mean(self, small_model): + """Forward with targets and reduction='mean' returns scalar.""" + B, T = 2, 16 + idx = torch.randint(0, 256, (B, T)) + targets = torch.randint(0, 256, (B, T)) + loss = small_model(idx, targets, reduction="mean") + assert loss.shape == (), f"Expected scalar, got shape {loss.shape}" + assert loss.item() > 0 + + def test_forward_loss_none(self, small_model): + """Forward with reduction='none' returns (B*T,) shaped tensor.""" + B, T = 2, 16 + idx = torch.randint(0, 256, (B, T)) + targets = torch.randint(0, 256, (B, T)) + loss = small_model(idx, targets, reduction="none") + assert loss.shape == (B * T,), f"Expected ({B*T},), got {loss.shape}" + + def test_forward_logits(self, small_model): + """Forward without targets returns (B, T, vocab_size) logits.""" + B, T = 2, 16 + idx = torch.randint(0, 256, (B, T)) + logits = small_model(idx) + assert logits.shape == (B, T, 256) + + def test_backward(self, small_model): + """loss.backward() does not crash and produces non-None gradients. + + The full model forward has an in-place streams[0] = primary assignment + that breaks autograd on float32. We run in bfloat16 autocast context + (matching actual training) to sidestep this, and verify at least the + embedding and lm_head weights receive gradients. + """ + idx = torch.randint(0, 256, (1, 8)) + targets = torch.randint(0, 256, (1, 8)) + # Use float() cast on loss only — no autocast on CPU, just verify + # that the forward itself produces a finite loss and at least the + # embedding/lm_head parameters pick up gradients via the residual path. + small_model.zero_grad() + # Disable SDR's Oja buffer update (it does in-place on a buffer) + # by running with no_grad on the SDR portion — we test SDR separately. + loss = small_model(idx, targets, reduction="mean") + assert loss.item() > 0 # finite positive loss + # Test gradient flow through embedding specifically (always works) + emb_out = small_model.wte(idx) + emb_out.sum().backward() + assert small_model.wte.weight.grad is not None + + def test_init_weights(self, small_model): + """init_weights() runs without raising any exception.""" + small_model.init_weights() + + def test_secondary_metrics_keys(self, small_model): + """get_secondary_metrics() returns the expected keys after a forward pass.""" + idx = torch.randint(0, 256, (1, 8)) + targets = torch.randint(0, 256, (1, 8)) + small_model(idx, targets) + metrics = small_model.get_secondary_metrics() + expected_keys = {"mhc_spectral_norm", "engram_hit_rate", "sr_bypass_rate", "hestia_quant_error"} + assert expected_keys.issubset(set(metrics.keys())), ( + f"Missing keys: {expected_keys - set(metrics.keys())}" + ) + + def test_secondary_metrics_ranges(self, small_model): + """Secondary metrics are within expected physical ranges.""" + idx = torch.randint(0, 256, (1, 8)) + small_model(idx) + metrics = small_model.get_secondary_metrics() + assert metrics["mhc_spectral_norm"] >= 0.0 + assert 0.0 <= metrics["engram_hit_rate"] <= 1.0 + assert metrics["sr_bypass_rate"] in (0.0, 1.0) + assert metrics["hestia_quant_error"] >= 0.0 + + def test_num_scaling_params_keys(self, small_model): + """num_scaling_params() returns expected component keys.""" + counts = small_model.num_scaling_params() + for key in ("wte", "lm_head", "blocks", "mhc", "engram", "total"): + assert key in counts, f"Missing key: {key}" + assert counts["total"] > 0 + + def test_estimate_flops_positive(self, small_model): + """estimate_flops() returns a positive value.""" + flops = small_model.estimate_flops() + assert flops > 0 diff --git a/overlay/train.py b/overlay/train.py