[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.

tests/python/test_predict.py

@@ -28,7 +28,7 @@ def run_threaded_predict(X, rows, predict_func):
         assert f.result()
 
 
-def run_predict_leaf(predictor):
+def run_predict_leaf(gpu_id: int) -> np.ndarray:
     rows = 100
     cols = 4
     classes = 5
@@ -42,13 +42,13 @@ def run_predict_leaf(predictor):
         {
             "num_parallel_tree": num_parallel_tree,
             "num_class": classes,
-            "predictor": predictor,
             "tree_method": "hist",
         },
         m,
         num_boost_round=num_boost_round,
     )
 
+    booster = tm.set_ordinal(gpu_id, booster)
     empty = xgb.DMatrix(np.ones(shape=(0, cols)))
     empty_leaf = booster.predict(empty, pred_leaf=True)
     assert empty_leaf.shape[0] == 0
@@ -74,13 +74,14 @@ def run_predict_leaf(predictor):
 
     # When there's only 1 tree, the output is a 1 dim vector
     booster = xgb.train({"tree_method": "hist"}, num_boost_round=1, dtrain=m)
+    booster = tm.set_ordinal(gpu_id, booster)
     assert booster.predict(m, pred_leaf=True).shape == (rows,)
 
     return leaf
 
 
-def test_predict_leaf():
-    run_predict_leaf("cpu_predictor")
+def test_predict_leaf() -> None:
+    run_predict_leaf(-1)
 
 
 def test_predict_shape():