fix: drop zero-cost views of ak.Array

src/awkward/_connect/dlpack.py

@@ -1,12 +1,9 @@
 # BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
 from __future__ import annotations
 
-__all__ = ("DLPackDevice", "get_layout_device", "to_dlpack")
+__all__ = ("DLPackDevice",)
 from enum import IntEnum
 
-from awkward._typing import Any
-from awkward.contents import Content
-
 
 class DLPackDevice(IntEnum):
     CPU = 1  # CPU
@@ -19,26 +16,3 @@ class DLPackDevice(IntEnum):
     ROCM = 10  # GPU
     ROCM_PINNED = 11  # GPU & CPU
     CUDA_MANAGED = 13  # GPU & CPU
-
-
-def get_layout_device(layout: Content) -> tuple[int, int]:
-    while True:
-        if layout.is_numpy:
-            break
-        elif layout.is_regular:
-            layout = layout.content
-        else:
-            raise TypeError(
-                "Cannot determine the DLPack device for this array layout."
-                "DLPack is only supported for regular arrays."
-            )
-
-    return layout.data.__dlpack_device__()
-
-
-def to_dlpack(layout: Content, stream: Any = None) -> Any:
-    array = layout.to_backend_array(allow_missing=False)
-    if stream is None:
-        return array.__dlpack__()
-    else:
-        return array.__dlpack__(stream)

src/awkward/_connect/numpy.py

@@ -434,3 +434,11 @@ def action(inputs, **ignore):
 
 def action_for_matmul(inputs):
     raise NotImplementedError
+
+
+def convert_to_array(layout, dtype=None):
+    out = ak.operations.to_numpy(layout, allow_missing=False)
+    if dtype is None:
+        return out
+    else:
+        return numpy.array(out, dtype=dtype)

src/awkward/highlevel.py

@@ -23,7 +23,6 @@
 from awkward._backends.dispatch import register_backend_lookup_factory
 from awkward._backends.numpy import NumpyBackend
 from awkward._behavior import behavior_of, get_array_class, get_record_class
-from awkward._connect.dlpack import get_layout_device, to_dlpack
 from awkward._layout import wrap_layout
 from awkward._nplikes.numpy import Numpy
 from awkward._nplikes.numpylike import NumpyMetadata
@@ -1320,30 +1319,37 @@ def _repr_mimebundle_(self, include=None, exclude=None):
             "text/plain": repr(self),
         }
 
-    @non_inspectable_property
-    def __cuda_array_interface__(self):
-        with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__cuda_array_interface__", (self,), {}
-        ):
-            array = ak.operations.to_cupy(self)
-            return array.__cuda_array_interface__
+    def __array__(self, dtype=None):
+        """
+        Intercepts attempts to convert this Array into a NumPy array and
+        either performs a zero-copy conversion or raises an error.
 
-    @non_inspectable_property
-    def __array_interface__(self):
-        with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__array_interface__", (self,), {}
-        ):
-            array = ak.operations.to_numpy(self)
-            return array.__array_interface__
+        This function is also called by the
+        [np.asarray](https://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html)
+        family of functions, which have `copy=False` by default.
+
+            >>> np.asarray(ak.Array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]]))
+            array([[1.1, 2.2, 3.3],
+                   [4.4, 5.5, 6.6]])
+
+        If the data are numerical and regular (nested lists have equal lengths
+        in each dimension, as described by the #type), they can be losslessly
+        converted to a NumPy array and this function returns without an error.
 
-    def __dlpack_device__(self):
+        Otherwise, the function raises an error. It does not create a NumPy
+        array with dtype `"O"` for `np.object_` (see the
+        [note on object_ type](https://docs.scipy.org/doc/numpy/reference/arrays.scalars.html#arrays-scalars-built-in))
+        since silent conversions to dtype `"O"` arrays would not only be a
+        significant performance hit, but would also break functionality, since
+        nested lists in a NumPy `"O"` array are severed from the array and
+        cannot be sliced as dimensions.
+        """
         with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__dlpack_device__", (self,), {}
+            "numpy.asarray", (self,), {"dtype": dtype}
         ):
-            return get_layout_device(self._layout)
+            from awkward._connect.numpy import convert_to_array
 
-    def __dlpack__(self, stream=None):
-        return to_dlpack(self._layout, stream)
+            return convert_to_array(self._layout, dtype=dtype)
 
     def __arrow_array__(self, type=None):
         with ak._errors.OperationErrorContext(
@@ -2453,27 +2459,20 @@ def show(self, limit_rows=20, limit_cols=80, type=False, stream=sys.stdout):
             limit_rows=limit_rows, limit_cols=limit_cols, type=type, stream=stream
         )
 
-    @property
-    def __cuda_array_interface__(self):
-        with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__cuda_array_interface__", (self,), {}
-        ):
-            array = ak.operations.to_cupy(self)
-            return array.__cuda_array_interface__
+    def __array__(self, dtype=None):
+        """
+        Intercepts attempts to convert a #snapshot of this array into a
+        NumPy array and either performs a zero-copy conversion or raises
+        an error.
 
-    @property
-    def __array_interface__(self):
+        See #ak.Array.__array__ for a more complete description.
+        """
         with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__array_interface__", (self,), {}
+            "numpy.asarray", (self,), {"dtype": dtype}
         ):
-            array = ak.operations.to_numpy(self)
-            return array.__array_interface__
+            from awkward._connect.numpy import convert_to_array
 
-    def __dlpack_device__(self):
-        with ak._errors.OperationErrorContext(
-            f"{type(self).__name__}.__dlpack_device__", (self,), {}
-        ):
-            return get_layout_device(self.snapshot())
+            return convert_to_array(self.snapshot(), dtype=dtype)
 
     def __arrow_array__(self, type=None):
         with ak._errors.OperationErrorContext(

tests/test_2649_dlpack_support.py

@@ -4,7 +4,6 @@
 import pytest
 
 import awkward as ak
-from awkward._connect.dlpack import DLPackDevice
 
 
 def test_from_dlpack_numpy():
@@ -14,14 +13,6 @@ def test_from_dlpack_numpy():
     assert np.shares_memory(np_array, np_from_ak)
 
 
-def test_to_dlpack_numpy():
-    np_array = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
-    array = ak.from_numpy(np_array, regulararray=True)
-    np_from_ak = np.from_dlpack(array)
-    assert np.shares_memory(np_array, np_from_ak)
-    assert array.__dlpack_device__()[0] == DLPackDevice.CPU
-
-
 def test_from_dlpack_cupy():
     # This test only checks cupy usage, it doesn't explicitly test GPU & CPU
     cp = pytest.importorskip("cupy")
@@ -31,15 +22,6 @@ def test_from_dlpack_cupy():
     assert cp.shares_memory(cp_array, cp_from_ak)
 
 
-def test_to_dlpack_cupy():
-    # This test only checks cupy usage, it doesn't explicitly test GPU & CPU
-    cp = pytest.importorskip("cupy")
-    cp_array = cp.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
-    array = ak.from_cupy(cp_array, regulararray=True)
-    cp_from_ak = cp.from_dlpack(array)
-    assert cp.shares_memory(cp_array, cp_from_ak)
-
-
 class DLPackOf:
     def __init__(self, array):
         self._array = array