import numpy as np import torch import torch.nn as nn from torch.cuda.amp import custom_bwd, custom_fwd from transformers.models.llama.modeling_llama import LlamaMLP try: import triton import triton.language as tl from . import custom_autotune # code based https://github.com/fpgaminer/GPTQ-triton @custom_autotune.autotune( configs=[ triton.Config({ 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), triton.Config({ 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), triton.Config({ 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), triton.Config({ 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), triton.Config({ 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), triton.Config({ 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), # 3090 triton.Config({ 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), # 3090 triton.Config({ 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8 }, num_stages=2, num_warps=4), # 3090 triton.Config({ 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8 }, num_stages=4, num_warps=4), # 3090 ], key=['M', 'N', 'K'], nearest_power_of_two=True, prune_configs_by={ 'early_config_prune': custom_autotune.matmul248_kernel_config_pruner, 'perf_model': None, 'top_k': None, }, ) @triton.jit def fusedmatmul_248_kernel(a_ptr, c_ptr, b1_ptr, scales1_ptr, zeros1_ptr, g1_ptr, b2_ptr, scales2_ptr, zeros2_ptr, g2_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr): """ Computes: C = silu(A * B1) * (A * B2) A is of shape (M, K) float16 B is of shape (K//8, N) int32 C is of shape (M, N) float16 scales is of shape (1, N) float16 zeros is of shape (1, N//8) int32 """ infearure_per_bits = 32 // bits pid = tl.program_id(axis=0) num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) num_pid_in_group = GROUP_SIZE_M * num_pid_n group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + (pid % group_size_m) pid_n = (pid % num_pid_in_group) // group_size_m offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K) a_mask = (offs_am[:, None] < M) # b_ptrs is set up such that it repeats elements along the K axis 8 times b1_ptrs = b1_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) b2_ptrs = b2_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) g1_ptrs = g1_ptr + offs_k g2_ptrs = g2_ptr + offs_k # shifter is used to extract the N bits of each element in the 32-bit word from B scales1_ptrs = scales1_ptr + offs_bn[None, :] scales2_ptrs = scales2_ptr + offs_bn[None, :] zeros1_ptrs = zeros1_ptr + (offs_bn[None, :] // infearure_per_bits) zeros2_ptrs = zeros2_ptr + (offs_bn[None, :] // infearure_per_bits) shifter = (offs_k % infearure_per_bits) * bits zeros_shifter = (offs_bn % infearure_per_bits) * bits accumulator1 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) accumulator2 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, num_pid_k): g1_idx = tl.load(g1_ptrs) g2_idx = tl.load(g2_ptrs) # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop scales1 = tl.load(scales1_ptrs + g1_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) scales2 = tl.load(scales2_ptrs + g2_idx[:, None] * stride_scales) zeros1 = tl.load(zeros1_ptrs + g1_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros1 = (zeros1 >> zeros_shifter[None, :]) & maxq zeros1 = (zeros1 + 1) zeros2 = tl.load(zeros2_ptrs + g2_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros2 = (zeros2 >> zeros_shifter[None, :]) & maxq zeros2 = (zeros2 + 1) a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K) b1 = tl.load(b1_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated b2 = tl.load(b2_ptrs) # Now we need to unpack b (which is N-bit values) into 32-bit values b1 = (b1 >> shifter[:, None]) & maxq # Extract the N-bit values b1 = (b1 - zeros1) * scales1 # Scale and shift accumulator1 += tl.dot(a, b1) b2 = (b2 >> shifter[:, None]) & maxq b2 = (b2 - zeros2) * scales2 accumulator2 += tl.dot(a, b2) a_ptrs += BLOCK_SIZE_K b1_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk b2_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk g1_ptrs += BLOCK_SIZE_K g2_ptrs += BLOCK_SIZE_K accumulator1 = silu(accumulator1) c = accumulator1 * accumulator2 c = c.to(tl.float16) c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) tl.store(c_ptrs, c, mask=c_mask) @triton.jit def silu(x): return x * tl.sigmoid(x) except: print('triton not installed.') class QuantLlamaMLP(nn.Module): def __init__( self, gate_proj, down_proj, up_proj, ): super().