You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient (paper)

@misc{zhang2021compress,
      title={You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient}, 
      author={Shaokun Zhang and Xiawu Zheng and Chenyi Yang and Yuchao Li and Yan Wang and Fei Chao and Mengdi Wang and Shen Li and Jun Yang and Rongrong Ji},
      year={2021},
      eprint={2106.02435},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      } 

Overview

This repository is the official implementation of You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient

📋 We propose a novel approach, YOCO-BERT, to achieve compress once and deploy everywhere. Compared with state of-the-art algorithms, YOCO-BERT provides more compact models, yet achieving superior average accuracy improvement on the GLUE.

Requirements

• Python > 3.6
• Pytorch = 1.7.0
• transformers = 3.5.0

Training

To train the super-BERTs in the paper, run this command:

python train_superbert.py --cfg /path_to_superbert_training_config/config.yaml

Searching

To search the optimal sub-BERTs given any constraints in the paper, run this command:

python search_subbert.py --cfg /path_to_subbert_searching_config/config.yaml

Evaluation

The evaluation results will be reported after the searching process.

Config

We release all the traning and searching configs in config

Results

Our model achieves the following performance on :

GLUE

Results given various FlOPs and parameters.

Results under common constraints (compress to no more than 66M)

📋 The detailed metrics used in this code are reported in the paper.

Licence

This repository is released under the MIT license. See LICENSE for more information.

Contact

Any problem regarding this code re-implementation, feel free to contact the first author: [email protected]