jaxtyping v0.2.24

New features

• Error messages will now include useful shape information for debugging. (!!!) This closes the venerable #6, which is is one of the oldest feature requests for jaxtyping. This is enabled by using the following syntax, instead of the old double-decorator syntax:

  from jaxtyping import jaxtyped
  from beartype/typeguard import beartype/typechecked as typechecker
  
  @jaxtyped(typechecker=typechecker)  # passing as keyword argument is important
  def foo(...):
      ...

  and moreover this is what install_import_hook now does.
  As an example of this done, consider this buggy code:

  import jax.numpy as jnp
  from jaxtyping import Array, Float, jaxtyped
  from beartype import beartype
  
  @jaxtyped(typechecker=beartype)
  def f(x: Float[Array, "foo bar"], y: Float[Array, "foo"]):
      ...
  
  f(jnp.zeros((3, 4)), jnp.zeros(5))

  will now produce the error message

  jaxtyping.TypeCheckError: Type-check error whilst checking the parameters of f.
  The problem arose whilst typechecking argument 'y'.
  Called with arguments: {'x': f32[3,4], 'y': f32[5]}
  Parameter annotations: (x: Float[Array, 'foo bar'], y: Float[Array, 'foo']).
  The current values for each jaxtyping axis annotation are as follows.
  foo=3
  bar=4
  

  Hurrah! I'm really glad to have this important quality-of-life improvement in. (#6, #138)
• Added support for the following:

  def make_zeros(size: int) -> Float[Array, "{size}"]:
      return jnp.zeros(size)

  in which axis names enclosed in {...} are evaluated as f-strings using the value of the argument of the function. This closes the long-standing feature request #93. (#93, #140) (Heads-up @MilesCranmer!)
• Added support for declaring PyTree structures, which like array shapes must match across all arguments. For example

  def f(x: PyTree[int, "T"], y: PyTree[float, "T"])

  demands that x and y be PyTrees with the same jax.tree_util.tree_structure as each other. (#135)
• Added support for treepath-dependent sizes using ?. This makes it possible for the value of a dimension to vary across its position within a pytree, but must still be consistent with its value in other pytrees of the same structure. Such annotations look like PyTree[Float[Array, "?foo"], "T"]. Together with the previous point, this means that you can now declare that two pytrees must have the exact same structure and array shapes as each other: use PyTree[Float[Array, "?*shape"], "T"] as the annotation for both. (#136)
• Added jaxtyping.Real, which admits any float, signed integer, or unsigned integer. (But not bools or complexes.) (#128)
• If JAX is installed, then jaxtyping.DTypeLike is now available (it is just a forwarding on of jax.typing.DTypeLike). (#129)

Bugfixes

• Fixed no error being raised when having mismatched variadic+broadcast and variadic+nonbroadcast dimensions; see #134 for details. (#134)
• Fixed jaxtyping.Key not being compatible with the new-style jax.random.key. (As opposed to the old-style jax.random.PRNGKey.) (#142, #143)
• Fixed install_import_hook(..., None) crashing (#145, #146).
• Variadic shapes combined with bool/int/float/complex now work correctly, e.g. Float[float, "..."] is now valid (and equivalent to just float). This is useful in particular for Float[ArrayLike, "..."] to work correctly (as ArrayLike includes float). (#133)

Better error messages

• The error message due to a nonexist symbolic dimension -- e.g. def f(x: Float[Array, "dim*2"]) leaves dim unspecified -- are now fixed. (#131)
• The error message due to the wrong dataclass attribute type -- e.g.

  @dataclass
  class Foo:
      attribute_name: int
  Foo("strings are not integers")

  will now correctly include the attribute_name. (#132)

---

Note that this release may result in new errors being raised, due to the inclusion of #134. If so then you then the appropriate thing to do is to fix your code -- this is a correct error that jaxtyping was previously failing to raise.

Full Changelog: v0.2.23...v0.2.24