xmmutablemap

JAX-compatible Immutable Mapping

JAX prefers immutable objects, but before Python 3.15 there is no built-in immutable dictionary. 😢
This repository defines a light-weight immutable map (lower-level than a dict) that JAX understands as a PyTree. 🎉 🕶️ On Python 3.15+, prefer the built-in frozendict (see below).

Installation

pip install xmmutablemap

using uv

uv add xmmutablemap

from source, using pip

pip install git+https://github.com/GalacticDynamics/xmmutablemap.git

building from source

cd /path/to/parent
git clone https://github.com/GalacticDynamics/xmmutablemap.git
cd xmmutablemap
pip install -e .  # editable mode

Documentation

xmutablemap provides the class ImmutableMap, which is a full implementation of Python's Mapping ABC. If you've used a dict then you already know how to use ImmutableMap! The things ImmutableMap adds is 1) immutability (and related benefits like hashability) and 2) compatibility with JAX.

from xmmutablemap import ImmutableMap

print(ImmutableMap(a=1, b=2, c=3))
# ImmutableMap({'a': 1, 'b': 2, 'c': 3})

print(ImmutableMap({"a": 1, "b": 2.0, "c": "3"}))
# ImmutableMap({'a': 1, 'b': 2.0, 'c': '3'})

Relationship to frozendict (PEP 814)

Python 3.15 adds a built-in frozendict (PEP 814), which covers most of what ImmutableMap was written for. ImmutableMap is now a subclass of it, so the two interoperate directly:

from xmmutablemap import ImmutableMap, frozendict

d = ImmutableMap(a=1, b=2)

print(isinstance(d, frozendict))
# True
print(d == frozendict(a=1, b=2) == {"a": 1, "b": 2})
# True

On Python 3.15+, xmmutablemap.frozendict is the built-in. On older Pythons it is a behaviour-matched stand-in, so this code works the same on every supported version and you can migrate ahead of your Python floor.

Prefer the built-in frozendict on Python 3.15+

Since JAX 0.11.2, JAX registers the built-in frozendict as a PyTree node. On Python 3.15+ with jax>=0.11.2, use the built-in frozendict directly; it does everything ImmutableMap does, with no extra dependency. ImmutableMap remains for Python < 3.15, where the built-in does not exist (and JAX treats xmmutablemap.frozendict, the stand-in, as an opaque leaf).

import jax

print(jax.tree.structure(ImmutableMap(a=1, b=2)))
# PyTreeDef(CustomNode(ImmutableMap[('a', 'b')], [*, *]))
print(jax.tree.structure(frozendict(a=1, b=2)))
# Python 3.15+, jax>=0.11.2: PyTreeDef(frozendict({'a': *, 'b': *}))
# older Pythons (the stand-in): PyTreeDef(*)

Warning

Leaf order. JAX flattens a built-in frozendict like a dict: leaves come out in sorted key order, unflattening rebuilds the map in sorted order, and keys that cannot be sorted against each other raise an error. ImmutableMap does the same, so switching to the built-in keeps leaf order. Earlier releases of ImmutableMap flattened in insertion order (for ImmutableMap(b=1, a=2), jax.tree.leaves gave [1, 2]; it now gives [2, 1]). The treedefs of the two types also still differ. When upgrading or switching, check anything that depends on leaf position or tree structure, such as indexing jax.tree.leaves, saved optimizer states, or checkpoints.

One deliberate difference: ImmutableMap | other merges any Mapping, while frozendict | other merges only a dict or frozendict (so, for example, a custom Mapping with no | of its own raises TypeError). Convert such mappings with dict(...) when switching to the built-in.

JAX Integration

One of the key benefits of ImmutableMap is its compatibility with JAX. Since it's immutable and hashable, it can be used in places where JAX would normally complain about mutable objects like regular dictionaries.

Using ImmutableMap as a Default in JAX Dataclasses

Here's an example showing how ImmutableMap can be used as a default value in a dataclass, which is particularly useful with JAX:

import functools
import jax
import jax.numpy as jnp
from dataclasses import dataclass
from xmmutablemap import ImmutableMap


@functools.partial(
    jax.tree_util.register_dataclass, data_fields=["params"], meta_fields=["batch_size"]
)
@dataclass(frozen=True)
class Config:
    """Configuration with immutable default parameters."""

    # This works! ImmutableMap is immutable and hashable
    params: ImmutableMap[str, float] = ImmutableMap(
        learning_rate=0.001, momentum=0.9, weight_decay=1e-4
    )
    batch_size: int = 32


# JAX can safely transform functions using this dataclass
@jax.jit
def train_step(config: Config, data: jnp.ndarray) -> jnp.ndarray:
    """Example training step that uses config parameters."""
    lr = config.params["learning_rate"]
    return data * lr


# This works perfectly
config = Config()
data = jnp.array([1.0, 2.0, 3.0])
result = train_step(config, data)
print(f"Result: {result}")
# Result: [0.001 0.002 0.003]

Key Benefits for JAX

• Immutability: Once created, ImmutableMap cannot be modified, preventing accidental mutations that could break JAX's functional programming model
• Hashability: JAX can safely cache and memoize functions that use ImmutableMap instances
• PyTree Support: ImmutableMap is registered as a JAX PyTree, so it works seamlessly with JAX transformations like jit, grad, vmap, etc.
• Safe Defaults: Can be used as default values in dataclasses without the typical pitfalls of mutable defaults

Development

We welcome contributions!