Skip to content

Latest commit

 

History

History
 
 

BinaryBERT

readme.md

BinaryBERT: Pushing the Limit of BERT Quantization

This repository contains the implementation of our paper "BinaryBERT: Pushing the Limit of BERT Quantization" in ACL 2021. The overall workflow of training BinaryBERT is shown below. We first train a half-sized ternary BERT model, and then apply ternary weight splitting to initalize the full-sized BinaryBERT. We then fine-tune BinaryBERT for further refinement. BinaryBERT

Dependencies

pip install -r requirements.txt

Datasets

We train and test BinaryBERT on GLUE and SQuAD benchmarks. Both dataset are available online:

For data augmentation on GLUE, please follow the instruction in TinyBERT.

Execution

Our experiments are based on the fine-tuned full-precision DynaBERT, which can be found here. Complete running scripts and more detailed tips are provided in ./scripts. There are two steps for execution, and we illustrate them with training BinaryBERT with 4-bit activations on MRPC.

Step one: Train a half-sized ternary BERT

This correponds to scripts/ternary_glue.sh. For example

sh scripts/terarny_glue.sh mrpc data/mrpc/ models/dynabert_model/mrpc/width_0.5_depth_1.0/ models/dynabert_model/mrpc/width_0.5_depth_1.0/ 2 4

Step two: Apply TWS and finetune BinaryBERT

This correponds to scripts/tws_glue.sh. Based on the model checkpoint of ternary BERT, execute:

sh scripts/tws_glue.sh mrpc data/mrpc/ models/dynabert_model/mrpc/width_0.5_depth_1.0/ output/Ternary_W2A8/mrpc/kd_stage2/ 1 4

Go through each script for more detail.

Citation

If you find this repo helpful for your research, please:

@inproceedings{bai2021binarybert,
	title={BinaryBERT: Pushing the Limit of BERT Quantization},
	author={Bai, H. and Zhang, W. and Hou, L. and Shang, L. and Jin, J. and Jiang, X. and Liu, Q. and Lyu, M. and King, I.},
	booktitle={Annual Meeting of the Association for Computational Linguistics},
	year={2021}
}