mirror of
https://github.com/deepseek-ai/DeepGEMM
synced 2025-06-26 23:15:49 +00:00
8dfa329827
* Grouped GEMM skip useless computation for unaligned Ms * Update readme.md * small typo * Rename variables * Restore previous indent * Format * Refactor tests * Add `SkipComputation` types * Bug fixed * Format * Fix tests * Add assertions * Minor fix --------- Co-authored-by: yukuai <yukuai@deepseek.com> Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
313 lines
15 KiB
Python
313 lines
15 KiB
Python
# PyTorch has its own NVRTC, which may have a lower version than the system
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# So try to disable PyTorch's NVRTC, or import NVRTC before PyTorch
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import cuda.bindings.nvrtc as nvrtc
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print(f'NVRTC version: {nvrtc.nvrtcVersion()[1:]}')
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import random
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import torch
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from typing import List, Tuple
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import deep_gemm
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from deep_gemm import bench_kineto, calc_diff, ceil_div, get_col_major_tma_aligned_tensor
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from deep_gemm.jit_kernels.utils import get_m_alignment_for_contiguous_layout
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def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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pad_size = (128 - (n % 128)) % 128
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x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
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x_view = x.view(m, -1, 128)
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x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
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fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
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return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
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def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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x_padded = torch.zeros((ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device)
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x_padded[:m, :n] = x
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x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
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x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
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x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
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return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
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def construct(m: int, k: int, n: int) -> \
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Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
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x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
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y = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
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out = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
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ref_out = x @ y.t()
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x_fp8, y_fp8 = per_token_cast_to_fp8(x), per_block_cast_to_fp8(y)
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# Transpose earlier so that the testing will not trigger transposing kernels
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x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
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return x_fp8, y_fp8, out, ref_out
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def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int) -> \
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Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
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alignment = get_m_alignment_for_contiguous_layout()
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group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3)) for _ in range(num_groups)]
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m = sum([ceil_div(x, alignment) * alignment for x in group_ms])
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x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
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y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
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m_indices = torch.empty(m, device='cuda', dtype=torch.int32)
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out = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
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ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)
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start = 0
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for i, group_m in enumerate(group_ms):
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actual_end = start + group_m
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aligned_end = start + ceil_div(group_m, alignment) * alignment
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m_indices[start:actual_end] = i
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m_indices[actual_end:aligned_end] = -1
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ref_out[start:aligned_end] = x[start:aligned_end] @ y[i].t()
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start = aligned_end
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ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
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assert m % 4 == 0, f'TMA alignment error: {m}'
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x_fp8 = per_token_cast_to_fp8(x)
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y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), k // 128), device='cuda', dtype=torch.float))
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for i in range(num_groups):
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y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
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return m, x_fp8, y_fp8, m_indices, out, ref_out
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def construct_masked_grouped(num_groups: int, max_m: int, expected_m_per_group: int, k: int, n: int) -> \
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Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
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x = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16)
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y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
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out = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16)
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ref_out = torch.einsum('gmk,gnk->gmn', x, y)
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assert max_m % 4 == 0, f'TMA alignment error: {max_m}'
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x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, max_m, k // 128), device='cuda', dtype=torch.float))
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y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), k // 128), device='cuda', dtype=torch.float))
