Awesome

torchprune

Main contributors of this code base: Lucas Liebenwein, Cenk Baykal.

Please check individual paper folders for authors of each paper.

Papers

This repository contains code to reproduce the results from the following papers:

Packages

In addition, the repo also contains two stand-alone python packages that can be used for any desired pruning experiment:

Paper Reproducibility

The code for each paper is implemented in the respective packages. In addition, for each paper we have a separate folder that contains additional information about the paper and scripts and parameter configuration to reproduce the exact results from the paper.

Setup

We provide three ways to install the codebase:

1. Github repo + full conda environment
2. Installation via pip
3. Docker image

1. Github Repo

Clone the github repo:

git pull git@github.com:lucaslie/torchprune.git
# (or your favorite way to pull a repo)

We recommend installing the packages in a separate conda environment. Then to create a new conda environment run

conda create -n prune python=3.8 pip
conda activate prune

To install all required dependencies and both packages, run:

pip install -r misc/requirements.txt

Note that this will also install pre-commit hooks for clean commits :-)

2. Pip Installation

To separately install each package with minimal dependencies without cloning the repo manually, run the following commands:

# "torchprune" package
pip install git+https://github.com/lucaslie/torchprune/#subdirectory=src/torchprune

# "experiment" package
pip install git+https://github.com/lucaslie/torchprune/#subdirectory=src/experiment

Note that the experiment package does not automatically install the torchprune package.

3. Docker Image

You can simply pull the docker image from our docker hub:

docker pull liebenwein/torchprune

You can run it interactively with

docker run -it liebenwein/torchprune bash

For your reference you can find the Dockerfile here.

More Information and Usage

Check out the following READMEs in the sub-directories to find out more about using the codebase.

Citations

Please cite the respective papers when using our work.

Sparse flows: Pruning continuous-depth models

@article{liebenwein2021sparse,
  title={Sparse flows: Pruning continuous-depth models},
  author={Liebenwein, Lucas and Hasani, Ramin and Amini, Alexander and Rus, Daniela},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={22628--22642},
  year={2021}
}

Towards Determining the Optimal Layer-wise Decomposition

@inproceedings{liebenwein2021alds,
 author = {Lucas Liebenwein and Alaa Maalouf and Dan Feldman and Daniela Rus},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition},
 url = {https://arxiv.org/abs/2107.11442},
 volume = {34},
 year = {2021}
}

Lost In Pruning

@article{liebenwein2021lost,
title={Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy},
author={Liebenwein, Lucas and Baykal, Cenk and Carter, Brandon and Gifford, David and Rus, Daniela},
journal={Proceedings of Machine Learning and Systems},
volume={3},
year={2021}
}

Provable Filter Pruning

@inproceedings{liebenwein2020provable,
title={Provable Filter Pruning for Efficient Neural Networks},
author={Lucas Liebenwein and Cenk Baykal and Harry Lang and Dan Feldman and Daniela Rus},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BJxkOlSYDH}
}

SiPPing Neural Networks (Weight Pruning)

@article{baykal2022sensitivity,
  title={Sensitivity-informed provable pruning of neural networks},
  author={Baykal, Cenk and Liebenwein, Lucas and Gilitschenski, Igor and Feldman, Dan and Rus, Daniela},
  journal={SIAM Journal on Mathematics of Data Science},
  volume={4},
  number={1},
  pages={26--45},
  year={2022},
  publisher={SIAM}
}