Merge branch 'main' into release

Author: asmeurer

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

@@ -49,6 +49,7 @@ jobs:
       run: |
         # Parameterizing this in the CI matrix is wasteful. Just do a loop here.
         for ARRAY_API_STRICT_API_VERSION in ${API_VERSIONS}; do
+            export ARRAY_API_STRICT_API_VERSION
             cd ${GITHUB_WORKSPACE}/array-api-tests
             pytest array_api_tests/ --skips-file ${GITHUB_WORKSPACE}/array-api-strict/array-api-tests-xfails.txt ${PYTEST_ARGS}
         done

.github/workflows/publish-package.yml

@@ -97,7 +97,7 @@ jobs:
       if: >-
         (github.event_name == 'push' && startsWith(github.ref, 'refs/tags'))
         || (github.event_name == 'workflow_dispatch' && github.event.inputs.publish == 'true')
-      uses: pypa/gh-action-pypi-publish@v1.9.0
+      uses: pypa/gh-action-pypi-publish@v1.11.0
       with:
         repository-url: https://test.pypi.org/legacy/
         print-hash: true
@@ -110,6 +110,6 @@ jobs:
 
     - name: Publish distribution 📦 to PyPI
       if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
-      uses: pypa/gh-action-pypi-publish@v1.9.0
+      uses: pypa/gh-action-pypi-publish@v1.11.0
       with:
         print-hash: true

array_api_strict/_array_object.py

@@ -66,6 +66,8 @@ def __hash__(self):
 
 _default = object()
 
+_allow_array = False
+
 class Array:
     """
     n-d array object for the array API namespace.
@@ -145,30 +147,35 @@ def __repr__(self: Array, /) -> str:
             mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
         return prefix + mid + suffix
 
-    # This function is not required by the spec, but we implement it here for
-    # convenience so that np.asarray(array_api_strict.Array) will work.
+    # Disallow __array__, meaning 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 alternative of course is to just support __array__, which is what we
+    # used to do. But this isn't actually supported by the standard, so it can
+    # lead to code assuming np.asarray(other_array) would always work in the
+    # standard.
     def __array__(self, dtype: None | np.dtype[Any] = None, copy: None | bool = None) -> npt.NDArray[Any]:
-        """
-        Warning: this method is NOT part of the array API spec. Implementers
-        of other libraries need not include it, and users should not assume it
-        will be present in other implementations.
-
-        """
-        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:
+        # 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)
-        else:
-            return np.asarray(self._array, dtype=dtype, copy=copy)
+            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")
 
     # 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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-
+from contextlib import contextmanager
 from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
 if TYPE_CHECKING:
@@ -16,6 +16,19 @@
 
 import numpy as np
 
+@contextmanager
+def allow_array():
+    """
+    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):
     # Note: Only spelling dtypes as the dtype objects is supported.
@@ -99,7 +112,8 @@ 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")
-    res = np.array(obj, dtype=_np_dtype, copy=copy)
+    with allow_array():
+        res = np.array(obj, dtype=_np_dtype, copy=copy)
     return Array._new(res, device=device)
 
 

array_api_strict/_linalg.py

@@ -267,6 +267,8 @@ def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
     # default tolerance by max(M, N).
     if rtol is None:
         rtol = max(x.shape[-2:]) * finfo(x.dtype).eps
+    if isinstance(rtol, Array):
+        rtol = rtol._array
     return Array._new(np.linalg.pinv(x._array, rcond=rtol), device=x.device)
 
 @requires_extension('linalg')

array_api_strict/tests/test_array_object.py

@@ -350,23 +350,19 @@ def test_array_properties():
     assert isinstance(b.mT, Array)
     assert b.mT.shape == (3, 2)
 
-def test___array__():
-    a = ones((2, 3), dtype=int16)
-    assert np.asarray(a) is a._array
-    b = np.asarray(a, dtype=np.float64)
-    assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
-    assert b.dtype == np.float64
 
 def test_array_conversion():
     # Check that arrays on the CPU device can be converted to NumPy
-    # but arrays on other devices can't
+    # but arrays on other devices can't. Note this is testing the logic in
+    # __array__, which is only used in asarray when converting lists of
+    # arrays.
     a = ones((2, 3))
-    np.asarray(a)
+    asarray([a])
 
     for device in ("device1", "device2"):
         a = ones((2, 3), device=array_api_strict.Device(device))
         with pytest.raises(RuntimeError, match="Can not convert array"):
-            np.asarray(a)
+            asarray([a])
 
 def test_allow_newaxis():
     a = ones(5)

array_api_strict/tests/test_creation_functions.py

@@ -22,7 +22,7 @@
     zeros,
     zeros_like,
 )
-from .._dtypes import float32, float64
+from .._dtypes import int16, float32, float64
 from .._array_object import Array, CPU_DEVICE, Device
 from .._flags import set_array_api_strict_flags
 
@@ -97,6 +97,19 @@ def test_asarray_copy():
     a[0] = 0
     assert all(b[0] == 0)
 
+def test_asarray_list_of_lists():
+    a = asarray(1, dtype=int16)
+    b = asarray([1], dtype=int16)
+    res = asarray([a, a])
+    assert res.shape == (2,)
+    assert res.dtype == int16
+    assert all(res == asarray([1, 1]))
+
+    res = asarray([b, b])
+    assert res.shape == (2, 1)
+    assert res.dtype == int16
+    assert all(res == asarray([[1], [1]]))
+
 
 def test_asarray_device_inference():
     assert asarray([1, 2, 3]).device == CPU_DEVICE