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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:

Paper Venue Title & Link
Node NeurIPS 2021 Sparse Flows: Pruning Continuous-depth Models
ALDS NeurIPS 2021 Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Lost MLSys 2021 Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
PFP ICLR 2020 Provable Filter Pruning for Efficient Neural Networks
SiPP SIAM 2022 SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks

Packages

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

Packages Location Description
torchprune ./src/torchprune This package can be used to run any of the implemented pruning algorithms. It also contains utilities to use pre-defined networks (or use your own network) and utilities for standard datasets.
experiment ./src/experiment This package can be used to run pruning experiments and compare multiple pruning methods for different prune ratios. Each experiment is configured using a .yaml-configuration files.

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.

Paper Location
Node paper/node
ALDS paper/alds
Lost paper/lost
PFP paper/pfp
SiPP paper/sipp

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 [email protected]: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.

READMEs More Information
src/torchprune/README.md more details to prune neural networks, how to use and setup the data sets, how to implement custom pruning methods, and how to add your data sets and networks.
src/experiment/README.md more details on how to configure and run your own experiments, and more information on how to re-produce the results.
paper/node/README.md check out for more information on the Node paper.
paper/alds/README.md check out for more information on the ALDS paper.
paper/lost/README.md check out for more information on the Lost paper.
paper/pfp/README.md check out for more information on the PFP paper.
paper/sipp/README.md check out for more information on the SiPP paper.

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}
}