import copy import numpy as np import random import torch import deep_gemm from deep_gemm.testing import ( bench_kineto, calc_diff, count_bytes, ignore_env, get_arch_major ) from generators import ( KernelType, get_ue8m0_usage, layout_masked_to_psum, align, enumerate_normal, enumerate_m_grouped_contiguous, enumerate_m_grouped_masked, enumerate_k_grouped_contiguous, generate_normal, generate_m_grouped_contiguous, generate_m_grouped_masked, generate_k_grouped_contiguous, get_mk_alignment_for_contiguous_layout ) def test_gemm() -> None: print('Testing GEMM:') scores = [] for kernel_type, quant_config, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(torch.float8_e4m3fn): major_opt = 'N' if major_a.is_k_major() else 'T' major_opt += 'T' if major_b.is_k_major() else 'N' out_opt = 'FP32' if out_dtype == torch.float else 'BF16' acc_opt = f'acc={int(accumulate)}' kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D' use_ue8m0 = get_ue8m0_usage(kernel_type) disable_ue8m0_cast = not use_ue8m0 recipe, recipe_a, recipe_b = quant_config.get_recipes(is_wgrad=(kernel_type.is_1d1d() and accumulate)) for test_alias in (False, True): a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0, quant_config=quant_config) func_name = f'fp8_fp4_gemm_{major_opt.lower() if test_alias else "nt"}' if test_alias: a = a if major_a.is_k_major() else (a[0].T, a[1].T) b = b if major_b.is_k_major() else (b[0].T, b[1].T) assert a[0].is_contiguous() and b[0].is_contiguous() getattr(deep_gemm, func_name)(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast, recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b) diff = calc_diff(d, ref_d) assert diff < quant_config.max_diff(), (f'{m=}, {n=}, {k=}, {kernel_opt}, {major_opt=}, {accumulate=}, {out_dtype=}, ' f'{diff:.5f}, alias={test_alias}') a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0, quant_config=quant_config) t = bench_kineto(lambda: deep_gemm.fp8_fp4_gemm_nt(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast, recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b), 'gemm_', suppress_kineto_output=True) cublas_t, split_k_t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a[0], b[0], d, c=c), ('nvjet', 'reduce'), suppress_kineto_output=True) \ if not quant_config.is_fp4_a and not quant_config.is_fp4_b else (0, 0) print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, {kernel_opt}, layout={major_opt}, {out_opt}, {acc_opt}): ' f'{t * 1e6:6.1f} us | {2 * m * n * k / t / 1e12:4.0f} TFLOPS | ' f'{(count_bytes(a, b, d) + count_bytes(c) * int(accumulate)) / 1e9 / t:4.0f} GB/s | ' f'{(cublas_t + split_k_t) / t:.2f}x cuBLAS') if cublas_t > 0: scores.append((cublas_t + split_k_t) / t) print(f"Average FP8xFP8 GEMM speedup over cuBLASLt: {float(np.prod(scores)) ** (1.0 / len(scores)):.3f}x\n") def test_m_grouped_gemm_contiguous() -> None: print('Testing m-grouped contiguous GEMM:') for kernel_type, quant_config, num_groups, expected_m_per_group, n, k, major_a, major_b, use_psum_layout in enumerate_m_grouped_contiguous(dtype=torch.float8_e4m3fn): major_opt = 'N' if major_a.is_k_major() else 'T' major_opt += 'T' if major_b.is_k_major() else 'N' kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D' use_ue8m0 = get_ue8m0_usage(kernel_type) disable_ue8m0_cast = not use_ue8m0 recipe, recipe_a, recipe_b = quant_config.get_recipes() # Select best alignment alignment = deep_gemm.get_theoretical_mk_alignment_for_contiguous_layout() deep_gemm.set_mk_alignment_for_contiguous_layout(alignment) for test_alias in (False, True): m, a, b, grouped_layout, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b, use_ue8m0=use_ue8m0, use_psum_layout=use_psum_layout, quant_config=quant_config) func_name = f"m_grouped_fp8_fp4_gemm_{(major_opt.lower() if test_alias else 'nt')}_contiguous" if test_alias: assert major_a.is_k_major() b = b if major_b.is_k_major() else (b[0].mT, b[1].mT) assert a[0].is_contiguous() and b[0].is_contiguous() getattr(deep_gemm, func_name)(a, b, d, grouped_layout, disable_ue8m0_cast=disable_ue8m0_cast, use_psum_layout=use_psum_layout, recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b) if use_psum_layout: for j in range(num_groups): start = 0 if j == 0 else align(grouped_layout[j - 1], get_mk_alignment_for_contiguous_layout()) end = grouped_layout[j] diff = calc_diff(d[start : end], ref_d[start : end]) assert diff < quant_config.max_diff(), f'{m=}, {n=}, {k=}, {major_opt}, {kernel_opt}, {diff:.5f}, alias={test_alias}' else: diff = calc_diff(d, ref_d) assert diff < quant_config.max_diff(), f'{m=}, {n=}, {k=}, {major_opt}, {kernel_opt}, {diff:.5f}, alias={test_alias}' m, a, b, grouped_layout, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b, use_ue8m0=use_ue8m0, use_psum_layout=use_psum_layout, quant_config=quant_config) # noinspection