65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import random
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import torch
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from typing import Tuple
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import deep_gemm
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from deep_gemm.testing import bench_kineto, calc_diff, count_bytes
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from deep_gemm.utils import ceil_div, per_custom_dims_cast_to_fp8
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from generators import get_arch_major, generate_normal, get_ue8m0_usage, get_kernel_types, MajorTypeAB
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def apply_skip_head_mid(d: torch.Tensor, head_splits: Tuple[int, int, int]):
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left, mid, right = head_splits
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m, n = d.shape
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assert n % (left + right) == 0
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num_heads = n // (left + right)
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# Split and insert padding tensor
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d = d.view(m, num_heads, -1)
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d_left = d[:, :, :left]
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d_right = d[:, :, -right:]
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d_mid = torch.zeros((m, num_heads, mid), dtype=d.dtype, device=d.device)
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return torch.cat([d_left, d_mid, d_right], dim=2).view(m, -1)
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def test_gemm_skip_head_mid() -> None:
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print('Testing GEMM skip head mid:')
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head_splits = (128, 64, 128)
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major_a, major_b = MajorTypeAB.KMajor, MajorTypeAB.KMajor
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out_dtype, accumulate = torch.bfloat16, False
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for kernel_type in get_kernel_types(dtype=torch.float8_e4m3fn):
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for m in (128, 4096):
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for n, k in [(32768, 512), (8192, 512)]:
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kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D'
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use_ue8m0 = get_ue8m0_usage(kernel_type)
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disable_ue8m0_cast = not use_ue8m0
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a, b, _, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0)
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d = apply_skip_head_mid(d, head_splits)
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ref_d = apply_skip_head_mid(ref_d, head_splits)
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deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast)
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diff = calc_diff(d, ref_d)
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assert diff < 0.001, f'{m=}, {n=}, {k=}, {kernel_opt}, {diff:.5f}'
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t = bench_kineto(lambda: deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast),
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'fp8_gemm', suppress_kineto_output=True)
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print(f' > Perf (m={m:5}, n={n:5}, k={k:5}, {kernel_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.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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test_gemm_skip_head_mid()
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