SynJax Project Introduction
What is SynJax?
SynJax is a cutting-edge neural network library designed specifically for JAX, a popular platform for high-performance numerical computing. Its primary focus is on providing an extensive suite of structured probability distributions. This means it offers tools and models that help describe and compute the likelihood of various structured data representations. The types of distributions and models currently supported by SynJax include:
- Linear Chain CRF: This is used for sequential data where each element depends on the previous one, useful in tasks like sequence labeling.
- Semi-Markov CRF: An extension of the linear chain model that considers segments of the sequence.
- Constituency Tree CRF: This handles hierarchical data structures, such as trees in natural language processing.
- Spanning Tree CRF: Helps in constructing trees that span across subsets, with constraints on direction, root edges, and more.
- Alignment CRF: Supports different modes of data alignment, crucial for tasks like translation alignment.
- CTC Alignment: Used in scenarios like speech recognition where alignment without predefined timing is necessary.
- PCFG (Probabilistic Context-Free Grammar): Useful in parsing structures like grammar in languages.
- Tensor-Decomposition PCFG: An advanced version that decomposes grammar into tensors for efficient computation.
- HMM (Hidden Markov Model): A widely-used model for capturing temporal and sequential data dependencies.
These distributions can perform essential operations such as calculating the log-probability of a structure, finding the most likely structure, sampling, and evaluating various entropy measures. SynJax also aligns with standard JAX transformations like jax.vmap, jax.jit, jax.pmap, and jax.grad, except for a few functions like argmax and sampling.
For those interested in diving deeper into the intricate workings of SynJax, a detailed research paper is available here.
Installation
Installing SynJax is straightforward. Although it's written in pure Python, it relies on some C++ code through JAX, making the installation process vary depending on the user's CUDA version. Therefore, it doesn't automatically include JAX as a dependency. To install SynJax:
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First, install JAX by following these instructions, ensuring the appropriate support for your hardware accelerator.
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Then, you can install SynJax via pip with the following command:
$ pip install git+https://github.com/google-deepmind/synjax
Examples
The SynJax library comes with an array of examples to help users understand and utilize its capabilities effectively. These can be found in the notebooks directory. Among these is an introductory notebook that showcases the essential features of SynJax, available to be run directly via Google Colab.
Citing SynJax
For those who find SynJax useful in their research or projects and wish to cite it, two citations are recommended. The first is for the SynJax paper as follows:
@article{synjax2023,
title="{SynJax: Structured Probability Distributions for JAX}",
author={Milo\v{s} Stanojevi\'{c} and Laurent Sartran},
year={2023},
journal={arXiv preprint arXiv:2308.03291},
url={https://arxiv.org/abs/2308.03291},
}
Additionally, users should reference the current DeepMind JAX Ecosystem citation available in their repository. This ensures acknowledgment of the foundational work supporting SynJax's operations.
In summary, SynJax is a versatile and powerful library for any practitioner or researcher interested in structured probability distributions and neural networks, offering extensive functionality built on top of the JAX platform.