Make 2.0 release. (#9567)

---------

Co-authored-by: Bobby Wang <wbo4958@gmail.com>

.github/workflows/r_tests.yml

@@ -25,7 +25,7 @@ jobs:
       with:
         submodules: 'true'
 
-    - uses: r-lib/actions/setup-r@50d1eae9b8da0bb3f8582c59a5b82225fa2fe7f2 # v2.3.1
+    - uses: r-lib/actions/setup-r@11a22a908006c25fe054c4ef0ac0436b1de3edbe # v2.6.4
       with:
         r-version: ${{ matrix.config.r }}
 
@@ -64,7 +64,7 @@ jobs:
       with:
         submodules: 'true'
 
-    - uses: r-lib/actions/setup-r@50d1eae9b8da0bb3f8582c59a5b82225fa2fe7f2 # v2.3.1
+    - uses: r-lib/actions/setup-r@11a22a908006c25fe054c4ef0ac0436b1de3edbe # v2.6.4
       with:
         r-version: ${{ matrix.config.r }}
 

.readthedocs.yaml

@@ -32,4 +32,3 @@ formats:
 python:
   install:
    - requirements: doc/requirements.txt
-  system_packages: true

R-package/DESCRIPTION

@@ -2,7 +2,7 @@ Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
 Version: 2.0.0.1
-Date: 2022-10-18
+Date: 2023-09-11
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),

R-package/src/xgboost_R.cc

@@ -120,11 +120,25 @@ XGB_DLL SEXP XGDMatrixCreateFromMat_R(SEXP mat, SEXP missing, SEXP n_threads) {
   ctx.nthread = asInteger(n_threads);
   std::int32_t threads = ctx.Threads();
 
-  xgboost::common::ParallelFor(nrow, threads, [&](xgboost::omp_ulong i) {
+  if (is_int) {
-    for (size_t j = 0; j < ncol; ++j) {
+    xgboost::common::ParallelFor(nrow, threads, [&](xgboost::omp_ulong i) {
-      data[i * ncol + j] = is_int ? static_cast<float>(iin[i + nrow * j]) : din[i + nrow * j];
+      for (size_t j = 0; j < ncol; ++j) {
-    }
+        auto v = iin[i + nrow * j];
-  });
+        if (v == NA_INTEGER) {
+          data[i * ncol + j] = std::numeric_limits<float>::quiet_NaN();
+        } else {
+          data[i * ncol + j] = static_cast<float>(v);
+        }
+      }
+    });
+  } else {
+    xgboost::common::ParallelFor(nrow, threads, [&](xgboost::omp_ulong i) {
+      for (size_t j = 0; j < ncol; ++j) {
+        data[i * ncol + j] = din[i + nrow * j];
+      }
+    });
+  }
+
   DMatrixHandle handle;
   CHECK_CALL(XGDMatrixCreateFromMat_omp(BeginPtr(data), nrow, ncol,
                                         asReal(missing), &handle, threads));

R-package/tests/testthat/test_dmatrix.R

@@ -56,6 +56,42 @@ test_that("xgb.DMatrix: basic construction", {
   expect_equal(raw_fd, raw_dgc)
 })
 
+test_that("xgb.DMatrix: NA", {
+  n_samples <- 3
+  x <- cbind(
+    x1 = sample(x = 4, size = n_samples, replace = TRUE),
+    x2 = sample(x = 4, size = n_samples, replace = TRUE)
+  )
+  x[1, "x1"] <- NA
+
+  m <- xgb.DMatrix(x)
+  xgb.DMatrix.save(m, "int.dmatrix")
+
+  x <- matrix(as.numeric(x), nrow = n_samples, ncol = 2)
+  colnames(x) <- c("x1", "x2")
+  m <- xgb.DMatrix(x)
+
+  xgb.DMatrix.save(m, "float.dmatrix")
+
+  iconn <- file("int.dmatrix", "rb")
+  fconn <- file("float.dmatrix", "rb")
+
+  expect_equal(file.size("int.dmatrix"), file.size("float.dmatrix"))
+
+  bytes <- file.size("int.dmatrix")
+  idmatrix <- readBin(iconn, "raw", n = bytes)
+  fdmatrix <- readBin(fconn, "raw", n = bytes)
+
+  expect_equal(length(idmatrix), length(fdmatrix))
+  expect_equal(idmatrix, fdmatrix)
+
+  close(iconn)
+  close(fconn)
+
+  file.remove("int.dmatrix")
+  file.remove("float.dmatrix")
+})
+
 test_that("xgb.DMatrix: saving, loading", {
   # save to a local file
   dtest1 <- xgb.DMatrix(test_data, label = test_label)

doc/parameter.rst

@@ -329,7 +329,7 @@ Parameters for Linear Booster (``booster=gblinear``)
   - Choice of algorithm to fit linear model
 
     - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
-    - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
+    - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. When the ``device`` parameter is set to ``cuda`` or ``gpu``, a GPU variant would be used.
 
 * ``feature_selector`` [default= ``cyclic``]
 

include/xgboost/global_config.h

@@ -1,5 +1,5 @@
-/*!
+/**
- * Copyright 2020 by Contributors
+ * Copyright 2020-2023, XGBoost Contributors
  * \file global_config.h
  * \brief Global configuration for XGBoost
  * \author Hyunsu Cho
@@ -7,24 +7,22 @@
 #ifndef XGBOOST_GLOBAL_CONFIG_H_
 #define XGBOOST_GLOBAL_CONFIG_H_
 
-#include <xgboost/parameter.h>
+#include <dmlc/thread_local.h>  // for ThreadLocalStore
-#include <vector>
+#include <xgboost/parameter.h>  // for XGBoostParameter
-#include <string>
+
+#include <cstdint>  // for int32_t
 
 namespace xgboost {
-class Json;
-
 struct GlobalConfiguration : public XGBoostParameter<GlobalConfiguration> {
-  int verbosity { 1 };
+  std::int32_t verbosity{1};
-  bool use_rmm { false };
+  bool use_rmm{false};
   DMLC_DECLARE_PARAMETER(GlobalConfiguration) {
     DMLC_DECLARE_FIELD(verbosity)
         .set_range(0, 3)
         .set_default(1)  // shows only warning
         .describe("Flag to print out detailed breakdown of runtime.");
-    DMLC_DECLARE_FIELD(use_rmm)
+    DMLC_DECLARE_FIELD(use_rmm).set_default(false).describe(
-        .set_default(false)
+        "Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost");
-        .describe("Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost");
   }
 };
 

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.0</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>

jvm-packages/xgboost4j-example/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
     <name>xgboost4j-example</name>
     <artifactId>xgboost4j-example_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.0</version>
     <packaging>jar</packaging>
     <build>
         <plugins>

jvm-packages/xgboost4j-flink/pom.xml

@@ -6,12 +6,12 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
 
     <name>xgboost4j-flink</name>
     <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.0</version>
     <properties>
       <flink-ml.version>2.2.0</flink-ml.version>
     </properties>

jvm-packages/xgboost4j-gpu/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
     <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
     <name>xgboost4j-gpu</name>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.0</version>
     <packaging>jar</packaging>
 
     <dependencies>

jvm-packages/xgboost4j-spark-gpu/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
     <name>xgboost4j-spark-gpu</name>
     <artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>

jvm-packages/xgboost4j-spark/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
     <name>xgboost4j-spark</name>
     <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>

jvm-packages/xgboost4j/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm</artifactId>
-        <version>2.0.0-SNAPSHOT</version>
+        <version>2.0.0</version>
     </parent>
     <name>xgboost4j</name>
     <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>2.0.0</version>
     <packaging>jar</packaging>
 
     <dependencies>

python-package/pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "packager.pep517"
 
