[pyspark] unify the way for determining whether runs on the GPU. (#9724)

tests/test_distributed/test_with_spark/test_spark_local.py

@@ -888,6 +888,22 @@ class TestPySparkLocal:
         clf = SparkXGBClassifier(device="cuda")
         clf._validate_params()
 
+    def test_gpu_params(self) -> None:
+        clf = SparkXGBClassifier()
+        assert not clf._run_on_gpu()
+
+        clf = SparkXGBClassifier(device="cuda", tree_method="hist")
+        assert clf._run_on_gpu()
+
+        clf = SparkXGBClassifier(device="cuda")
+        assert clf._run_on_gpu()
+
+        clf = SparkXGBClassifier(tree_method="gpu_hist")
+        assert clf._run_on_gpu()
+
+        clf = SparkXGBClassifier(use_gpu=True)
+        assert clf._run_on_gpu()
+
     def test_gpu_transform(self, clf_data: ClfData) -> None:
         """local mode"""
         classifier = SparkXGBClassifier(device="cpu")
@@ -898,23 +914,23 @@ class TestPySparkLocal:
             model.write().overwrite().save(path)
 
             # The model trained with CPU, transform defaults to cpu
-            assert not model._gpu_transform()
+            assert not model._run_on_gpu()
 
             # without error
             model.transform(clf_data.cls_df_test).collect()
 
             model.set_device("cuda")
-            assert model._gpu_transform()
+            assert model._run_on_gpu()
 
             model_loaded = SparkXGBClassifierModel.load(path)
 
             # The model trained with CPU, transform defaults to cpu
-            assert not model_loaded._gpu_transform()
+            assert not model_loaded._run_on_gpu()
             # without error
             model_loaded.transform(clf_data.cls_df_test).collect()
 
             model_loaded.set_device("cuda")
-            assert model_loaded._gpu_transform()
+            assert model_loaded._run_on_gpu()
 
 
 class XgboostLocalTest(SparkTestCase):