From 1169f83c3685613f5e156cd1984005dcedddc202 Mon Sep 17 00:00:00 2001 From: Chenggang Zhao Date: Tue, 27 May 2025 11:32:05 +0800 Subject: [PATCH] Refactor tests --- tests/test_core.py | 79 +++++++++++++++++++--------------------------- 1 file changed, 33 insertions(+), 46 deletions(-) diff --git a/tests/test_core.py b/tests/test_core.py index 375bae2..3cccdb7 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -49,13 +49,10 @@ def construct(m: int, k: int, n: int) -> \ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int) -> \ Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]: - m = 0 - m_aligned = get_m_alignment_for_contiguous_layout() - group_m_list = [] - for i in range(num_groups): - group_m = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3)) - m += ceil_div(group_m, m_aligned) * m_aligned - group_m_list.append(group_m) + alignment = get_m_alignment_for_contiguous_layout() + group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3))] + m = sum([ceil_div(x, alignment) * alignment for x in group_ms]) + x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16) y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16) m_indices = torch.empty(m, device='cuda', dtype=torch.int32) @@ -63,13 +60,14 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16) start = 0 - for i, group_m in enumerate(group_m_list): + for i, group_m in enumerate(group_ms): actual_end = start + group_m - aligned_end = start + ceil_div(group_m, m_aligned) * m_aligned + aligned_end = start + ceil_div(group_m, alignment) * alignment m_indices[start:actual_end] = i m_indices[actual_end:aligned_end] = -1 ref_out[start:aligned_end] = x[start:aligned_end] @ y[i].t() start = aligned_end + ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out) assert m % 4 == 0, f'TMA alignment error: {m}' x_fp8 = per_token_cast_to_fp8(x) @@ -80,15 +78,15 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: return m, x_fp8, y_fp8, m_indices, out, ref_out -def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \ - Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]: - x = torch.randn((num_groups, m, k), device='cuda', dtype=torch.bfloat16) +def construct_masked_grouped(num_groups: int, max_m: int, expected_m_per_group: int, k: int, n: int) -> \ + Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]: + x = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16) y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16) - out = torch.empty((num_groups, m, n), device='cuda', dtype=torch.bfloat16) + out = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16) ref_out = torch.einsum('gmk,gnk->gmn', x, y) - assert m % 4 == 0, f'TMA alignment error: {m}' - x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, m, k // 128), device='cuda', dtype=torch.float)) + assert max_m % 4 == 0, f'TMA alignment error: {max_m}' + x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, max_m, k // 128), device='cuda', dtype=torch.float)) 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)) for i in range(num_groups): x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i]) @@ -96,7 +94,12 @@ def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \ # Transpose earlier so that the testing will not trigger transposing kernels x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1])) - return x_fp8, y_fp8, out, ref_out + + # Construct mask + masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int) + for j in range(num_groups): + masked_m[j] = int(expected_m_per_group * random.uniform(0.7, 1.3)) + return x_fp8, y_fp8, masked_m, out, ref_out def construct_wgrad(m: int, k: int, n: int) -> \ @@ -172,9 +175,6 @@ def test_gemm() -> None: diff = calc_diff(out, ref_out) assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}' - # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2) - x_fp8, y_fp8, out, ref_out = construct(m, k, n) - # noinspection PyShadowingNames def test_func(): deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out) @@ -190,63 +190,50 @@ def test_m_grouped_gemm_contiguous() -> None: print('Testing grouped contiguous GEMM:') 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)): - # TODO: make a stronger test + # NOTES: we should mask the unfilled part before calculating difference m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n) deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices) out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out) - ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out) diff = calc_diff(out, ref_out) assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}' - # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2) - m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n) - # noinspection PyShadowingNames def test_func(): deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices) t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True) - sum_m = (m_indices != -1).sum().item() + valid_m = (m_indices != -1).sum().item() 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 | ' - f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, ' - f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s') + f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, ' + f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s') print() def test_m_grouped_gemm_masked() -> None: print('Testing grouped masked GEMM:') - m = 4096 + max_m = 4096 for num_groups, expected_m_per_group in ((1, 1024), (2, 512), (4, 256)): for k, n in ((7168, 4096), (2048, 7168), ): # Test correctness for i in range(10): - x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n) - masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int) - for j in range(num_groups): - masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3)) - expected_m = min(int(masked_m.float().mean()) + 1, m) - deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m) + x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, max_m, expected_m_per_group, k, n) + deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group) for j in range(num_groups): diff = calc_diff(out[j, :masked_m[j].item()], ref_out[j, :masked_m[j].item()]) - assert diff < 0.001, f'{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}' - - # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2) - x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n) - for j in range(num_groups): - masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3)) - expected_m = min(int(masked_m.float().mean()) + 1, m) - sum_m = masked_m.sum().item() + assert diff < 0.001, f'{max_m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}' # noinspection PyShadowingNames def test_func(): - deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m) + deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group) # Test performance with fixed shapes + # noinspection PyUnboundLocalVariable + valid_m = masked_m.sum().item() t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True) 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 | ' - f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, ' - f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s') + f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, ' + f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s') print() @@ -320,4 +307,4 @@ if __name__ == '__main__': test_m_grouped_gemm_masked() test_wgrad_gemm() - test_k_grouped_wgrad_gemm() \ No newline at end of file + test_k_grouped_wgrad_gemm()