 [project]
 name = "xgboost"
-version = "2.0.0-dev"
+version = "2.0.0"
 authors = [
     { name = "Hyunsu Cho", email = "chohyu01@cs.washington.edu" },
     { name = "Jiaming Yuan", email = "jm.yuan@outlook.com" }

python-package/xgboost/VERSION

@@ -1 +1 @@
-2.0.0-dev
+2.0.0

python-package/xgboost/compat.py

@@ -88,6 +88,18 @@ def is_cudf_available() -> bool:
         return False
 
 
+def is_cupy_available() -> bool:
+    """Check cupy package available or not"""
+    if importlib.util.find_spec("cupy") is None:
+        return False
+    try:
+        import cupy
+
+        return True
+    except ImportError:
+        return False
+
+
 try:
     import scipy.sparse as scipy_sparse
     from scipy.sparse import csr_matrix as scipy_csr

python-package/xgboost/spark/core.py

@@ -59,11 +59,12 @@ from scipy.special import expit, softmax  # pylint: disable=no-name-in-module
 
 import xgboost
 from xgboost import XGBClassifier
-from xgboost.compat import is_cudf_available
+from xgboost.compat import is_cudf_available, is_cupy_available
 from xgboost.core import Booster, _check_distributed_params
 from xgboost.sklearn import DEFAULT_N_ESTIMATORS, XGBModel, _can_use_qdm
 from xgboost.training import train as worker_train
 
+from .._typing import ArrayLike
 from .data import (
     _read_csr_matrix_from_unwrapped_spark_vec,
     alias,
@@ -241,6 +242,13 @@ class _SparkXGBParams(
         TypeConverters.toList,
     )
 
+    def set_device(self, value: str) -> "_SparkXGBParams":
+        """Set device, optional value: cpu, cuda, gpu"""
+        _check_distributed_params({"device": value})
+        assert value in ("cpu", "cuda", "gpu")
+        self.set(self.device, value)
+        return self
+
     @classmethod
     def _xgb_cls(cls) -> Type[XGBModel]:
         """
@@ -1117,12 +1125,111 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
             )
         return features_col, feature_col_names
 
+    def _get_pred_contrib_col_name(self) -> Optional[str]:
+        """Return the pred_contrib_col col name"""
+        pred_contrib_col_name = None
+        if (
+            self.isDefined(self.pred_contrib_col)
+            and self.getOrDefault(self.pred_contrib_col) != ""
+        ):
+            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
+
+        return pred_contrib_col_name
+
+    def _out_schema(self) -> Tuple[bool, str]:
+        """Return the bool to indicate if it's a single prediction, true is single prediction,
+        and the returned type of the user-defined function. The value must
+        be a DDL-formatted type string."""
+
+        if self._get_pred_contrib_col_name() is not None:
+            return False, f"{pred.prediction} double, {pred.pred_contrib} array<double>"
+
+        return True, "double"
+
+    def _get_predict_func(self) -> Callable:
+        """Return the true prediction function which will be running on the executor side"""
+
+        predict_params = self._gen_predict_params_dict()
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
+
+        def _predict(
+            model: XGBModel, X: ArrayLike, base_margin: Optional[ArrayLike]
+        ) -> Union[pd.DataFrame, pd.Series]:
+            data = {}
+            preds = model.predict(
+                X,
+                base_margin=base_margin,
+                validate_features=False,
+                **predict_params,
+            )
+            data[pred.prediction] = pd.Series(preds)
+
+            if pred_contrib_col_name is not None:
+                contribs = pred_contribs(model, X, base_margin)
+                data[pred.pred_contrib] = pd.Series(list(contribs))
+                return pd.DataFrame(data=data)
+
+            return data[pred.prediction]
+
+        return _predict
+
+    def _post_transform(self, dataset: DataFrame, pred_col: Column) -> DataFrame:
+        """Post process of transform"""
+        prediction_col_name = self.getOrDefault(self.predictionCol)
+        single_pred, _ = self._out_schema()
+
+        if single_pred:
+            if prediction_col_name:
+                dataset = dataset.withColumn(prediction_col_name, pred_col)
+        else:
+            pred_struct_col = "_prediction_struct"
+            dataset = dataset.withColumn(pred_struct_col, pred_col)
+
+            if prediction_col_name:
+                dataset = dataset.withColumn(
+                    prediction_col_name, getattr(col(pred_struct_col), pred.prediction)
+                )
+
+            pred_contrib_col_name = self._get_pred_contrib_col_name()
+            if pred_contrib_col_name is not None:
+                dataset = dataset.withColumn(
+                    pred_contrib_col_name,
+                    array_to_vector(getattr(col(pred_struct_col), pred.pred_contrib)),
+                )
+
+            dataset = dataset.drop(pred_struct_col)
+        return dataset
+
+    def _gpu_transform(self) -> bool:
+        """If gpu is used to do the prediction, true to gpu prediction"""
+
+        if _is_local(_get_spark_session().sparkContext):
+            # if it's local model, we just use the internal "device"
+            return use_cuda(self.getOrDefault(self.device))
+
+        gpu_per_task = (
+            _get_spark_session()
+            .sparkContext.getConf()
+            .get("spark.task.resource.gpu.amount")
+        )
+
+        # User don't set gpu configurations, just use cpu
+        if gpu_per_task is None:
+            if use_cuda(self.getOrDefault(self.device)):
+                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".
+        return use_cuda(self.getOrDefault(self.device))
+
     def _transform(self, dataset: DataFrame) -> DataFrame:
         # pylint: disable=too-many-statements, too-many-locals
         # Save xgb_sklearn_model and predict_params to be local variable
         # to avoid the `self` object to be pickled to remote.
         xgb_sklearn_model = self._xgb_sklearn_model
-        predict_params = self._gen_predict_params_dict()
 
         has_base_margin = False
         if (
@@ -1137,79 +1244,92 @@ class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
         features_col, feature_col_names = self._get_feature_col(dataset)
         enable_sparse_data_optim = self.getOrDefault(self.enable_sparse_data_optim)
 
-        pred_contrib_col_name = None
+        predict_func = self._get_predict_func()
-        if (
-            self.isDefined(self.pred_contrib_col)
-            and self.getOrDefault(self.pred_contrib_col) != ""
-        ):
-            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
 
-        single_pred = True
+        _, schema = self._out_schema()
-        schema = "double"
+
-        if pred_contrib_col_name:
+        is_local = _is_local(_get_spark_session().sparkContext)
-            single_pred = False
+        run_on_gpu = self._gpu_transform()
-            schema = f"{pred.prediction} double, {pred.pred_contrib} array<double>"
 
         @pandas_udf(schema)  # type: ignore
         def predict_udf(iterator: Iterator[pd.DataFrame]) -> Iterator[pd.Series]:
             assert xgb_sklearn_model is not None
             model = xgb_sklearn_model
+
+            from pyspark import TaskContext
+
+            context = TaskContext.get()
+            assert context is not None
+
+            dev_ordinal = -1
+
+            if is_cudf_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()
+                        if total_gpus > 0:
+                            partition_id = context.partitionId()
+                            # For transform local mode, default the dev_ordinal to
+                            # (partition id) % gpus.
+                            dev_ordinal = partition_id % total_gpus
+                elif run_on_gpu:
+                    dev_ordinal = _get_gpu_id(context)
+
+                if dev_ordinal >= 0:
+                    device = "cuda:" + str(dev_ordinal)
+                    get_logger("XGBoost-PySpark").info(
+                        "Do the inference with device: %s", device
+                    )
+                    model.set_params(device=device)
+                else:
+                    get_logger("XGBoost-PySpark").info("Do the inference on the CPUs")
+            else:
+                msg = (
+                    "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:
+                """Move the data to gpu if possible"""
+                if dev_ordinal >= 0:
+                    import cudf  # pylint: disable=import-error
+                    import cupy as cp  # pylint: disable=import-error
+
+                    # We must set the device after import cudf, which will change the device id to 0
+                    # See https://github.com/rapidsai/cudf/issues/11386
+                    cp.cuda.runtime.setDevice(dev_ordinal)  # pylint: disable=I1101
+                    df = cudf.DataFrame(data)
+                    del data
+                    return df
+                return data
+
             for data in iterator:
                 if enable_sparse_data_optim:
                     X = _read_csr_matrix_from_unwrapped_spark_vec(data)
                 else:
                     if feature_col_names is not None:
-                        X = data[feature_col_names]
+                        tmp = data[feature_col_names]
                     else:
-                        X = stack_series(data[alias.data])
+                        tmp = stack_series(data[alias.data])
+                    X = to_gpu_if_possible(tmp)
 
                 if has_base_margin:
-                    base_margin = data[alias.margin].to_numpy()
+                    base_margin = to_gpu_if_possible(data[alias.margin])
                 else:
                     base_margin = None
 
