Merge pull request #141 from asmeurer/release

1.7 release

Author: asmeurer

array_api_compat/__init__.py

@@ -17,6 +17,6 @@
 this implementation for the default when working with NumPy arrays.
 
 """
-__version__ = '1.6'
+__version__ = '1.7'
 
 from .common import *  # noqa: F401, F403

array_api_compat/common/_helpers.py

@@ -50,7 +50,7 @@ def is_numpy_array(x):
     is_torch_array
     is_dask_array
     is_jax_array
-    is_pydata_sparse
+    is_pydata_sparse_array
     """
     # Avoid importing NumPy if it isn't already
     if 'numpy' not in sys.modules:
@@ -80,7 +80,7 @@ def is_cupy_array(x):
     is_torch_array
     is_dask_array
     is_jax_array
-    is_pydata_sparse
+    is_pydata_sparse_array
     """
     # Avoid importing NumPy if it isn't already
     if 'cupy' not in sys.modules:
@@ -107,7 +107,7 @@ def is_torch_array(x):
     is_cupy_array
     is_dask_array
     is_jax_array
-    is_pydata_sparse
+    is_pydata_sparse_array
     """
     # Avoid importing torch if it isn't already
     if 'torch' not in sys.modules:
@@ -134,7 +134,7 @@ def is_dask_array(x):
     is_cupy_array
     is_torch_array
     is_jax_array
-    is_pydata_sparse
+    is_pydata_sparse_array
     """
     # Avoid importing dask if it isn't already
     if 'dask.array' not in sys.modules:
@@ -161,7 +161,7 @@ def is_jax_array(x):
     is_cupy_array
     is_torch_array
     is_dask_array
-    is_pydata_sparse
+    is_pydata_sparse_array
     """
     # Avoid importing jax if it isn't already
     if 'jax' not in sys.modules:
@@ -172,7 +172,7 @@ def is_jax_array(x):
     return isinstance(x, jax.Array) or _is_jax_zero_gradient_array(x)
 
 
-def is_pydata_sparse(x) -> bool:
+def is_pydata_sparse_array(x) -> bool:
     """
     Return True if `x` is an array from the `sparse` package.
 
@@ -219,7 +219,7 @@ def is_array_api_obj(x):
         or is_torch_array(x) \
         or is_dask_array(x) \
         or is_jax_array(x) \
-        or is_pydata_sparse(x) \
+        or is_pydata_sparse_array(x) \
         or hasattr(x, '__array_namespace__')
 
 def _check_api_version(api_version):
@@ -288,7 +288,7 @@ def your_function(x, y):
     is_torch_array
     is_dask_array
     is_jax_array
-    is_pydata_sparse
+    is_pydata_sparse_array
 
     """
     if use_compat not in [None, True, False]:
@@ -348,7 +348,7 @@ def your_function(x, y):
                 # not have a wrapper submodule for it.
                 import jax.experimental.array_api as jnp
             namespaces.add(jnp)
-        elif is_pydata_sparse(x):
+        elif is_pydata_sparse_array(x):
             if use_compat is True:
                 _check_api_version(api_version)
                 raise ValueError("`sparse` does not have an array-api-compat wrapper")
@@ -451,7 +451,7 @@ def device(x: Array, /) -> Device:
             return x.device()
         else:
             return x.device
-    elif is_pydata_sparse(x):
+    elif is_pydata_sparse_array(x):
         # `sparse` will gain `.device`, so check for this first.
         x_device = getattr(x, 'device', None)
         if x_device is not None:
@@ -583,7 +583,7 @@ def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]]
         # This import adds to_device to x
         import jax.experimental.array_api # noqa: F401
         return x.to_device(device, stream=stream)
-    elif is_pydata_sparse(x) and device == _device(x):
+    elif is_pydata_sparse_array(x) and device == _device(x):
         # Perform trivial check to return the same array if
         # device is same instead of err-ing.
         return x
@@ -613,7 +613,7 @@ def size(x):
     "is_jax_array",
     "is_numpy_array",
     "is_torch_array",
-    "is_pydata_sparse",
+    "is_pydata_sparse_array",
     "size",
     "to_device",
 ]

docs/changelog.md

@@ -1,6 +1,6 @@
 # Changelog
 
-## 1.6 (2024-03-29)
+## 1.7 (2024-05-24)
 
 ## Major Changes
 
@@ -10,7 +10,23 @@
   `array_api_compat.sparse` submodule, and
   `array_namespace(<pydata/sparse array>)` returns the `sparse` module.
 