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for i in range(num_groups):
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x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
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y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
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# Transpose earlier so that the testing will not trigger transposing kernels
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x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
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# Construct mask
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masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
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for j in range(num_groups):
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masked_m[j] = int(expected_m_per_group * random.uniform(0.7, 1.3))
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assert masked_m.amax().item() <= max_m
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return x_fp8, y_fp8, masked_m, out, ref_out
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def construct_wgrad(m: int, k: int, n: int) -> \
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Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
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x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
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y = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
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residual = torch.randn((m, n), device='cuda', dtype=torch.float) * 10
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out = residual.clone()
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ref_out = residual + (x.float() @ y.float().t())
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x_fp8 = per_token_cast_to_fp8(x)
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y_fp8 = per_token_cast_to_fp8(y)
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# NOTES: please do inplace add on the `out` later
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return x_fp8, y_fp8, residual, out, ref_out
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def construct_k_grouped_wgrad(m: int, n: int, k_sizes: List[int]) -> \
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Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, List[int]]:
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num_groups, total_k = len(k_sizes), sum(k_sizes)
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x_flat = torch.empty((m * total_k,), device='cuda', dtype=torch.bfloat16)
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y_flat = torch.empty((n * total_k,), device='cuda', dtype=torch.bfloat16)
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out = torch.zeros((num_groups, m, n), device='cuda', dtype=torch.float)
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ref_out = torch.zeros((num_groups, m, n), device='cuda', dtype=torch.float)
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# Fill tensors with data and compute reference output
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x_offset, y_offset = 0, 0
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for idx, k in enumerate(k_sizes):
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x_chunk = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
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y_chunk = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
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x_flat[x_offset:x_offset + m * k].copy_(x_chunk.flatten())
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y_flat[y_offset:y_offset + n * k].copy_(y_chunk.flatten())
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ref_out[idx] = x_chunk.float() @ y_chunk.float().t()
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x_offset += m * k
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y_offset += n * k
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x_fp8_flat = torch.empty_like(x_flat, dtype=torch.float8_e4m3fn)
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y_fp8_flat = torch.empty_like(y_flat, dtype=torch.float8_e4m3fn)
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total_scale_factors = sum(ceil_div(k, 128) for k in k_sizes)
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x_scales = torch.empty((total_scale_factors, m), device='cuda', dtype=torch.float)
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y_scales = torch.empty((total_scale_factors, n), device='cuda', dtype=torch.float)
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# Cast to FP8 and prepare scale factors
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x_offset, y_offset, scale_offset = 0, 0, 0
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for k in k_sizes:
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x_fp8_chunk, x_scale_chunk = per_token_cast_to_fp8(x_flat[x_offset:x_offset + m * k].view(m, k))
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y_fp8_chunk, y_scale_chunk = per_token_cast_to_fp8(y_flat[y_offset:y_offset + n * k].view(n, k))
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x_fp8_flat[x_offset:x_offset + m * k].copy_(x_fp8_chunk.flatten())
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y_fp8_flat[y_offset:y_offset + n * k].copy_(y_fp8_chunk.flatten())
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num_scales = ceil_div(k, 128)
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x_scales[scale_offset:scale_offset + num_scales].copy_(x_scale_chunk.T)
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y_scales[scale_offset:scale_offset + num_scales].copy_(y_scale_chunk.T)
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x_offset += m * k
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y_offset += n * k
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scale_offset += num_scales
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return (x_fp8_flat, x_scales), (y_fp8_flat, y_scales), out, ref_out, k_sizes
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def test_gemm() -> None:
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print('Testing GEMM:')
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for m in (64, 128, 4096):
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for k, n in [(576, 7168), (7168, 2112), (1536, 24576), (512, 32768), (16384, 7168), (7168, 4096), (2048, 7168)]:
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x_fp8, y_fp8, out, ref_out = construct(m, k, n)
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deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
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diff = calc_diff(out, ref_out)
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assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
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t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
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print(f' > Perf (m={m:5}, n={n:5}, k={k:5}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(m * k + k * n + m * n * 2) / 1e9 / t:4.0f} GB/s')
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print()
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def test_m_grouped_gemm_contiguous() -> None:
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print('Testing grouped contiguous GEMM:')
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for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168),
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(8, 4096, 7168, 4096), (8, 4096, 2048, 7168),