-                data = {}
+                yield predict_func(model, X, base_margin)
-                preds = model.predict(
-                    X,
-                    base_margin=base_margin,
-                    validate_features=False,
-                    **predict_params,
-                )
-                data[pred.prediction] = pd.Series(preds)
-
-                if pred_contrib_col_name:
-                    contribs = pred_contribs(model, X, base_margin)
-                    data[pred.pred_contrib] = pd.Series(list(contribs))
-                    yield pd.DataFrame(data=data)
-                else:
-                    yield data[pred.prediction]
 
         if has_base_margin:
             pred_col = predict_udf(struct(*features_col, base_margin_col))
         else:
             pred_col = predict_udf(struct(*features_col))
 
-        prediction_col_name = self.getOrDefault(self.predictionCol)
+        return self._post_transform(dataset, pred_col)
-
-        if single_pred:
-            dataset = dataset.withColumn(prediction_col_name, pred_col)
-        else:
-            pred_struct_col = "_prediction_struct"
-            dataset = dataset.withColumn(pred_struct_col, pred_col)
-
-            dataset = dataset.withColumn(
-                prediction_col_name, getattr(col(pred_struct_col), pred.prediction)
-            )
-
-            if pred_contrib_col_name:
-                dataset = dataset.withColumn(
-                    pred_contrib_col_name,
-                    array_to_vector(getattr(col(pred_struct_col), pred.pred_contrib)),
-                )
-
-            dataset = dataset.drop(pred_struct_col)
-
-        return dataset
 
 
 class _ClassificationModel(  # pylint: disable=abstract-method
@@ -1221,22 +1341,21 @@ class _ClassificationModel(  # pylint: disable=abstract-method
     .. Note:: This API is experimental.
     """
 
-    def _transform(self, dataset: DataFrame) -> DataFrame:
+    def _out_schema(self) -> Tuple[bool, str]:
-        # pylint: disable=too-many-statements, too-many-locals
+        schema = (
-        # Save xgb_sklearn_model and predict_params to be local variable
+            f"{pred.raw_prediction} array<double>, {pred.prediction} double,"
-        # to avoid the `self` object to be pickled to remote.
+            f" {pred.probability} array<double>"
-        xgb_sklearn_model = self._xgb_sklearn_model
+        )
-        predict_params = self._gen_predict_params_dict()
+        if self._get_pred_contrib_col_name() is not None:
+            # We will force setting strict_shape to True when predicting contribs,
+            # So, it will also output 3-D shape result.
+            schema = f"{schema}, {pred.pred_contrib} array<array<double>>"
 
-        has_base_margin = False
+        return False, schema
-        if (
+
-            self.isDefined(self.base_margin_col)
+    def _get_predict_func(self) -> Callable:
-            and self.getOrDefault(self.base_margin_col) != ""
+        predict_params = self._gen_predict_params_dict()
-        ):
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
-            has_base_margin = True
-            base_margin_col = col(self.getOrDefault(self.base_margin_col)).alias(
-                alias.margin
-            )
 
         def transform_margin(margins: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
             if margins.ndim == 1:
@@ -1251,76 +1370,38 @@ class _ClassificationModel(  # pylint: disable=abstract-method
                 class_probs = softmax(raw_preds, axis=1)
             return raw_preds, class_probs
 
-        features_col, feature_col_names = self._get_feature_col(dataset)
+        def _predict(
-        enable_sparse_data_optim = self.getOrDefault(self.enable_sparse_data_optim)
+            model: XGBModel, X: ArrayLike, base_margin: Optional[np.ndarray]
+        ) -> Union[pd.DataFrame, pd.Series]:
+            margins = model.predict(
+                X,
+                base_margin=base_margin,
+                output_margin=True,
+                validate_features=False,
+                **predict_params,
+            )
+            raw_preds, class_probs = transform_margin(margins)
 
-        pred_contrib_col_name = None
+            # It seems that they use argmax of class probs,
-        if (
+            # not of margin to get the prediction (Note: scala implementation)
-            self.isDefined(self.pred_contrib_col)
+            preds = np.argmax(class_probs, axis=1)
-            and self.getOrDefault(self.pred_contrib_col) != ""
+            result: Dict[str, pd.Series] = {
-        ):
+                pred.raw_prediction: pd.Series(list(raw_preds)),
-            pred_contrib_col_name = self.getOrDefault(self.pred_contrib_col)
+                pred.prediction: pd.Series(preds),
+                pred.probability: pd.Series(list(class_probs)),
+            }
 
-        schema = (
+            if pred_contrib_col_name is not None:
-            f"{pred.raw_prediction} array<double>, {pred.prediction} double,"
+                contribs = pred_contribs(model, X, base_margin, strict_shape=True)
-            f" {pred.probability} array<double>"
+                result[pred.pred_contrib] = pd.Series(list(contribs.tolist()))
-        )
-        if pred_contrib_col_name:
-            # We will force setting strict_shape to True when predicting contribs,
-            # So, it will also output 3-D shape result.
-            schema = f"{schema}, {pred.pred_contrib} array<array<double>>"
 
-        @pandas_udf(schema)  # type: ignore
+            return pd.DataFrame(data=result)
-        def predict_udf(
-            iterator: Iterator[Tuple[pd.Series, ...]]
-        ) -> Iterator[pd.DataFrame]:
-            assert xgb_sklearn_model is not None
-            model = xgb_sklearn_model
-            for data in iterator:
-                if enable_sparse_data_optim:
-                    X = _read_csr_matrix_from_unwrapped_spark_vec(data)
-                else:
-                    if feature_col_names is not None:
-                        X = data[feature_col_names]  # type: ignore
-                    else:
-                        X = stack_series(data[alias.data])
 
-                if has_base_margin:
+        return _predict
-                    base_margin = stack_series(data[alias.margin])
-                else:
-                    base_margin = None
-
-                margins = model.predict(
-                    X,
-                    base_margin=base_margin,
-                    output_margin=True,
-                    validate_features=False,
-                    **predict_params,
-                )
-                raw_preds, class_probs = transform_margin(margins)
-
-                # It seems that they use argmax of class probs,
-                # not of margin to get the prediction (Note: scala implementation)
-                preds = np.argmax(class_probs, axis=1)
-                result: Dict[str, pd.Series] = {
-                    pred.raw_prediction: pd.Series(list(raw_preds)),
-                    pred.prediction: pd.Series(preds),
-                    pred.probability: pd.Series(list(class_probs)),
-                }
-
-                if pred_contrib_col_name:
-                    contribs = pred_contribs(model, X, base_margin, strict_shape=True)
-                    result[pred.pred_contrib] = pd.Series(list(contribs.tolist()))
-
-                yield pd.DataFrame(data=result)
-
-        if has_base_margin:
-            pred_struct = predict_udf(struct(*features_col, base_margin_col))
-        else:
-            pred_struct = predict_udf(struct(*features_col))
 
