7f2a703ed5
* Merge with private repo * Update README * Update README * Update README * Add PyTorch requirements * Fix sync scopes for MQA logits (#256) * Update README
224 lines
11 KiB
Python
224 lines
11 KiB
Python
import copy
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import numpy as np
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import random
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import torch
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import deep_gemm
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from deep_gemm.testing import (
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bench_kineto,
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calc_diff, count_bytes
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)
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from generators import (
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get_arch_major, layout_masked_to_psum, align,
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enumerate_normal, enumerate_m_grouped_contiguous, enumerate_m_grouped_masked, enumerate_k_grouped_contiguous,
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generate_normal, generate_m_grouped_contiguous, generate_m_grouped_masked, generate_k_grouped_contiguous,
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get_mk_alignment_for_contiguous_layout
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)
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def test_gemm() -> None:
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print('Testing GEMM:')
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scores = []
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for kernel_type, _, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(torch.bfloat16):
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major_opt = 'N' if major_a.is_k_major() else 'T'
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major_opt += 'T' if major_b.is_k_major() else 'N'
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out_opt = 'FP32' if out_dtype == torch.float else 'BF16'
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acc_opt = f'acc={int(accumulate)}'
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for test_alias in (False, True):
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a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
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func_name = f'bf16_gemm_{major_opt.lower() if test_alias else "nt"}'
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if test_alias:
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a = a if major_a.is_k_major() else a.T
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b = b if major_b.is_k_major() else b.T
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assert a.is_contiguous() and b.is_contiguous()
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getattr(deep_gemm, func_name)(a, b, d, c=c)
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diff = calc_diff(d, ref_d)
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assert diff < 1e-5, (f'{m=}, {n=}, {k=}, {major_opt=}, {accumulate=}, {out_dtype=}, '
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f'{diff:.5f}, alias={test_alias}')
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a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
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t = bench_kineto(lambda: deep_gemm.bf16_gemm_nt(a, b, d, c=c), 'bf16_gemm', suppress_kineto_output=True)
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cublas_t, split_k_t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a, b, d, c=c), ('nvjet', 'reduce'), suppress_kineto_output=True)
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print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, layout={major_opt}, {out_opt}, {acc_opt}): '
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f'{t * 1e6:7.1f} us | '
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f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
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f'{(count_bytes(a, b, d) + count_bytes(c) * int(accumulate)) / 1e9 / t:4.0f} GB/s | '
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f'{(cublas_t + split_k_t) / t:.2f}x cuBLAS')
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if cublas_t > 0:
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scores.append((cublas_t + split_k_t) / t)
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print(f"Average speedup over cuBLASLt: {float(np.prod(scores)) ** (1.0 / len(scores)):.3f}x\n")
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def test_m_grouped_gemm_contiguous() -> None:
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print('Testing m-grouped contiguous GEMM:')
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for _, _, num_groups, expected_m_per_group, n, k, major_a, major_b, use_psum_layout in enumerate_m_grouped_contiguous(torch.bfloat16):
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major_opt = 'N' if major_a.is_k_major() else 'T'
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major_opt += 'T' if major_b.is_k_major() else 'N'
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# Select best alignment
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alignment = deep_gemm.get_theoretical_mk_alignment_for_contiguous_layout()
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deep_gemm.set_mk_alignment_for_contiguous_layout(alignment)
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for test_alias in (False, True):
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m, a, b, grouped_layout, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b,
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use_bf16=True, use_psum_layout=use_psum_layout)
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func_name = f"m_grouped_bf16_gemm_{(major_opt.lower() if test_alias else 'nt')}_contiguous"
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if test_alias:
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assert major_a.is_k_major()
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b = b if major_b.is_k_major() else b.mT
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assert a[0].is_contiguous() and b[0].is_contiguous()
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getattr(deep_gemm, func_name)(a, b, d, grouped_layout, use_psum_layout=use_psum_layout)
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if use_psum_layout:
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for j in range(num_groups):
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start = 0 if j == 0 else align(grouped_layout[j - 1], get_mk_alignment_for_contiguous_layout())
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end = grouped_layout[j]
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diff = calc_diff(d[start : end], ref_d[start : end])
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assert diff < 1e-5, f'{m=}, {n=}, {k=}, {major_opt}, {diff:.5f}, alias={test_alias}'
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else:
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diff = calc_diff(d, ref_d)
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assert diff < 1e-5, f'{m=}, {n=}, {k=}, {major_opt}, {diff:.5f}, alias={test_alias}'
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m, a, b, grouped_layout, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b,
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use_bf16=True, use_psum_layout=use_psum_layout)
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.m_grouped_bf16_gemm_nt_contiguous(a, b, d, grouped_layout, use_psum_layout=use_psum_layout)
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t = bench_kineto(test_func, 'bf16_gemm', suppress_kineto_output=True)
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print(f' > Perf ({num_groups=}, m={m:5}, n={n:5}, k={k:5}, layout={major_opt}, psum={use_psum_layout}): '
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f'{t * 1e6:4.0f} us | '
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f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
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f'{count_bytes(a, b, d) / 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 m-grouped masked GEMM:')
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# TODO: when the actual `m` is greater than `expected_m_per_group`, efficiency may significantly decrease.