PyShadowingNames def test_func(): deep_gemm.m_grouped_fp8_fp4_gemm_nt_contiguous(a, b, d, grouped_layout, disable_ue8m0_cast=disable_ue8m0_cast, use_psum_layout=use_psum_layout, recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b) t = bench_kineto(test_func, 'gemm_', suppress_kineto_output=True) print(f' > Perf ({num_groups=}, m={m:5}, n={n:6}, k={k:5}, {kernel_opt}, layout={major_opt}, psum={use_psum_layout}): ' f'{t * 1e6:4.0f} us | ' f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | ' f'{count_bytes(a, b, d) / 1e9 / t:4.0f} GB/s') print() 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, quant_config, num_groups, max_m, expected_m_per_group, n, k, use_psum_layout 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 recipe, recipe_a, recipe_b = quant_config.get_recipes() num_tests = 8 sum_t, max_t = 0, 0 sum_ops, sum_bytes = 0, 0 # Select best alignment alignment = deep_gemm.get_theoretical_mk_alignment_for_contiguous_layout(int(expected_m_per_group * 1.2)) deep_gemm.set_mk_alignment_for_contiguous_layout(alignment) for i in range(num_tests): a, b, masked_m, psum_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_ue8m0=use_ue8m0, use_psum_layout=use_psum_layout, quant_config=quant_config) if use_psum_layout: a_psum = (layout_masked_to_psum(a[0], psum_m), layout_masked_to_psum(a[1], psum_m)) d_psum = layout_masked_to_psum(d, psum_m) # noinspection PyShadowingNames def test_func(): if use_psum_layout: deep_gemm.m_grouped_fp8_fp4_gemm_nt_contiguous(a_psum, b, d_psum, psum_m, disable_ue8m0_cast=disable_ue8m0_cast, use_psum_layout=True, expected_m_for_psum_layout=int(expected_m_per_group * 1.2), recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b) else: deep_gemm.m_grouped_fp8_fp4_gemm_nt_masked(a, b, d, masked_m, int(expected_m_per_group * 1.2), disable_ue8m0_cast=disable_ue8m0_cast, recipe=recipe, recipe_a=recipe_a, recipe_b=recipe_b) test_func() for j in range(num_groups): if masked_m[j].item() == 0: continue if use_psum_layout: d_slice = d_psum[: psum_m[j]] if j == 0 else d_psum[align(psum_m[j - 1], get_mk_alignment_for_contiguous_layout()): psum_m[j]] else: d_slice = d[j, :masked_m[j].item()] diff = calc_diff(d_slice, ref_d[j, :masked_m[j].item()]) assert diff < quant_config.max_diff(), f'{max_m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {kernel_opt}, {num_groups=}, {diff:.5f}' # Test performance with fixed shapes valid_m = masked_m.sum().item() t = bench_kineto(test_func, 'gemm_', suppress_kineto_output=True) sum_t += t max_t = max(max_t, t) sum_ops += 2 * valid_m * n * k sum_bytes += count_bytes(a, d) * valid_m / (max_m * num_groups) + count_bytes(b) print(f' > Perf (num_groups={num_groups:2}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}, ' f'{kernel_opt}, psum={1 if use_psum_layout else 0}): ' f'{sum_t / num_tests * 1e6:4.0f} us (max: {max_t * 1e6:3.0f} us) | ' f'{sum_ops / sum_t / 1e12:4.0f} TFLOPS | ' f'{sum_bytes / sum_t / 1e9:4.0f} GB/s') print() def test_k_grouped_gemm_contiguous() -> None: print('Testing k-grouped contiguous GEMM:') k_grouped_fp8_gemm_contiguous = deep_gemm.k_grouped_fp8_gemm_nt_contiguous if get_arch_major() == 9 \ else deep_gemm.k_grouped_fp8_gemm_tn_contiguous for num_groups, m, n, major_a, major_b, ks, expected_k_per_group, gran_k in enumerate_k_grouped_contiguous(torch.float8_e4m3fn): recipe = (1, 1, gran_k) use_ue8m0 = get_ue8m0_usage(KernelType.Kernel1D1D) for test_empty_groups in (False, True): new_ks = copy.deepcopy(ks) if test_empty_groups and len(ks) > 1: new_ks[random.randint(0, num_groups - 1)] = 0 k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, new_ks, use_ue8m0=use_ue8m0, gran_k=gran_k) new_ks_tensor = torch.tensor(new_ks, dtype=torch.int, device='cuda') k_grouped_fp8_gemm_contiguous(a, b, d, new_ks, new_ks_tensor, c, recipe=recipe) diff = calc_diff(d, ref_d) assert diff < 0.001, f'{m=}, {n=}, {k=}, {ks=}, {diff:.5f}' # Test performance k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, ks, use_ue8m0=use_ue8m0, gran_k=gran_k) ks_tensor = torch.tensor(ks, dtype=torch.int, device='cuda') # noinspection PyShadowingNames def test_func(): k_grouped_fp8_gemm_contiguous(a, b, d, ks, ks_tensor, c, recipe=recipe) t = bench_kineto(test_func, 'gemm_', suppress_kineto_output=True) print(f' > Perf ({num_groups=:2}, m={m:5}, n={n:5}, k={k:5}, gran_k={gran_k:3}): ' f'{t * 1e6:4.0f} us | ' f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | ' f'{count_bytes(a, b, c, d) / 1e9 / t:4.0f} GB/s') print() if __name__ == '__main__': torch.manual_seed(0) random.seed(0) print('Library path:') print(f' > {deep_gemm.__path__}\n') test_gemm() test_m_grouped_gemm_contiguous() test_m_grouped_gemm_masked() test_k_grouped_gemm_contiguous()