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(32, 256, 7168, 4096), (32, 256, 2048, 7168)):
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# NOTES: we should mask the unfilled part before calculating difference
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m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
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out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out)
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diff = calc_diff(out, ref_out)
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assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
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t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
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valid_m = (m_indices != -1).sum().item()
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print(f' > Perf ({num_groups=:2}, {expected_m_per_group=:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
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print()
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def test_m_grouped_gemm_masked() -> None:
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print('Testing grouped masked GEMM:')
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for num_groups, expected_m_per_group in ((1, 1024), (2, 512), (4, 256)):
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for k, n in ((7168, 4096), (2048, 7168), ):
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# Test correctness
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for i in range(10):
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x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, 4096, expected_m_per_group, k, n)
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
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for j in range(num_groups):
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diff = calc_diff(out[j, :masked_m[j].item()], ref_out[j, :masked_m[j].item()])
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assert diff < 0.001, f'{expected_m_per_group=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
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# Test performance with fixed shapes
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# noinspection PyUnboundLocalVariable
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valid_m = masked_m.sum().item()
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t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
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print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
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print()
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def test_wgrad_gemm():
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print('Testing weight gradient GEMM:')
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for k in (4096, 8192):
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for m, n in ((7168, 2112), (1536, 24576), (512, 32768), (16384, 7168), (7168, 4096), (2048, 7168)):
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# Test correctness
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x_fp8, y_fp8, residual, out, ref_out = construct_wgrad(m, k, n)
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deep_gemm.wgrad_gemm_fp8_fp8_fp32_nt(x_fp8, y_fp8, out)
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diff = calc_diff(out, ref_out)
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assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
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# Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
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x_fp8, y_fp8, residual, out, ref_out = construct_wgrad(m, k, n)
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.wgrad_gemm_fp8_fp8_fp32_nt(x_fp8, y_fp8, out)
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t = bench_kineto(test_func, 'fp8_wgrad_gemm', suppress_kineto_output=True)
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print(f' > Performance (m={m:5}, n={n:5}, k={k:5}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(m * k + k * n + m * n * 2) / 1e9 / t:4.0f} GB/s')
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print()
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def test_k_grouped_wgrad_gemm():
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print('Testing grouped weight gradient GEMM:')
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for num_groups, base_k in ((4, 4096), (4, 8192), (8, 4096)):
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for m, n in ((7168, 4096), (2048, 7168)):
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# Vary k sizes around base_k
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k_sizes = [base_k + random.randint(-1, 1) * 128 for _ in range(num_groups - 1)]
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k_sizes.append(base_k * num_groups - sum(k_sizes))
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# Test correctness
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x_fp8, y_fp8, out, ref_out, k_sizes = construct_k_grouped_wgrad(m, n, k_sizes)
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deep_gemm.k_grouped_wgrad_gemm_fp8_fp8_fp32_nt(x_fp8, y_fp8, out, k_sizes)
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for idx in range(num_groups):
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diff = calc_diff(out[idx], ref_out[idx])
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assert diff < 0.001, f'{num_groups=}, {m=}, {n=}, k={k_sizes[idx]}, batch={idx}, {diff:.5f}'
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# Construct new tensors to avoid L2 cache acceleration
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x_fp8, y_fp8, out, ref_out, k_sizes = construct_k_grouped_wgrad(m, n, k_sizes)
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total_k = sum(k_sizes)
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def test_func():
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deep_gemm.k_grouped_wgrad_gemm_fp8_fp8_fp32_nt(x_fp8, y_fp8, out, k_sizes)
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t = bench_kineto(test_func, 'fp8_wgrad_gemm', suppress_kineto_output=True, with_multiple_kernels=True) * num_groups
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print(f' > Performance ({num_groups=}, m={m:5}, n={n:5}, avg_k={total_k//num_groups:5}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * num_groups * m * n * (total_k/num_groups) / t / 1e12:4.0f} TFLOPS, '
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f'{(m * total_k + n * total_k + num_groups * m * n * 2) / 1e9 / t:4.0f} GB/s')
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print()
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if __name__ == '__main__':
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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torch.manual_seed(0)
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random.seed(0)
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print('Library path:')
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print(f' > {deep_gemm.__path__}\n')
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test_gemm()
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test_m_grouped_gemm_contiguous()
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test_m_grouped_gemm_masked()
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test_wgrad_gemm()
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test_k_grouped_wgrad_gemm()
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