+    def _post_transform(self, dataset: DataFrame, pred_col: Column) -> DataFrame:
         pred_struct_col = "_prediction_struct"
-        dataset = dataset.withColumn(pred_struct_col, pred_struct)
+        dataset = dataset.withColumn(pred_struct_col, pred_col)
 
         raw_prediction_col_name = self.getOrDefault(self.rawPredictionCol)
         if raw_prediction_col_name:
@@ -1342,7 +1423,8 @@ class _ClassificationModel(  # pylint: disable=abstract-method
                 array_to_vector(getattr(col(pred_struct_col), pred.probability)),
             )
 
-        if pred_contrib_col_name:
+        pred_contrib_col_name = self._get_pred_contrib_col_name()
+        if pred_contrib_col_name is not None:
             dataset = dataset.withColumn(
                 pred_contrib_col_name,
                 getattr(col(pred_struct_col), pred.pred_contrib),

python-package/xgboost/spark/utils.py

@@ -10,7 +10,7 @@ from threading import Thread
 from typing import Any, Callable, Dict, Optional, Set, Type
 
 import pyspark
-from pyspark import BarrierTaskContext, SparkContext, SparkFiles
+from pyspark import BarrierTaskContext, SparkContext, SparkFiles, TaskContext
 from pyspark.sql.session import SparkSession
 
 from xgboost import Booster, XGBModel, collective
@@ -129,7 +129,7 @@ def _is_local(spark_context: SparkContext) -> bool:
     return spark_context._jsc.sc().isLocal()
 
 
-def _get_gpu_id(task_context: BarrierTaskContext) -> int:
+def _get_gpu_id(task_context: TaskContext) -> int:
     """Get the gpu id from the task resources"""
     if task_context is None:
         # This is a safety check.

src/common/categorical.h

@@ -52,7 +52,7 @@ inline XGBOOST_DEVICE bool InvalidCat(float cat) {
  *
  *   Go to left if it's NOT the matching category, which matches one-hot encoding.
  */
-inline XGBOOST_DEVICE bool Decision(common::Span<uint32_t const> cats, float cat) {
+inline XGBOOST_DEVICE bool Decision(common::Span<CatBitField::value_type const> cats, float cat) {
   KCatBitField const s_cats(cats);
   if (XGBOOST_EXPECT(InvalidCat(cat), false)) {
     return true;

src/common/error_msg.cc

@@ -3,9 +3,11 @@
  */
 #include "error_msg.h"
 
+#include <mutex>    // for call_once, once_flag
 #include <sstream>  // for stringstream
 
 #include "../collective/communicator-inl.h"  // for GetRank
+#include "xgboost/context.h"                 // for Context
 #include "xgboost/logging.h"
 
 namespace xgboost::error {
@@ -26,34 +28,43 @@ void WarnDeprecatedGPUHist() {
 }
 
 void WarnManualUpdater() {
-  bool static thread_local logged{false};
+  static std::once_flag flag;
-  if (logged) {
+  std::call_once(flag, [] {
-    return;
+    LOG(WARNING)
-  }
+        << "You have manually specified the `updater` parameter. The `tree_method` parameter "
-  LOG(WARNING)
+           "will be ignored. Incorrect sequence of updaters will produce undefined "
-      << "You have manually specified the `updater` parameter. The `tree_method` parameter "
+           "behavior. For common uses, we recommend using `tree_method` parameter instead.";
-         "will be ignored. Incorrect sequence of updaters will produce undefined "
+  });
-         "behavior. For common uses, we recommend using `tree_method` parameter instead.";
-  logged = true;
 }
 
 void WarnDeprecatedGPUId() {
-  static thread_local bool logged{false};
+  static std::once_flag flag;
-  if (logged) {
+  std::call_once(flag, [] {
-    return;
+    auto msg = DeprecatedFunc("gpu_id", "2.0.0", "device");
-  }
+    msg += " E.g. device=cpu/cuda/cuda:0";
-  auto msg = DeprecatedFunc("gpu_id", "2.0.0", "device");
+    LOG(WARNING) << msg;
-  msg += " E.g. device=cpu/cuda/cuda:0";
+  });
-  LOG(WARNING) << msg;
-  logged = true;
 }
 
 void WarnEmptyDataset() {
-  static thread_local bool logged{false};
+  static std::once_flag flag;
-  if (logged) {
+  std::call_once(flag,
-    return;
+                 [] { LOG(WARNING) << "Empty dataset at worker: " << collective::GetRank(); });
-  }
+}
-  LOG(WARNING) << "Empty dataset at worker: " << collective::GetRank();
+
-  logged = true;
+void MismatchedDevices(Context const* booster, Context const* data) {
+  static std::once_flag flag;
+  std::call_once(flag, [&] {
+    LOG(WARNING)
+        << "Falling back to prediction using DMatrix due to mismatched devices. This might "
+           "lead to higher memory usage and slower performance. XGBoost is running on: "
+        << booster->DeviceName() << ", while the input data is on: " << data->DeviceName() << ".\n"
+        << R"(Potential solutions:
+- Use a data structure that matches the device ordinal in the booster.
+- Set the device for booster before call to inplace_predict.
+
+This warning will only be shown once.
+)";
+  });
 }
 }  // namespace xgboost::error

src/common/error_msg.h

@@ -10,7 +10,8 @@
 #include <limits>     // for numeric_limits
 #include <string>     // for string
 
-#include "xgboost/base.h"  // for bst_feature_t
+#include "xgboost/base.h"     // for bst_feature_t
+#include "xgboost/context.h"  // for Context
 #include "xgboost/logging.h"
 #include "xgboost/string_view.h"  // for StringView
 
@@ -94,5 +95,7 @@ constexpr StringView InvalidCUDAOrdinal() {
   return "Invalid device. `device` is required to be CUDA and there must be at least one GPU "
          "available for using GPU.";
 }
+
+void MismatchedDevices(Context const* booster, Context const* data);
 }  // namespace xgboost::error
 #endif  // XGBOOST_COMMON_ERROR_MSG_H_

src/data/proxy_dmatrix.cc

@@ -55,6 +55,7 @@ std::shared_ptr<DMatrix> CreateDMatrixFromProxy(Context const *ctx,
   }
 
   CHECK(p_fmat) << "Failed to fallback.";
+  p_fmat->Info() = proxy->Info().Copy();
   return p_fmat;
 }
 }  // namespace xgboost::data

src/gbm/gblinear.cc

@@ -1,5 +1,5 @@
-/*!
+/**
- * Copyright 2014-2022 by XGBoost Contributors
+ * Copyright 2014-2023, XGBoost Contributors
  * \file gblinear.cc
  * \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
  *        the update rule is parallel coordinate descent (shotgun)
@@ -26,9 +26,9 @@
 #include "../common/timer.h"
 #include "../common/common.h"
 #include "../common/threading_utils.h"
+#include "../common/error_msg.h"
 
-namespace xgboost {
+namespace xgboost::gbm {
-namespace gbm {
 
 DMLC_REGISTRY_FILE_TAG(gblinear);
 
