gongyu-lightmatter / jaxtyping

Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees.

jaxtyping

Type annotations and runtime checking for:

1. shape and dtype of JAX arrays; (Now also supports PyTorch, NumPy, and TensorFlow!)
2. PyTrees.

For example:

from jaxtyping import Array, Float, PyTree

# Accepts floating-point 2D arrays with matching dimensions
def matrix_multiply(x: Float[Array, "dim1 dim2"],
                    y: Float[Array, "dim2 dim3"]
                  ) -> Float[Array, "dim1 dim3"]:
    ...

def accepts_pytree_of_ints(x: PyTree[int]):
    ...

def accepts_pytree_of_arrays(x: PyTree[Float[Array, "batch c1 c2"]]):
    ...

Installation

pip install jaxtyping

Requires Python 3.8+.

JAX is an optional dependency, required for jaxtyping.{Array, ArrayLike, PyTree}. If JAX is not installed then these types will not be available, but you may still use jaxtyping alongside PyTorch/NumPy/etc.

Also install your favourite runtime type-checking package. The two most popular are typeguard (which exhaustively checks every argument) and beartype (which checks random pieces of arguments).

Documentation

Full API reference

FAQ (static type checking, flake8, etc.)

Finally

See also: other tools in the JAX ecosystem

Neural networks: Equinox.

Numerical differential equation solvers: Diffrax.

Computer vision models: Eqxvision.

SymPy<->JAX conversion; train symbolic expressions via gradient descent: sympy2jax.

Disclaimer

This is not an official Google product.