Jax

* New Features
* Added {obj}`jax.nn.squareplus`.

* Changes
* The minimum jaxlib version is now 0.4.19.
* Released wheels are built now with clang instead of gcc.
* Enforce that the device backend has not been initialized prior to calling `jax.distributed.initialize()`.
* Automate arguments to `jax.distributed.initialize()` in cloud TPU environments.

* Deprecations
* The previously-deprecated `sym_pos` argument has been removed from
{func}`jax.scipy.linalg.solve`. Use `assume_a='pos'` instead.
* Passing `None` to {func}`jax.array` or {func}`jax.asarray`, either directly or
within a list or tuple, is deprecated and now raises a {obj}`FutureWarning`.
It currently is converted to NaN, and in the future will raise a {obj}`TypeError`.
* Passing the `condition`, `x`, and `y` parameters to `jax.numpy.where` by
keyword arguments has been deprecated, to match `numpy.where`.
* Passing arguments to {func}`jax.numpy.array_equal` and {func}`jax.numpy.array_equiv`
that cannot be converted to a JAX array is deprecated and now raises a
{obj}`DeprecationWaning`. Currently the functions return False, in the future this
will raise an exception.
* The `device()` method of JAX arrays is deprecated. Depending on the context, it may
be replaced with one of the following:
- {meth}`jax.Array.devices` returns the set of all devices used by the array.
- {attr}`jax.Array.sharding` gives the sharding configuration used by the array.

jaxlib 0.4.21 (Dec 4 2023)

* Changes
* In preparation for adding distributed CPU support, JAX now treats CPU
devices identically to GPU and TPU devices, that is:

* `jax.devices()` includes all devices present in a distributed job, even
those not local to the current process. `jax.local_devices()` still only
includes devices local to the current process, so if the change to
`jax.devices()` breaks you, you most likely want to use
`jax.local_devices()` instead.
* CPU devices now receive a globally unique ID number within a distributed
job; previously CPU devices would receive a process-local ID number.
* The `process_index` of each CPU device will now match any GPU or TPU
devices within the same process; previously the `process_index` of a CPU
device was always 0.

* On NVIDIA GPU, JAX now prefers a Jacobi SVD solver for matrices up to
1024x1024. The Jacobi solver appears faster than the non-Jacobi version.

* Bug fixes
* Fixed error/hang when an array with non-finite values is passed to a
non-symmetric eigendecomposition (18226). Arrays with non-finite values now
produce arrays full of NaNs as outputs.

jaxlib 0.4.20 (Nov 2, 2023)

* Bug fixes
* Fixed some type confusion between E4M3 and E5M2 float8 types.

* New Features
* Added {obj}`jax.typing.DTypeLike`, which can be used to annotate objects that
are convertible to JAX dtypes.
* Added `jax.numpy.fill_diagonal`.

* Changes
* JAX now requires SciPy 1.9 or newer.

* Bug fixes
* Only process 0 in a multicontroller distributed JAX program will write
persistent compilation cache entries. This fixes write contention if the
cache is placed on a network file system such as GCS.
* The version check for cusolver and cufft no longer considers the patch
versions when determining if the installed version of these libraries is at
least as new as the versions against which JAX was built.

jaxlib 0.4.19 (Oct 19, 2023)

* Changes
* jaxlib will now always prefer pip-installed NVIDIA CUDA libraries
(nvidia-... packages) over any other CUDA installation if they are
installed, including installations named in `LD_LIBRARY_PATH`. If this
causes problems and the intent is to use a system-installed CUDA, the fix is
to remove the pip installed CUDA library packages.

jaxlib 0.4.18 (Oct 6, 2023)

* Changes
* CUDA jaxlibs now depend on the user to install a compatible NCCL version.
If using the recommended `cuda12_pip` installation, NCCL should be installed
automatically. Currently, NCCL 2.16 or newer is required.
* We now provide Linux aarch64 wheels, both with and without NVIDIA GPU
support.
* {meth}`jax.Array.item` now supports optional index arguments.

