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import os
import gc
import sys
import time
import contextlib
import torch
from modules.errors import log
from modules import cmd_args, shared, memstats, errors
if sys.platform == "darwin":
from modules import mac_specific # pylint: disable=ungrouped-imports
previous_oom = 0
backup_sdpa = None
debug = os.environ.get('SD_DEVICE_DEBUG', None) is not None
def has_mps() -> bool:
if sys.platform != "darwin":
return False
else:
return mac_specific.has_mps # pylint: disable=used-before-assignment
def get_gpu_info():
def get_driver():
import subprocess
if torch.cuda.is_available() and torch.version.cuda:
try:
result = subprocess.run('nvidia-smi --query-gpu=driver_version --format=csv,noheader', shell=True, check=False, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
version = result.stdout.decode(encoding="utf8", errors="ignore").strip()
return version
except Exception:
return ''
else:
return ''
def get_package_version(pkg: str):
import pkg_resources
spec = pkg_resources.working_set.by_key.get(pkg, None) # more reliable than importlib
version = pkg_resources.get_distribution(pkg).version if spec is not None else ''
return version
if not torch.cuda.is_available():
try:
if shared.cmd_opts.use_openvino:
return {
'device': get_openvino_device(),
'openvino': get_package_version("openvino"),
}
elif shared.cmd_opts.use_directml:
return {
'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()}',
'directml': get_package_version("torch-directml"),
}
else:
return {}
except Exception:
return {}
else:
try:
if hasattr(torch, "xpu") and torch.xpu.is_available():
return {
'device': f'{torch.xpu.get_device_name(torch.xpu.current_device())} n={torch.xpu.device_count()}',
'ipex': get_package_version('intel-extension-for-pytorch'),
}
elif torch.version.cuda:
return {
'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()} arch={torch.cuda.get_arch_list()[-1]} cap={torch.cuda.get_device_capability(device)}',
'cuda': torch.version.cuda,
'cudnn': torch.backends.cudnn.version(),
'driver': get_driver(),
}
elif torch.version.hip:
return {
'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()}',
'hip': torch.version.hip,
}
else:
return {
'device': 'unknown'
}
except Exception as ex:
if debug:
errors.display(ex, 'Device exception')
return { 'error': ex }
def extract_device_id(args, name): # pylint: disable=redefined-outer-name
for x in range(len(args)):
if name in args[x]:
return args[x + 1]
return None
def get_cuda_device_string():
if backend == 'ipex':
if shared.cmd_opts.device_id is not None:
return f"xpu:{shared.cmd_opts.device_id}"
return "xpu"
elif backend == 'directml' and torch.dml.is_available():
if shared.cmd_opts.device_id is not None:
return f"privateuseone:{shared.cmd_opts.device_id}"
return torch.dml.get_device_string(torch.dml.default_device().index)
else:
if shared.cmd_opts.device_id is not None:
return f"cuda:{shared.cmd_opts.device_id}"
return "cuda"
def get_optimal_device_name():
if cuda_ok or backend == 'directml':
return get_cuda_device_string()
if has_mps() and backend != 'openvino':
return "mps"
return "cpu"
def get_optimal_device():
return torch.device(get_optimal_device_name())
def get_device_for(task):
if task in shared.cmd_opts.use_cpu:
log.debug(f'Forcing CPU for task: {task}')
return cpu
return get_optimal_device()
def torch_gc(force=False):
t0 = time.time()
mem = memstats.memory_stats()
gpu = mem.get('gpu', {})
ram = mem.get('ram', {})
oom = gpu.get('oom', 0)
if backend == "directml":
used_gpu = round(100 * torch.cuda.memory_allocated() / (1 << 30) / gpu.get('total', 1)) if gpu.get('total', 1) > 1 else 0
else:
used_gpu = round(100 * gpu.get('used', 0) / gpu.get('total', 1)) if gpu.get('total', 1) > 1 else 0
used_ram = round(100 * ram.get('used', 0) / ram.get('total', 1)) if ram.get('total', 1) > 1 else 0
global previous_oom # pylint: disable=global-statement
if oom > previous_oom:
previous_oom = oom
log.warning(f'GPU out-of-memory error: {mem}')
force = True
if used_gpu >= shared.opts.torch_gc_threshold or used_ram >= shared.opts.torch_gc_threshold:
log.info(f'High memory utilization: GPU={used_gpu}% RAM={used_ram}% {mem}')
force = True
if not force:
return
# actual gc
collected = gc.collect() # python gc
if cuda_ok:
try:
with torch.cuda.device(get_cuda_device_string()):
torch.cuda.empty_cache() # cuda gc
torch.cuda.ipc_collect()
except Exception:
pass
t1 = time.time()
log.debug(f'GC: collected={collected} device={torch.device(get_optimal_device_name())} {memstats.memory_stats()} time={round(t1 - t0, 2)}')
def set_cuda_sync_mode(mode):
"""
Set the CUDA device synchronization mode: auto, spin, yield or block.
auto: Chooses spin or yield depending on the number of available CPU cores.
spin: Runs one CPU core per GPU at 100% to poll for completed operations.
yield: Gives control to other threads between polling, if any are waiting.
block: Lets the thread sleep until the GPU driver signals completion.
