Treeo
A small library for creating and manipulating custom JAX Pytree classes
- Light-weight: has no dependencies other than
jax. - Compatible: Treeo
Treeobjects are compatible with anyjaxfunction that accepts Pytrees. - Standards-based:
treeo.fieldis built on top of python'sdataclasses.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.
Installation
Install using pip:
pip install treeo
Basics
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
@dataclass
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')
Kinds
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.
Examples
A simple Tree
from dataclasses import dataclass
import treeo as to
@dataclass
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
@jax.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
@dataclass
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)
@jax.jit
def update(counter: Counter):
counter.inc()
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 jnp.dot(x, self.w) + self.b
@jax.value_and_grad
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
@jax.jit
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")
plt.legend()
plt.show()
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