91 lines
4.4 KiB
Python
91 lines
4.4 KiB
Python
import torch
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import random
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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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enumerate_m_grouped_contiguous, enumerate_k_grouped_contiguous,
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generate_m_grouped_contiguous, generate_k_grouped_contiguous,
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)
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def test_m_grouped_gemm_contiguous_tl() -> None:
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print('Testing m-grouped contiguous Triton GEMM:')
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for _, _, num_groups, expected_m_per_group, n, k, major_a, major_b, _ 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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for expand in (False, True):
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for test_alias in (False, True):
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m, a, b, m_indices, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b, use_bf16=True)
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func_name = f"{'a_fused_' if expand else ''}m_grouped_bf16_gemm_{major_opt.lower() if test_alias else 'nt'}_contiguous_tl"
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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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if expand:
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m_row_indices = torch.arange(0, m, dtype=torch.int32, device='cuda')
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getattr(deep_gemm.legacy, func_name)(a, b, d, (m_indices, m_row_indices))
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else:
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getattr(deep_gemm.legacy, func_name)(a, b, d, m_indices)
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d = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(d), d)
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diff = calc_diff(d, ref_d)
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assert diff < 0.001, f'{m=}, {n=}, {k=}, {major_opt}, {diff:.5f}, alias={test_alias}'
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m, a, b, m_indices, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b, use_bf16=True)
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.legacy.m_grouped_bf16_gemm_nt_contiguous_tl(a, b, d, m_indices)
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t = bench_kineto(test_func, 'm_grouped_bf16_gemm_contiguous_tl_impl', 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}): '
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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_k_grouped_gemm_contiguous_tl() -> None:
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print('Testing k-grouped contiguous Triton GEMM:')
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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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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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for fused_operand in ('a', 'b'):
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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_ue8m0=False, use_bf16=True)
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func_name = f"{fused_operand}_fused_k_grouped_bf16_gemm_{major_opt.lower()}_contiguous_tl"
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k_indices = torch.arange(0, k, dtype=torch.int32, device='cuda')
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k_start = torch.empty(len(ks), dtype=torch.int32, device='cuda')
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k_end = torch.empty(len(ks), dtype=torch.int32, device='cuda')
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for i, group_k in enumerate(ks):
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k_start[i] = k_end[i-1] if i > 0 else 0
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k_end[i] = k_start[i] + group_k
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getattr(deep_gemm.legacy, func_name)(a, b, c, (k_indices, k_start, k_end), True)
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diff = calc_diff(c, ref_d)
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assert diff < 0.001, f'{m=}, {n=}, {k=}, {major_opt}, {diff:.5f}'
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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_ue8m0=False, use_bf16=True)
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# noinspection PyShadowingNames
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def test_func():
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deep_gemm.legacy.b_fused_k_grouped_bf16_gemm_tn_contiguous_tl(a, b, c, (k_indices, k_start, k_end), True)
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t = bench_kineto(test_func, 'b_fused_k_grouped_bf16_gemm_contiguous_tl_impl', 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}): '
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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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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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test_m_grouped_gemm_contiguous_tl()
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test_k_grouped_gemm_contiguous_tl()
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