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

python-package/xgboost/spark/core.py

@@ -347,7 +347,7 @@ class _SparkXGBParams(
     def _validate_gpu_params(self) -> None:
         """Validate the gpu parameters and gpu configurations"""
 
-        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
+        if self._run_on_gpu():
             ss = _get_spark_session()
             sc = ss.sparkContext
 
@@ -414,9 +414,7 @@ class _SparkXGBParams(
             )
 
         if self.getOrDefault(self.features_cols):
-            if not use_cuda(self.getOrDefault(self.device)) and not self.getOrDefault(
+            if not self._run_on_gpu():
-                self.use_gpu
-            ):
                 raise ValueError(
                     "features_col param with list value requires `device=cuda`."
                 )
@@ -473,6 +471,15 @@ class _SparkXGBParams(
 
         self._validate_gpu_params()
 
+    def _run_on_gpu(self) -> bool:
+        """If train or transform on the gpu according to the parameters"""
+
+        return (
+            use_cuda(self.getOrDefault(self.device))
+            or self.getOrDefault(self.use_gpu)
+            or self.getOrDefault(self.getParam("tree_method")) == "gpu_hist"
+        )
+
 
 def _validate_and_convert_feature_col_as_float_col_list(
     dataset: DataFrame, features_col_names: List[str]
@@ -905,7 +912,7 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
         """Check if stage-level scheduling is not needed,
         return true to skip stage-level scheduling"""
 
-        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
+        if self._run_on_gpu():
             ss = _get_spark_session()
             sc = ss.sparkContext
 
@@ -1022,9 +1029,8 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
             dmatrix_kwargs,
         ) = self._get_xgb_parameters(dataset)
 
-        run_on_gpu = use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(
+        run_on_gpu = self._run_on_gpu()
-            self.use_gpu
+
-        )
         is_local = _is_local(_get_spark_session().sparkContext)
 
         num_workers = self.getOrDefault(self.num_workers)
@@ -1318,12 +1324,15 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
             dataset = dataset.drop(pred_struct_col)
         return dataset
 
-    def _gpu_transform(self) -> bool:
+    def _run_on_gpu(self) -> bool:
-        """If gpu is used to do the prediction, true to gpu prediction"""
+        """If gpu is used to do the prediction according to the parameters
+        and spark configurations"""
+
+        use_gpu_by_params = super()._run_on_gpu()
 
         if _is_local(_get_spark_session().sparkContext):
-            # if it's local model, we just use the internal "device"
+            # if it's local model, no need to check the spark configurations
-            return use_cuda(self.getOrDefault(self.device))
+            return use_gpu_by_params
 
         gpu_per_task = (
             _get_spark_session()
@@ -1333,15 +1342,15 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
 
         # User don't set gpu configurations, just use cpu
         if gpu_per_task is None:
-            if use_cuda(self.getOrDefault(self.device)):
+            if use_gpu_by_params:
                 get_logger("XGBoost-PySpark").warning(
                     "Do the prediction on the CPUs since "
                     "no gpu configurations are set"
                 )
             return False
 
-        # User already sets the gpu configurations, we just use the internal "device".
+        # User already sets the gpu configurations.
-        return use_cuda(self.getOrDefault(self.device))
+        return use_gpu_by_params
 
     def _transform(self, dataset: DataFrame) -> DataFrame:
         # pylint: disable=too-many-statements, too-many-locals
@@ -1367,7 +1376,7 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
         _, schema = self._out_schema()
 
         is_local = _is_local(_get_spark_session().sparkContext)
-        run_on_gpu = self._gpu_transform()
+        run_on_gpu = self._run_on_gpu()
 
         @pandas_udf(schema)  # type: ignore
         def predict_udf(iterator: Iterator[pd.DataFrame]) -> Iterator[pd.Series]:
@@ -1381,9 +1390,10 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
 
             dev_ordinal = -1
 
-            if is_cudf_available():
+            msg = "Do the inference on the CPUs"
+            if run_on_gpu:
+                if is_cudf_available() and is_cupy_available():
                     if is_local:
-                    if run_on_gpu and is_cupy_available():
                         import cupy as cp  # pylint: disable=import-error
 
                         total_gpus = cp.cuda.runtime.getDeviceCount()
@@ -1392,23 +1402,18 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
                             # For transform local mode, default the dev_ordinal to
                             # (partition id) % gpus.
                             dev_ordinal = partition_id % total_gpus
-                elif run_on_gpu:
+                    else:
                         dev_ordinal = _get_gpu_id(context)
 
                     if dev_ordinal >= 0:
                         device = "cuda:" + str(dev_ordinal)
-                    get_logger("XGBoost-PySpark").info(
+                        msg = "Do the inference with device: " + device
-                        "Do the inference with device: %s", device
-                    )
                         model.set_params(device=device)
                     else:
-                    get_logger("XGBoost-PySpark").info("Do the inference on the CPUs")
+                        msg = "Couldn't get the correct gpu id, fallback the inference on the CPUs"
                 else:
-                msg = (
+                    msg = "CUDF or Cupy is unavailable, fallback the inference on the CPUs"
-                    "CUDF is unavailable, fallback the inference on the CPUs"
+
-                    if run_on_gpu
-                    else "Do the inference on the CPUs"
-                )
             get_logger("XGBoost-PySpark").info(msg)
 
             def to_gpu_if_possible(data: ArrayLike) -> ArrayLike:

tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py

@@ -251,10 +251,10 @@ def test_gpu_transform(spark_diabetes_dataset) -> None:
     model: SparkXGBRegressorModel = regressor.fit(train_df)
 
     # The model trained with GPUs, and transform with GPU configurations.
-    assert model._gpu_transform()
+    assert model._run_on_gpu()
 
     model.set_device("cpu")
-    assert not model._gpu_transform()
+    assert not model._run_on_gpu()
     # without error
     cpu_rows = model.transform(test_df).select("prediction").collect()
 
@@ -263,11 +263,11 @@ def test_gpu_transform(spark_diabetes_dataset) -> None:
 
     # The model trained with CPUs. Even with GPU configurations,
     # still prefer transforming with CPUs
-    assert not model._gpu_transform()
+    assert not model._run_on_gpu()
 
     # Set gpu transform explicitly.
     model.set_device("cuda")
-    assert model._gpu_transform()
+    assert model._run_on_gpu()
     # without error
     gpu_rows = model.transform(test_df).select("prediction").collect()
 

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):