treex 0.7.0a3

No project description provided

Treex

A Pytree Module system for Deep Learning in JAX

Main Features

• 💡 Intuitive: Modules contain their own parameters and respect Object Oriented semantics like in PyTorch and Keras.
• 🌳 Pytree-based: Modules are Pytrees whose leaves are its parameters, meaning they are fully compatible with jit, grad, vmap, etc.

Treex is implemented on top of Treeo and reexports all of its API for convenience.

Getting Started | User Guide | Examples | Documentation

What is included?

• A base Module class.
• A nn module for with common layers implemented as wrappers over Flax layers.
• A losses module with common loss functions.
• A metrics module with common metrics.
• An Optimizer class that can wrap any optax optimizer.

Why Treex?

Show

Despite all JAX benefits, current Module systems are not intuitive to new users and add additional complexity not present in frameworks like PyTorch or Keras. Treex takes inspiration from S4TF and delivers an intuitive experience using JAX Pytree infrastructure.

Current Alternative's Drawbacks and Solutions

Currently we have many alternatives like Flax, Haiku, Objax, that have one or more of the following drawbacks:

• Module structure and parameter structure are separate, and parameters have to be manipulated around by the end-user, which is not intuitive. In Treex, parameters are stored in the modules themselves and can be accessed directly.
• Monadic architecture adds complexity. Flax and Haiku use an apply method to call modules that set a context with parameters, rng, and different metadata, which adds additional overhead to the API and creates an asymmetry in how Modules are being used inside and outside a context. In Treex, modules can be called directly.
• Among different frameworks, parameter surgery requires special consideration and is challenging to implement. Consider a standard workflow such as transfer learning, transferring parameters and state from a pre-trained module or submodule as part of a new module; in different frameworks, we have to know precisely how to extract their parameters and how to insert them into the new parameter structure/dictionaries such that it is in agreement with the new module structure. In Treex, just as in PyTorch / Keras, we enable to pass the (sub)module to the new module, and parameters are automatically added to the new structure.
• Multiple frameworks deviate from JAX semantics and require particular versions of jit, grad, vmap, etc., which makes it harder to integrate with other JAX libraries. Treex's Modules are plain old JAX PyTrees and are compatible with any JAX library that supports them.
• Other Pytree-based approaches like Parallax and Equinox do not have a total state management solution to handle complex states as encountered in Flax. Treex has the Filter and Update API, which is very expressive and can effectively handle systems with a complex state.

Installation

Install using pip:

pip install treex

Getting Started

This is a small appetizer to give you a feel for how using Treex looks like, be sure to checkout the User Guide for a more in-depth explanation.

import treex as tx
import numpy as np
import jax, optax


# create some data
x = np.random.uniform(size=(50, 1))
y = 1.3 * x ** 2 - 0.3 + np.random.normal(size=x.shape)



# initialize a Module, its simple
model = tx.MLP([64, 1]).init(key=42, inputs=x)
# define an optimizer, init with model params
optimizer = tx.Optimizer(optax.adam(4e-3)).init(model)



# define loss function, notice
# Modules are jit-abel and differentiable 🤯
@jax.jit
@jax.grad
def loss_fn(model: tx.MLP, x, y):
    # forward is a simple call
    preds = model(x)
    # MSE
    return ((preds - y) ** 2).mean()



# basic training loop
for step in range(500):

    # grads have the same type as model
    grads: tx.MLP = loss_fn(model, x, y)
    # apply the gradient updates
    model = optimizer.update(grads, model)



# Pytorch-like eval mode
model = model.eval()
preds = model(x)

Custom Modules

Show

Modules are Treeo Trees, which are Pytrees. When creating core layers you often mark fields that will contain state that JAX should be aware as nodes by assigning class variables to the output of functions like tx.Parameter.node():

import treex as tx

class Linear(tx.Module):
    # use Treeo's API to define Parameter nodes
    w: jnp.ndarray = tx.Parameter.node()
    b: jnp.ndarray = tx.Parameter.node()

    def __init__(self, features_out: int):
        self.features_out = features_out

    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        # init will call forward, we can know if we are inside it
        if self.initializing():
            # `next_key` only available during `init`
            key = tx.next_key() 
            # leverage shape inference
            self.w = jax.random.uniform(
                key, shape=[x.shape[-1], self.features_out]
            )
            self.b = jnp.zeros(shape=[self.features_out])

        # linear forward
        return jnp.dot(x, self.w) + self.b

model = Linear(10).init(key=42, inputs=x)

Node field types (e.g. tx.Parameter) are called Kinds and Treex exports a whole family of Kinds which serve for differente purposes such as holding non-differentiable state (tx.BatchStats), metric's state (tx.MetricState), logging, etc. Checkout the kinds section for more information.

