ci: further fp32 GPU green CI enabling

onedal/linear_model/linear_model.py

@@ -198,6 +198,9 @@ def fit(self, X, y, queue=None):
         if not isinstance(X, np.ndarray):
             X = np.asarray(X)
 
+        policy = self._get_policy(queue, X, y)
+        X, y = _convert_to_supported(policy, X, y)
+
         dtype = get_dtype(X)
         if dtype not in [np.float32, np.float64]:
             dtype = np.float64
@@ -207,11 +210,8 @@ def fit(self, X, y, queue=None):
 
         X, y = _check_X_y(X, y, force_all_finite=False, accept_2d_y=True)
 
-        policy = self._get_policy(queue, X, y)
-
         self.n_features_in_ = _num_features(X, fallback_1d=True)
 
-        X, y = _convert_to_supported(policy, X, y)
         params = self._get_onedal_params(get_dtype(X))
         X_table, y_table = to_table(X, y)
 

onedal/linear_model/tests/test_linear_regression.py

@@ -248,7 +248,7 @@ def test_multioutput_regression(queue, dtype, fit_intercept, problem_type):
 
     pred = model.predict(X, queue=queue)
     expected_pred = X @ model.coef_.T + model.intercept_.reshape((1, -1))
-    tol = 1e-5 if pred.dtype == np.float32 else 1e-7
+    tol = 2e-5 if pred.dtype == np.float32 else 1e-7
     assert_allclose(pred, expected_pred, rtol=tol)
 
     # check that it also works when 'y' is a list of lists

onedal/linear_model/tests/test_logistic_regression.py

@@ -89,7 +89,7 @@ def test_csr(queue, dtype, dims):
         model_sp.fit(X_sp, y, queue=queue)
         pred_sp = model_sp.predict(X_sp, queue=queue)
 
-        rtol = 2e-4
+        rtol = 2e-3
         assert_allclose(pred, pred_sp, rtol=rtol)
         assert_allclose(model.coef_, model_sp.coef_, rtol=rtol)
         assert_allclose(model.intercept_, model_sp.intercept_, rtol=rtol)

sklearnex/linear_model/tests/test_incremental_linear.py

@@ -49,7 +49,7 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, fit_intercept, macro_block
     y_pred = inclin.predict(X_df)
     np_y_pred = _as_numpy(y_pred)
 
-    tol = 5e-5 if dtype == np.float32 else 1e-7
+    tol = 5e-5 if y_pred.dtype == np.float32 else 1e-7
     assert_allclose(inclin.coef_, [1], atol=tol)
     if fit_intercept:
         assert_allclose(inclin.intercept_, [0], atol=tol)
@@ -89,7 +89,7 @@ def test_sklearnex_partial_fit_on_gold_data(
     np_y_pred = _as_numpy(y_pred)
 
     assert inclin.n_features_in_ == 1
-    tol = 1e-5 if dtype == np.float32 else 1e-7
+    tol = 1e-5 if y_pred.dtype == np.float32 else 1e-7
     assert_allclose(inclin.coef_, [[1]], atol=tol)
     if fit_intercept:
         assert_allclose(inclin.intercept_, 3, atol=tol)
@@ -131,7 +131,7 @@ def test_sklearnex_partial_fit_multitarget_on_gold_data(
 
     assert inclin.n_features_in_ == 2
     tol = 1e-7
-    if dtype == np.float32:
+    if y_pred.dtype == np.float32:
         tol = 7e-6 if _IS_INTEL else 2e-5
 
     assert_allclose(inclin.coef_, [1.0, 2.0], atol=tol)

sklearnex/linear_model/tests/test_incremental_ridge.py

@@ -76,10 +76,13 @@ def test_inc_ridge_fit_coefficients(
         coefficients_manual, intercept_manual = _compute_ridge_coefficients(
             X, y, alpha, fit_intercept
         )
+
+        tol = 2e-4 if inc_ridge.coef_.dtype == np.float32 else 1e-6
+
         if fit_intercept:
-            assert_allclose(inc_ridge.intercept_, intercept_manual, rtol=1e-6, atol=1e-6)
+            assert_allclose(inc_ridge.intercept_, intercept_manual, rtol=tol, atol=tol)
 
