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Changelog

1.7 (2024-05-24)

Major Changes

  • Add support for sparse. Note that unlike other array libraries, array-api-compat does not contain any wrappers for sparse functions. All sparse array API support is in sparse itself. Thus, there is no array_api_compat.sparse submodule, and array_namespace(<pydata/sparse array>) returns the sparse module.

  • Added the function is_pydata_sparse_array(x).

Minor Changes

  • Fix JAX float0 arrays. See jax-ml/jax#20620. (@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.

  • NumPy 2.0 is now left completely unwrapped.

  • New flag use_compat to {func}~.array_namespace to force the use or non-use of the compat wrapper namespace. The default is to return a compat namespace when it is appropiate.

  • Fix the copy flag to asarray for NumPy, CuPy, and Dask.

  • Fix the device flag to asarray for CuPy.

  • Fix various issues with asarray for Dask.

Minor Changes

  • Test Python 3.12 on CI.

  • Add more tests for {func}~.array_namespace.

  • Add more tests for asarray.

  • Add a test that there are no hard dependencies.

1.5.1 (2024-03-20)

Minor Changes

  • Add HTML documentation. Includes new documentation on the scope of the package and new developer documentation.

  • Fix array_api_compat.numpy.asarray(torch.Tensor) to return a NumPy array.

  • Allow Python scalars in torch functions.

  • Fix the torch.std wrapper when correction is an int.

  • Fix issues with qr and svd in the Dask wrappers.

1.5 (2024-03-07)

Major Changes

  • Add support for Dask (@lithomas1).

  • Add support for JAX. Note that unlike other array libraries, array-api-compat does not contain any wrappers for JAX functions. All JAX array API support is in JAX itself. Thus, there is no array_api_compat.jax submodule, and array_namespace(<JAX array>) returns the jax.experimental.array_api module.

  • The functions is_numpy_array(x), is_cupy_array(x), is_torch_array(x), is_dask_array(x), is_jax_array(x) are now part of the public array_api_compat API.

  • Add wrappers for the fft extension module for NumPy, CuPy, and PyTorch.

Minor Changes

  • Allow '2022.12' as the api_version in {func}~.array_namespace(). '2021.12' is also supported but will issue a warning since the returned namespace will still be a 2022.12 compliant one.

  • Add wrapper for numpy.linalg.solve, which broadcasts the inputs according to the standard.

  • Add wrappers for various PyTorch linalg functions.

  • Fix a bug with numpy.linalg.vector_norm(keepdims=True).

  • BREAKING: Update vecdot wrappers to apply axes before broadcasting, not after. This matches the updated 2023.12 standard wording, and also the behavior of the new numpy.vecdot gufunc in NumPy 2.0.

  • Fix some linalg functions which were supposed to be in both the main namespace and the linalg extension namespace.

  • Add Ruff to CI. (@adonath)

  • Test that internal definitions of __all__ are self-consistent, which should help to avoid issues where wrappers are accidentally not exported to the compat namespaces properly.

1.4.1 (2024-01-18)

Minor Changes

  • Add support for the upcoming NumPy 2.0 release.

  • Added a torch wrapper for trace (torch.trace doesn't support the offset argument or stacking)

  • Wrap numpy, cupy, and torch nonzero to raise an error for zero-dimensional input arrays.

  • Add torch wrapper for newaxis.

  • Improve error message for array_namespace

  • Fix linalg.cholesky returning the conjugate of the expected upper decomposition for numpy and cupy.

1.4 (2023-09-13)

Major Changes

Minor Changes

  • Fix torch.result_type() cross-kind promotion (@lucascolley).

  • Fix the torch.take() wrapper to make axis optional for ndim = 1.

  • Add requires-python metadata to the package (@matthewfeickert).

1.3 (2023-06-20)

Major Changes

  • Add 2022.12 standard support. This includes things like adding complex dtype support, adding the new take function, and various minor changes in the specification.

Minor Changes

  • Support "cpu" in CuPy to_device().

  • Return a new array in NumPy/CuPy reshape(copy=False).

  • Fix signatures for PyTorch broadcast_to and permute_dims.

1.2 (2023-04-03)

Major Changes

  • Support the linalg extension in the array_api_compat.torch namespace.

  • Add isdtype().

Minor Changes

  • Fix the k keyword argument to tril and triu in torch.

1.1.1 (2023-03-10)

Major Changes

  • Rename get_namespace() to array_namespace() (get_namespace() is maintained as a backwards compatible alias).

Minor Changes

  • The minimum supported NumPy version is now 1.21. Fixed a few issues with NumPy 1.21 (with unique_* and asarray), although there are also a few known issues with this version (see the README).

  • Add api_version to get_namespace().

  • array_namespace() (née get_namespace()) now works correctly with torch tensors.

  • array_namespace() (née get_namespace()) now works correctly with numpy.array_api arrays.

  • array_namespace() (née get_namespace()) now raises TypeError instead of ValueError.

  • Fix the torch.std wrapper.

  • Add torch wrappers for ones, empty, and zeros so that shape can be passed as a keyword argument.

1.1 (2023-02-24)

Major Changes

  • Added support for PyTorch.

  • Add helper function size() (required if torch is used as torch.Tensor.size is a method that is incompatible with the array API .size).

  • All wrapper functions that wrap existing library functions now pass through arbitrary **kwargs.

Minor Changes

  • Added CI to run against the array API testsuite.

  • Fix sort(stable=False) and argsort(stable=False) with CuPy.

1.0 (2022-12-05)

Major Changes

  • Initial release. Includes support for NumPy and CuPy.