test_fp8_gemm.py

import pytest
import torch
from sgl_kernel import fp8_scaled_mm


def torch_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias):
    o = torch.matmul(a.to(torch.float32), b.to(torch.float32))
    o = o.to(torch.float32)
    temp1 = o * scale_a.view(-1, 1)
    temp2 = temp1 * scale_b.view(1, -1)
    final = temp2.to(out_dtype)
    if bias is not None:
        final = final + bias.view(1, -1)
    return final


def _test_accuracy_once(M, N, K, with_bias, out_dtype, device):
    fp8_info = torch.finfo(torch.float8_e4m3fn)
    fp8_max, fp8_min = fp8_info.max, fp8_info.min
    a_fp32 = (torch.rand(M, K, dtype=torch.float32, device=device) - 0.5) * 2 * fp8_max
    a_fp8 = a_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
    b_fp32 = (torch.rand(N, K, dtype=torch.float32, device=device) - 0.5) * 2 * fp8_max
    b_fp8 = b_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
    scale_a = torch.randn((M,), device=device, dtype=torch.float32) * 0.001
    scale_b = torch.randn((N,), device=device, dtype=torch.float32) * 0.001
    if with_bias:
        bias = torch.randn((N,), device=device, dtype=out_dtype)
    else:
        bias = None
    b_fp8 = b_fp8.t()
    o = torch_scaled_mm(a_fp8, b_fp8, scale_a, scale_b, out_dtype, bias)
    o1 = fp8_scaled_mm(a_fp8, b_fp8, scale_a, scale_b, out_dtype, bias)
    rtol = 0.02
    atol = 1
    torch.testing.assert_close(o, o1, rtol=rtol, atol=atol)
    print(f"M={M}, N={N}, K={K}, with_bias={with_bias}, out_dtype={out_dtype}: OK")


@pytest.mark.parametrize("M", [1, 128, 512, 1024, 4096])
@pytest.mark.parametrize("N", [16, 128, 512, 1024, 4096])
@pytest.mark.parametrize("K", [512, 1024, 4096, 8192, 16384])
@pytest.mark.parametrize("with_bias", [True, False])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16])
def test_accuracy(M, N, K, with_bias, out_dtype):
    _test_accuracy_once(M, N, K, with_bias, out_dtype, "cuda")


if __name__ == "__main__":
    pytest.main([__file__])