-        assert_allclose(inc_ridge.coef_, coefficients_manual, rtol=1e-6, atol=1e-6)
+        assert_allclose(inc_ridge.coef_, coefficients_manual, rtol=tol, atol=tol)
 
     @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
     @pytest.mark.parametrize("batch_size", [2, 5])
@@ -106,8 +109,10 @@ def test_inc_ridge_partial_fit_coefficients(dataframe, queue, alpha, batch_size)
         inverse_term = np.linalg.inv(np.dot(X.T, X) + lambda_identity)
         xt_y = np.dot(X.T, y)
         coefficients_manual = np.dot(inverse_term, xt_y)
+
+        tol = 5e-3 if inc_ridge.coef_.dtype == np.float32 else 1e-6
 
-        assert_allclose(inc_ridge.coef_, coefficients_manual, rtol=1e-6, atol=1e-6)
+        assert_allclose(inc_ridge.coef_, coefficients_manual, rtol=tol, atol=tol)
 
     def test_inc_ridge_score_before_fit():
         X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
@@ -149,5 +154,7 @@ def test_inc_ridge_predict_after_fit(dataframe, queue, fit_intercept):
         y_pred_manual = np.dot(X, coefficients_manual)
         if fit_intercept:
             y_pred_manual += intercept_manual
+
+        tol = 1e-5 if inc_ridge.coef_.dtype == np.float32 else 1e-6
 
-        assert_allclose(_as_numpy(y_pred), y_pred_manual, rtol=1e-6, atol=1e-6)
+        assert_allclose(_as_numpy(y_pred), y_pred_manual, rtol=tol, atol=tol)

sklearnex/linear_model/tests/test_logreg.py

@@ -127,7 +127,7 @@ def test_csr(queue, dtype, dims):
             pred_sp = model_sp.predict(X_sp)
             prob_sp = model_sp.predict_proba(X_sp)
 
-        rtol = 2e-4
+        rtol = 1e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 2e-4
         assert_allclose(pred, pred_sp, rtol=rtol)
         assert_allclose(prob, prob_sp, rtol=rtol)
         assert_allclose(model.coef_, model_sp.coef_, rtol=rtol)

sklearnex/linear_model/tests/test_ridge.py

@@ -129,8 +129,10 @@ def test_ridge_coefficients(
         X, y, alpha, fit_intercept=fit_intercept
     )
 
-    assert_allclose(ridge_reg.coef_, coefficients_manual, rtol=1e-6, atol=1e-6)
-    assert_allclose(ridge_reg.intercept_, intercept_manual, rtol=1e-6, atol=1e-6)
+    tol = 1e-5 if ridge_reg.coef_.dtype == np.float32 else 1e-6
+
+    assert_allclose(ridge_reg.coef_, coefficients_manual, rtol=tol, atol=tol)
+    assert_allclose(ridge_reg.intercept_, intercept_manual, rtol=tol, atol=tol)
 
 
 @pytest.mark.skipif(

sklearnex/preview/decomposition/tests/test_incremental_pca.py

@@ -200,7 +200,7 @@ def test_sklearnex_partial_fit_on_gold_data(dataframe, queue, whiten, num_blocks
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.transform(X_df)
-    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
+    check_pca_on_gold_data(incpca, transformed_data.dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -217,7 +217,7 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, whiten, num_blocks, dtype)
     incpca.fit(X_df)
     transformed_data = incpca.transform(X_df)
 
-    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
+    check_pca_on_gold_data(incpca, transformed_data.dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -235,7 +235,7 @@ def test_sklearnex_fit_transform_on_gold_data(
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.fit_transform(X_df)
 
-    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
+    check_pca_on_gold_data(incpca, transformed_data.dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -263,7 +263,7 @@ def test_sklearnex_partial_fit_on_random_data(
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.transform(X_df)
-    check_pca(incpca, dtype, whiten, X, transformed_data)
+    check_pca(incpca, transformed_data.dtype, whiten, X, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())

sklearnex/spmd/basic_statistics/tests/test_basic_statistics_spmd.py

@@ -65,8 +65,9 @@ def test_basic_stats_spmd_gold(dataframe, queue):
     spmd_result = BasicStatistics_SPMD().fit(local_dpt_data)
     batch_result = BasicStatistics_Batch().fit(data)
 
