ENH: rm __array__, add __buffer__

On python 3.12 and above, delegate to numpy's __buffer__

On earlier python's raise an error and ask the user to updgrade.
Otherwise, on python 3.11 and below, np.array(array_api_strict_array)
becomes a 0D object array.

Co-authored-by: Tim Head <betatim@gmail.com>

Author: ev-br

.github/workflows/array-api-tests.yml

@@ -11,8 +11,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
-        numpy-version: ['1.26', 'dev']
+        python-version: ['3.12', '3.13']
+        numpy-version: ['1.26', '2.2', 'dev']
         exclude:
           - python-version: '3.13'
             numpy-version: '1.26'
@@ -38,7 +38,7 @@ jobs:
         if [[ "${{ matrix.numpy-version }}" == "dev" ]]; then
           python -m pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy;
         else
-          python -m pip install 'numpy>=1.26,<2.0';
+          python -m pip install 'numpy=='${{ matrix.numpy-version }};
         fi
         python -m pip install ${GITHUB_WORKSPACE}/array-api-strict
         python -m pip install -r ${GITHUB_WORKSPACE}/array-api-tests/requirements.txt

array_api_strict/_array_object.py

@@ -67,9 +67,7 @@ def __hash__(self) -> int:
 CPU_DEVICE = Device()
 ALL_DEVICES = (CPU_DEVICE, Device("device1"), Device("device2"))
 
-# See https://github.com/data-apis/array-api-strict/issues/67 and the comment
-# on __array__ below.
-_allow_array = True
+_default = object()
 
 
 class Array:
@@ -151,40 +149,28 @@ def __repr__(self) -> str:
 
     __str__ = __repr__
 
-    # In the future, _allow_array will be set to False, which will disallow
-    # __array__. This means calling `np.func()` on an array_api_strict array
-    # will give an error. If we don't explicitly disallow it, NumPy defaults
-    # to creating an object dtype array, which would lead to confusing error
-    # messages at best and surprising bugs at worst. The reason for doing this
-    # is that __array__ is not actually supported by the standard, so it can
-    # lead to code assuming np.asarray(other_array) would always work in the
-    # standard.
-    #
-    # This was implemented historically for compatibility, and removing it has
+    # `__array__` was implemented historically for compatibility, and removing it has
     # caused issues for some libraries (see
     # https://github.com/data-apis/array-api-strict/issues/67).
-    def __array__(
-        self, dtype: None | np.dtype[Any] = None, copy: None | bool = None
-    ) -> npt.NDArray[Any]:
-        # We have to allow this to be internally enabled as there's no other
-        # easy way to parse a list of Array objects in asarray().
-        if _allow_array:
-            if self._device != CPU_DEVICE:
-                raise RuntimeError(f"Can not convert array on the '{self._device}' device to a Numpy array.")
-            # copy keyword is new in 2.0.0; for older versions don't use it
-            # retry without that keyword.
-            if np.__version__[0] < '2':
-                return np.asarray(self._array, dtype=dtype)
-            elif np.__version__.startswith('2.0.0-dev0'):
-                # Handle dev version for which we can't know based on version
-                # number whether or not the copy keyword is supported.
-                try:
-                    return np.asarray(self._array, dtype=dtype, copy=copy)
-                except TypeError:
-                    return np.asarray(self._array, dtype=dtype)
-            else:
-                return np.asarray(self._array, dtype=dtype, copy=copy)
-        raise ValueError("Conversion from an array_api_strict array to a NumPy ndarray is not supported")
+
+    # Instead of `__array__` we now implement the buffer protocol.
+    # Note that it makes array-apis-strict requiring python>=3.12
+    def __buffer__(self, flags):
+        if self._device != CPU_DEVICE:
+            raise RuntimeError(f"Can not convert array on the '{self._device}' device to a Numpy array.")
+        return self._array.__buffer__(flags)
+
+    # We do not define __release_buffer__, per the discussion at
+    # https://github.com/data-apis/array-api-strict/pull/115#pullrequestreview-2917178729
+
+    def __array__(self, *args, **kwds):
+        # a stub for python < 3.12; otherwise numpy silently produces object arrays
+        import sys
+        minor, major = sys.version_info.minor, sys.version_info.major
+        if major < 3 or minor < 12:
+            raise TypeError(
+                "Interoperation with NumPy requires python >= 3.12. Please upgrade."
+            )
 