@@ -83,7 +83,16 @@ class GBLinear : public GradientBooster {
     }
     param_.UpdateAllowUnknown(cfg);
     param_.CheckGPUSupport();
-    updater_.reset(LinearUpdater::Create(param_.updater, ctx_));
+    if (param_.updater == "gpu_coord_descent") {
+      LOG(WARNING) << error::DeprecatedFunc("gpu_coord_descent", "2.0.0",
+                                            R"(device="cuda", updater="coord_descent")");
+    }
+
+    if (param_.updater == "coord_descent" && ctx_->IsCUDA()) {
+      updater_.reset(LinearUpdater::Create("gpu_coord_descent", ctx_));
+    } else {
+      updater_.reset(LinearUpdater::Create(param_.updater, ctx_));
+    }
     updater_->Configure(cfg);
     monitor_.Init("GBLinear");
   }
@@ -354,5 +363,4 @@ XGBOOST_REGISTER_GBM(GBLinear, "gblinear")
     .set_body([](LearnerModelParam const* booster_config, Context const* ctx) {
       return new GBLinear(booster_config, ctx);
     });
-}  // namespace gbm
+}  // namespace xgboost::gbm
-}  // namespace xgboost

src/gbm/gbtree.cc

@@ -85,25 +85,6 @@ bool UpdatersMatched(std::vector<std::string> updater_seq,
                       return name == up->Name();
                     });
 }
-
-void MismatchedDevices(Context const* booster, Context const* data) {
-  bool thread_local static logged{false};
-  if (logged) {
-    return;
-  }
-  LOG(WARNING) << "Falling back to prediction using DMatrix due to mismatched devices. This might "
-                  "lead to higher memory usage and slower performance. XGBoost is running on: "
-               << booster->DeviceName() << ", while the input data is on: " << data->DeviceName()
-               << ".\n"
-               << R"(Potential solutions:
-- Use a data structure that matches the device ordinal in the booster.
-- Set the device for booster before call to inplace_predict.
-
-This warning will only be shown once for each thread. Subsequent warnings made by the
-current thread will be suppressed.
-)";
-  logged = true;
-}
 }  // namespace
 
 void GBTree::Configure(Args const& cfg) {
@@ -146,14 +127,6 @@ void GBTree::Configure(Args const& cfg) {
   if (specified_updater_) {
     error::WarnManualUpdater();
   }
-
-  if (model_.learner_model_param->IsVectorLeaf()) {
-    CHECK(tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAuto)
-        << "Only the hist tree method is supported for building multi-target trees with vector "
-           "leaf.";
-    CHECK(ctx_->IsCPU()) << "GPU is not yet supported for vector leaf.";
-  }
-
   LOG(DEBUG) << "Using tree method: " << static_cast<int>(tparam_.tree_method);
 
   if (!specified_updater_) {
@@ -225,6 +198,13 @@ void GBTree::UpdateTreeLeaf(DMatrix const* p_fmat, HostDeviceVector<float> const
 
 void GBTree::DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
                      PredictionCacheEntry* predt, ObjFunction const* obj) {
+  if (model_.learner_model_param->IsVectorLeaf()) {
+    CHECK(tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAuto)
+        << "Only the hist tree method is supported for building multi-target trees with vector "
+           "leaf.";
+    CHECK(ctx_->IsCPU()) << "GPU is not yet supported for vector leaf.";
+  }
+
   TreesOneIter new_trees;
   bst_target_t const n_groups = model_.learner_model_param->OutputLength();
   monitor_.Start("BoostNewTrees");
@@ -555,7 +535,7 @@ void GBTree::InplacePredict(std::shared_ptr<DMatrix> p_m, float missing,
   auto [tree_begin, tree_end] = detail::LayerToTree(model_, layer_begin, layer_end);
   CHECK_LE(tree_end, model_.trees.size()) << "Invalid number of trees.";
   if (p_m->Ctx()->Device() != this->ctx_->Device()) {
-    MismatchedDevices(this->ctx_, p_m->Ctx());
+    error::MismatchedDevices(this->ctx_, p_m->Ctx());
     CHECK_EQ(out_preds->version, 0);
     auto proxy = std::dynamic_pointer_cast<data::DMatrixProxy>(p_m);
     CHECK(proxy) << error::InplacePredictProxy();
@@ -808,7 +788,7 @@ class Dart : public GBTree {
     auto n_groups = model_.learner_model_param->num_output_group;
 
     if (ctx_->Device() != p_fmat->Ctx()->Device()) {
-      MismatchedDevices(ctx_, p_fmat->Ctx());
+      error::MismatchedDevices(ctx_, p_fmat->Ctx());
       auto proxy = std::dynamic_pointer_cast<data::DMatrixProxy>(p_fmat);
       CHECK(proxy) << error::InplacePredictProxy();
       auto p_fmat = data::CreateDMatrixFromProxy(ctx_, proxy, missing);

src/linear/updater_coordinate.cc

@@ -9,8 +9,7 @@
 #include "coordinate_common.h"
 #include "xgboost/json.h"
 
-namespace xgboost {
+namespace xgboost::linear {
-namespace linear {
 
 DMLC_REGISTER_PARAMETER(CoordinateParam);
 DMLC_REGISTRY_FILE_TAG(updater_coordinate);
@@ -39,8 +38,9 @@ class CoordinateUpdater : public LinearUpdater {
     FromJson(config.at("linear_train_param"), &tparam_);
     FromJson(config.at("coordinate_param"), &cparam_);
   }
-  void SaveConfig(Json* p_out) const override {
+  void SaveConfig(Json *p_out) const override {
-    auto& out = *p_out;
+    LOG(DEBUG) << "Save config for CPU updater.";
+    auto &out = *p_out;
     out["linear_train_param"] = ToJson(tparam_);
     out["coordinate_param"] = ToJson(cparam_);
   }
@@ -99,5 +99,4 @@ class CoordinateUpdater : public LinearUpdater {
 XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")
     .describe("Update linear model according to coordinate descent algorithm.")
     .set_body([]() { return new CoordinateUpdater(); });
-}  // namespace linear
+}  // namespace xgboost::linear
-}  // namespace xgboost

src/linear/updater_gpu_coordinate.cu

@@ -15,8 +15,7 @@
 #include "../common/timer.h"
 #include "./param.h"
 
-namespace xgboost {
+namespace xgboost::linear {
-namespace linear {
 
 DMLC_REGISTRY_FILE_TAG(updater_gpu_coordinate);
 
@@ -29,7 +28,7 @@ DMLC_REGISTRY_FILE_TAG(updater_gpu_coordinate);
 class GPUCoordinateUpdater : public LinearUpdater {  // NOLINT
  public:
   // set training parameter
-  void Configure(Args const& args) override {
+  void Configure(Args const &args) override {
     tparam_.UpdateAllowUnknown(args);
     coord_param_.UpdateAllowUnknown(args);
     selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
@@ -41,8 +40,9 @@ class GPUCoordinateUpdater : public LinearUpdater {  // NOLINT
     FromJson(config.at("linear_train_param"), &tparam_);
     FromJson(config.at("coordinate_param"), &coord_param_);
   }
-  void SaveConfig(Json* p_out) const override {
+  void SaveConfig(Json *p_out) const override {
-    auto& out = *p_out;
+    LOG(DEBUG) << "Save config for GPU updater.";
+    auto &out = *p_out;
     out["linear_train_param"] = ToJson(tparam_);
     out["coordinate_param"] = ToJson(coord_param_);
   }
@@ -101,10 +101,9 @@ class GPUCoordinateUpdater : public LinearUpdater {  // NOLINT
     monitor_.Stop("LazyInitDevice");
 
     monitor_.Start("UpdateGpair");
-    auto &in_gpair_host = in_gpair->ConstHostVector();
     // Update gpair
     if (ctx_->gpu_id >= 0) {
-      this->UpdateGpair(in_gpair_host);
+      this->UpdateGpair(in_gpair->ConstHostVector());
     }
     monitor_.Stop("UpdateGpair");
 