+    tol = 1e-7 if queue.sycl_device.has_aspect_fp64 else 1e-6
     for option in options_and_tests:
-        assert_allclose(getattr(spmd_result, option), getattr(batch_result, option))
+        assert_allclose(getattr(spmd_result, option), getattr(batch_result, option), rtol=tol)
 
 
 @pytest.mark.skipif(
@@ -97,7 +98,7 @@ def test_basic_stats_spmd_synthetic(n_samples, n_features, dataframe, queue, dty
     spmd_result = BasicStatistics_SPMD().fit(local_dpt_data)
     batch_result = BasicStatistics_Batch().fit(data)
 
-    tol = 1e-5 if dtype == np.float32 else 1e-7
+    tol = 1e-5 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
     for option in options_and_tests:
         assert_allclose(
             getattr(spmd_result, option),

sklearnex/spmd/basic_statistics/tests/test_incremental_basic_statistics_spmd.py

@@ -260,7 +260,7 @@ def test_incremental_basic_statistics_partial_fit_spmd_synthetic(
         IncrementalBasicStatistics as IncrementalBasicStatistics_SPMD,
     )
 
-    tol = 2e-3 if dtype == np.float32 else 1e-7
+    tol = 2e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
 
     # Create gold data and process into dpt
     data = _generate_statistic_data(n_samples, n_features, dtype=dtype)

sklearnex/spmd/cluster/tests/test_dbscan_spmd.py

@@ -43,7 +43,7 @@ def test_dbscan_spmd_gold(dataframe, queue):
     from sklearnex.cluster import DBSCAN as DBSCAN_Batch
     from sklearnex.spmd.cluster import DBSCAN as DBSCAN_SPMD
 
-    data = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])
+    data = np.array([[1., 2.], [2., 2.], [2., 3.], [8., 7.], [8., 8.], [25., 80.]])
 
     local_dpt_data = _convert_to_dataframe(
         _get_local_tensor(data), sycl_queue=queue, target_df=dataframe

sklearnex/spmd/cluster/tests/test_kmeans_spmd.py

@@ -47,22 +47,21 @@ def test_kmeans_spmd_gold(dataframe, queue):
 
     X_train = np.array(
         [
-            [1, 2],
-            [2, 2],
-            [2, 3],
-            [8, 7],
-            [8, 8],
-            [25, 80],
-            [5, 65],
-            [2, 8],
-            [1, 3],
-            [2, 2],
-            [1, 3],
-            [2, 2],
+            [1., 2.],
+            [2., 2.],
+            [2., 3.],
+            [8., 7.],
+            [8., 8.],
+            [25., 80.],
+            [5., 65.],
+            [2., 8.],
+            [1., 3.],
+            [2., 2.],
+            [1., 3.],
+            [2., 2.],
         ],
-        dtype=np.float64,
     )
-    X_test = np.array([[0, 0], [12, 3], [2, 2], [7, 8]], dtype=np.float64)
+    X_test = np.array([[0., 0.], [12., 3.], [2., 2.], [7., 8.]])
 
     local_dpt_X_train = _convert_to_dataframe(
         _get_local_tensor(X_train), sycl_queue=queue, target_df=dataframe
@@ -146,7 +145,7 @@ def test_kmeans_spmd_synthetic(
         n_clusters=n_clusters, init=spmd_model_init.cluster_centers_, random_state=0
     ).fit(X_train)
 
-    atol = 1e-5 if dtype == np.float32 else 1e-7
+    atol = 1e-5 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
     _assert_unordered_allclose(
         spmd_model.cluster_centers_, batch_model.cluster_centers_, atol=atol
     )

sklearnex/spmd/covariance/tests/test_covariance_spmd.py

@@ -64,8 +64,9 @@ def test_covariance_spmd_gold(dataframe, queue):
     spmd_result = EmpiricalCovariance_SPMD().fit(local_dpt_data)
     batch_result = EmpiricalCovariance_Batch().fit(data)
 
