# Treex

*A Pytree Module system for Deep Learning in JAX*

- ðŸ’¡
**Intuitive**: Modules contain their own parameters and respect Object Oriented semantics like in PyTorch and Keras. - ðŸŒ³
**Pytree-based**: Modules are Pytrees whos 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 ðŸ¤¯
@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 `Tree`s, 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)
```

#### 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)
```

## Status

Treex is in an early stage, things might brake 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 ðŸ’š

- 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`

. For a real use cases use the metrics inside `treex.metrics`

.

```
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)
```