Composite Modules

Show

Composite Modules usually hold and call other Modules within them, while they would be instantiate inside __init__ and used later in __call__ like in Pytorch / Keras, in Treex you usually leverage the @tx.compact decorator over the __call__ method to define the submodules inline.

class MLP(tx.Module):
    def __init__(self, features: Sequence[int]):
        self.features = features

    # compact lets you define submodules on the fly
    @tx.compact
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        for units in self.features[:-1]:
            x = Linear(units)(x)
            x = jax.nn.relu(x)

        return Linear(self.features[-1])(x)

model = MLP([32, 10]).init(key=42, inputs=x)

Under the hood all calls to submodule constructors (e.g. Linear(...)) inside compact are assigned to fields in the parent Module (MLP) so they are part of the same Pytree, their field names are available under the ._subtrees attribute. compact must always define submodules in the same order.

Status

Treex is in an early stage, things might break between versions but we will respect semanting versioning. Since Treex layers are numerically equivalent to Flax, it borrows some maturity and yields more confidence over its results. Feedback is much appreciated.

Roadmap:

• Wrap all Flax Linen Modules
• Implement more layers, losses, and metrics.
• Create applications and pretrained Modules.

Contributions are welcomed!

Sponsors 💚

• Quansight - paid development time

Examples

Checkout the /examples directory for more detailed examples. Here are a few additional toy examples:

Linear Regression

This is a simple but realistic example of how Treex is used.

from functools import partial
from typing import Union
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
import treex as tx

x = np.random.uniform(size=(500, 1))
y = 1.4 * x - 0.3 + np.random.normal(scale=0.1, size=(500, 1))


# differentiate only w.r.t. parameters
def loss_fn(params, model, x, y):
    # merge params into model
    model = model.merge(params)

    preds = model(x)
    loss = jnp.mean((preds - y) ** 2)

    # the model may contain state updates
    # so it should be returned
    return loss, model


grad_fn = jax.value_and_grad(loss_fn, has_aux=True)

# both model and optimizer are jit-able
@jax.jit
def train_step(model, x, y, optimizer):
    # select only the parameters
    params = model.parameters()

    (loss, model), grads = grad_fn(params, model, x, y)

    # update params and model
    params = optimizer.update(grads, params)
    model = model.merge(params)

    # return new model and optimizer
    return loss, model, optimizer


model = tx.Linear(1).init(42, x)
optimizer = tx.Optimizer(optax.adam(0.01)).init(model)

for step in range(300):
    loss, model, optimizer = train_step(model, x, y, optimizer)
    if step % 50 == 0:
        print(f"loss: {loss:.4f}")

# eval mode "turns off" layers like Dropout / BatchNorm
model = model.eval()

X_test = np.linspace(x.min(), x.max(), 100)[:, None]
preds = model(X_test)

plt.scatter(x, y, c="k", label="data")
plt.plot(X_test, preds, c="b", linewidth=2, label="prediction")
plt.legend()
plt.show()

A Stateful Module

Here is an example of creating a stateful module of a RollingMean metric and using them with jax.jit.

class RollingMean(tx.Module):
    count: jnp.ndarray = tx.State.node()
    total: jnp.ndarray = tx.State.node()

    def __init__(self):
        self.count = jnp.array(0, dtype=jnp.int32)
        self.total = jnp.array(0.0, dtype=jnp.float32)

    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        self.count += np.prod(x.shape)
        self.total += x.sum()

        return self.total / self.count

@jax.jit
def update(x: jnp.ndarray, metric: RollingMean) -> Tuple[jnp.ndarray, RollingMean]:
    mean = metric(x)
    return mean, metric # return mean value and updated metric


metric = RollingMean()

for i in range(10):
    x = np.random.uniform(-1, 1, size=(100, 1))
    mean, metric = update(x, metric)

print(mean)