-    assert_allclose(spmd_result.covariance_, batch_result.covariance_)
-    assert_allclose(spmd_result.location_, batch_result.location_)
+    atol = 1e-7 if queue.sycl_device.has_aspect_fp64 else 1e-5
+    assert_allclose(spmd_result.covariance_, batch_result.covariance_, atol=atol)
+    assert_allclose(spmd_result.location_, batch_result.location_, atol=atol)
 
 
 @pytest.mark.skipif(
@@ -102,6 +103,6 @@ def test_covariance_spmd_synthetic(
     )
     batch_result = EmpiricalCovariance_Batch(assume_centered=assume_centered).fit(data)
 
-    atol = 1e-5 if dtype == np.float32 else 1e-7
+    atol = 1e-5 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
     assert_allclose(spmd_result.covariance_, batch_result.covariance_, atol=atol)
     assert_allclose(spmd_result.location_, batch_result.location_, atol=atol)

sklearnex/spmd/covariance/tests/test_incremental_covariance_spmd.py

@@ -178,7 +178,7 @@ def test_incremental_covariance_partial_fit_spmd_synthetic(
 
     inccov.fit(dpt_data)
 
-    tol = 1e-7
+    tol = 1e-7 if queue.sycl_device.has_aspect_fp64 else 1e-6
 
     assert_allclose(inccov_spmd.covariance_, inccov.covariance_, atol=tol)
     assert_allclose(inccov_spmd.location_, inccov.location_, atol=tol)

sklearnex/spmd/decomposition/tests/test_incremental_pca_spmd.py

@@ -174,7 +174,7 @@ def test_incremental_pca_fit_spmd_random(
     from sklearnex.spmd.decomposition import IncrementalPCA as IncrementalPCA_SPMD
 
     # Increased test dataset size requires a higher tol setting in comparison to other tests
-    tol = 7e-5 if dtype == np.float32 else 1e-7
+    tol = 3e-2 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
 
     # Create data and process into dpt
     X = _generate_statistic_data(num_samples, num_features, dtype)
@@ -233,7 +233,7 @@ def test_incremental_pca_partial_fit_spmd_random(
     from sklearnex.preview.decomposition import IncrementalPCA
     from sklearnex.spmd.decomposition import IncrementalPCA as IncrementalPCA_SPMD
 
-    tol = 3e-4 if dtype == np.float32 else 1e-7
+    tol = 3e-4 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
 
     # Create data and process into dpt
     X = _generate_statistic_data(num_samples, num_features, dtype)

sklearnex/spmd/decomposition/tests/test_pca_spmd.py

@@ -65,16 +65,18 @@ def test_pca_spmd_gold(dataframe, queue):
     spmd_result = PCA_SPMD(n_components=2).fit(local_dpt_data)
     batch_result = PCA_Batch(n_components=2).fit(data)
 
+    tol = 1e-7 if queue.sycl_device.has_aspect_fp64 else 1e-5
+
     assert_allclose(spmd_result.mean_, batch_result.mean_)
     assert_allclose(spmd_result.components_, batch_result.components_)
-    assert_allclose(spmd_result.singular_values_, batch_result.singular_values_)
+    assert_allclose(spmd_result.singular_values_, batch_result.singular_values_, rtol=tol)
     assert_allclose(
         spmd_result.noise_variance_,
         batch_result.noise_variance_,
-        atol=1e-7,
+        atol=tol,
     )
     assert_allclose(
-        spmd_result.explained_variance_ratio_, batch_result.explained_variance_ratio_
+        spmd_result.explained_variance_ratio_, batch_result.explained_variance_ratio_, rtol=tol
     )
 
 
@@ -116,7 +118,7 @@ def test_pca_spmd_synthetic(
     spmd_result = PCA_SPMD(n_components=n_components, whiten=whiten).fit(local_dpt_data)
     batch_result = PCA_Batch(n_components=n_components, whiten=whiten).fit(data)
 
