elegy 0.8.6

Elegy is a Neural Networks framework based on Jax and Haiku.

Elegy

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A High Level API for Deep Learning in JAX

Main Features

• 😀 Easy-to-use: Elegy provides a Keras-like high-level API that makes it very easy to use for most common tasks.
• 💪‍ Flexible: Elegy provides a Pytorch Lightning-like low-level API that offers maximum flexibility when needed.
• 🔌 Compatible: Elegy supports various frameworks and data sources including Flax & Haiku Modules, Optax Optimizers, TensorFlow Datasets, Pytorch DataLoaders, and more.

Elegy is built on top of Treex and Treeo and reexports their APIs for convenience.

Getting Started | Examples | Documentation

What is included?

• A Model class with an Estimator-like API.
• A callbacks module with common Keras callbacks.

From Treex

• A Module class.
• A nn module for with common layers.
• A losses module with common loss functions.
• A metrics module with common metrics.

Installation

Install using pip:

pip install elegy

For Windows users, we recommend the Windows subsystem for Linux 2 WSL2 since jax does not support it yet.

Quick Start: High-level API

Elegy's high-level API provides a straightforward interface you can use by implementing the following steps:

1. Define the architecture inside a Module:

import jax
import elegy as eg

class MLP(eg.Module):
    @eg.compact
    def __call__(self, x):
        x = eg.Linear(300)(x)
        x = jax.nn.relu(x)
        x = eg.Linear(10)(x)
        return x

2. Create a Model from this module and specify additional things like losses, metrics, and optimizers:

import optax optax
import elegy as eg

model = eg.Model(
    module=MLP(),
    loss=[
        eg.losses.Crossentropy(),
        eg.regularizers.L2(l=1e-5),
    ],
    metrics=eg.metrics.Accuracy(),
    optimizer=optax.rmsprop(1e-3),
)

3. Train the model using the fit method:

model.fit(
    inputs=X_train,
    labels=y_train,
    epochs=100,
    steps_per_epoch=200,
    batch_size=64,
    validation_data=(X_test, y_test),
    shuffle=True,
    callbacks=[eg.callbacks.TensorBoard("summaries")]
)

Using Flax

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To use Flax with Elegy just create a flax.linen.Module and pass it to Model.

import jax
import elegy as eg
import optax optax
import flax.linen as nn

class MLP(nn.Module):
    @nn.compact
    def __call__(self, x, training: bool):
        x = nn.Dense(300)(x)
        x = jax.nn.relu(x)
        x = nn.Dense(10)(x)
        return x


model = eg.Model(
    module=MLP(),
    loss=[
        eg.losses.Crossentropy(),
        eg.regularizers.L2(l=1e-5),
    ],
    metrics=eg.metrics.Accuracy(),
    optimizer=optax.rmsprop(1e-3),
)

As shown here, Flax Modules can optionally request a training argument to __call__ which will be provided by Elegy / Treex.

Using Haiku

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To use Haiku with Elegy do the following:

• Create a forward function.
• Create a TransformedWithState object by feeding forward to hk.transform_with_state.
• Pass your TransformedWithState to Model.

You can also optionally create your own hk.Module and use it in forward if needed. Putting everything together should look like this:

import jax
import elegy as eg
import optax optax
import haiku as hk


def forward(x, training: bool):
    x = hk.Linear(300)(x)
    x = jax.nn.relu(x)
    x = hk.Linear(10)(x)
    return x


model = eg.Model(
    module=hk.transform_with_state(forward),
    loss=[
        eg.losses.Crossentropy(),
        eg.regularizers.L2(l=1e-5),
    ],
    metrics=eg.metrics.Accuracy(),
    optimizer=optax.rmsprop(1e-3),
)

As shown here, forward can optionally request a training argument which will be provided by Elegy / Treex.

Quick Start: Low-level API

Elegy's low-level API lets you explicitly define what goes on during training, testing, and inference. Let's define our own custom Model to implement a LinearClassifier with pure JAX:

1. Define a custom init_step method:

class LinearClassifier(eg.Model):
    # use treex's API to declare parameter nodes
    w: jnp.ndarray = eg.Parameter.node()
    b: jnp.ndarray = eg.Parameter.node()

    def init_step(self, key: jnp.ndarray, inputs: jnp.ndarray):
        self.w = jax.random.uniform(
            key=key,
            shape=[features_in, 10],
        )
        self.b = jnp.zeros([10])

        self.optimizer = self.optimizer.init(self)

        return self

Here we declared the parameters w and b using Treex's Parameter.node() for pedagogical reasons, however normally you don't have to do this since you typically use a sub-Module instead.

2. Define a custom test_step method:

    def test_step(self, inputs, labels):
        # flatten + scale
        inputs = jnp.reshape(inputs, (inputs.shape[0], -1)) / 255

        # forward
        logits = jnp.dot(inputs, self.w) + self.b

        # crossentropy loss
        target = jax.nn.one_hot(labels["target"], 10)
        loss = optax.softmax_cross_entropy(logits, target).mean()

        # metrics
        logs = dict(
            acc=jnp.mean(jnp.argmax(logits, axis=-1) == labels["target"]),
            loss=loss,
        )

        return loss, logs, self

3. Instantiate our LinearClassifier with an optimizer:

model = LinearClassifier(
    optimizer=optax.rmsprop(1e-3),
)

4. Train the model using the fit method:

model.fit(
    inputs=X_train,
    labels=y_train,
    epochs=100,
    steps_per_epoch=200,
    batch_size=64,
    validation_data=(X_test, y_test),
    shuffle=True,
    callbacks=[eg.callbacks.TensorBoard("summaries")]
)

Using other JAX Frameworks

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It is straightforward to integrate other functional JAX libraries with this low-level API, here is an example with Flax:

import elegy as eg
import flax.linen as nn

class LinearClassifier(eg.Model):
    params: Mapping[str, Any] = eg.Parameter.node()
    batch_stats: Mapping[str, Any] = eg.BatchStat.node()
    next_key: eg.KeySeq

    def __init__(self, module: nn.Module, **kwargs):
        self.flax_module = module
        super().__init__(**kwargs)

    def init_step(self, key, inputs):
        self.next_key = eg.KeySeq(key)

        variables = self.flax_module.init(
            {"params": self.next_key(), "dropout": self.next_key()}, x
        )
        self.params = variables["params"]
        self.batch_stats = variables["batch_stats"]

        self.optimizer = self.optimizer.init(self.parameters())

    def test_step(self, inputs, labels):
        # forward
        variables = dict(
            params=self.params,
            batch_stats=self.batch_stats,
        )
        logits, variables = self.flax_module.apply(
            variables,
            inputs, 
            rngs={"dropout": self.next_key()}, 
            mutable=True,
        )
        self.batch_stats = variables["batch_stats"]
        
        # loss
        target = jax.nn.one_hot(labels["target"], 10)
        loss = optax.softmax_cross_entropy(logits, target).mean()

        # logs
        logs = dict(
            accuracy=accuracy,
            loss=loss,
        )
        return loss, logs, self

Examples

Check out the /example directory for some inspiration. To run an example, first install some requirements:

pip install -r examples/requirements.txt

And the run it normally with python e.g.

python examples/flax/mnist_vae.py

Contributing

If your are interested in helping improve Elegy check out the Contributing Guide.

Sponsors 💚

• Quansight - paid development time

Citing Elegy

BibTeX

@software{elegy2020repository,
	title        = {Elegy: A High Level API for Deep Learning in JAX},
	author       = {PoetsAI},
	year         = 2021,
	url          = {https://github.com/poets-ai/elegy},
	version      = {0.8.1}
}