inox 0.3.1

Stainless neural networks in JAX

Stainless neural networks in JAX

Inox is a minimal JAX library for neural networks with an intuitive PyTorch-like interface. As with Equinox, modules are represented as PyTrees, which allows to pass networks in and out of JAX transformations, like jax.jit or jax.vmap. However, Inox modules automatically detect non-array leaves, like hyper-parameters or boolean flags, and consider them as static. Consequently, Inox modules are compatible with native JAX transformations out of the box, and do not require custom lifted transformations.

Inox means "stainless steel" in French 🔪

Installation

The inox package is available on PyPI, which means it is installable via pip.

pip install inox

Alternatively, if you need the latest features, you can install it from the repository.

pip install git+https://github.com/francois-rozet/inox

Getting started

Networks are defined with an intuitive PyTorch-like syntax,

import jax
import inox.nn as nn

init_key, data_key = jax.random.split(jax.random.key(0))

class MLP(nn.Module):
    def __init__(self, key):
        keys = jax.random.split(key, 3)

        self.l1 = nn.Linear(keys[0], 3, 64)
        self.l2 = nn.Linear(keys[1], 64, 64)
        self.l3 = nn.Linear(keys[2], 64, 3)
        self.relu = nn.ReLU()

    def __call__(self, x):
        x = self.l1(x)
        x = self.l2(self.relu(x))
        x = self.l3(self.relu(x))

        return x

network = MLP(init_key)

and are fully compatible with native JAX transformations.

X = jax.random.normal(data_key, (1024, 3))
Y = jax.numpy.sort(X, axis=-1)

@jax.jit
def loss_fn(network, x, y):
    pred = jax.vmap(network)(x)
    return jax.numpy.mean((y - pred) ** 2)

grads = jax.grad(loss_fn)(network, X, Y)

For more information, check out the documentation at inox.readthedocs.io.

Contributing

If you have a question, an issue or would like to contribute, please read our contributing guidelines.