-- Added the function `is_pydata_sparse(x)`.
+- Added the function `is_pydata_sparse_array(x)`.
+
+## Minor Changes
+
+- Fix JAX `float0` arrays. See https://github.com/google/jax/issues/20620.
+  ([@NeilGirdhar](https://github.com/NeilGirdhar))
+
+- Fix `torch.linalg.vector_norm()` when `axis=()`.
+
+- Fix `torch.linalg.solve()` to apply the array API standard rules for when
+  `x2` should be treated as a vector vs. a matrix.
+
+- Fix PyTorch test failures on CI by skipping uint16, uint32, uint64 tests.
+
+## 1.6 (2024-03-29)
+
+## Major Changes
 
 - Drop support for Python 3.8.
 

docs/dev/tests.md

@@ -11,7 +11,8 @@ dependencies from `requirements-dev.txt` (array-api-compat has [no hard
 runtime dependencies](no-dependencies)).
 
 array-api-tests is run against all supported libraries are tested on CI
-([except for JAX](jax-support)). This is achieved by a [reusable GitHub Actions
+([except for JAX](jax-support) and [Sparse](sparse-support)). This is achieved
+by a [reusable GitHub Actions
 Workflow](https://github.com/data-apis/array-api-compat/blob/main/.github/workflows/array-api-tests.yml).
 Most libraries have tests that must be xfailed or skipped for various reasons.
 These are defined in specific `<library>-xfails.txt` files and are

docs/helper-functions.rst

@@ -49,3 +49,4 @@ yet.
 .. autofunction:: is_torch_array
 .. autofunction:: is_dask_array
 .. autofunction:: is_jax_array
+.. autofunction:: is_pydata_sparse_array

docs/index.md

@@ -2,8 +2,8 @@
 
 This is a small wrapper around common array libraries that is compatible with
 the [Array API standard](https://data-apis.org/array-api/latest/). Currently,
-NumPy, CuPy, PyTorch, Dask, and JAX are supported. If you want support for other array
-libraries, or if you encounter any issues, please [open an
+NumPy, CuPy, PyTorch, Dask, JAX, and Sparse are supported. If you want support
+for other array libraries, or if you encounter any issues, please [open an
 issue](https://github.com/data-apis/array-api-compat/issues).
 
 Note that some of the functionality in this library is backwards incompatible

docs/supported-array-libraries.md

@@ -133,5 +133,7 @@ Other methods may only be partially implemented or return incorrect results at t
 
 The minimum supported Dask version is 2023.12.0.
 
-## [`sparse`](https://sparse.pydata.org/en/stable/)
+(sparse-support)=
+## [Sparse](https://sparse.pydata.org/en/stable/)
+
 Similar to JAX, `sparse` Array API support is contained directly in `sparse`.

setup.py

@@ -21,6 +21,7 @@
         "jax": "jax",
         "pytorch": "pytorch",
         "dask": "dask",
+        "sprase": "sparse >=0.15.1",
     },
     classifiers=[
         "Programming Language :: Python :: 3",

tests/test_common.py

@@ -1,5 +1,5 @@
 from array_api_compat import (is_numpy_array, is_cupy_array, is_torch_array, # noqa: F401
-                              is_dask_array, is_jax_array, is_pydata_sparse)
+                              is_dask_array, is_jax_array, is_pydata_sparse_array)
 
 from array_api_compat import is_array_api_obj, device, to_device
 
@@ -16,7 +16,7 @@
     'torch': 'is_torch_array',
     'dask.array': 'is_dask_array',
     'jax.numpy': 'is_jax_array',
-    'sparse': 'is_pydata_sparse',
+    'sparse': 'is_pydata_sparse_array',
 }
 
 @pytest.mark.parametrize('library', is_functions.keys())