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[xpu][test] Port 2 test/prototype/test_{nvfp4_tensor, spinquant} UT files to intel XPU #3423

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[xpu][test] Port 2 test/prototype/test_{nvfp4_tensor, spinquant} UT files to intel XPU #3423

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ff57d77
add test/prototype/mx_formats/test_nvfp4_tensor.py
zxd1997066 Dec 3, 2025
9c797df
add test/prototype/mx_formats/test_nvfp4_tensor.py
zxd1997066 Dec 3, 2025
08a600d
add test/prototype/mx_formats/test_nvfp4_tensor.py
zxd1997066 Dec 3, 2025
284959f
test/prototype/test_spinquant.py
zxd1997066 Dec 3, 2025
6523234
fix format issue
zxd1997066 Dec 3, 2025
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62 changes: 34 additions & 28 deletions test/prototype/mx_formats/test_nvfp4_tensor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,11 +22,13 @@
from torchao.quantization.utils import compute_error
from torchao.testing.utils import skip_if_rocm
from torchao.utils import (
get_current_accelerator_device,
is_sm_at_least_100,
torch_version_at_least,
)

torch.manual_seed(2)
_DEVICE = get_current_accelerator_device()

if not torch_version_at_least("2.8.0"):
pytest.skip("Unsupported PyTorch version", allow_module_level=True)
Expand All @@ -42,12 +44,12 @@
(torch.bfloat16, (1, 32, 64), False),
],
)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="torch.compile requires PyTorch 2.8+"
)
def test_nvfp4_reconstruction(dtype, shape, use_per_tensor_scale):
x = torch.randn(shape, dtype=dtype, device="cuda")
x = torch.randn(shape, dtype=dtype, device=_DEVICE)
if use_per_tensor_scale:
tensor_amax = torch.max(torch.abs(x))
scale = per_tensor_amax_to_scale(tensor_amax)
Expand Down Expand Up @@ -113,14 +115,14 @@ def assert_sqnr_gt_threshold(orig, new, threshold):
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="torch.compile requires PyTorch 2.8+"
)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
def test_nvfp4_swizzled_scales_construction(is_swizzled_scales, shape):
"""
Test that NVFP4Tensor can be constructed with swizzled scales and
that the _is_swizzled_scales flag is set correctly.
"""

data = torch.randn(*shape, device="cuda", dtype=torch.bfloat16)
data = torch.randn(*shape, device=_DEVICE, dtype=torch.bfloat16)

tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=is_swizzled_scales)
assert tensor._is_swizzled_scales == is_swizzled_scales
Expand All @@ -146,7 +148,7 @@ def test_nvfp4_swizzled_scales_construction(is_swizzled_scales, shape):
pytest.param(1, slice(1024, 2048), id="slice_cols[1024:2048]_quarter"),
],
)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -164,7 +166,7 @@ def test_nvfp4_swizzled_scales_slicing(slice_dim, slice_spec):
# For column slicing, need multiples of 64 columns for alignment
M, K = 128, 4096

data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
data = torch.randn(M, K, device=_DEVICE, dtype=torch.bfloat16)

tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True)
assert tensor._is_swizzled_scales == True
Expand Down Expand Up @@ -240,7 +242,7 @@ def test_nvfp4_swizzled_scales_slicing(slice_dim, slice_spec):
),
],
)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -250,7 +252,7 @@ def test_nvfp4_swizzled_scales_slicing_errors(slice_dim, slice_spec, expected_er
"""

M, K = 256, 4096
data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
data = torch.randn(M, K, device=_DEVICE, dtype=torch.bfloat16)
tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True)

with pytest.raises(RuntimeError, match=expected_error):
Expand All @@ -260,7 +262,7 @@ def test_nvfp4_swizzled_scales_slicing_errors(slice_dim, slice_spec, expected_er
_ = tensor[:, slice_spec]


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -270,7 +272,7 @@ def test_nvfp4_swizzled_scales_view_semantics():
"""

M, K = 256, 4096
data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
data = torch.randn(M, K, device=_DEVICE, dtype=torch.bfloat16)
tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True)

# Test row slicing (should maintain views)
Expand All @@ -286,7 +288,7 @@ def test_nvfp4_swizzled_scales_view_semantics():
assert full_width_slice.qdata.data_ptr() == tensor.qdata.data_ptr()


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -296,7 +298,7 @@ def test_nvfp4_swizzled_scales_serialization():
"""

M, K = 32, 64
data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
data = torch.randn(M, K, device=_DEVICE, dtype=torch.bfloat16)

# Create tensor with swizzled scales
original_tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True)
Expand Down Expand Up @@ -327,7 +329,7 @@ def test_nvfp4_swizzled_scales_serialization():
torch.testing.assert_close(original_dq, reconstructed_dq, atol=1e-6, rtol=1e-6)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -337,7 +339,7 @@ def test_nvfp4_swizzled_scales_get_scales_method():
"""

M, K = 32, 64
data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
data = torch.randn(M, K, device=_DEVICE, dtype=torch.bfloat16)