-    tol = 1e-3 if dtype == np.float32 else 1e-7
+    tol = 1e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
     assert_allclose(spmd_result.mean_, batch_result.mean_, atol=tol)
     assert_allclose(spmd_result.components_, batch_result.components_, atol=tol, rtol=tol)
     assert_allclose(spmd_result.singular_values_, batch_result.singular_values_, atol=tol)

sklearnex/spmd/linear_model/tests/test_incremental_linear_spmd.py

@@ -208,7 +208,7 @@ def test_incremental_linear_regression_fit_spmd_random(
         IncrementalLinearRegression as IncrementalLinearRegression_SPMD,
     )
 
-    tol = 5e-3 if dtype == np.float32 else 1e-7
+    tol = 5e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
 
     # Generate random data and process into dpt
     X_train, X_test, y_train, _ = _generate_regression_data(
@@ -279,7 +279,7 @@ def test_incremental_linear_regression_partial_fit_spmd_random(
         IncrementalLinearRegression as IncrementalLinearRegression_SPMD,
     )
 
-    tol = 5e-3 if dtype == np.float32 else 1e-7
+    tol = 5e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
 
     # Generate random data and process into dpt
     X_train, X_test, y_train, _ = _generate_regression_data(

sklearnex/spmd/linear_model/tests/test_linear_regression_spmd.py

@@ -82,14 +82,16 @@ def test_linear_spmd_gold(dataframe, queue):
     spmd_model = LinearRegression_SPMD().fit(local_dpt_X_train, local_dpt_y_train)
     batch_model = LinearRegression_Batch().fit(X_train, y_train)
 
-    assert_allclose(spmd_model.coef_, batch_model.coef_)
-    assert_allclose(spmd_model.intercept_, batch_model.intercept_)
+    tol = 1e-7 if queue.sycl_device.has_aspect_fp64 else 1e-5
+
+    assert_allclose(spmd_model.coef_, batch_model.coef_, rtol=tol)
+    assert_allclose(spmd_model.intercept_, batch_model.intercept_, rtol=tol)
 
     # ensure predictions of batch algo match spmd
     spmd_result = spmd_model.predict(local_dpt_X_test)
     batch_result = batch_model.predict(X_test)
 
-    _spmd_assert_allclose(spmd_result, batch_result)
+    _spmd_assert_allclose(spmd_result, batch_result, rtol=tol)
 
 
 @pytest.mark.skipif(
@@ -134,7 +136,7 @@ def test_linear_spmd_synthetic(n_samples, n_features, dataframe, queue, dtype):
     spmd_model = LinearRegression_SPMD().fit(local_dpt_X_train, local_dpt_y_train)
     batch_model = LinearRegression_Batch().fit(X_train, y_train)
 
-    tol = 1e-3 if dtype == np.float32 else 1e-7
+    tol = 1e-3 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-7
     assert_allclose(spmd_model.coef_, batch_model.coef_, rtol=tol, atol=tol)
     assert_allclose(spmd_model.intercept_, batch_model.intercept_, rtol=tol, atol=tol)
 

sklearnex/spmd/neighbors/tests/test_neighbors_spmd.py

@@ -155,7 +155,7 @@ def test_knncls_spmd_synthetic(
     batch_result = batch_model.predict(X_test)
 
     tol = 1e-4
-    if dtype == np.float64:
+    if dtype == np.float64 and queue.sycl_device.has_aspect_fp64:
         _assert_unordered_allclose(spmd_indcs, batch_indcs, localize=True)
         _assert_unordered_allclose(
             spmd_dists, batch_dists, localize=True, rtol=tol, atol=tol
@@ -279,8 +279,8 @@ def test_knnreg_spmd_synthetic(
     spmd_result = spmd_model.predict(local_dpt_X_test)
     batch_result = batch_model.predict(X_test)
 
-    tol = 0.005 if dtype == np.float32 else 1e-4
-    if dtype == np.float64:
+    tol = 0.005 if (dtype == np.float32 or not queue.sycl_device.has_aspect_fp64) else 1e-4
+    if dtype == np.float64 and queue.sycl_device.has_aspect_fp64:
         _assert_unordered_allclose(spmd_indcs, batch_indcs, localize=True)
         _assert_unordered_allclose(
             spmd_dists, batch_dists, localize=True, rtol=tol, atol=tol