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Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
This is the offical repo of the ICML 2023 paper Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Requirements
This code has been tested with Python 3.7 and the following packages:
numba==0.56.4
numpy==1.21.6
scikit_learn==1.0.2
torch==1.12.1+cu113
torchvision==0.13.1+cu113
Pruned models
We provide checkpoints for our best pruned models, obtained with the gradual pruning procedure described in the paper.
MobileNetV1
| Sparsity | Checkpoint |
|---|---|
| 75.28 | link |
| 89.00 | link |
ResNet50
| Sparsity | Checkpoint |
|---|---|
| 90.00 | link |
| 95.00 | link |
| 98.00 | link |
Structure of the repo
Scripts to run the algorithms are located in scripts/. The current code supports the following architectures (datasets): MLPNet (MNIST), ResNet20 (Cifar10), MobileNetV1 (Imagenet) and ResNet50 (Imagenet). Adding new models can be done through model_factory function in utils/main_utils.py.
Citing CHITA
If you find CHITA useful in your research, please consider citing the following paper.
@InProceedings{pmlr-v202-benbaki23a,
title = {Fast as {CHITA}: Neural Network Pruning with Combinatorial Optimization},
author = {Benbaki, Riade and Chen, Wenyu and Meng, Xiang and Hazimeh, Hussein and Ponomareva, Natalia and Zhao, Zhe and Mazumder, Rahul},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {2031--2049},
year = {2023},
}