__init__() self.register_buffer('gate_proj_qweight', gate_proj.qweight) self.register_buffer('gate_proj_scales', gate_proj.scales) self.register_buffer('gate_proj_qzeros', gate_proj.qzeros) self.register_buffer('gate_proj_g_idx', gate_proj.g_idx) self.register_buffer('up_proj_qweight', up_proj.qweight) self.register_buffer('up_proj_scales', up_proj.scales) self.register_buffer('up_proj_qzeros', up_proj.qzeros) self.register_buffer('up_proj_g_idx', up_proj.g_idx) self.infeatures = gate_proj.infeatures self.intermediate_size = gate_proj.outfeatures self.outfeatures = down_proj.outfeatures self.bits = gate_proj.bits self.maxq = gate_proj.maxq self.down_proj = down_proj def forward(self, x): return self.down_proj(self.triton_llama_mlp(x)) def triton_llama_mlp(self, x): with torch.cuda.device(x.device): out_shape = x.shape[:-1] + (self.intermediate_size, ) x = x.reshape(-1, x.shape[-1]) M, K = x.shape N = self.intermediate_size c = torch.empty((M, N), device='cuda', dtype=torch.float16) grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), ) fusedmatmul_248_kernel[grid](x, c, self.gate_proj_qweight, self.gate_proj_scales, self.gate_proj_qzeros, self.gate_proj_g_idx, self.up_proj_qweight, self.up_proj_scales, self.up_proj_qzeros, self.up_proj_g_idx, M, N, K, self.bits, self.maxq, x.stride(0), x.stride(1), self.gate_proj_qweight.stride(0), self.gate_proj_qweight.stride(1), c.stride(0), c.stride(1), self.gate_proj_scales.stride(0), self.gate_proj_qzeros.stride(0)) c = c.reshape(out_shape) return c def fused2cuda(self): self.gate_proj_qweight = self.gate_proj_qweight.cuda() self.gate_proj_scales = self.gate_proj_scales.cuda() self.gate_proj_qzeros = self.gate_proj_qzeros.cuda() self.gate_proj_g_idx = self.gate_proj_g_idx.cuda() self.up_proj_qweight = self.up_proj_qweight.cuda() self.up_proj_scales = self.up_proj_scales.cuda() self.up_proj_qzeros = self.up_proj_qzeros.cuda() self.up_proj_g_idx = self.up_proj_g_idx.cuda() def fused2cpu(self): self.gate_proj_qweight = self.gate_proj_qweight.cpu() self.gate_proj_scales = self.gate_proj_scales.cpu() self.gate_proj_qzeros = self.gate_proj_qzeros.cpu() self.gate_proj_g_idx = self.gate_proj_g_idx.cpu() self.up_proj_qweight = self.up_proj_qweight.cpu() self.up_proj_scales = self.up_proj_scales.cpu() self.up_proj_qzeros = self.up_proj_qzeros.cpu() self.up_proj_g_idx = self.up_proj_g_idx.cpu() def make_fused_mlp(m, parent_name=''): """ Replace all LlamaMLP modules with QuantLlamaMLP modules, which fuses many of the operations. """ if isinstance(m, LlamaMLP): return QuantLlamaMLP(m.gate_proj, m.down_proj, m.up_proj) for name, child in m.named_children(): child = make_fused_mlp(child, parent_name=f"{parent_name}.{name}") if isinstance(child, QuantLlamaMLP): setattr(m, name, child) return m def autotune_warmup_fused(model): """ Pre-tunes the quantized kernel """ from tqdm import tqdm kn_values = {} for _, m in model.named_modules(): if not isinstance(m, QuantLlamaMLP): continue k = m.infeatures n = m.intermediate_size m.fused2cuda() if (k, n) not in kn_values: kn_values[(k, n)] = m print(f'Found {len(kn_values)} unique fused mlp KN values.') print('Warming up autotune cache ...') with torch.no_grad(): for m in tqdm(range(0, 12)): m = 2**m # [1, 2048] for (k, n), (modules) in kn_values.items(): a = torch.randn(m, k, dtype=torch.float16, device='cuda') modules.triton_llama_mlp(a) for (k, n), (modules) in kn_values.items(): a = torch.randn(m, k, dtype=torch.float16, device='cuda') modules.fused2cpu() del kn_values