cheshire / flax

Flax is a neural network library for JAX that is designed for flexibility.

Flax: A neural network library and ecosystem for JAX designed for flexibility

Overview | Quick install | What does Flax look like? | Documentation

This README is a very short intro. To learn everything you need to know about Flax, see our full documentation

Flax was originally started by engineers and researchers within the Brain Team in Google Research (in close collaboration with the JAX team), and is now developed jointly with the open source community.

Flax is being used by a growing community of hundreds of folks in various Alphabet research departments for their daily work, as well as a growing community of open source projects.

The Flax team's mission is to serve the growing JAX neural network research ecosystem -- both within Alphabet and with the broader community, and to explore the use-cases where JAX shines. We use GitHub for almost all of our coordination and planning, as well as where we discuss upcoming design changes. We welcome feedback on any of our discussion, issue and pull request thread. We are in the process of moving some remaining internal design docs and conversation threads to GitHub discussions, issues and pull requests. We hope to increasingly engage with the needs and clarifications of the broader ecosystem. Please let us know how we can help!

Please report any feature requests, issues, questions or concerns in our discussion forum, or just let us know what you're working on!

We expect to improve Flax, but we don't anticipate significant breaking changes to the core API. We use Changelog entries and deprecation warnings when possible.

In case you want to reach us directly, we're at [email protected].

Overview

Flax is a high-performance neural network library and ecosystem for JAX that is designed for flexibility: Try new forms of training by forking an example and by modifying the training loop, not by adding features to a framework.

Flax is being developed in close collaboration with the JAX team and comes with everything you need to start your research, including:

• Neural network API (flax.linen): Dense, Conv, {Batch|Layer|Group} Norm, Attention, Pooling, {LSTM|GRU} Cell, Dropout

• Optimizers (flax.optim): SGD, Momentum, Adam, LARS, Adagrad, LAMB, RMSprop

• Utilities and patterns: replicated training, serialization and checkpointing, metrics, prefetching on device

• Educational examples that work out of the box: MNIST, LSTM seq2seq, Graph Neural Networks, Sequence Tagging

• Fast, tuned large-scale end-to-end examples: CIFAR10, ResNet on ImageNet, Transformer LM1b

Quick install

You will need Python 3.6 or later and a working JAX installation (with or without GPU support, see instructions there). For a CPU-only version:

> pip install --upgrade pip # To support manylinux2010 wheels.
> pip install --upgrade jax jaxlib # CPU-only

Then install Flax from PyPi:

> pip install flax

To upgrade to the latest version of Flax, you can use:

> pip install --upgrade git+https://github.com/google/flax.git

What does Flax look like?

We provide three examples using the Flax API: a simple multi-layer perceptron, a CNN and an auto-encoder.

To learn more about the Module abstraction, see our docs, our broad intro to the Module abstraction. For additional concrete demonstrations of best practices, see our HOWTO guides.

from typing import Sequence

import jax
import jax.numpy as jnp
import flax.linen as nn

class MLP(nn.Module):
  features: Sequence[int]

  @nn.compact
  def __call__(self, x):
    for feat in self.features[:-1]:
      x = nn.relu(nn.Dense(feat)(x))
    x = nn.Dense(self.features[-1])(x)
    return x

model = MLP([12, 8, 4])
batch = jnp.ones((32, 10))
variables = model.init(jax.random.PRNGKey(0), batch)
output = model.apply(variables, batch)

class CNN(nn.Module):
  @nn.compact
  def __call__(self, x):
    x = nn.Conv(features=32, kernel_size=(3, 3))(x)
    x = nn.relu(x)
    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
    x = nn.Conv(features=64, kernel_size=(3, 3))(x)
    x = nn.relu(x)
    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
    x = x.reshape((x.shape[0], -1))  # flatten
    x = nn.Dense(features=256)(x)
    x = nn.relu(x)
    x = nn.Dense(features=10)(x)
    x = nn.log_softmax(x)
    return x

model = CNN()
batch = jnp.ones((32, 64, 64, 10))  # (N, H, W, C) format
variables = model.init(jax.random.PRNGKey(0), batch)
output = model.apply(variables, batch)

class AutoEncoder(nn.Module):
  encoder_widths: Sequence[int]
  decoder_widths: Sequence[int]
  input_shape: Sequence[int]

  def setup(self):
    input_dim = jnp.prod(jnp.asarray(self.input_shape))
    self.encoder = MLP(self.encoder_widths)
    self.decoder = MLP(self.decoder_widths + (input_dim,))

  def __call__(self, x):
    return self.decode(self.encode(x))

  def encode(self, x):
    assert x.shape[1:] == self.input_shape
    return self.encoder(jnp.reshape(x, (x.shape[0], -1)))

  def decode(self, z):
    z = self.decoder(z)
    x = nn.sigmoid(z)
    x = jnp.reshape(x, (x.shape[0],) + self.input_shape)
    return x

model = AutoEncoder(encoder_widths=[20, 10, 5],
                    decoder_widths=[5, 10, 20],
                    input_shape=(12,))
batch = jnp.ones((16, 12))
variables = model.init(jax.random.PRNGKey(0), batch)
encoded = model.apply(variables, batch, method=model.encode)
decoded = model.apply(variables, encoded, method=model.decode)

Citing Flax

To cite this repository:

@software{flax2020github,
  author = {Jonathan Heek and Anselm Levskaya and Avital Oliver and Marvin Ritter and Bertrand Rondepierre and Andreas Steiner and Marc van {Z}ee},
  title = {{F}lax: A neural network library and ecosystem for {JAX}},
  url = {http://github.com/google/flax},
  version = {0.3.4},
  year = {2020},
}

In the above bibtex entry, names are in alphabetical order, the version number is intended to be that from flax/version.py, and the year corresponds to the project's open-source release.

Note

Flax is an open source project maintained by a dedicated team in Google Research, but is not an official Google product.