[Breaking] Accept multi-dim meta info. (#7405)

This PR changes base_margin into a 3-dim array, with one of them being reserved for multi-target classification. Also, a breaking change is made for binary serialization due to extra dimension along with a fix for saving the feature weights. Lastly, it unifies the prediction initialization between CPU and GPU. After this PR, the meta info setter in Python will be based on array interface.

python-package/xgboost/data.py

@@ -5,7 +5,7 @@ import ctypes
 import json
 import warnings
 import os
-from typing import Any, Tuple, Callable, Optional, List
+from typing import Any, Tuple, Callable, Optional, List, Union
 
 import numpy as np
 
@@ -138,14 +138,14 @@ def _is_numpy_array(data):
     return isinstance(data, (np.ndarray, np.matrix))
 
 
-def _ensure_np_dtype(data, dtype):
+def _ensure_np_dtype(data, dtype) -> Tuple[np.ndarray, np.dtype]:
     if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
         data = data.astype(np.float32, copy=False)
         dtype = np.float32
     return data, dtype
 
 
-def _maybe_np_slice(data, dtype):
+def _maybe_np_slice(data: np.ndarray, dtype) -> np.ndarray:
     '''Handle numpy slice.  This can be removed if we use __array_interface__.
     '''
     try:
@@ -852,23 +852,17 @@ def _validate_meta_shape(data: Any, name: str) -> None:
 
 
 def _meta_from_numpy(
-    data: np.ndarray, field: str, dtype, handle: ctypes.c_void_p
+    data: np.ndarray,
+    field: str,
+    dtype: Optional[Union[np.dtype, str]],
+    handle: ctypes.c_void_p,
 ) -> None:
-    data = _maybe_np_slice(data, dtype)
+    data, dtype = _ensure_np_dtype(data, dtype)
     interface = data.__array_interface__
-    assert interface.get('mask', None) is None, 'Masked array is not supported'
-    size = data.size
-
-    c_type = _to_data_type(str(data.dtype), field)
-    ptr = interface['data'][0]
-    ptr = ctypes.c_void_p(ptr)
-    _check_call(_LIB.XGDMatrixSetDenseInfo(
-        handle,
-        c_str(field),
-        ptr,
-        c_bst_ulong(size),
-        c_type
-    ))
+    if interface.get("mask", None) is not None:
+        raise ValueError("Masked array is not supported.")
+    interface_str = _array_interface(data)
+    _check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface_str))
 
 
 def _meta_from_list(data, field, dtype, handle):
@@ -911,7 +905,9 @@ def _meta_from_dt(data, field: str, dtype, handle: ctypes.c_void_p):
     _meta_from_numpy(data, field, dtype, handle)
 
 
-def dispatch_meta_backend(matrix: DMatrix, data, name: str, dtype: str = None):
+def dispatch_meta_backend(
+    matrix: DMatrix, data, name: str, dtype: Optional[Union[str, np.dtype]] = None
+):
     '''Dispatch for meta info.'''
     handle = matrix.handle
     assert handle is not None