[ICLR24] Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging

Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta

This repository contains the code to reproduce the experiments from the ICLR24 paper "Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging". The code is based on PyTorch 1.9 and the experiment-tracking platform Weights & Biases.

Structure and Usage

Experiments are started from the following file:

• main.py: Starts experiments using the dictionary format of Weights & Biases.

The rest of the project is structured as follows:

• strategies: Contains the strategies used for training, pruning and model averaging.
• runners: Contains classes to control the training and collection of metrics.
• metrics: Contains all metrics as well as FLOP computation methods.
• models: Contains all model architectures used.
• utilities: Contains useful auxiliary functions and classes.

Citation

In case you find the paper or the implementation useful for your own research, please consider citing:

@inproceedings{zimmer2024sparse,
title={Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging},
author={Max Zimmer and Christoph Spiegel and Sebastian Pokutta},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=xx0ITyHp3u}
}