* Deprecations
* A number of internal utilities and inadvertent exports in {mod}`jax.lax` have
been deprecated, and will be removed in a future release.
* `jax.lax.dtypes`: use `jax.dtypes` instead.
* `jax.lax.itertools`: use `itertools` instead.
* `naryop`, `naryop_dtype_rule`, `standard_abstract_eval`, `standard_naryop`,
`standard_primitive`, `standard_unop`, `unop`, and `unop_dtype_rule` are
internal utilities, now deprecated without replacement.

* Bug fixes
* Fixed Cloud TPU regression where compilation would OOM due to smem.

* New features
* Added new {func}`jax.numpy.bitwise_count` function, matching the API of the similar
function recently added to NumPy.
* Deprecations
* Removed the deprecated module `jax.abstract_arrays` and all its contents.
* Named key constructors in {mod}`jax.random` are deprecated. Pass the `impl` argument
to {func}`jax.random.PRNGKey` or {func}`jax.random.key` instead:
* `random.threefry2x32_key(seed)` becomes `random.PRNGKey(seed, impl='threefry2x32')`
* `random.rbg_key(seed)` becomes `random.PRNGKey(seed, impl='rbg')`
* `random.unsafe_rbg_key(seed)` becomes `random.PRNGKey(seed, impl='unsafe_rbg')`
* Changes:
* CUDA: JAX now verifies that the CUDA libraries it finds are at least as new
as the CUDA libraries that JAX was built against. If older libraries are
found, JAX raises an exception since that is preferable to mysterious
failures and crashes.
* Removed the "No GPU/TPU" found warning. Instead warn if, on Linux, an
NVIDIA GPU or a Google TPU are found but not used and `--jax_platforms` was
not specified.
* {func}`jax.scipy.stats.mode` now returns a 0 count if the mode is taken
across a size-0 axis, matching the behavior of `scipy.stats.mode` in SciPy
1.11.
* Most `jax.numpy` functions and attributes now have fully-defined type stubs.
Previously many of these were treated as `Any` by static type checkers like
`mypy` and `pytype`.

jaxlib 0.4.17 (Oct 3, 2023)

* Changes:
* Python 3.12 wheels were added in this release.
* The CUDA 12 wheels now require CUDA 12.2 or newer and cuDNN 8.9.4 or newer.

* Bug fixes:
* Fixed log spam from ABSL when the JAX CPU backend was initialized.

* Changes
* Added {class}`jax.numpy.ufunc`, as well as {func}`jax.numpy.frompyfunc`, which can convert
any scalar-valued function into a {func}`numpy.ufunc`-like object, with methods such as
{meth}`~jax.numpy.ufunc.outer`, {meth}`~jax.numpy.ufunc.reduce`,
{meth}`~jax.numpy.ufunc.accumulate`, {meth}`~jax.numpy.ufunc.at`, and
{meth}`~jax.numpy.ufunc.reduceat` ({jax-issue}`17054`).
* Added {func}`jax.scipy.integrate.trapezoid`.
* When not running under IPython: when an exception is raised, JAX now filters out the
entirety of its internal frames from tracebacks. (Without the "unfiltered stack trace"
that previously appeared.) This should produce much friendlier-looking tracebacks. See
[here](https://github.com/jax-ml/jax/pull/16949) for an example.
This behavior can be changed by setting `JAX_TRACEBACK_FILTERING=remove_frames` (for two
separate unfiltered/filtered tracebacks, which was the old behavior) or
`JAX_TRACEBACK_FILTERING=off` (for one unfiltered traceback).
* jax2tf default serialization version is now 7, which introduces new shape
[safety assertions](https://github.com/jax-ml/jax/blob/main/jax/experimental/jax2tf/README.md#errors-in-presence-of-shape-polymorphism).
* Devices passed to `jax.sharding.Mesh` should be hashable. This specifically
applies to mock devices or user created devices. `jax.devices()` are
already hashable.

* Breaking changes:
* jax2tf now uses native serialization by default. See
the [jax2tf documentation](https://github.com/jax-ml/jax/blob/main/jax/experimental/jax2tf/README.md)
for details and for mechanisms to override the default.
* The option `--jax_coordination_service` has been removed. It is now always
`True`.
* `jax.jaxpr_util` has been removed from the public JAX namespace.
* `JAX_USE_PJRT_C_API_ON_TPU` no longer has an effect (i.e. it always defaults to true).
* The backwards compatibility flag `--jax_host_callback_ad_transforms`
introduced in December 2021, has been removed.

