mirror of
https://github.com/deepseek-ai/DeepGEMM
synced 2025-04-03 13:00:45 +00:00
6cbff5778f
The previous behaviour is potentially representative of some use cases (e.g. previous kernel filling L2 with the data in a very specific way) but not standard benchmarking practice.
162 lines
7.9 KiB
Python
162 lines
7.9 KiB
Python
import random
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import torch
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from typing import 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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def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2 and x.size(1) % 128 == 0
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m, n = x.shape
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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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return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(m, 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_grouped(num_groups: int, m: int, k: int, n: int, is_masked: bool) -> \
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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((num_groups, 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, 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 m % 4 == 0, f'TMA alignment error: {m}'
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x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, 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, (n + 127) // 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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# For non-masked input, we must merge the group and M dims
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if not is_masked:
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x_fp8 = (x_fp8[0].view(-1, k), per_token_cast_to_fp8(x.view(-1, k))[1])
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out, ref_out = out.view(-1, n), ref_out.view(-1, n)
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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 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 [(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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# Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
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x_fp8, y_fp8, out, ref_out = construct(m, k, n)
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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' > 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_m_grouped_gemm_contiguous() -> None:
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print('Testing grouped contiguous GEMM:')
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for num_groups, m, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
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# TODO: make a stronger test
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x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=False)
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m_indices = torch.arange(0, num_groups, device='cuda', dtype=torch.int)
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m_indices = m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
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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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diff = calc_diff(out, ref_out)
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assert diff < 0.001, f'm={m * num_groups}, {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, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=False)
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m_indices = torch.arange(0, num_groups, device='cuda', dtype=torch.int)
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m_indices = m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
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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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print(f' > Performance ({num_groups=}, m_per_group={m:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * num_groups * m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(num_groups * (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_masked() -> None:
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print('Testing grouped masked GEMM:')
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for num_groups, m 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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masked_m_candidates = list(filter(lambda candidate: candidate <= m, (64, 128, 192, 256, 320, 384)))
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for i in range(10):
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x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=True)
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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] = random.choice(masked_m_candidates)
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expected_m = min(int(masked_m.float().mean()) + 1, m)
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
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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'{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {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, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=True)
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masked_m = torch.ones((num_groups, ), device='cuda', dtype=torch.int) * m
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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, m)
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# Test performance with fixed shapes
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t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
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print(f' > Performance ({num_groups=}, m_per_group={m:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
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f'throughput: {2 * num_groups * m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(num_groups * (m * k + k * n + 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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