Sum-Product Probabilistic Language

SPPL is a probabilistic programming language that delivers exact solutions to a broad range of probabilistic inference queries. The language handles continuous, discrete, and mixed-type probability distributions; many-to-one numerical transformations; and a query language that includes general predicates on random variables.

Users express generative models as probabilistic programs with standard imperative constructs, such as arrays, if/else branches, for loops, etc. The program is then translated to a sum-product expression (a generalization of sum-product networks) that statically represents the probability distribution of all random variables in the program. This expression is used to deliver answers to probabilistic inference queries.

A system description of SPPL is given in the following paper:

SPPL: Probabilistic Programming with Fast Exact Symbolic Inference. Saad, F. A.; Rinard, M. C.; and Mansinghka, V. K. In PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, June 20-25, Virtual, Canada. ACM, New York, NY, USA. 2021. https://doi.org/10.1145/3453483.3454078.

Installation

This software is tested on Ubuntu 20.04 and Python 3.8. SPPL is available on the PyPI repository

$ python -m pip install sppl

To install the Jupyter interface, first obtain the system-wide dependencies in requirements.sh and then run

$ python -m pip install 'sppl[magics]'

Examples

The easiest way to use SPPL is via the browser-based Jupyter interface, which allows for interactive modeling, querying, and plotting. Refer to the .ipynb notebooks under the examples directory.

Benchmarks

Please refer to the artifact at the ACM Digital Library: https://doi.org/10.1145/3453483.3454078

Guide to Source Code

Please refer to GUIDE.md for a description of the main source files in this repository.

Tests

To run the test suite as a user, first install the test dependencies:

$ python -m pip install 'sppl[tests]'

Then run the test suite:

$ python -m pytest --pyargs sppl

To run the test suite as a developer:

To run crash tests: $ ./check.sh
To run integration tests: $ ./check.sh ci
To run a specific test: $ ./check.sh [<pytest-opts>] /path/to/test.py
To run the examples: $ ./check.sh examples
To build a docker image: $ ./check.sh docker
To generate a coverage report: $ ./check.sh coverage

To view the coverage report, open htmlcov/index.html in the browser.

Language Reference

Coming Soon!

Citation

To cite this work, please use the following BibTeX.

@inproceedings{saad2021sppl,
title           = {{SPPL:} Probabilistic Programming with Fast Exact Symbolic Inference},
author          = {Saad, Feras A. and Rinard, Martin C. and Mansinghka, Vikash K.},
booktitle       = {PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Design and Implementation},
pages           = {804--819},
year            = 2021,
location        = {Virtual, Canada},
publisher       = {ACM},
address         = {New York, NY, USA},
doi             = {10.1145/3453483.3454078},
address         = {New York, NY, USA},
keywords        = {probabilistic programming, symbolic execution, static analysis},
}

License

Apache 2.0; see LICENSE.txt

Acknowledgments

The logo was designed by McCoy R. Becker.

probsys / sppl

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