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Support torch validation and add to spmd tests #3003

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71641da
Support torch validation and add to spmd tests
ethanglaser Mar 6, 2026
f767365
formatting
ethanglaser Mar 6, 2026
a3f3407
Merge branch 'main' into dev/eglaser-add-torch-spmd-validation
ethanglaser Apr 8, 2026
676fead
Add torch to remaining spmd tests
ethanglaser Apr 8, 2026
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18 changes: 17 additions & 1 deletion onedal/tests/utils/_dataframes_support.py
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Expand Up @@ -27,6 +27,13 @@
except ImportError:
dpnp_available = False

try:
import torch

torch_available = True
except ImportError:
torch_available = False

try:
# This should be lazy imported in the
# future along with other popular
Expand All @@ -50,7 +57,7 @@
from onedal.tests.utils._device_selection import get_queues

test_frameworks = os.environ.get(
"ONEDAL_PYTEST_FRAMEWORKS", "numpy,pandas,dpnp,array_api"
"ONEDAL_PYTEST_FRAMEWORKS", "numpy,pandas,dpnp,array_api,torch"
)


Expand Down Expand Up @@ -116,6 +123,8 @@ def get_df_and_q(dataframe: str):
or array_api_enabled()
):
dataframes_and_queues.append(pytest.param("array_api", None, id="array_api"))
if torch_available and "torch" in dataframe_filter_:
dataframes_and_queues.extend(get_df_and_q("torch"))

return dataframes_and_queues

Expand All @@ -124,6 +133,8 @@ def _as_numpy(obj, *args, **kwargs):
"""Converted input object to numpy.ndarray format."""
if dpnp_available and isinstance(obj, dpnp.ndarray):
return obj.asnumpy(*args, **kwargs)
if torch_available and isinstance(obj, torch.Tensor):
return obj.cpu().detach().numpy(*args, **kwargs)
if isinstance(obj, pd.DataFrame) or isinstance(obj, pd.Series):
return obj.to_numpy(*args, **kwargs)
if sp.issparse(obj):
Expand Down Expand Up @@ -162,5 +173,10 @@ def _convert_to_dataframe(obj, sycl_queue=None, target_df=None, *args, **kwargs)

xp = array_api_modules[target_df]
return xp.asarray(obj)
elif target_df == "torch":
if hasattr(torch, "xpu") and torch.xpu.is_available():
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@icfaust icfaust Mar 7, 2026

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should probably be documented to prevent confusion

return torch.as_tensor(obj, device="xpu", *args, **kwargs)
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@yuejiaointel yuejiaointel Mar 10, 2026

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It seems we are testing np.float32/np.float64 dtypes in convert_to_dataframe and torch does not take this, maybe we need to conver to torch dtype before passing to as_tensor

else:
return torch.as_tensor(obj, device="cpu", *args, **kwargs)

