Penzai

盆 ("pen", tray) 栽 ("zai", planting) - an ancient Chinese art of forming trees and landscapes in miniature, also called penjing and an ancestor of the Japanese art of bonsai.

Penzai is a JAX library for writing models as legible, functional pytree data structures, along with tools for visualizing, modifying, and analyzing them. Penzai focuses on making it easy to do stuff with models after they have been trained, making it a great choice for research involving reverse-engineering or ablating model components, inspecting and probing internal activations, performing model surgery, debugging architectures, and more. (But if you just want to build and train a model, you can do that too!)

With Penzai, your neural networks could look like this:

Penzai is structured as a collection of modular tools, designed together but each useable independently:

• penzai.nn (pz.nn): A declarative combinator-based neural network library and an alternative to other neural network libraries like Flax, Haiku, Keras, or Equinox, which exposes the full structure of your model's forward pass in the model pytree. This means you can see everything your model does by pretty printing it, and inject new runtime logic with jax.tree_util. Like Equinox, there's no magic: models are just callable pytrees under the hood.

• penzai.treescope (pz.ts): A superpowered interactive Python pretty-printer, which works as a drop-in replacement for the ordinary IPython/Colab renderer. It's designed to help understand Penzai models and other deeply-nested JAX pytrees, with built-in support for visualizing arbitrary-dimensional NDArrays.

• penzai.core.selectors (pz.select): A pytree swiss-army-knife, generalizing JAX's .at[...].set(...) syntax to arbitrary type-driven pytree traversals, and making it easy to do complex rewrites or on-the-fly patching of Penzai models and other data structures.

• penzai.core.named_axes (pz.nx): A lightweight named axis system which lifts ordinary JAX functions to vectorize over named axes, and allows you to seamlessly switch between named and positional programming styles without having to learn a new array API.

• penzai.data_effects (pz.de): An opt-in system for side arguments, random numbers, and state variables that is built on pytree traversal and puts you in control, without getting in the way of writing or using your model.

Documentation on Penzai can be found at https://penzai.readthedocs.io.

[!WARNING] Penzai's API is currently unstable and may change in future releases.

In particular, the way Penzai handles parameter initialization, parameter sharing, and local mutable state in penzai.nn and penzai.data_effects is likely to be simplified in the future. Some internal details of the treescope pretty-printer intermediate representation may also change to make it easier to extend and configure.

Projects that use Penzai's neural network components or model implementations, or that define their own handlers for treescope, are encouraged to pin the 0.1.x release series (e.g. penzai>=0.1,<0.2) to avoid breaking changes.

Getting Started

If you haven't already installed JAX, you should do that first, since the installation process depends on your platform. You can find instructions in the JAX documentation. Afterward, you can install Penzai using

pip install penzai

and import it using

import penzai
from penzai import pz

(penzai.pz is an alias namespace, which makes it easier to reference common Penzai objects.)

When working in an Colab or IPython notebook, we recommend also configuring Penzai as the default pretty printer, and enabling some utilities for interactive use:

pz.ts.register_as_default()
pz.ts.register_autovisualize_magic()
pz.enable_interactive_context()

# Optional: enables automatic array visualization
pz.ts.active_autovisualizer.set_interactive(pz.ts.ArrayAutovisualizer())

Here's how you could initialize and visualize a simple neural network:

from penzai.example_models import simple_mlp
mlp = pz.nn.initialize_parameters(
    simple_mlp.MLP.from_config([8, 32, 32, 8]),
    jax.random.key(42),
)

# Models and arrays are visualized automatically when you output them from a
# Colab/IPython notebook cell:
mlp

Here's how you could capture and extract the activations after the elementwise nonlinearities:

mlp_with_captured_activations = pz.de.CollectingSideOutputs.handling(
    pz.select(mlp)
    .at_instances_of(pz.nn.Elementwise)
    .insert_after(pz.de.TellIntermediate())
)

output, intermediates = mlp_with_captured_activations(
  pz.nx.ones({"features": 8})
)

To learn more about how to build and manipulate neural networks with Penzai, we recommend starting with the "How to Think in Penzai" tutorial, or one of the other tutorials in the Penzai documentation.

This is not an officially supported Google product.