# Create tensors with both storage methods
regular_tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=False)
Expand Down Expand Up @@ -371,7 +373,7 @@ def test_triton_nvfp4_quantize_equivalence(M, N, use_per_tensor_scale, dtype):
"""Test that Triton and PyTorch NVFP4 quantization produce equivalent results."""

torch.manual_seed(42)
x = torch.randn(M, N, dtype=dtype, device="cuda")
x = torch.randn(M, N, dtype=dtype, device=_DEVICE)

per_tensor_scale = None
if use_per_tensor_scale:
Expand Down Expand Up @@ -413,7 +415,7 @@ def test_triton_nvfp4_quantize_equivalence(M, N, use_per_tensor_scale, dtype):
)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="torch.compile requires PyTorch 2.8+"
)
Expand Down Expand Up @@ -454,7 +456,11 @@ def test_nvfp4_matmul_with_amax(
shapes: tuple,
):
# DYNAMIC mode requires SM100+, but WEIGHT_ONLY works on older GPUs
if quant_type == "dynamic" and not is_sm_at_least_100():
if (
quant_type == "dynamic"
and torch.cuda.is_available()
and not is_sm_at_least_100()
):
pytest.skip("CUDA capability >= 10.0 required for DYNAMIC float4 gemm")

if bias and inpt_dtype == torch.float32:
Expand All @@ -467,13 +473,13 @@ def test_nvfp4_matmul_with_amax(

# Create activation tensor
if use_gelu:
x = torch.randn(m, k, dtype=inpt_dtype, device="cuda")
x = torch.randn(m, k, dtype=inpt_dtype, device=_DEVICE)
A = torch.nn.functional.gelu(x)
else:
A = torch.randn(m, k, dtype=inpt_dtype, device="cuda")
A = torch.randn(m, k, dtype=inpt_dtype, device=_DEVICE)

B = torch.randn(n, k, dtype=inpt_dtype, device="cuda")
bias_tensor = torch.randn(n, dtype=inpt_dtype, device="cuda") if bias else None
B = torch.randn(n, k, dtype=inpt_dtype, device=_DEVICE)
bias_tensor = torch.randn(n, dtype=inpt_dtype, device=_DEVICE) if bias else None

# Compute reference
C_ref = F.linear(A, B, bias_tensor)
Expand Down Expand Up @@ -511,12 +517,12 @@ def test_nvfp4_matmul_with_amax(
)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
def test_nvfp4_to_copy():
x = NVFP4Tensor.to_nvfp4(torch.randn((32, 128))).cuda()
x = NVFP4Tensor.to_nvfp4(torch.randn((32, 128))).to(_DEVICE)
y = torch.ops.aten._to_copy(x, dtype=torch.bfloat16)
assert torch.equal(x.qdata, y.qdata)
assert torch.equal(x.scale, y.scale)
Expand All @@ -531,7 +537,7 @@ def test_nvfp4_to_copy():
assert y.dtype == torch.bfloat16


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
Expand All @@ -551,14 +557,14 @@ def test_nvfp4_to_copy():
def test_scale_shape_matches_qdata(
transpose, use_triton_kernel, is_swizzled_scales, shape
):
if use_triton_kernel and not is_sm_at_least_100():
if use_triton_kernel and torch.cuda.is_available() and not is_sm_at_least_100():
pytest.skip("CUDA capability >= 10.0 required for nvfp4 triton kernel")
if use_triton_kernel and not is_swizzled_scales:
pytest.skip("triton kernel requires swizzled scales")

block_size = 16

x_hp = torch.randn(*shape, device="cuda")
x_hp = torch.randn(*shape, device=_DEVICE)
x = NVFP4Tensor.to_nvfp4(
x_hp, is_swizzled_scales=is_swizzled_scales, use_triton_kernel=use_triton_kernel
)
Expand Down Expand Up @@ -599,14 +605,14 @@ def test_scale_shape_matches_qdata(
)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
@pytest.mark.skipif(
not torch_version_at_least("2.8.0"), reason="NVFP4 requires PyTorch 2.8+"
)
@pytest.mark.parametrize("dims", ((1, 2), (2, 1), (-1, -2), (-2, -1)))
@pytest.mark.parametrize("is_swizzled_scales", [True, False])
def test_3d_transpose(dims, is_swizzled_scales):
x_hp = torch.randn(2, 128, 256, device="cuda")
x_hp = torch.randn(2, 128, 256, device=_DEVICE)
x_nvfp4 = NVFP4Tensor.to_nvfp4(x_hp, is_swizzled_scales=is_swizzled_scales)
x_hp_t = x_hp.transpose(dims[0], dims[1])
x_nvfp4_t = x_nvfp4.transpose(dims[0], dims[1])
Expand Down
6 changes: 5 additions & 1 deletion test/prototype/test_spinquant.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,11 @@ def _init_model(name="7B", device="cpu", precision=torch.bfloat16):
return model.eval()


_AVAILABLE_DEVICES = ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
_AVAILABLE_DEVICES = (
["cpu"]
+ (["cuda"] if torch.cuda.is_available() else [])
+ (["xpu"] if torch.xpu.is_available() else [])
)


@pytest.mark.parametrize("device", _AVAILABLE_DEVICES)
Expand Down