From a01ab1aabc042da8a64b0e36784a2d678197674a Mon Sep 17 00:00:00 2001 From: Eric Wong Date: Sun, 9 Nov 2025 14:59:50 +0800 Subject: [PATCH] feat: add test for signal GEMM. Co-authored-by: Zqy11 <841971412@qq.com> Co-authored-by: AniZpZ --- deep_gemm/testing/numeric.py | 15 +++++++++++++++ tests/generators.py | 14 +++++++++----- tests/test_fp8.py | 22 +++++++++++++++------- 3 files changed, 39 insertions(+), 12 deletions(-) diff --git a/deep_gemm/testing/numeric.py b/deep_gemm/testing/numeric.py index d06a03b..37a88d4 100644 --- a/deep_gemm/testing/numeric.py +++ b/deep_gemm/testing/numeric.py @@ -17,3 +17,18 @@ def count_bytes(*tensors): elif t is not None: total += t.numel() * t.element_size() return total + +def check_signal(num_local_expert, max_m, block_m, threshold, signal, masked_m): + ceil_div = lambda a, b: (a + b - 1) // b + + expert_len = max_m // block_m + for expert in range(num_local_expert): + mask = masked_m[expert] + start = expert * expert_len + end = expert * expert_len + expert_len + valid_len = ceil_div(mask, block_m) + for i in range(start, end): + if i < start + valid_len: + assert signal[i] == threshold, f'{i=}, {signal[i]=}, {threshold=}' + else: + assert signal[i] == 0, f'{i=}, {signal[i]=}' diff --git a/tests/generators.py b/tests/generators.py index d856217..0d06505 100644 --- a/tests/generators.py +++ b/tests/generators.py @@ -113,9 +113,10 @@ def enumerate_m_grouped_contiguous(dtype: torch.dtype) -> Generator: def enumerate_m_grouped_masked(dtype: torch.dtype) -> Generator: max_m = 4096 for kernel_type in get_kernel_types(dtype): - for num_groups, m in ((1, 1024), (2, 512), (4, 256)): - for n, k in ((4096, 7168), (7168, 2048), ): - yield kernel_type, num_groups, max_m, m, n, k + for enable_overlap in (False, True): + for num_groups, m in ((1, 1024), (2, 512), (4, 256), (16, 64), (16, 32)): + for n, k in ((4096, 7168), (7168, 2048), ): + yield kernel_type, enable_overlap, num_groups, max_m, m, n, k def enumerate_k_grouped_contiguous(): @@ -218,7 +219,7 @@ def generate_m_grouped_contiguous(num_groups: int, expected_m_per_group: int, n: def generate_m_grouped_masked(num_groups: int, max_m: int, expected_m_per_group: int, n: int, k: int, - use_ue8m0: bool = False, use_bf16: bool = False): + use_ue8m0: bool = False, use_bf16: bool = False, enable_overlap: bool = False): a = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16) b = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16) d = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16) @@ -238,7 +239,10 @@ def generate_m_grouped_masked(num_groups: int, max_m: int, expected_m_per_group: a_fp8[0][i], a_fp8[1][i] = per_token_cast_to_fp8(a[i], use_ue8m0=use_ue8m0) b_fp8[0][i], b_fp8[1][i] = per_block_cast_to_fp8(b[i], use_ue8m0=use_ue8m0) - return a_fp8, b_fp8, masked_m, d, ref_d + max_signal_size = num_groups * ceil_div(max_m, 64) + signal = torch.zeros(max_signal_size, dtype=torch.int32, device='cuda') if enable_overlap else None + + return a_fp8, b_fp8, masked_m, d, ref_d, signal def generate_k_grouped_contiguous(num_groups: int, m: int, n: int, major_a: MajorTypeAB, major_b: MajorTypeAB, ks: List[int], use_ue8m0: bool): diff --git a/tests/test_fp8.py b/tests/test_fp8.py index 7415e07..1a8e424 100644 --- a/tests/test_fp8.py +++ b/tests/test_fp8.py @@ -6,7 +6,8 @@ import torch import deep_gemm from deep_gemm.testing import ( bench, bench_kineto, - calc_diff, count_bytes + calc_diff, count_bytes, + check_signal, ) from generators import ( @@ -90,30 +91,37 @@ def test_m_grouped_gemm_masked() -> None: print('Testing m-grouped masked GEMM:') # TODO: when the actual `m` is greater than `expected_m_per_group`, efficiency may significantly decrease. - for kernel_type, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked(torch.float8_e4m3fn): + for kernel_type, enable_overlap, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked(torch.float8_e4m3fn): kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D' use_ue8m0 = get_ue8m0_usage(kernel_type) disable_ue8m0_cast = not use_ue8m0 # Test correctness for i in range(10): - a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_ue8m0=use_ue8m0) - deep_gemm.m_grouped_fp8_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group, disable_ue8m0_cast=disable_ue8m0_cast) + a, b, masked_m, d, ref_d, signal = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_ue8m0=use_ue8m0, enable_overlap=enable_overlap) + result = deep_gemm.m_grouped_fp8_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group, disable_ue8m0_cast=disable_ue8m0_cast, enable_overlap=enable_overlap, signal=signal) + + if enable_overlap: + block_m, threshold = result + check_signal(num_groups, max_m, block_m, threshold, signal, masked_m) + for j in range(num_groups): diff = calc_diff(d[j, :masked_m[j].item()], ref_d[j, :masked_m[j].item()]) assert diff < 0.001, f'{max_m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {kernel_opt}, {num_groups=}, {diff:.5f}' # Construct full cases - a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_ue8m0=use_ue8m0) + a, b, masked_m, d, ref_d, signal = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_ue8m0=use_ue8m0, enable_overlap=enable_overlap) + # noinspection PyShadowingNames def test_func(): - deep_gemm.m_grouped_fp8_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group, disable_ue8m0_cast=disable_ue8m0_cast) + deep_gemm.m_grouped_fp8_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group, disable_ue8m0_cast=disable_ue8m0_cast, enable_overlap=enable_overlap, signal=signal) + # Test performance with fixed shapes valid_m = masked_m.sum().item() t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True) - print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}, {kernel_opt}): ' + print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}, {kernel_opt}, enable_overlap={enable_overlap}): ' f'{t * 1e6:4.0f} us | ' f'{2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS | ' f'{(count_bytes(a, d) * valid_m / (max_m * num_groups) + count_bytes(b)) / 1e9 / t:4.0f} GB/s')