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jax_dataclasses

jax_dataclasses provides a wrapper around dataclasses.dataclass for use in JAX, which enables automatic support for:

• Pytree registration. This allows dataclasses to be used at API boundaries in JAX. (necessary for function transformations, JIT, etc)
• Serialization via flax.serialization.

Notably, jax_dataclasses is designed to work seamlessly with tooling that relies on static analysis. (mypy, jedi, etc)

Heavily influenced by some great existing work (the obvious one being flax.struct.dataclass); see Alternatives for comparisons.

Installation

pip install jax_dataclasses

Core interface

jax_dataclasses is meant to provide a drop-in replacement for dataclasses.dataclass:

• jax_dataclasses.pytree_dataclass has the same interface as dataclasses.dataclass, but also registers the target class as a pytree container.
• jax_dataclasses.static_field has the same interface as dataclasses.field, but will also mark the field as static. In a pytree node, static fields will be treated as part of the treedef instead of as a child of the node; all fields that are not explicitly marked static should contain arrays or child nodes.

We also provide several aliases: jax_dataclasses.[field, asdict, astuples, is_dataclass, replace] are all identical to their counterparts in the standard dataclasses library.

Mutations

All dataclasses are automatically marked as frozen and thus immutable (even when no frozen= parameter is passed in). To make changes to nested structures easier, we provide an interface that will (a) make a copy of a pytree and (b) return a context in which any of that copy's contained dataclasses are temporarily mutable:

from jax import numpy as jnp
import jax_dataclasses

@jax_dataclasses.pytree_dataclass
class Node:
  child: jnp.ndarray

obj = Node(child=jnp.zeros(3))

with jax_dataclasses.copy_and_mutate(obj) as obj_updated:
  # Make mutations to the dataclass. This is primarily useful for nested
  # dataclasses.
  #
  # Also does input validation: if the treedef, leaf shapes, or dtypes of `obj`
  # and `obj_updated` don't match, an AssertionError will be raised.
  # This can be disabled with a `validate=False` argument.
  obj_updated.child = jnp.ones(3)

print(obj)
print(obj_updated)

Alternatives

A few other solutions exist for automatically integrating dataclass-style objects into pytree structures. Great ones include: chex.dataclass, flax.struct, and tjax.dataclass. These all influenced this library.

The main differentiators of jax_dataclasses are:

• Static analysis support. Libraries like dataclasses and attrs rely on tooling-specific plugins for static analysis, which don't exist for chex or flax. tjax has a custom mypy plugin to enable type checking, but isn't supported by other tools. Because @jax_dataclasses.pytree_dataclass has the same API as @dataclasses.dataclass, it can include pytree registration behavior at runtime while being treated as the standard decorator during static analysis. This means that all static checkers, language servers, and autocomplete engines that support the standard dataclasses library should work out of the box with jax_dataclasses.

• Nested dataclasses. Making replacements/modifications in deeply nested dataclasses is generally very frustrating. The three alternatives all introduce a .replace(self, ...) method to dataclasses that's a bit more convenient than the traditional dataclasses.replace(obj, ...) API for shallow changes, but still becomes really cumbersome to use when dataclasses are nested. jax_dataclasses.copy_and_mutate() is introduced to address this.

• Static field support. Parameters that should not be traced in JAX should be marked as static. This is supported in flax, tjax, and jax_dataclasses, but not chex.

• Serialization. When working with flax, being able to serialize dataclasses is really handy. This is supported in flax.struct (naturally) and jax_dataclasses, but not chex or tjax.

Misc

This code was originally written for and factored out of jaxfg, where Nick Heppert provided valuable feedback!