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unify linear test cases #307

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unify linear test cases #307

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50 changes: 5 additions & 45 deletions test/test_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,58 +232,18 @@ def _test_linear_impl(
@pytest.mark.parametrize(
"scaling_type_dL_dY", [TensorScalingType.DELAYED, TensorScalingType.DYNAMIC]
)
@pytest.mark.parametrize("linear_dtype", [torch.bfloat16, torch.float32])
@pytest.mark.parametrize("linear_bias", [False, True])
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_linear_nobias(
def test_linear(
self,
x_shape,
emulate: bool,
scaling_type_x: TensorScalingType,
scaling_type_w: TensorScalingType,
scaling_type_dL_dY: TensorScalingType,
):
if not emulate:
if not torch.cuda.is_available():
warnings.warn("CUDA not available")
pytest.skip()
elif torch.cuda.get_device_capability() < (9, 0):
warnings.warn(
f"CUDA capability {torch.cuda.get_device_capability()} < (9.0)"
)
pytest.skip()
x = torch.randn(*x_shape, device="cuda")
m_ref = nn.Linear(16, 32, bias=False, device="cuda")
self._test_linear_impl(
x,
m_ref,
emulate,
scaling_type_x,
scaling_type_w,
scaling_type_dL_dY,
)

@pytest.mark.parametrize("emulate", [True, False] if is_H100 else [True])
@pytest.mark.parametrize("x_shape", [(16, 16), (2, 16, 16), (3, 2, 16, 16)])
@pytest.mark.parametrize(
"scaling_type_x", [TensorScalingType.DELAYED, TensorScalingType.DYNAMIC]
)
@pytest.mark.parametrize(
"scaling_type_w", [TensorScalingType.DELAYED, TensorScalingType.DYNAMIC]
)
@pytest.mark.parametrize(
"scaling_type_dL_dY", [TensorScalingType.DELAYED, TensorScalingType.DYNAMIC]
)
@pytest.mark.parametrize(
"linear_dtype", [torch.float16, torch.bfloat16, torch.float32]
)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_linear_bias(
self,
x_shape,
scaling_type_x: TensorScalingType,
scaling_type_w: TensorScalingType,
scaling_type_dL_dY: TensorScalingType,
emulate: bool,
linear_dtype: torch.dtype,
linear_bias: bool,
):
if not emulate:
if not torch.cuda.is_available():
Expand All @@ -295,7 +255,7 @@ def test_linear_bias(
)
pytest.skip()
x = torch.randn(*x_shape, device="cuda", dtype=linear_dtype)
m_ref = nn.Linear(16, 32, bias=True, device="cuda", dtype=linear_dtype)
m_ref = nn.Linear(16, 32, bias=linear_bias, device="cuda", dtype=linear_dtype)
self._test_linear_impl(
x,
m_ref,
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