"""
if mode == -1 or mode == 'none' or not cuda_ok:
return
try:
import ctypes
log.info(f'Set cuda synch: mode={mode}')
torch.cuda.set_device(torch.device(get_optimal_device_name()))
ctypes.CDLL('libcudart.so').cudaSetDeviceFlags({'auto': 0, 'spin': 1, 'yield': 2, 'block': 4}[mode])
except Exception:
pass
def test_fp16():
if shared.cmd_opts.experimental:
return True
try:
x = torch.tensor([[1.5,.0,.0,.0]]).to(device=device, dtype=torch.float16)
layerNorm = torch.nn.LayerNorm(4, eps=0.00001, elementwise_affine=True, dtype=torch.float16, device=device)
_y = layerNorm(x)
return True
except Exception as ex:
log.warning(f'Torch FP16 test failed: Forcing FP32 operations: {ex}')
shared.opts.cuda_dtype = 'FP32'
shared.opts.no_half = True
shared.opts.no_half_vae = True
return False
def test_bf16():
if shared.cmd_opts.experimental:
return True
try:
import torch.nn.functional as F
image = torch.randn(1, 4, 32, 32).to(device=device, dtype=torch.bfloat16)
_out = F.interpolate(image, size=(64, 64), mode="nearest")
return True
except Exception:
log.warning('Torch BF16 test failed: Fallback to FP16 operations')
return False
def set_cuda_params():
# log.debug('Verifying Torch settings')
if cuda_ok:
try:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
except Exception:
pass
if torch.backends.cudnn.is_available():
try:
torch.backends.cudnn.deterministic = shared.opts.cudnn_deterministic
torch.backends.cudnn.benchmark = True
if shared.opts.cudnn_benchmark:
log.debug('Torch enable cuDNN benchmark')
torch.backends.cudnn.benchmark_limit = 0
torch.backends.cudnn.allow_tf32 = True
except Exception:
pass
try:
if shared.opts.cross_attention_optimization == "Scaled-Dot-Product" or shared.opts.cross_attention_optimization == "Dynamic Attention SDP":
torch.backends.cuda.enable_flash_sdp('Flash attention' in shared.opts.sdp_options)
torch.backends.cuda.enable_mem_efficient_sdp('Memory attention' in shared.opts.sdp_options)
torch.backends.cuda.enable_math_sdp('Math attention' in shared.opts.sdp_options)
if backend == "rocm":
global backup_sdpa # pylint: disable=global-statement
if 'Flash attention' in shared.opts.sdp_options:
try:
# https://github.com/huggingface/diffusers/discussions/7172
from flash_attn import flash_attn_func
if backup_sdpa is None:
backup_sdpa = torch.nn.functional.scaled_dot_product_attention
def sdpa_hijack(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
if query.shape[3] <= 128 and attn_mask is None:
return flash_attn_func(q=query.transpose(1, 2), k=key.transpose(1, 2), v=value.transpose(1, 2), dropout_p=dropout_p, causal=is_causal, softmax_scale=scale).transpose(1, 2)
else:
return backup_sdpa(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
torch.nn.functional.scaled_dot_product_attention = sdpa_hijack
shared.log.debug('ROCm Flash Attention Hijacked')
except Exception as err:
log.error(f'ROCm Flash Attention failed: {err}')
elif backup_sdpa is not None: # Restore original SDPA
torch.nn.functional.scaled_dot_product_attention = backup_sdpa
except Exception:
pass
if shared.cmd_opts.profile:
shared.log.debug(f'Torch info: {torch.__config__.show()}')
global dtype, dtype_vae, dtype_unet, unet_needs_upcast, inference_context # pylint: disable=global-statement
if shared.opts.cuda_dtype == 'FP32':
dtype = torch.float32
dtype_vae = torch.float32
dtype_unet = torch.float32
fp16_ok = None
bf16_ok = None
elif shared.opts.cuda_dtype == 'BF16' or dtype == torch.bfloat16:
fp16_ok = test_fp16()
bf16_ok = test_bf16()
dtype = torch.bfloat16 if bf16_ok else torch.float16
dtype_vae = torch.bfloat16 if bf16_ok else torch.float16
dtype_unet = torch.bfloat16 if bf16_ok else torch.float16
elif shared.opts.cuda_dtype == 'FP16' or dtype == torch.float16:
fp16_ok = test_fp16()
bf16_ok = None
dtype = torch.float16 if fp16_ok else torch.float32
dtype_vae = torch.float16 if fp16_ok else torch.float32
dtype_unet = torch.float16 if fp16_ok else torch.float32
if shared.opts.no_half:
log.info('Torch override dtype: no-half set')
dtype = torch.float32
dtype_vae = torch.float32
dtype_unet = torch.float32
if shared.opts.no_half_vae: # set dtype again as no-half-vae options take priority