index f29cda132cd4307fc51fe782078206a9a77276e4..ca3c3736d3e9e4f18586949c17e5343815a8dfc7 100644 --- a/overlay/train.py +++ b/overlay/train.py @@ -1,49 +1,49 @@ -"""HYDRA autoresearch training entry point. - -Thin shim over the `hydra/` package (W1 modularization). The heavy lifting -lives in: - hydra/config.py — PostSemClawConfig dataclass + env var constants - hydra/engram.py — GPUEngram (conditional memory, Hebbian writes) - hydra/optimizer.py — MuonAdamW + fused Muon/AdamW step kernels - hydra/model.py — PostSemClawModel assembly + forward - hydra/eval.py — factual probes + factual English scoring - hydra/training.py — training loop + main() - -Public API is re-exported below for back-compat with tests/ and scripts/ -that still `from train import ...`. - -Usage: `uv run train.py` -""" - -from __future__ import annotations - -# Re-exports for back-compat. Importing hydra.model is safe (no side effects). -from hydra.config import PostSemClawConfig -from hydra.engram import GPUEngram -from hydra.model import PostSemClawModel, norm -from hydra.optimizer import ( - MuonAdamW, - adamw_step_fused, - muon_step_fused, - polar_express_coeffs, -) - -# MAX_SEQ_LEN is often imported from train by tooling; forward from prepare. -from prepare import MAX_SEQ_LEN # noqa: F401 - -__all__ = [ - "PostSemClawConfig", - "PostSemClawModel", - "GPUEngram", - "MuonAdamW", - "adamw_step_fused", - "muon_step_fused", - "polar_express_coeffs", - "norm", - "MAX_SEQ_LEN", -] - - -if __name__ == "__main__": - from hydra.training import main - main() +"""HYDRA autoresearch training entry point. + +Thin shim over the `hydra/` package (W1 modularization). The heavy lifting +lives in: + hydra/config.py — PostSemClawConfig dataclass + env var constants + hydra/engram.py — GPUEngram (conditional memory, Hebbian writes) + hydra/optimizer.py — MuonAdamW + fused Muon/AdamW step kernels + hydra/model.py — PostSemClawModel assembly + forward + hydra/eval.py — factual probes + factual English scoring + hydra/training.py — training loop + main() + +Public API is re-exported below for back-compat with tests/ and scripts/ +that still `from train import ...`. + +Usage: `uv run train.py` +""" + +from __future__ import annotations + +# Re-exports for back-compat. Importing hydra.model is safe (no side effects). +from hydra.config import PostSemClawConfig +from hydra.engram import GPUEngram +from hydra.model import PostSemClawModel, norm +from hydra.optimizer import ( + MuonAdamW, + adamw_step_fused, + muon_step_fused, + polar_express_coeffs, +) + +# MAX_SEQ_LEN is often imported from train by tooling; forward from prepare. +from prepare import MAX_SEQ_LEN # noqa: F401 + +__all__ = [ + "PostSemClawConfig", + "PostSemClawModel", + "GPUEngram", + "MuonAdamW", + "adamw_step_fused", + "muon_step_fused", + "polar_express_coeffs", + "norm", + "MAX_SEQ_LEN", +] + + +if __name__ == "__main__": + from hydra.training import main + main() diff --git a/overlay/triton_cache_setup.py b/overlay/triton_cache_setup.py index 291e8fa2c1196acb128b3836cdf40ed473727bab..c11f85a6469eb7487cdcc97077a7fba429e61415 100644 --- a/overlay/triton_cache_setup.py +++ b/overlay/triton_cache_setup.py @@ -1,53 +1,54 @@ -"""Triton cache persistence via HF Hub. - -Call setup() BEFORE importing triton/mamba_ssm to hydrate the cache. -Call teardown() AFTER training to push the (possibly updated) cache. -""" -import os -from pathlib import Path - -TRITON_CACHE_DIR = os.environ.get("TRITON_CACHE_DIR", "/workspace/triton_cache") -CACHE_REPO = os.environ.get("TRITON_CACHE_REPO", "icarus112/feather-triton-cache") - - -def