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for _, _, num_groups, max_m, expected_m_per_group, n, k, use_psum_layout in enumerate_m_grouped_masked(torch.bfloat16):
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num_tests = 8
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sum_t, max_t = 0, 0
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sum_ops, sum_bytes = 0, 0
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# Select best alignment
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alignment = deep_gemm.get_theoretical_mk_alignment_for_contiguous_layout(int(expected_m_per_group * 1.2))
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deep_gemm.set_mk_alignment_for_contiguous_layout(alignment)
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for i in range(num_tests):
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a, b, masked_m, psum_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k,
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use_bf16=True, use_psum_layout=use_psum_layout)
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if use_psum_layout:
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a_psum = layout_masked_to_psum(a, psum_m)
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d_psum = layout_masked_to_psum(d, psum_m)
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# noinspection PyShadowingNames
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def test_func():
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if use_psum_layout:
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deep_gemm.m_grouped_bf16_gemm_nt_contiguous(a_psum, b, d_psum, psum_m,
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use_psum_layout=True, expected_m_for_psum_layout=expected_m_per_group)
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else:
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deep_gemm.m_grouped_bf16_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group)
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test_func()
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for j in range(num_groups):
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if masked_m[j].item() == 0:
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continue
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if use_psum_layout:
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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]]
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else:
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d_slice = d[j, :masked_m[j].item()]
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diff = calc_diff(d_slice, ref_d[j, :masked_m[j].item()])
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assert diff < 1e-5, f'{max_m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
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# Test performance with fixed shapes
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valid_m = masked_m.sum().item()
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t = bench_kineto(test_func, 'bf16_gemm', suppress_kineto_output=True)
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sum_t += t
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max_t = max(max_t, t)
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sum_ops += 2 * valid_m * n * k
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sum_bytes += count_bytes(a, d) * valid_m / (max_m * num_groups) + count_bytes(b)
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print(f' > Perf (num_groups={num_groups:2}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}, '
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f'psum={1 if use_psum_layout else 0}): '
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f'{sum_t / num_tests * 1e6:4.0f} us (max: {max_t * 1e6:3.0f} us) | '
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f'{sum_ops / sum_t / 1e12:4.0f} TFLOPS | '
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f'{sum_bytes / sum_t / 1e9:4.0f} GB/s')
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print()
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def test_k_grouped_gemm_contiguous() -> None:
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print('Testing k-grouped contiguous GEMM:')
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# TODO: Support arbitrary alignment
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deep_gemm.set_mk_alignment_for_contiguous_layout(128)
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for num_groups, m, n, major_a, major_b, ks, expected_k_per_group in enumerate_k_grouped_contiguous(torch.bfloat16):
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for test_empty_groups in (False, True):
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new_ks = copy.deepcopy(ks)
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if test_empty_groups and len(ks) > 1:
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new_ks[random.randint(0, num_groups - 1)] = 0
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k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, new_ks, use_bf16=True)
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new_ks_tensor = torch.tensor(new_ks, dtype=torch.int, device='cuda')
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deep_gemm.k_grouped_bf16_gemm_tn_contiguous(a, b, d, new_ks, new_ks_tensor, c)
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diff = calc_diff(d, ref_d)
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assert diff < 1e-5, f'{m=}, {n=}, {k=}, {ks=}, {diff:.7f}'
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# Test performance
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k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, ks, use_bf16=True)
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ks_tensor = torch.tensor(ks, dtype=torch.int, device='cuda')
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.k_grouped_bf16_gemm_tn_contiguous(a, b, d, ks, ks_tensor, c)
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t = bench_kineto(test_func, 'bf16_gemm', suppress_kineto_output=True)
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print(f' > Perf ({num_groups=:2}, m={m:5}, n={n:5}, k={k:5}): '
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f'{t * 1e6:4.0f} us | '
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f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
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f'{count_bytes(a, b, c, d) / 1e9 / t:4.0f} GB/s')
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print()
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def test_cublaslt_gemm() -> None:
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print('Testing cuBLASLt GEMM:')
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for kernel_type, _, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(dtype=torch.bfloat16):
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major_opt = 'N' if major_a.is_k_major() else 'T'
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major_opt += 'T' if major_b.is_k_major() else 'N'
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out_opt = 'FP32' if out_dtype == torch.float else 'BF16'
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acc_opt = f'acc={int(accumulate)}'
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a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
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deep_gemm.cublaslt_gemm_nt(a, b, d, c=c)
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diff = calc_diff(d, ref_d)
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assert diff < 6e-7, f'{diff=}, ({m=}, {n=}, {k=}, {major_opt=}, {accumulate=}, {out_dtype=})'
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t_nvjet, t_gemv, t_gemm = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a, b, d, c=c), ('nvjet', 'gemv', 'gemm'), suppress_kineto_output=True)
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t = t_nvjet + t_gemv + t_gemm
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print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, layout={major_opt}, {out_opt}, {acc_opt}): '
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f'{t * 1e6:5.0f} us | '
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f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
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f'{(count_bytes(a, b, d) + count_bytes(c) * int(accumulate)) / 1e9 / t:4.0f} GB/s')
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print()
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if __name__ == '__main__':
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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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if get_arch_major() >= 9:
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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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test_k_grouped_gemm_contiguous()
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test_cublaslt_gemm()
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