* Deprecations:
* Several `jax.numpy` APIs have been deprecated following
[NumPy NEP-52](https://numpy.org/neps/nep-0052-python-api-cleanup.html):
* `jax.numpy.NINF` has been deprecated. Use `-jax.numpy.inf` instead.
* `jax.numpy.PZERO` has been deprecated. Use `0.0` instead.
* `jax.numpy.NZERO` has been deprecated. Use `-0.0` instead.
* `jax.numpy.issubsctype(x, t)` has been deprecated. Use `jax.numpy.issubdtype(x.dtype, t)`.
* `jax.numpy.row_stack` has been deprecated. Use `jax.numpy.vstack` instead.
* `jax.numpy.in1d` has been deprecated. Use `jax.numpy.isin` instead.
* `jax.numpy.trapz` has been deprecated. Use `jax.scipy.integrate.trapezoid` instead.
* `jax.scipy.linalg.tril` and `jax.scipy.linalg.triu` have been deprecated,
following SciPy. Use `jax.numpy.tril` and `jax.numpy.triu` instead.
* `jax.lax.prod` has been removed after being deprecated in JAX v0.4.11.
Use the built-in `math.prod` instead.
* A number of exports from `jax.interpreters.xla` related to defining
HLO lowering rules for custom JAX primitives have been deprecated. Custom
primitives should be defined using the StableHLO lowering utilities in
`jax.interpreters.mlir` instead.
* The following previously-deprecated functions have been removed after a
three-month deprecation period:
* `jax.abstract_arrays.ShapedArray`: use `jax.core.ShapedArray`.
* `jax.abstract_arrays.raise_to_shaped`: use `jax.core.raise_to_shaped`.
* `jax.numpy.alltrue`: use `jax.numpy.all`.
* `jax.numpy.sometrue`: use `jax.numpy.any`.
* `jax.numpy.product`: use `jax.numpy.prod`.
* `jax.numpy.cumproduct`: use `jax.numpy.cumprod`.

* Deprecations/removals:
* The internal submodule `jax.prng` is now deprecated. Its contents are available at
{mod}`jax.extend.random`.
* The internal submodule path `jax.linear_util` has been deprecated. Use
{mod}`jax.extend.linear_util` instead (Part of {ref}`jax-extend-jep`)
* `jax.random.PRNGKeyArray` and `jax.random.KeyArray` are deprecated. Use {class}`jax.Array`
for type annotations, and `jax.dtypes.issubdtype(arr.dtype, jax.dtypes.prng_key)` for
runtime detection of typed prng keys.
* The method `PRNGKeyArray.unsafe_raw_array` is deprecated. Use
{func}`jax.random.key_data` instead.
* `jax.experimental.pjit.with_sharding_constraint` is deprecated. Use
`jax.lax.with_sharding_constraint` instead.
* The internal utilities `jax.core.is_opaque_dtype` and `jax.core.has_opaque_dtype`
have been removed. Opaque dtypes have been renamed to Extended dtypes; use
`jnp.issubdtype(dtype, jax.dtypes.extended)` instead (available since jax v0.4.14).
* The utility `jax.interpreters.xla.register_collective_primitive` has been
removed. This utility did nothing useful in recent JAX releases and calls
to it can be safely removed.
* The internal submodule path `jax.linear_util` has been deprecated. Use
{mod}`jax.extend.linear_util` instead (Part of {ref}`jax-extend-jep`)

jaxlib 0.4.16 (Sept 18, 2023)

* Changes:
* Sparse CSR matrix multiplications via the experimental jax sparse APIs
no longer uses a deterministic algorithm on NVIDIA GPUs. This change was
made to improve compatibility with CUDA 12.2.1.

* Bug fixes:
* Fixed a crash on Windows due to a fatal LLVM error related to out-of-order
sections and IMAGE_REL_AMD64_ADDR32NB relocations
(https://github.com/openxla/xla/commit/cb732a921f0c4184995cbed82394931011d12bd4).