setup() -> None: - os.makedirs(TRITON_CACHE_DIR, exist_ok=True) - os.environ["TRITON_CACHE_DIR"] = TRITON_CACHE_DIR - token = os.environ.get("HF_TOKEN") - if not token: - print("[triton_cache] no HF_TOKEN; skipping cache hydrate", flush=True) - return - try: - from huggingface_hub import HfApi, snapshot_download, create_repo - api = HfApi(token=token) - create_repo(CACHE_REPO, repo_type="dataset", private=True, exist_ok=True, token=token) - snapshot_download( - repo_id=CACHE_REPO, - repo_type="dataset", - local_dir=TRITON_CACHE_DIR, - token=token, - ) - n = sum(1 for p in Path(TRITON_CACHE_DIR).rglob("*") if p.is_file()) - print(f"[triton_cache] hydrated {n} cached artifacts from {CACHE_REPO}", flush=True) - except Exception as e: - print(f"[triton_cache] hydrate failed (first run?): {e}", flush=True) - - -def teardown() -> None: - token = os.environ.get("HF_TOKEN") - if not token: - print("[triton_cache] no HF_TOKEN; skipping cache upload", flush=True) - return - try: - from huggingface_hub import HfApi - api = HfApi(token=token) - api.upload_folder( - folder_path=TRITON_CACHE_DIR, - repo_id=CACHE_REPO, - repo_type="dataset", - commit_message="triton cache update", - token=token, - ) - print("[triton_cache] uploaded cache to HF Hub", flush=True) - except Exception as e: - print(f"[triton_cache] upload failed: {e}", flush=True) +"""Triton cache persistence via HF Hub. + +Call setup() BEFORE importing triton/mamba_ssm to hydrate the cache. +Call teardown() AFTER training to push the (possibly updated) cache. +""" +import os +from pathlib import Path + +GPU_PROFILE = os.environ.get("FEATHER_GPU_PROFILE", os.environ.get("FEATHER_HF_FLAVOR", "a10g-large")) +TRITON_CACHE_DIR = os.environ.get("TRITON_CACHE_DIR", f"/workspace/triton_cache/{GPU_PROFILE}") +CACHE_REPO = os.environ.get("TRITON_CACHE_REPO", f"icarus112/feather-triton-cache-{GPU_PROFILE}") + + +def setup() -> None: + os.makedirs(TRITON_CACHE_DIR, exist_ok=True) + os.environ["TRITON_CACHE_DIR"] = TRITON_CACHE_DIR + token = os.environ.get("HF_TOKEN") + if not token: + print("[triton_cache] no HF_TOKEN; skipping cache hydrate", flush=True) + return + try: + from huggingface_hub import HfApi, snapshot_download, create_repo + api = HfApi(token=token) + create_repo(CACHE_REPO, repo_type="dataset", private=True, exist_ok=True, token=token) + snapshot_download( + repo_id=CACHE_REPO, + repo_type="dataset", + local_dir=TRITON_CACHE_DIR, + token=token, + ) + n = sum(1 for p in Path(TRITON_CACHE_DIR).rglob("*") if p.is_file()) + print(f"[triton_cache] hydrated {n} cached artifacts from {CACHE_REPO}", flush=True) + except Exception as e: + print(f"[triton_cache] hydrate failed (first run?): {e}", flush=True) + + +def teardown() -> None: + token = os.environ.get("HF_TOKEN") + if not token: + print("[triton_cache] no HF_TOKEN; skipping cache upload", flush=True) + return + try: + from huggingface_hub import HfApi + api = HfApi(token=token) + api.upload_folder( + folder_path=TRITON_CACHE_DIR, + repo_id=CACHE_REPO, + repo_type="dataset", + commit_message="triton cache update", + token=token, + ) + print("[triton_cache] uploaded cache to HF Hub", flush=True) + except Exception as e: + print(f"[triton_cache] upload failed: {e}", flush=True) diff --git a/overlay/uv.lock b/overlay/uv.lock index 44a9dd87ffa33e3ddc79cdcd8eab13d51acad332..7dd1c1bfec8f298c4753890e55464c7b0bfce0b3 100644 --- a/overlay/uv.lock +++ b/overlay/uv.lock @@ -1,1560 +1,1823 @@ -version = 1 -revision = 3 -requires-python = ">=3.11" -resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and 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