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Learning to Grow Pretrained Models for Efficient Transformer Training

The official implementation of ICLR 2023 paper Learning to Grow Pretrained Models for Efficient Transformer Training.

Peihao Wang¹, Rameswar Panda², Lucas Torroba Hennigen⁴, Philip Greengard³, Leonid Karlinsky², Rogerio Feris², David Cox², Zhangyang (Atlas) Wang¹, Yoon Kim⁴

¹University of Texas at Austin, ²MIT-IBM Watson Lab, ³Columbia University, ⁴MIT

Getting Started

Dependency

To run our code, the following libraries which are required:

torch
torchvision
transformers==4.21.0
tensorboardX

# For GLUE evaluation
sklearn

# Fiass supports fast indexing.
# The code has a torch-implemented GPU indexing, so do not worry if you could not install faiss.
faiss-gpu>=1.6.3

# Spacy is used in sentence segmentation where the sentences are the input the cross-modality matching model.
spacy

# A higher h5py version to support h5py.VirtualLayout
h5py>=2.10.0

Data Preparation

We re-use the data preparation pipeline provided by Vokenization.

1. Download and Pre-Process Pure-Language Data

We provide scripts to get the English-Wikipeida dataset.

The script to download and process wiki data are modified from XLM. Note that the data processing pipelines for BERT and RoBERTa are different as they use different tokenizers.

To get data for training BERT, use the following command:

bash data/wiki/get_data_cased.bash en

For RoBERTa, it requires an untokenized version of English Wikipedia, so please use the following command:

bash data/wiki/get_data_cased_untokenized.bash en

2. Tokenize Language Data

We next tokenize the language corpus. It would locally save three files: <dataset_name>.<tokenizer_name>, <dataset_name>.<tokenizer_name>.hdf5, and <dataset_name>.<tokenizer_name>.line. Use the following commands accordingly to tokenize the dataset:

# For BERT
bash tokenization/tokenize_wiki_bert.bash

# For RoBERTa
bash tokenization/tokenize_wiki_roberta.bash 

Usage

Please use the following commands to pretrain BERT or RoBERTa:

Training BERT from Scratch

(6L, 512H) BERT

python run_lm_distributed.py --config configs/bert_wiki.txt --config_name configs/bert-6L-512H.json --output_dir <output_path> --max_steps 400000 --warmup_steps 10000 --should_continue

(12L, 768H) BERT

python run_lm_distributed.py --config configs/bert_wiki.txt --config_name configs/bert-12L-768H.json --output_dir <output_path> --max_steps 400000 --warmup_steps 10000 --should_continue

Training BERT with LiGO

First train a LiGO operator using the following command:

python run_grow_distributed.py --config configs/bert_wiki.txt --config_name configs/bert-12L-768H.json --output_dir <path_to_save_LiGO> --tune_width --tune_depth --source_model_path <path_to_small_model> --fuse_init_scheme stackbert_noisy rand --max_steps 100 --logging_steps 100 --ckpt_steps 100 --should_continue

Then use pre-trained LiGO operator to grow the model:

python run_lm_distributed.py --config configs/bert_wiki.txt --config_name configs/bert-12L-768H.json --output_dir <output_path> --grow_scheme ligo --source_model_path <path_to_small_model>  --pretrained_ligo_path <path_to_save_LiGO> --fuse_init_scheme stackbert_noisy rand --learning_rate 2e-4 --warmup_steps 0 --should_continue

Training RoBERTa from Scratch

(6L, 512H) RoBERTa

python run_lm_distributed.py --config configs/roberta_wiki.txt --config_name configs/roberta-6L-512H.json --per_gpu_train_batch_size 64 --gradient_accumulation_steps 4 --learning_rate 2e-4 --output_dir <output_path> --should_continue

(12L, 512H) RoBERTa

python run_lm_distributed.py --config configs/roberta_wiki.txt --config_name configs/roberta-12L-768H.json --per_gpu_train_batch_size 64 --gradient_accumulation_steps 4 --learning_rate 2e-4 --output_dir <output_path> --should_continue

Note that the argument --gradient_accumulation_steps 4 is necessary to gaurantee the batch size of RoBERTa is 4 times of BERT. One can use 4 times number of GPUs to achieve the same batch size.

Training RoBERTa with LiGO

# Train LiGO
python run_grow_distributed.py --config configs/roberta_wiki.txt --config_name configs/roberta-12L-768H.json --per_gpu_train_batch_size 64 --gradient_accumulation_steps 4 --learning_rate 2e-4 --output_dir <path_to_save_LiGO> --tune_width --tune_depth --source_model_path <path_to_small_model> --fuse_init_scheme stackbert_noisy rand --max_steps 100 --logging_steps 100 --ckpt_steps 100 --should_continue

# Apply pre-trained LiGO operator to grow the model
python vlm/run_lm_distributed.py --config configs/roberta_wiki.txt --config_name configs/roberta-12L-768H.json --per_gpu_train_batch_size 64 --gradient_accumulation_steps 4 --output_dir <output_dir> --grow_scheme ligo --source_model_path <path_to_small_model> --pretrained_ligo_path <path_to_save_LiGO> --fuse_init_scheme stackbert_noisy rand --learning_rate 2e-4 --warmup_steps 10000 --should_continue

Citation

This repository is based on the project Vokenization. If you find this work or our work helpful for your own research, please cite our paper.

@inproceedings{wang2023learning,
title={Learning to grow pretrained models for efficient transformer training},
author={Wang, Peihao and Panda, Rameswar and Hennigen, Lucas Torroba and Greengard, Philip and Karlinsky, Leonid and Feris, Rogerio and Cox, David Daniel and Wang, Zhangyang and Kim, Yoon},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=cDYRS5iZ16f},
}