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[Feature] use pytest for sgl-kernel (#4896)

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This commit is contained in:
Adarsh Shirawalmath
2025-03-30 23:06:52 +05:30
committed by GitHub
parent 4ede6770cd
commit 9fccda3111
10 changed files with 263 additions and 290 deletions
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76
sgl-kernel/tests/test_fp8_gemm.py
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@@ -1,67 +1,49 @@
import unittest
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
class TestFp8Gemm(unittest.TestCase):
def _test_accuracy_once(self, 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
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")
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
o1 = torch.empty((M, N), device=device, dtype=torch.bfloat16)
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")
def test_accuracy(self):
Ms = [1, 128, 512, 1024, 4096]
Ns = [16, 128, 512, 1024, 4096]
Ks = [512, 1024, 4096, 8192, 16384]
bias_opts = [True, False]
out_dtypes = [torch.bfloat16, torch.float16]
for M in Ms:
for N in Ns:
for K in Ks:
for with_bias in bias_opts:
for out_dtype in out_dtypes:
self._test_accuracy_once(
M, N, K, with_bias, out_dtype, "cuda"
)
@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__":
unittest.main()
pytest.main([__file__])