@@ -249,5 +248,4 @@ XGBOOST_REGISTER_LINEAR_UPDATER(GPUCoordinateUpdater, "gpu_coord_descent")
         "Update linear model according to coordinate descent algorithm. GPU "
         "accelerated.")
     .set_body([]() { return new GPUCoordinateUpdater(); });
-}  // namespace linear
+}  // namespace xgboost::linear
-}  // namespace xgboost

src/tree/gpu_hist/evaluate_splits.cu

@@ -1,5 +1,5 @@
-/*!
+/**
- * Copyright 2020-2022 by XGBoost Contributors
+ * Copyright 2020-2023, XGBoost Contributors
  */
 #include <algorithm>  // std::max
 #include <vector>
@@ -11,9 +11,7 @@
 #include "evaluate_splits.cuh"
 #include "expand_entry.cuh"
 
-namespace xgboost {
+namespace xgboost::tree {
-namespace tree {
-
 // With constraints
 XGBOOST_DEVICE float LossChangeMissing(const GradientPairInt64 &scan,
                                        const GradientPairInt64 &missing,
@@ -315,11 +313,11 @@ __device__ void SetCategoricalSplit(const EvaluateSplitSharedInputs &shared_inpu
                                     common::Span<common::CatBitField::value_type> out,
                                     DeviceSplitCandidate *p_out_split) {
   auto &out_split = *p_out_split;
-  out_split.split_cats = common::CatBitField{out};
+  auto out_cats = common::CatBitField{out};
 
   // Simple case for one hot split
   if (common::UseOneHot(shared_inputs.FeatureBins(fidx), shared_inputs.param.max_cat_to_onehot)) {
-    out_split.split_cats.Set(common::AsCat(out_split.thresh));
+    out_cats.Set(common::AsCat(out_split.thresh));
     return;
   }
 
@@ -339,7 +337,7 @@ __device__ void SetCategoricalSplit(const EvaluateSplitSharedInputs &shared_inpu
   assert(partition > 0 && "Invalid partition.");
   thrust::for_each(thrust::seq, beg, beg + partition, [&](size_t c) {
     auto cat = shared_inputs.feature_values[c - node_offset];
-    out_split.SetCat(cat);
+    out_cats.Set(common::AsCat(cat));
   });
 }
 
@@ -427,8 +425,7 @@ void GPUHistEvaluator::EvaluateSplits(
 
     if (split.is_cat) {
       SetCategoricalSplit(shared_inputs, d_sorted_idx, fidx, i,
-                          device_cats_accessor.GetNodeCatStorage(input.nidx),
+                          device_cats_accessor.GetNodeCatStorage(input.nidx), &out_splits[i]);
-                          &out_splits[i]);
     }
 
     float base_weight =
@@ -460,6 +457,4 @@ GPUExpandEntry GPUHistEvaluator::EvaluateSingleSplit(
                                 cudaMemcpyDeviceToHost));
   return root_entry;
 }
-
+}  // namespace xgboost::tree
-}  // namespace tree
-}  // namespace xgboost

src/tree/gpu_hist/evaluate_splits.cuh

@@ -37,8 +37,8 @@ struct EvaluateSplitSharedInputs {
   common::Span<const float> feature_values;
   common::Span<const float> min_fvalue;
   bool is_dense;
-  XGBOOST_DEVICE auto Features() const { return feature_segments.size() - 1; }
+  [[nodiscard]] XGBOOST_DEVICE auto Features() const { return feature_segments.size() - 1; }
-  __device__ auto FeatureBins(bst_feature_t fidx) const {
+  [[nodiscard]] __device__ std::uint32_t FeatureBins(bst_feature_t fidx) const {
     return feature_segments[fidx + 1] - feature_segments[fidx];
   }
 };
@@ -102,7 +102,7 @@ class GPUHistEvaluator {
   }
 
   /**
-   * \brief Get device category storage of nidx for internal calculation.
+   * @brief Get device category storage of nidx for internal calculation.
    */
   auto DeviceCatStorage(const std::vector<bst_node_t> &nidx) {
     if (!has_categoricals_) return CatAccessor{};
@@ -117,8 +117,8 @@ class GPUHistEvaluator {
   /**
    * \brief Get sorted index storage based on the left node of inputs.
    */
-  auto SortedIdx(int num_nodes, bst_feature_t total_bins) {
+  auto SortedIdx(int num_nodes, bst_bin_t total_bins) {
-    if(!need_sort_histogram_) return common::Span<bst_feature_t>();
+    if (!need_sort_histogram_) return common::Span<bst_feature_t>{};
     cat_sorted_idx_.resize(num_nodes * total_bins);
     return dh::ToSpan(cat_sorted_idx_);
   }
@@ -142,12 +142,22 @@ class GPUHistEvaluator {
    * \brief Get host category storage for nidx.  Different from the internal version, this
    *        returns strictly 1 node.
    */
-  common::Span<CatST const> GetHostNodeCats(bst_node_t nidx) const {
+  [[nodiscard]] common::Span<CatST const> GetHostNodeCats(bst_node_t nidx) const {
     copy_stream_.View().Sync();
     auto cats_out = common::Span<CatST const>{h_split_cats_}.subspan(
         nidx * node_categorical_storage_size_, node_categorical_storage_size_);
     return cats_out;
   }
+
+  [[nodiscard]] auto GetDeviceNodeCats(bst_node_t nidx) {
+    copy_stream_.View().Sync();
+    if (has_categoricals_) {
+      CatAccessor accessor = {dh::ToSpan(split_cats_), node_categorical_storage_size_};
+      return common::KCatBitField{accessor.GetNodeCatStorage(nidx)};
+    } else {
+      return common::KCatBitField{};
+    }
+  }
   /**
    * \brief Add a split to the internal tree evaluator.
    */

src/tree/updater_gpu_common.cuh

@@ -64,7 +64,6 @@ struct DeviceSplitCandidate {
   // split.
   bst_cat_t thresh{-1};
 
-  common::CatBitField split_cats;
   bool is_cat { false };
 
   GradientPairInt64 left_sum;
@@ -72,12 +71,6 @@ struct DeviceSplitCandidate {
 
   XGBOOST_DEVICE DeviceSplitCandidate() {}  // NOLINT
 
-  template <typename T>
-  XGBOOST_DEVICE void SetCat(T c) {
-    this->split_cats.Set(common::AsCat(c));
-    fvalue = std::max(this->fvalue, static_cast<float>(c));
-  }
-
   XGBOOST_DEVICE void Update(float loss_chg_in, DefaultDirection dir_in, float fvalue_in,
                              int findex_in, GradientPairInt64 left_sum_in,
                              GradientPairInt64 right_sum_in, bool cat,
@@ -100,22 +93,23 @@ struct DeviceSplitCandidate {
    */
   XGBOOST_DEVICE void UpdateCat(float loss_chg_in, DefaultDirection dir_in, bst_cat_t thresh_in,
                                 bst_feature_t findex_in, GradientPairInt64 left_sum_in,
-                                GradientPairInt64 right_sum_in, GPUTrainingParam const& param, const GradientQuantiser& quantiser) {
+                                GradientPairInt64 right_sum_in, GPUTrainingParam const& param,
-    if (loss_chg_in > loss_chg &&
+                                const GradientQuantiser& quantiser) {
-        quantiser.ToFloatingPoint(left_sum_in).GetHess() >= param.min_child_weight &&
+      if (loss_chg_in > loss_chg &&
-        quantiser.ToFloatingPoint(right_sum_in).GetHess() >= param.min_child_weight) {
+          quantiser.ToFloatingPoint(left_sum_in).GetHess() >= param.min_child_weight &&
-      loss_chg = loss_chg_in;
+          quantiser.ToFloatingPoint(right_sum_in).GetHess() >= param.min_child_weight) {
-      dir = dir_in;
+        loss_chg = loss_chg_in;
-      fvalue = std::numeric_limits<float>::quiet_NaN();
+        dir = dir_in;
-      thresh = thresh_in;
+        fvalue = std::numeric_limits<float>::quiet_NaN();
-      is_cat = true;
+        thresh = thresh_in;
-      left_sum = left_sum_in;
+        is_cat = true;
-      right_sum = right_sum_in;
+        left_sum = left_sum_in;
-      findex = findex_in;
+        right_sum = right_sum_in;
-    }
+        findex = findex_in;
+      }
   }
 
