testax

🧪 testax

.. image:: https://img.shields.io/pypi/v/testax :target: https://pypi.org/project/testax .. image:: https://github.com/tillahoffmann/testax/actions/workflows/build.yml/badge.svg :target: https://github.com/tillahoffmann/testax/actions/workflows/build.yml .. image:: https://readthedocs.org/projects/testax/badge/?version=latest :target: https://testax.readthedocs.io/en/latest/?badge=latest

testax provides runtime assertions for JAX through the testing interface familiar to NumPy users.

import jax from jax import numpy as jnp import testax

def safe_log(x): ... testax.assert_array_less(0, x) ... return jnp.log(x)

safe_log(jnp.arange(2)) Traceback (most recent call last): ... jax._src.checkify.JaxRuntimeError: Arrays are not less-ordered

Mismatched elements: 1 / 2 (50%) Max absolute difference: 1 Max relative difference: 1 x: Array(0, dtype=int32, weak_type=True) y: Array([0, 1], dtype=int32)

testax assertions are :code:jit-able, although errors need to be functionalized to conform to JAX's requirement that functions are pure and do not have side effects (see the :code:checkify guide <https://jax.readthedocs.io/en/latest/debugging/checkify_guide.html>__ for details). In short, a :code:checkify-d function returns a tuple :code:(error, value). The first element is an error that may have occurred, and the second is the return value of the original function.

jitted = jax.jit(safe_log) checkified = testax.checkify(jitted) error, y = checkified(jnp.arange(2)) error.throw() Traceback (most recent call last): ... jax._src.checkify.JaxRuntimeError: Arrays are not less-ordered

Mismatched elements: 1 / 2 (50%) Max absolute difference: 1 Max relative difference: 1 x: Array(0, dtype=int32, weak_type=True) y: Array([0, 1], dtype=int32)

y Array([-inf, 0.], dtype=float32)

Installation

testax is pip-installable and can be installed by running

.. code-block:: bash

pip install testax

Interface

testax mirrors the testing <https://numpy.org/doc/stable/reference/routines.testing.html>__ interface familiar to NumPy users, such as :code:assert_allclose.