PGMax

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

• General factor graphs: PGMax supports easy specification of general factor graphs with potentially complicated topology, factor definitions, and discrete variables with a varying number of states.
• LBP in JAX: PGMax generates pure JAX functions implementing LBP for a given factor graph. The generated pure JAX functions run on modern accelerators (GPU/TPU), work with JAX transformations (e.g. vmap for processing batches of models/samples, grad for differentiating through the LBP iterative process), and can be easily used as part of a larger end-to-end differentiable system.

See our blog post and companion paper for more details.

PGMax is under active development. APIs may change without notice, and expect rough edges!

Installation | Getting started

Installation

Install from PyPI

pip install pgmax

Install latest version from GitHub

pip install git+https://github.com/vicariousinc/PGMax.git

Developer

git clone https://github.com/vicariousinc/PGMax.git
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python3 -
cd PGMax
poetry shell
poetry install
pre-commit install

Install on GPU

By default the above commands install JAX for CPU. If you have access to a GPU, follow the official instructions here to install JAX for GPU.

Getting Started

Here are a few self-contained Colab notebooks to help you get started on using PGMax:

• Tutorial on basic PGMax usage
• Implementing max-product LBP for Recursive Cortical Networks
• End-to-end differentiable LBP for gradient-based PGM training
• 2D binary deconvolution

Citing PGMax

Please consider citing our companion paper if you use PGMax in your work:

@article{zhou2022pgmax,
  author = {Zhou, Guangyao and Kumar, Nishanth and L{\'a}zaro-Gredilla, Miguel and Kushagra, Shrinu and George, Dileep},
  title = {{PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX}},
  journal = {arXiv preprint arXiv:2202.04110},
  year={2022}
}

First two authors contributed equally.