-  XGBOOST_DEVICE bool IsValid() const { return loss_chg > 0.0f; }
+  [[nodiscard]] XGBOOST_DEVICE bool IsValid() const { return loss_chg > 0.0f; }
 
   friend std::ostream& operator<<(std::ostream& os, DeviceSplitCandidate const& c) {
     os << "loss_chg:" << c.loss_chg << ", "

src/tree/updater_gpu_hist.cu

@@ -7,9 +7,9 @@
 
 #include <algorithm>
 #include <cmath>
-#include <limits>
+#include <cstddef>  // for size_t
-#include <memory>
+#include <memory>   // for unique_ptr, make_unique
-#include <utility>
+#include <utility>  // for move
 #include <vector>
 
 #include "../collective/communicator-inl.cuh"
@@ -216,9 +216,9 @@ struct GPUHistMakerDevice {
   void InitFeatureGroupsOnce() {
     if (!feature_groups) {
       CHECK(page);
-      feature_groups.reset(new FeatureGroups(page->Cuts(), page->is_dense,
+      feature_groups = std::make_unique<FeatureGroups>(page->Cuts(), page->is_dense,
-                                             dh::MaxSharedMemoryOptin(ctx_->gpu_id),
+                                                       dh::MaxSharedMemoryOptin(ctx_->gpu_id),
-                                             sizeof(GradientPairPrecise)));
+                                                       sizeof(GradientPairPrecise));
     }
   }
 
@@ -244,10 +244,10 @@ struct GPUHistMakerDevice {
 
     this->evaluator_.Reset(page->Cuts(), feature_types, dmat->Info().num_col_, param, ctx_->gpu_id);
 
-    quantiser.reset(new GradientQuantiser(this->gpair));
+    quantiser = std::make_unique<GradientQuantiser>(this->gpair);
 
     row_partitioner.reset();  // Release the device memory first before reallocating
-    row_partitioner.reset(new RowPartitioner(ctx_->gpu_id, sample.sample_rows));
+    row_partitioner = std::make_unique<RowPartitioner>(ctx_->gpu_id, sample.sample_rows);
 
     // Init histogram
     hist.Init(ctx_->gpu_id, page->Cuts().TotalBins());
@@ -294,7 +294,7 @@ struct GPUHistMakerDevice {
     dh::TemporaryArray<GPUExpandEntry> entries(2 * candidates.size());
     // Store the feature set ptrs so they dont go out of scope before the kernel is called
     std::vector<std::shared_ptr<HostDeviceVector<bst_feature_t>>> feature_sets;
-    for (size_t i = 0; i < candidates.size(); i++) {
+    for (std::size_t i = 0; i < candidates.size(); i++) {
       auto candidate = candidates.at(i);
       int left_nidx = tree[candidate.nid].LeftChild();
       int right_nidx = tree[candidate.nid].RightChild();
@@ -327,14 +327,13 @@ struct GPUHistMakerDevice {
         d_node_inputs.data().get(), h_node_inputs.data(),
         h_node_inputs.size() * sizeof(EvaluateSplitInputs), cudaMemcpyDefault));
 
-    this->evaluator_.EvaluateSplits(nidx, max_active_features,
+    this->evaluator_.EvaluateSplits(nidx, max_active_features, dh::ToSpan(d_node_inputs),
-                                    dh::ToSpan(d_node_inputs), shared_inputs,
+                                    shared_inputs, dh::ToSpan(entries));
-                                    dh::ToSpan(entries));
     dh::safe_cuda(cudaMemcpyAsync(pinned_candidates_out.data(),
                                   entries.data().get(), sizeof(GPUExpandEntry) * entries.size(),
                                   cudaMemcpyDeviceToHost));
     dh::DefaultStream().Sync();
-    }
+  }
 
   void BuildHist(int nidx) {
     auto d_node_hist = hist.GetNodeHistogram(nidx);
@@ -366,23 +365,29 @@ struct GPUHistMakerDevice {
   struct NodeSplitData {
     RegTree::Node split_node;
     FeatureType split_type;
-    common::CatBitField node_cats;
+    common::KCatBitField node_cats;
   };
 
-  void UpdatePosition(const std::vector<GPUExpandEntry>& candidates, RegTree* p_tree) {
+  void UpdatePosition(std::vector<GPUExpandEntry> const& candidates, RegTree* p_tree) {
-    if (candidates.empty()) return;
+    if (candidates.empty()) {
-    std::vector<int> nidx(candidates.size());
+      return;
-    std::vector<int> left_nidx(candidates.size());
+    }
-    std::vector<int> right_nidx(candidates.size());
+
+    std::vector<bst_node_t> nidx(candidates.size());
+    std::vector<bst_node_t> left_nidx(candidates.size());
+    std::vector<bst_node_t> right_nidx(candidates.size());
     std::vector<NodeSplitData> split_data(candidates.size());
+
     for (size_t i = 0; i < candidates.size(); i++) {
-      auto& e = candidates[i];
+      auto const& e = candidates[i];
       RegTree::Node split_node = (*p_tree)[e.nid];
       auto split_type = p_tree->NodeSplitType(e.nid);
       nidx.at(i) = e.nid;
       left_nidx.at(i) = split_node.LeftChild();
       right_nidx.at(i) = split_node.RightChild();
-      split_data.at(i) = NodeSplitData{split_node, split_type, e.split.split_cats};
+      split_data.at(i) = NodeSplitData{split_node, split_type, evaluator_.GetDeviceNodeCats(e.nid)};
+
+      CHECK_EQ(split_type == FeatureType::kCategorical, e.split.is_cat);
     }
 
     auto d_matrix = page->GetDeviceAccessor(ctx_->gpu_id);
@@ -390,7 +395,7 @@ struct GPUHistMakerDevice {
         nidx, left_nidx, right_nidx, split_data,
         [=] __device__(bst_uint ridx, const NodeSplitData& data) {
           // given a row index, returns the node id it belongs to
-          bst_float cut_value = d_matrix.GetFvalue(ridx, data.split_node.SplitIndex());
+          float cut_value = d_matrix.GetFvalue(ridx, data.split_node.SplitIndex());
           // Missing value
           bool go_left = true;
           if (isnan(cut_value)) {
@@ -620,7 +625,6 @@ struct GPUHistMakerDevice {
       CHECK(common::CheckNAN(candidate.split.fvalue));
       std::vector<common::CatBitField::value_type> split_cats;
 