     # These are various helper functions to make the array behavior match the
     # spec in places where it either deviates from or is more strict than

array_api_strict/_creation_functions.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-from collections.abc import Generator
-from contextlib import contextmanager
 from enum import Enum
 from typing import TYPE_CHECKING, Literal
 
@@ -26,21 +24,6 @@ class Undef(Enum):
 _undef = Undef.UNDEF
 
 
-@contextmanager
-def allow_array() -> Generator[None]:
-    """
-    Temporarily enable Array.__array__. This is needed for np.array to parse
-    list of lists of Array objects.
-    """
-    from . import _array_object
-    original_value = _array_object._allow_array
-    try:
-        _array_object._allow_array = True
-        yield
-    finally:
-        _array_object._allow_array = original_value
-
-
 def _check_valid_dtype(dtype: DType | None) -> None:
     # Note: Only spelling dtypes as the dtype objects is supported.
     if dtype not in (None,) + _all_dtypes:
@@ -123,8 +106,8 @@ def asarray(
         # Give a better error message in this case. NumPy would convert this
         # to an object array. TODO: This won't handle large integers in lists.
         raise OverflowError("Integer out of bounds for array dtypes")
-    with allow_array():
-        res = np.array(obj, dtype=_np_dtype, copy=copy)
+
+    res = np.array(obj, dtype=_np_dtype, copy=copy)
     return Array._new(res, device=device)
 
 

array_api_strict/tests/test_array_object.py

@@ -1,3 +1,4 @@
+import sys
 import operator
 from builtins import all as all_
 
@@ -526,6 +527,10 @@ def test_array_properties():
     assert b.mT.shape == (3, 2)
 
 
+@pytest.mark.xfail(sys.version_info.major*100 + sys.version_info.minor < 312,
+                   reason="array conversion relies on buffer protocol, and "
+                          "requires python >= 3.12"
+)
 def test_array_conversion():
     # Check that arrays on the CPU device can be converted to NumPy
     # but arrays on other devices can't. Note this is testing the logic in
@@ -536,25 +541,23 @@ def test_array_conversion():
 
     for device in ("device1", "device2"):
         a = ones((2, 3), device=array_api_strict.Device(device))
-        with pytest.raises(RuntimeError, match="Can not convert array"):
+        with pytest.raises((RuntimeError, ValueError)):
             np.asarray(a)
 
-def test__array__():
-    # __array__ should work for now
+    # __buffer__ should work for now for conversion to numpy
     a = ones((2, 3))
-    np.array(a)
-
-    # Test the _allow_array private global flag for disabling it in the
-    # future.
-    from .. import _array_object
-    original_value = _array_object._allow_array
-    try:
-        _array_object._allow_array = False
-        a = ones((2, 3))
-        with pytest.raises(ValueError, match="Conversion from an array_api_strict array to a NumPy ndarray is not supported"):
-            np.array(a)
-    finally:
-        _array_object._allow_array = original_value
+    na = np.array(a)
+    assert na.shape == (2, 3)
+    assert na.dtype == np.float64
+
+@pytest.mark.skipif(not sys.version_info.major*100 + sys.version_info.minor < 312,
+                    reason="conversion to numpy errors out unless python >= 3.12"
+)
+def test_array_conversion_2():
+    a = ones((2, 3))
+    with pytest.raises(TypeError):
+        np.array(a)
+
 
 def test_allow_newaxis():
     a = ones(5)