JAX v0.10.0

New features:

• Added ResizeMethod.CUBIC_PYTORCH to jax.image.resize to match PyTorch's bicubic resize (#15768).
• We now support differentiation of jax.lax.linalg.qr for wide matrices and when full_matrices is True.
• LAPACK operations are now parallelized along the batch dimension on CPU.
• Added perturb_singular argument to jax.lax.linalg.tridiagonal_solve to handle singular matrices by perturbing near-zero pivots in the LU decomposition. This is useful for solving numerically singular systems when computing eigenvectors by inverse iteration.
• jax.scipy.linalg.eigh_tridiagonal now supports computing eigenvectors on CPU and GPU.
• Added the jax.numpy.ndarray.byteswap method.

Breaking changes:

• PartitionSpec objects no longer report themselves to be equal to tuples. Convert tuples to PartitionSpec objects before testing equality.
• The .vma property has been removed from jax.core.ShapedArray. Use .manual_axis_type.varying instead.
• JAX CPU devices now report their names as cpu:0, cpu:1, etc. instead of TFRT_CPU_0, TFRT_CPU_1.
• The config state jax_pmap_shmap_merge has been removed. jax.pmap will now always use the new implementation that wraps jax.jit(jax.shard_map). Please see https://docs.jax.dev/en/latest/migrate_pmap.html for more information.
• jax.device_put_sharded and jax.device_put_replicated have been removed from the public API and now raise an AttributeError when accessed. Please see https://docs.jax.dev/en/latest/migrate_pmap.html#drop-in-replacements for drop-in replacements.
• The C++ pmap infrastructure has been removed. The following public APIs are no longer available:
  • jax.sharding.PmapSharding
  • From jaxlib.xla_extension: PmapFunction, pmap, NoSharding, Chunked, Unstacked, ShardedAxis, Replicated, ShardingSpec.
  • From jax.interpreters.pxla: MapTracer, PmapExecutable, parallel_callable, shard_args, xla_pmap_p, Chunked, NoSharding, Replicated, ShardedAxis, ShardingSpec, Unstacked, spec_to_indices.
• The deprecated keyword arguments a, a_min, and a_max to jax.numpy.clip have been removed.
• Functions jax.numpy.hstack, jax.numpy.vstack, jax.numpy.dstack, jax.numpy.column_stack, jax.numpy.atleast_1d, jax.numpy.atleast_2d, and jax.numpy.atleast_3d no longer accept non-ArrayLike inputs. Doing so previously issued a DeprecationWarning.
• jax.scipy.stats.rankdata now returns floating point values in all cases, following a similar change in the SciPy 1.18 release.

Deprecations:

• A number of internal APIs in jax.core have been newly deprecated and some have been moved to jax.extend.core. These include CallPrimitive, DebugInfo, DropVar, Effect, Effects, InconclusiveDimensionOperation, JaxprTypeError, check_jaxpr, concrete_or_error, find_top_trace, gensym, get_opaque_trace_state, jaxprs_in_params, new_jaxpr_eqn, no_effects, nonempty_axis_env_DO_NOT_USE, primal_dtype_to_tangent_dtype, unsafe_am_i_under_a_jit_DO_NOT_USE, unsafe_am_i_under_a_vmap_DO_NOT_USE, unsafe_get_axis_names_DO_NOT_USE, valid_jaxtype, JaxprPpContext, JaxprPpSettings, OutputType, abstract_token, aval_mapping_handlers, call, concretization_function_error, custom_typechecks, is_concrete, is_constant_dim, is_constant_shape, literalable_types, no_axis_name, pytype_aval_mappings, and trace_ctx.

Changes:

• The minimum supported SciPy version is now 1.14.
• vma parameter of jax.ShapeDtypeStruct has been replaced with manual_axis_type: jax.sharding.ManualAxisType. The .vma property has been replaced with .manual_axis_type.varying.
• Removed experimental jax.experimental.custom_dce.custom_dce
• jax.scipy.linalg.cho_solve, jax.scipy.linalg.lu_solve, and jax.scipy.linalg.solve_triangular now show a deprecation warning for batched 1D solves with b.ndim > 1. In the future these will be treated as batched 2D solves.
• Added a new version 10 for the jax.export serialization format. This is an optimization for when there are multiple occurrences of the same abstract value, abstract mesh, or sharding.

Bug fixes:

• Fixed a bug that led to differing output between CPU and GPU for non-symmetric multidimensional IRFFTs (#29325).
• Fixed an error when tiny matrices were passed to jax.lax.linalg.tridiagonal_solve on GPU (#32487).
• Fixed a bug in jax.scipy.fft.dctn and idctn where axes=None incorrectly defaulted to all axes when s was specified, instead of the last len(s) axes to match SciPy behavior (#29426).
• Fixed a bug where calling jax.distributed.initialize() on a GCE TPU Managed Instance Group raised an IndexError (#36593). When jax.distributed.initialize() is called on a GCE VM, it uses the GCE metadata server to learn the addresses of all participating tasks. The format of this metadata on Managed Instance Groups was not a format JAX expected, leading to the exception. We now parse this format correctly.