-      CHECK_GT(candidate.split.split_cats.Bits().size(), 0);
       auto h_cats = this->evaluator_.GetHostNodeCats(candidate.nid);
       auto n_bins_feature = page->Cuts().FeatureBins(candidate.split.findex);
       split_cats.resize(common::CatBitField::ComputeStorageSize(n_bins_feature), 0);

tests/cpp/gbm/test_gblinear.cu

@@ -0,0 +1,42 @@
+/**
+ * Copyright 2023, XGBoost Contributors
+ */
+#include <gtest/gtest.h>
+#include <xgboost/global_config.h>  // for GlobalConfigThreadLocalStore
+#include <xgboost/json.h>           // for Json, Object
+#include <xgboost/learner.h>        // for Learner
+
+#include <algorithm>  // for transform
+#include <string>     // for string
+#include <utility>    // for swap
+
+#include "../helpers.h"  // for RandomDataGenerator
+
+namespace xgboost {
+TEST(GBlinear, DispatchUpdater) {
+  auto verbosity = 3;
+  std::swap(GlobalConfigThreadLocalStore::Get()->verbosity, verbosity);
+
+  auto test = [](std::string device) {
+    auto p_fmat = RandomDataGenerator{10, 10, 0.0f}.GenerateDMatrix(true);
+    std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
+    learner->SetParams(
+        Args{{"booster", "gblinear"}, {"updater", "coord_descent"}, {"device", device}});
+    learner->Configure();
+    for (std::int32_t iter = 0; iter < 3; ++iter) {
+      learner->UpdateOneIter(iter, p_fmat);
+    }
+    Json config{Object{}};
+    ::testing::internal::CaptureStderr();
+    learner->SaveConfig(&config);
+    auto str = ::testing::internal::GetCapturedStderr();
+    std::transform(device.cbegin(), device.cend(), device.begin(),
+                   [](char c) { return std::toupper(c); });
+    ASSERT_NE(str.find(device), std::string::npos);
+  };
+  test("cpu");
+  test("gpu");
+
+  std::swap(GlobalConfigThreadLocalStore::Get()->verbosity, verbosity);
+}
+}  // namespace xgboost

tests/cpp/gbm/test_gbtree.cu

@@ -58,21 +58,6 @@ void TestInplaceFallback(Context const* ctx) {
   HostDeviceVector<float>* out_predt{nullptr};
   ConsoleLogger::Configure(Args{{"verbosity", "1"}});
   std::string output;
-  // test whether the warning is raised
-#if !defined(_WIN32)
-  // Windows has issue with CUDA and thread local storage. For some reason, on Windows a
-  // cudaInitializationError is raised during destruction of `HostDeviceVector`. This
-  // might be related to https://github.com/dmlc/xgboost/issues/5793
-  ::testing::internal::CaptureStderr();
-  std::thread{[&] {
-    // Launch a new thread to ensure a warning is raised as we prevent over-verbose
-    // warning by using thread-local flags.
-    learner->InplacePredict(p_m, PredictionType::kValue, std::numeric_limits<float>::quiet_NaN(),
-                            &out_predt, 0, 0);
-  }}.join();
-  output = testing::internal::GetCapturedStderr();
-  ASSERT_NE(output.find("Falling back"), std::string::npos);
-#endif
 
   learner->InplacePredict(p_m, PredictionType::kValue, std::numeric_limits<float>::quiet_NaN(),
                           &out_predt, 0, 0);

tests/python-gpu/test_gpu_prediction.py

@@ -191,14 +191,32 @@ class TestGPUPredict:
         np.testing.assert_allclose(predt_0, predt_3)
         np.testing.assert_allclose(predt_0, predt_4)
 
-    def run_inplace_base_margin(self, booster, dtrain, X, base_margin):
+    def run_inplace_base_margin(
+        self, device: int, booster: xgb.Booster, dtrain: xgb.DMatrix, X, base_margin
+    ) -> None:
         import cupy as cp
 
+        booster.set_param({"device": f"cuda:{device}"})
         dtrain.set_info(base_margin=base_margin)
         from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
         from_dmatrix = booster.predict(dtrain)
         cp.testing.assert_allclose(from_inplace, from_dmatrix)
 
+        booster = booster.copy()  # clear prediction cache.
+        booster.set_param({"device": "cpu"})
+        from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
+        from_dmatrix = booster.predict(dtrain)
+        cp.testing.assert_allclose(from_inplace, from_dmatrix)
+
+        booster = booster.copy()  # clear prediction cache.
+        base_margin = cp.asnumpy(base_margin)
+        if hasattr(X, "values"):
+            X = cp.asnumpy(X.values)
+        booster.set_param({"device": f"cuda:{device}"})
+        from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
+        from_dmatrix = booster.predict(dtrain)
+        cp.testing.assert_allclose(from_inplace, from_dmatrix, rtol=1e-6)
+
     def run_inplace_predict_cupy(self, device: int) -> None:
         import cupy as cp
 
@@ -244,7 +262,7 @@ class TestGPUPredict:
             run_threaded_predict(X, rows, predict_dense)
 
         base_margin = cp_rng.randn(rows)
-        self.run_inplace_base_margin(booster, dtrain, X, base_margin)
+        self.run_inplace_base_margin(device, booster, dtrain, X, base_margin)
 
         # Create a wide dataset
         X = cp_rng.randn(100, 10000)
@@ -318,7 +336,7 @@ class TestGPUPredict:
             run_threaded_predict(X, rows, predict_df)
 
         base_margin = cudf.Series(rng.randn(rows))
-        self.run_inplace_base_margin(booster, dtrain, X, base_margin)
+        self.run_inplace_base_margin(0, booster, dtrain, X, base_margin)
 
     @given(
         strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy

tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py

@@ -2,6 +2,7 @@ import json
 import logging
 import subprocess
 
+import numpy as np
 import pytest
 import sklearn
 
@@ -13,7 +14,7 @@ from pyspark.ml.linalg import Vectors
 from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
 from pyspark.sql import SparkSession
 
-from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor
+from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor, SparkXGBRegressorModel
 
 gpu_discovery_script_path = "tests/test_distributed/test_gpu_with_spark/discover_gpu.sh"
 
@@ -242,3 +243,33 @@ def test_sparkxgb_regressor_feature_cols_with_gpu(spark_diabetes_dataset_feature
     evaluator = RegressionEvaluator(metricName="rmse")
     rmse = evaluator.evaluate(pred_result_df)
     assert rmse <= 65.0
+
+
+def test_gpu_transform(spark_diabetes_dataset) -> None:
+    regressor = SparkXGBRegressor(device="cuda", num_workers=num_workers)
+    train_df, test_df = spark_diabetes_dataset
+    model: SparkXGBRegressorModel = regressor.fit(train_df)
+
+    # The model trained with GPUs, and transform with GPU configurations.
+    assert model._gpu_transform()
+
+    model.set_device("cpu")
+    assert not model._gpu_transform()
+    # without error
+    cpu_rows = model.transform(test_df).select("prediction").collect()
+
+    regressor = SparkXGBRegressor(device="cpu", num_workers=num_workers)
+    model = regressor.fit(train_df)
+
+    # The model trained with CPUs. Even with GPU configurations,
+    # still prefer transforming with CPUs
+    assert not model._gpu_transform()
+
+    # Set gpu transform explicitly.
+    model.set_device("cuda")
+    assert model._gpu_transform()
+    # without error
+    gpu_rows = model.transform(test_df).select("prediction").collect()
+
+    for cpu, gpu in zip(cpu_rows, gpu_rows):
+        np.testing.assert_allclose(cpu.prediction, gpu.prediction, atol=1e-3)

tests/test_distributed/test_with_spark/test_spark_local.py

@@ -888,6 +888,34 @@ class TestPySparkLocal:
         clf = SparkXGBClassifier(device="cuda")
         clf._validate_params()
 
+    def test_gpu_transform(self, clf_data: ClfData) -> None:
+        """local mode"""
+        classifier = SparkXGBClassifier(device="cpu")
+        model: SparkXGBClassifierModel = classifier.fit(clf_data.cls_df_train)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = "file:" + tmpdir
+            model.write().overwrite().save(path)
+
+            # The model trained with CPU, transform defaults to cpu
+            assert not model._gpu_transform()
+
+            # without error
+            model.transform(clf_data.cls_df_test).collect()
+
+            model.set_device("cuda")
+            assert model._gpu_transform()
+
+            model_loaded = SparkXGBClassifierModel.load(path)
+
+            # The model trained with CPU, transform defaults to cpu
+            assert not model_loaded._gpu_transform()
+            # without error
+            model_loaded.transform(clf_data.cls_df_test).collect()
+
+            model_loaded.set_device("cuda")
+            assert model_loaded._gpu_transform()
+
 
 class XgboostLocalTest(SparkTestCase):
     def setUp(self):