raise RuntimeError("Unsupported dataframe conversion")
4 changes: 2 additions & 2 deletions sklearnex/spmd/basic_statistics/tests/test_basic_statistics_spmd.py
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Expand Up @@ -38,7 +38,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_basic_stats_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -82,7 +82,7 @@ def test_basic_stats_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("array_api_dispatch", [True, False])
@pytest.mark.mpi
Expand Down
8 changes: 4 additions & 4 deletions sklearnex/spmd/basic_statistics/tests/test_incremental_basic_statistics_spmd.py
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Expand Up @@ -38,7 +38,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("weighted", [True, False])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
Expand Down Expand Up @@ -104,7 +104,7 @@ def test_incremental_basic_statistics_fit_spmd_gold(dataframe, queue, weighted,
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("num_blocks", [1, 2])
@pytest.mark.parametrize("weighted", [True, False])
Expand Down Expand Up @@ -178,7 +178,7 @@ def test_incremental_basic_statistics_partial_fit_spmd_gold(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("num_blocks", [1, 2])
@pytest.mark.parametrize("weighted", [True, False])
Expand Down Expand Up @@ -247,7 +247,7 @@ def test_incremental_basic_statistics_single_option_partial_fit_spmd_gold(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("num_blocks", [1, 2])
@pytest.mark.parametrize("weighted", [True, False])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/cluster/tests/test_dbscan_spmd.py
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Expand Up @@ -36,7 +36,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_dbscan_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -67,7 +67,7 @@ def test_dbscan_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("min_samples", [2, 5, 15])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/cluster/tests/test_kmeans_spmd.py
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Expand Up @@ -39,7 +39,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_kmeans_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -107,7 +107,7 @@ def test_kmeans_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("n_clusters", [2, 5, 15])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/covariance/tests/test_covariance_spmd.py
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Expand Up @@ -37,7 +37,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_covariance_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -83,7 +83,7 @@ def test_covariance_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("assume_centered", [True, False])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
6 changes: 3 additions & 3 deletions sklearnex/spmd/covariance/tests/test_incremental_covariance_spmd.py
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Expand Up @@ -37,7 +37,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("assume_centered", [True, False])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
Expand Down Expand Up @@ -88,7 +88,7 @@ def test_incremental_covariance_fit_spmd_gold(dataframe, queue, assume_centered,
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("num_blocks", [1, 2])
@pytest.mark.parametrize("assume_centered", [True, False])
Expand Down Expand Up @@ -149,7 +149,7 @@ def test_incremental_covariance_partial_fit_spmd_gold(
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("array_api_dispatch", [True, False])
@pytest.mark.mpi
Expand Down
8 changes: 4 additions & 4 deletions sklearnex/spmd/decomposition/tests/test_incremental_pca_spmd.py
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Expand Up @@ -47,7 +47,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
Expand Down Expand Up @@ -95,7 +95,7 @@ def test_incremental_pca_fit_spmd_gold(dataframe, queue, whiten, dtype):
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize("num_blocks", [1, 2])
Expand Down Expand Up @@ -159,7 +159,7 @@ def test_incremental_pca_partial_fit_spmd_gold(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize("n_components", [None, 2, 5])
Expand Down Expand Up @@ -211,7 +211,7 @@ def test_incremental_pca_fit_spmd_random(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize("n_components", [None, 2, 5])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/decomposition/tests/test_pca_spmd.py
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Expand Up @@ -37,7 +37,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_pca_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -90,7 +90,7 @@ def test_pca_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
8 changes: 4 additions & 4 deletions sklearnex/spmd/ensemble/tests/test_forest_spmd.py
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Expand Up @@ -38,7 +38,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_rfcls_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -107,7 +107,7 @@ def test_rfcls_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("local_trees_mode", [False, True])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down Expand Up @@ -173,7 +173,7 @@ def test_rfcls_spmd_synthetic(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_rfreg_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -242,7 +242,7 @@ def test_rfreg_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("local_trees_mode", [False, True])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
8 changes: 4 additions & 4 deletions sklearnex/spmd/linear_model/tests/test_incremental_linear_spmd.py
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Expand Up @@ -37,7 +37,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("macro_block", [None, 1024])
Expand Down Expand Up @@ -112,7 +112,7 @@ def test_incremental_linear_regression_fit_spmd_gold(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("num_blocks", [1, 2])
Expand Down Expand Up @@ -196,7 +196,7 @@ def test_incremental_linear_regression_partial_fit_spmd_gold(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("num_samples", [100, 1000])
Expand Down Expand Up @@ -261,7 +261,7 @@ def test_incremental_linear_regression_fit_spmd_random(
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("num_blocks", [1, 2])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/linear_model/tests/test_linear_regression_spmd.py
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Expand Up @@ -38,7 +38,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_linear_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -102,7 +102,7 @@ def test_linear_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("n_features", [10, 100])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
4 changes: 2 additions & 2 deletions sklearnex/spmd/linear_model/tests/test_logistic_regression_spmd.py
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Expand Up @@ -38,7 +38,7 @@
)
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.mpi
def test_logistic_spmd_gold(dataframe, queue):
Expand Down Expand Up @@ -112,7 +112,7 @@ def test_logistic_spmd_gold(dataframe, queue):
@pytest.mark.parametrize("tol", [1e-2, 1e-4])
@pytest.mark.parametrize(
"dataframe,queue",
get_dataframes_and_queues(dataframe_filter_="dpnp", device_filter_="gpu"),
get_dataframes_and_queues(dataframe_filter_="dpnp,torch", device_filter_="gpu"),
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("array_api_dispatch", [True, False])
Expand Down
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