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A small library for creating and manipulating custom JAX Pytree classes

  • Light-weight: has no dependencies other than jax.
  • Compatible: Treeo Tree objects are compatible with any jax function that accepts Pytrees.
  • Standards-based: treeo.field is built on top of python's dataclasses.field.
  • Flexible: Treeo is compatible with both dataclass and non-dataclass classes.

Treeo lets you easily create class-based Pytrees so your custom objects can easily interact seamlessly with JAX. Uses of Treeo can range from just creating simple simple JAX-aware utility classes to using it as the core abstraction for full-blown frameworks. Treeo was originally extracted from the core of Treex and shares a lot in common with flax.struct.

Documentation | User Guide


Install using pip:

pip install treeo


With Treeo you can easily define your own custom Pytree classes by inheriting from Treeo's Tree class and using the field function to declare which fields are nodes (children) and which are static (metadata):

import treeo as to

class Person(to.Tree):
    height: jnp.array = to.field(node=True) # I am a node field!
    name: str = to.field(node=False) # I am a static field!
field is just a wrapper around dataclasses.field so you can define your Pytrees as dataclasses, but Treeo fully supports non-dataclass classes as well. Since all Tree instances are Pytree they work with the various functions from thejax library as expected:

p = Person(height=jnp.array(1.8), name="John")

# Trees can be jitted!
jax.jit(lambda person: person)(p) # Person(height=array(1.8), name='John')

# Trees can be mapped!
jax.tree_map(lambda x: 2 * x, p) # Person(height=array(3.6), name='John')


Treeo also include a kind system that lets you give semantic meaning to fields (what a field represents within your application). A kind is just a type you pass to field via its kind argument:

class Parameter: pass
class BatchStat: pass

class BatchNorm(to.Tree):
    scale: jnp.ndarray = to.field(node=True, kind=Parameter)
    mean: jnp.ndarray = to.field(node=True, kind=BatchStat)

Kinds are very useful as a filtering mechanism via treeo.filter:

model = BatchNorm(...)

# select only Parameters, mean is filtered out
params = to.filter(model, Parameter) # BatchNorm(scale=array(...), mean=Nothing)
Nothing behaves like None in Python, but it is a special value that is used to represent the absence of a value within Treeo.

Treeo also offers the merge function which lets you rejoin filtered Trees with a logic similar to Python dict.update but done recursively:

def loss_fn(params, model, ...):
    # add traced params to model
    model = to.merge(model, params)

# gradient only w.r.t. params
params = to.filter(model, Parameter) # BatchNorm(scale=array(...), mean=Nothing)
grads = jax.grad(loss_fn)(params, model, ...)

For a more in-depth tour check out the User Guide.


A simple Tree

from dataclasses import dataclass
import treeo as to

class Character(to.Tree):
    position: jnp.ndarray = to.field(node=True)    # node field
    name: str = to.field(node=False, opaque=True)  # static field

character = Character(position=jnp.array([0, 0]), name='Adam')

# character can freely pass through jit
def update(character: Character, velocity, dt) -> Character:
    character.position += velocity * dt
    return character

character = update(character velocity=jnp.array([1.0, 0.2]), dt=0.1)

A Stateful Tree

from dataclasses import dataclass
import treeo as to

class Counter(to.Tree):
    n: jnp.array = to.field(default=jnp.array(0), node=True) # node
    step: int = to.field(default=1, node=False) # static

    def inc(self):
        self.n += self.step

counter = Counter(step=2) # Counter(n=jnp.array(0), step=2)

def update(counter: Counter):
    return counter

counter = update(counter) # Counter(n=jnp.array(2), step=2)

# map over the tree

Full Example - Linear Regression

import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np

import treeo as to

class Linear(to.Tree):
    w: jnp.ndarray = to.node()
    b: jnp.ndarray = to.node()

    def __init__(self, din, dout, key):
        self.w = jax.random.uniform(key, shape=(din, dout))
        self.b = jnp.zeros(shape=(dout,))

    def __call__(self, x):
        return, self.w) + self.b

def loss_fn(model, x, y):
    y_pred = model(x)
    loss = jnp.mean((y_pred - y) ** 2)

    return loss

def sgd(param, grad):
    return param - 0.1 * grad

def train_step(model, x, y):
    loss, grads = loss_fn(model, x, y)
    model = jax.tree_map(sgd, model, grads)

    return loss, model

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

key = jax.random.PRNGKey(0)
model = Linear(1, 1, key=key)

for step in range(1000):
    loss, model = train_step(model, x, y)
    if step % 100 == 0:
        print(f"loss: {loss:.4f}")

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

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