[Misc] Use pytest.mark.skipif in sgl-kernel test (#5137)

sgl-kernel/README.md

@@ -158,10 +158,19 @@ python -m uv build --wheel -Cbuild-dir=build --color=always .
 
 ### Testing & Benchmarking
 
-1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests)
+1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests), if you need to skip some test, please use `@pytest.mark.skipif`
+
+```python
+@pytest.mark.skipif(
+    skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
+)
+```
+
 2. Add benchmarks using [triton benchmark](https://triton-lang.org/main/python-api/generated/triton.testing.Benchmark.html) in [benchmark/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/benchmark)
 3. Run test suite
 
+
+
 ### Release new version
 
 Update version in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/pyproject.toml) and [version.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel/version.py)

sgl-kernel/tests/test_fp4_gemm.py

@@ -2,11 +2,7 @@ import pytest
 import torch
 from sgl_kernel import cutlass_scaled_fp4_mm, scaled_fp4_quant
 
-if torch.cuda.get_device_capability() < (10, 0):
-    pytest.skip(
-        reason="Nvfp4 Requires compute capability of 10 or above.",
-        allow_module_level=True,
-    )
+skip_condition = torch.cuda.get_device_capability() < (10, 0)
 
 DTYPES = [torch.float16, torch.bfloat16]
 # m, n, k
@@ -108,6 +104,9 @@ def get_ref_results(
     return torch.matmul(a_in_dtype, b_in_dtype.t())
 
 
+@pytest.mark.skipif(
+    skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
+)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("shape", SHAPES)
 @torch.inference_mode()
@@ -149,3 +148,7 @@ def test_nvfp4_gemm(
     )
 
     torch.testing.assert_close(out, expected_out.to(dtype=dtype), atol=1e-1, rtol=1e-1)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

sgl-kernel/tests/test_fp4_quantize.py

@@ -2,11 +2,7 @@ import pytest
 import torch
 from sgl_kernel import scaled_fp4_quant
 
-if torch.cuda.get_device_capability() < (10, 0):
-    pytest.skip(
-        reason="Nvfp4 Requires compute capability of 10 or above.",
-        allow_module_level=True,
-    )
+skip_condition = torch.cuda.get_device_capability() < (10, 0)
 
 DTYPES = [torch.float16, torch.bfloat16]
 SHAPES = [(128, 64), (128, 128), (256, 64), (256, 128)]
@@ -115,6 +111,9 @@ def recover_swizzled_scales(scale, m, n):
     return result[:m, :scale_n]
 
 
+@pytest.mark.skipif(
+    skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
+)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("shape", SHAPES)
 @torch.inference_mode()
@@ -140,6 +139,9 @@ def test_quantize_to_fp4(
     torch.testing.assert_close(scale_ans, scale_ref)
 
 
+@pytest.mark.skipif(
+    skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
+)
 @pytest.mark.parametrize("pad_shape", PAD_SHAPES)
 @torch.inference_mode()
 def test_quantize_to_fp4_padded(pad_shape: tuple[int, int]) -> None:
@@ -162,3 +164,7 @@ def test_quantize_to_fp4_padded(pad_shape: tuple[int, int]) -> None:
 
     torch.testing.assert_close(out_ans, out_ref)
     torch.testing.assert_close(scale_ans, scale_ref)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])