[breaking] Remove the predictor param, allow fallback to prediction using DMatrix. (#9129)

- A `DeviceOrd` struct is implemented to indicate the device. It will eventually replace the `gpu_id` parameter.
- The `predictor` parameter is removed.
- Fallback to `DMatrix` when `inplace_predict` is not available.
- The heuristic for choosing a predictor is only used during training.

doc/gpu/index.rst

@@ -45,7 +45,7 @@ XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as
 
 .. code-block:: python
 
-  model.set_param({"predictor": "gpu_predictor"})
+  model.set_param({"gpu_id": "0", "tree_method": "gpu_hist"})
   shap_values = model.predict(dtrain, pred_contribs=True)
   shap_interaction_values = model.predict(dtrain, pred_interactions=True)
 

doc/parameter.rst

@@ -199,18 +199,6 @@ Parameters for Tree Booster
   - Maximum number of discrete bins to bucket continuous features.
   - Increasing this number improves the optimality of splits at the cost of higher computation time.
 
-* ``predictor``, [default= ``auto``]
-
-  - The type of predictor algorithm to use. Provides the same results but allows the use of GPU or CPU.
-
-    - ``auto``: Configure predictor based on heuristics.
-    - ``cpu_predictor``: Multicore CPU prediction algorithm.
-    - ``gpu_predictor``: Prediction using GPU.  Used when ``tree_method`` is ``gpu_hist``.
-      When ``predictor`` is set to default value ``auto``, the ``gpu_hist`` tree method is
-      able to provide GPU based prediction without copying training data to GPU memory.
-      If ``gpu_predictor`` is explicitly specified, then all data is copied into GPU, only
-      recommended for performing prediction tasks.
-
 * ``num_parallel_tree``, [default=1]
 
   - Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.

doc/prediction.rst

@@ -87,15 +87,6 @@ with the native Python interface :py:meth:`xgboost.Booster.predict` and
 behavior.  Also the ``save_best`` parameter from :py:obj:`xgboost.callback.EarlyStopping`
 might be useful.
 
-*********
-Predictor
-*********
-
-There are 2 predictors in XGBoost (3 if you have the one-api plugin enabled), namely
-``cpu_predictor`` and ``gpu_predictor``.  The default option is ``auto`` so that XGBoost
-can employ some heuristics for saving GPU memory during training.  They might have slight
-different outputs due to floating point errors.
-
 
 ***********
 Base Margin
@@ -134,15 +125,6 @@ it.  Be aware that the output of in-place prediction depends on input data type,
 input is on GPU data output is :py:obj:`cupy.ndarray`, otherwise a :py:obj:`numpy.ndarray`
 is returned.
 
-****************
-Categorical Data
-****************
-
-Other than users performing encoding, XGBoost has experimental support for categorical
-data using ``gpu_hist`` and ``gpu_predictor``.  No special operation needs to be done on
-input test data since the information about categories is encoded into the model during
-training.
-
 *************
 Thread Safety
 *************
@@ -159,7 +141,6 @@ instance we might accidentally call ``clf.set_params()`` inside a predict functi
 
     def predict_fn(clf: xgb.XGBClassifier, X):
         X = preprocess(X)
-        clf.set_params(predictor="gpu_predictor")  # NOT safe!
         clf.set_params(n_jobs=1)  # NOT safe!
         return clf.predict_proba(X, iteration_range=(0, 10))
 

doc/tutorials/dask.rst

@@ -148,8 +148,8 @@ Also for inplace prediction:
 
 .. code-block:: python
 
-  booster.set_param({'predictor': 'gpu_predictor'})
-  # where X is a dask DataFrame or dask Array containing cupy or cuDF backed data.
+  # where X is a dask DataFrame or dask Array backed by cupy or cuDF.
+  booster.set_param({"gpu_id": "0"})
   prediction = xgb.dask.inplace_predict(client, booster, X)
 
 When input is ``da.Array`` object, output is always ``da.Array``.  However, if the input

doc/tutorials/saving_model.rst

@@ -173,7 +173,6 @@ Will print out something similar to (not actual output as it's too long for demo
         "gradient_booster": {
           "gbtree_train_param": {
             "num_parallel_tree": "1",
-            "predictor": "gpu_predictor",
             "process_type": "default",
             "tree_method": "gpu_hist",
             "updater": "grow_gpu_hist",