CVPR24 Paper

Code for CVPR24 Paper - Resource-Efficient Transformer Pruning for Finetuning of Large Models

Fatih Ilhan, Gong Su, Selim Furkan Tekin, Tiansheng Huang, Sihao Hu, and Ling Liu, "Resource-Efficient Transformer Pruning for Finetuning of Large Models," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 17-21, 2024.

Setup

Python 3.10

Pytorch 2.0.1

Transformers 4.33

https://github.com/microsoft/nni

Please check requirements.txt for the list of other packages.

Usage

CIFAR, TinyImageNet, GLUE datasets are automatically downloaded. You can download Cityscapes from https://www.cityscapes-dataset.com/ and KITTI from https://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015.

General usage for finetuning with RECAP:

python main.py --task <TASK> --data <DATASET> --arch <MODEL> --init_sparse <r_p> --iter_sparse -<r_f> -num_pi <N_p> -num_pr <NUM_RECAP_ITERATIONS>

Example: Finetune ViT-base at CIFAR100 with 33% pruning and 87.5% masking in 10 iterations:

python main.py --task img_class --data cifar100 --arch vit-base --init_sparse 0.33 --iter_sparse -0.875 -num_pi 2 -num_pr 10

Example: Finetune Mask2Former at Cityscapes with 50% pruning and 50% masking in 20 iterations:

python main.py --task img_seg --data cityscapes --arch m2f --init_sparse 0.5 --iter_sparse -0.5 -num_pi 3 -num_pr 20

Example: Finetune BERT-base at CoLA with 17% pruning and 50% masking in 5 iterations:

python main.py --task glue --data cola --arch bert-base-uncased --init_sparse 0.17 --iter_sparse -0.5 -num_pi 1 -num_pr 5

Parameters

All pruning/finetuning parameters are controlled from config.py.

git-disl/recap

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