tjax

============= Tools for JAX

|pypi| |py_versions|

.. |pypi| image:: https://img.shields.io/pypi/v/tjax :alt: PyPI - Version

.. |py_versions| image:: https://img.shields.io/pypi/pyversions/tjax :alt: PyPI - Python Version

.. role:: bash(code) :language: bash

.. role:: python(code) :language: python

This repository implements a variety of tools for the differential programming library JAX <https://github.com/google/jax>_.

---

Major components

Tjax's major components are:

• A dataclass <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/dataclasses>_ decorator :python:dataclass that facilitates defining structured JAX objects (so-called "pytrees"), which benefits from:

  • the ability to mark fields as static (not available in chex.dataclass), and
  • a display method that produces formatted text according to the tree structure.

• A shim <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/gradient>_ for the gradient transformation library optax <https://github.com/deepmind/optax>_ that supports:

  • easy differentiation and vectorization of “gradient transformation” (learning rule) parameters,
  • gradient transformation objects that can be passed dynamically to jitted functions, and
  • generic type annotations.

• A pretty printer :python:print_generic for aggregate and vector types, including dataclasses. (See display <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/display>_.) It features:

  • support for traced values,
  • colorized tree output for aggregate structures, and
  • formatted tabular output for arrays (or statistics when there's no room for tabular output).

---

Minor components

Tjax also includes:

• Versions of :python:custom_vjp and :python:custom_jvp that support being used on methods: :python:custom_vjp_method and :python:custom_vjp_method (See shims <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/shims.py>_.)

• Tools for working with cotangents. (See cotangent_tools <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/cotangent_tools.py>_.)

• JAX tree registration for NetworkX <https://networkx.github.io/>_ graph types. (See graph <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/graph.py>_.)

• Leaky integration :python:leaky_integrate and Ornstein-Uhlenbeck process iteration :python:diffused_leaky_integrate. (See leaky_integral <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/leaky_integral.py>_.)

• An improved version of :python:jax.tree_util.Partial. (See partial <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/partial.py>_.)

• A testing function :python:assert_tree_allclose that automatically produces testing code. And, a related function :python:tree_allclose. (See testing <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/testing.py>_.)

• Basic tools like :python:divide_where. (See tools <https://github.com/NeilGirdhar/tjax/blob/master/tjax/_src/tools.py>_.)

---

Contribution guidelines

• Conventions: PEP8.

• How to run tests: :bash:pytest .

• How to clean the source:

  • :bash:ruff check .
  • :bash:pyright
  • :bash:mypy
  • :bash:isort .
  • :bash:pylint tjax tests