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https://github.com/deepseek-ai/DeepGEMM
synced 2025-06-26 23:15:49 +00:00
Refactor tests
This commit is contained in:
Chenggang Zhao
parent
c4e31d121b
commit
1169f83c36
@ -49,13 +49,10 @@ def construct(m: int, k: int, n: int) -> \
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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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m = 0
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m_aligned = get_m_alignment_for_contiguous_layout()
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group_m_list = []
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for i in range(num_groups):
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group_m = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
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m += ceil_div(group_m, m_aligned) * m_aligned
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group_m_list.append(group_m)
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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))]
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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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@ -63,13 +60,14 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
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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_m_list):
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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, m_aligned) * m_aligned
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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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@ -80,15 +78,15 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
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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, 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((num_groups, m, k), device='cuda', dtype=torch.bfloat16)
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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, m, n), 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 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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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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@ -96,7 +94,12 @@ def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \
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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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# 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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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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@ -172,9 +175,6 @@ def test_gemm() -> None:
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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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@ -190,63 +190,50 @@ 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), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
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# TODO: make a stronger test
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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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ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_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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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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# 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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sum_m = (m_indices != -1).sum().item()
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valid_m = (m_indices != -1).sum().item()
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print(f' > Performance ({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 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
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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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m = 4096
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max_m = 4096
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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, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
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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.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
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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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x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, max_m, 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'{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_masked_grouped(num_groups, m, k, n)
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for j in range(num_groups):
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masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
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expected_m = min(int(masked_m.float().mean()) + 1, m)
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sum_m = masked_m.sum().item()
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assert diff < 0.001, f'{max_m=}, {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)
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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' > Performance ({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 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
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f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
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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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