joshuacook / bert-tickets

[NeurIPS 2020] "The Lottery Ticket Hypothesis for Pre-trained BERT Networks", Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin

The Lottery Ticket Hypothesis for Pre-trained BERT Networks

Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS 2020]

Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin.

Our implementation is based on Huggingface repo. Details are referred to README here. Pre-trained subnetworks are coming soon.

Overview

The Existence of Matching Subnetworks in BERT

Transfer Learning for BERT Winning Tickets

Method

Reproduce Details

Prerequisites and Installation

Details are referred to README here.

Iterative Magnitude Pruning (IMP)

MLM task:

python -u LT_pretrain.py 
	   --output_dir LT_pretrain_model
	   --model_type bert 
	   --model_name_or_path bert-base-uncased 
	   --train_data_file pretrain_data/en.train 
	   --do_train 
	   --eval_data_file pretrain_data/en.valid 
	   --do_eval 
	   --per_gpu_train_batch_size 16 
	   --per_gpu_eval_batch_size 16 
	   --evaluate_during_training 
	   --num_train_epochs 1 
	   --logging_steps 10000 
	   --save_steps 10000 
	   --mlm 
	   --overwrite_output_dir 
	   --seed 57

Glue task:

python -u LT_glue.py
	   --output_dir tmp/mnli 
	   --logging_steps 36813 
	   --task_name MNLI 
	   --data_dir glue_data/MNLI 
	   --model_type bert 
	   --model_name_or_path bert-base-uncased 
	   --do_train 
	   --do_eval 
	   --do_lower_case 
	   --max_seq_length 128 
	   --per_gpu_train_batch_size 32 
	   --learning_rate 2e-5 
	   --num_train_epochs 30 
	   --overwrite_output_dir 
	   --evaluate_during_training 
	   --save_steps 36813
	   --eval_all_checkpoints 
	   --seed 57

SQuAD task:

python -u squad_trans.py 
	   --output_dir tmp/530/squad 
	   --model_type bert 
	   --model_name_or_path bert-base-uncased 
       --do_train 
       --do_eval 
       --do_lower_case 
       --train_file SQuAD/train-v1.1.json 
       --predict_file SQuAD/dev-v1.1.json 
       --per_gpu_train_batch_size 16 
       --learning_rate 3e-5 
       --num_train_epochs 40 
       --max_seq_length 384 
       --doc_stride 128 
       --evaluate_during_training 
       --eval_all_checkpoints 
       --overwrite_output_dir 
       --logging_steps 22000 
       --save_steps 22000 
       --seed 57

One-shot Magnitude Pruning (OMP)

python oneshot.py --weight [pre or rand] --model [glue or squad or pretrain] --rate 0.5

Fine-tuning

MLM task:

python -u pretrain_trans.py 
	   --dir pre\  [using random weight or official pretrain weight]
	   --weight_pertub tmp/shuffle_weight.pt\ [weight for Bert (not required)]
	   --mask_dir tmp/dif_mask/pretrain_mask.pt \ [mask file]
	   --output_dir tmp/530/pre 
	   --model_type bert 
	   --model_name_or_path bert-base-uncased 
	   --train_data_file pretrain_data/en.train 
	   --do_train --eval_data_file pretrain_data/en.valid 
	   --do_eval 
	   --per_gpu_train_batch_size 8 
	   --per_gpu_eval_batch_size 8 
	   --evaluate_during_training 
	   --num_train_epochs 1 
	   --logging_steps 2000 
	   --save_steps 0 
	   --max_steps 20000  
	   --mlm 
	   --overwrite_output_dir 
	   --seed 57

Glue task:

python -u glue_trans.py 
       --dir pre \  [using random weight or official pretrain weight]
       --weight_pertub tmp/shuffle_weight.pt \ [weight for Bert (not required)]
       --mask_dir tmp/dif_mask/mnli_mask.pt \ [mask file]
       --output_dir tmp/530/mnli 
       --logging_steps 12271 
       --task_name MNLI 
       --data_dir glue_data/MNLI 
       --model_type bert 
       --model_name_or_path bert-base-uncased 
       --do_train 
       --do_eval 
       --do_lower_case 
       --max_seq_length 128 
       --per_gpu_train_batch_size 32 
       --learning_rate 2e-5 
       --num_train_epochs 3 
       --overwrite_output_dir 
       --evaluate_during_training 
       --save_steps 0 
       --eval_all_checkpoints 
       --seed 5

SQuAD task:

python -u squad_trans.py 
	   --dir pre \  [using random weight or official pretrain weight]
	   --weight_pertub tmp/shuffle_weight.pt \ [weight for Bert (not required)]
	   --mask_dir tmp/dif_mask/squad_mask.pt \ [mask file]
	   --output_dir tmp/530/squad 
	   --model_type bert 
	   --model_name_or_path bert-base-uncased 
	   --do_train 
	   --do_eval 
	   --do_lower_case 
	   --train_file SQuAD/train-v1.1.json 
	   --predict_file SQuAD/dev-v1.1.json 
	   --per_gpu_train_batch_size 16 
	   --learning_rate 3e-5 
	   --num_train_epochs 4 
	   --max_seq_length 384 
	   --doc_stride 128 
	   --evaluate_during_training 
	   --eval_all_checkpoints 
	   --overwrite_output_dir 
	   --logging_steps 5500 
	   --save_steps 0 
	   --seed 57

Subnetwork with Ramdomly Suffuled Pre-trined Weight

python pertub_weight.py

Citation

If you use this code for your research, please cite our paper:

@misc{chen2020lottery,
    title={The Lottery Ticket Hypothesis for Pre-trained BERT Networks},
    author={Tianlong Chen and Jonathan Frankle and Shiyu Chang and Sijia Liu and Yang Zhang and Zhangyang Wang and Michael Carbin},
    year={2020},
    eprint={2007.12223},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Acknowlegement

We would like to express our deepest gratitude to the MIT-IBM Watson AI Lab. In particular, we would like to thank John Cohn for his generous help in providing us with the computing resources necessary to conduct this research.