150 lines
7.1 KiB
Python
150 lines
7.1 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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get_arch_major,
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enumerate_normal, enumerate_m_grouped_contiguous, enumerate_m_grouped_masked, generate_normal,
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generate_m_grouped_contiguous, generate_m_grouped_masked
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)
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def test_gemm() -> None:
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print('Testing GEMM:')
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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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# TODO: support accumulation for SM90 BF16 GEMM
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if get_arch_major() == 9 and accumulate:
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continue
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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 < 0.0001, (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 * 1e6:5.0f} 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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f'{(cublas_t + split_k_t) / t:.2f}x cuBLAS')
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print()
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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 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 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"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, 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}, {kernel_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.m_grouped_bf16_gemm_nt_contiguous(a, b, d, m_indices)
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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}): '
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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 in enumerate_m_grouped_masked(torch.bfloat16):
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# Test correctness
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for i in range(10):
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a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_bf16=True)
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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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for j in range(num_groups):
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diff = calc_diff(d[j, :masked_m[j].item()], ref_d[j, :masked_m[j].item()])
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assert diff < 0.001, f'{m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
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# Construct full cases
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a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_bf16=True)
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# noinspection PyShadowingNames
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def test_func():
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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 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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print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): '
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f'{t * 1e6:4.0f} us | '
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f'{2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS | '
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f'{(count_bytes(a, d) * valid_m / (max_m * num_groups) + count_bytes(b)) / 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 < 5e-7, f'{diff=}, ({m=}, {n=}, {k=}, {major_opt=}, {accumulate=}, {out_dtype=})'
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t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a, b, d, c=c), 'nvjet', 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 * 1e6:5.0f} 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.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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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_gemm()
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# TODO: support SM100
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if get_arch_major() == 9:
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test_m_grouped_gemm_contiguous()
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test_m_grouped_gemm_masked()
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test_cublaslt_gemm()
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