log.info('Torch override VAE dtype: no-half set')
dtype_vae = torch.float32
unet_needs_upcast = shared.opts.upcast_sampling
if shared.opts.inference_mode == 'inference-mode':
inference_context = torch.inference_mode
elif shared.opts.inference_mode == 'none':
inference_context = contextlib.nullcontext
else:
inference_context = torch.no_grad
log_device_name = get_raw_openvino_device() if shared.cmd_opts.use_openvino else torch.device(get_optimal_device_name())
log.debug(f'Desired Torch parameters: dtype={shared.opts.cuda_dtype} no-half={shared.opts.no_half} no-half-vae={shared.opts.no_half_vae} upscast={shared.opts.upcast_sampling}')
log.info(f'Setting Torch parameters: device={log_device_name} dtype={dtype} vae={dtype_vae} unet={dtype_unet} context={inference_context.__name__} fp16={fp16_ok} bf16={bf16_ok} optimization={shared.opts.cross_attention_optimization}')
args = cmd_args.parser.parse_args()
backend = 'not set'
if args.use_openvino:
from modules.intel.openvino import get_openvino_device
from modules.intel.openvino import get_device as get_raw_openvino_device
backend = 'openvino'
if hasattr(torch, 'xpu') and torch.xpu.is_available():
torch.xpu.is_available = lambda *args, **kwargs: False
torch.cuda.is_available = lambda *args, **kwargs: False
elif args.use_ipex or (hasattr(torch, 'xpu') and torch.xpu.is_available()):
backend = 'ipex'
from modules.intel.ipex import ipex_init
ok, e = ipex_init()
if not ok:
log.error(f'IPEX initialization failed: {e}')
backend = 'cpu'
elif args.use_directml:
backend = 'directml'
from modules.dml import directml_init
ok, e = directml_init()
if not ok:
log.error(f'DirectML initialization failed: {e}')
backend = 'cpu'
elif torch.cuda.is_available() and torch.version.cuda:
backend = 'cuda'
elif torch.cuda.is_available() and torch.version.hip:
backend = 'rocm'
elif sys.platform == 'darwin':
backend = 'mps'
else:
backend = 'cpu'
inference_context = torch.no_grad
cuda_ok = torch.cuda.is_available()
cpu = torch.device("cpu")
device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
dtype = torch.float16
dtype_vae = torch.float16
dtype_unet = torch.float16
unet_needs_upcast = False
onnx = None
if args.profile:
log.info(f'Torch build config: {torch.__config__.show()}')
# set_cuda_sync_mode('block') # none/auto/spin/yield/block
def cond_cast_unet(tensor):
return tensor.to(dtype_unet) if unet_needs_upcast else tensor
def cond_cast_float(tensor):
return tensor.float() if unet_needs_upcast else tensor
def randn(seed, shape):
torch.manual_seed(seed)
if backend == 'ipex':
torch.xpu.manual_seed_all(seed)
if device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
if device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def autocast(disable=False):
if disable:
return contextlib.nullcontext()
if dtype == torch.float32 or shared.cmd_opts.precision == "Full":
return contextlib.nullcontext()
if shared.cmd_opts.use_directml:
return torch.dml.amp.autocast(dtype)
if cuda_ok:
return torch.autocast("cuda")
else:
return torch.autocast("cpu")
def without_autocast(disable=False):
if disable:
return contextlib.nullcontext()
if shared.cmd_opts.use_directml:
return torch.dml.amp.autocast(enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext() # pylint: disable=unexpected-keyword-arg
if cuda_ok:
return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext()
else:
return torch.autocast("cpu", enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext()
class NansException(Exception):
pass
def test_for_nans(x, where):
if shared.opts.disable_nan_check:
return
if not torch.all(torch.isnan(x)).item():
return
if where == "unet":
message = "A tensor with all NaNs was produced in Unet."
if not shared.opts.no_half:
message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
elif where == "vae":
message = "A tensor with all NaNs was produced in VAE."
if not shared.opts.no_half and not shared.opts.no_half_vae:
message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
else:
message = "A tensor with all NaNs was produced."
message += " Use --disable-nan-check commandline argument to disable this check."
raise NansException(message)
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