ExtremeBERT is a toolkit that accelerates the pretraining and finetuning of BERT on customized datasets. Take a quick look at our documentation and paper.
We simplify the installation process of dependencies required by the package. They can be easily installed by a single command source install.sh.
No other steps are needed!
Pretraining time can be reduced to a time-based value by defining --total_training_time=24.0 (24 hours for example).
Mixed precision is supported by adding --fp16.
Support a large number of pre-training datasets. Users can easily use them via huggingface dataset hub. In addition, we also support users' custom dataset.
It is hard to verify the effectiveness of a new optimization method in LM pre-training. With our package, we support integration of customized optimizers and schedulers into the pipeline, which help researchers in the optimization community to easily verify their algorithms. Efficient optimization and data processing algorithms will also be added to future releases.
| Model | Status |
|---|---|
| BERT | ✅ Supported |
| RoBERTa, ALBERT, DeBERTa | 🚧 Developing |
- Linux Ubuntu >= 18.04.
- At least 20 GB GPU memory, e.g. GPU 3090 x1, or 2080Ti GPU x2, GeForce RTX 3070 x4, etc.
- At least 200GB disk space.
First, one may refer to configs/bert-simple.yaml and make suitable configs for the pipeline, including datasets, number of gpus available, etc. Then, by simply running the following command, the whole pipeline will be executed stage by stage,
source install.sh; python main.py --config configs/bert-simple.yamlwhich will run environment installation, dataset prepration, pretraining, finetuning and test result collection one by one and generate the .zip file for GLUE test server submission under output_test_translated/finetune/*/*.zip.
Please refer to PIPELINE_CONFIG.md for more information about the YAML config file.
The details of each stages are illustrated in following sections.
run source install.sh
The dataset directory includes scripts to pre-process the datasets we used in our experiments (Wikipedia, Bookcorpus). See dedicated README for full details.
Pretraining script: run_pretraining.py
For all possible pretraining arguments see: python run_pretraining.py -h
Example for training with the best configuration presented in our paper (24-layers/1024H/time-based learning rate schedule/fp16):
deepspeed run_pretraining.py \
--model_type bert-mlm --tokenizer_name bert-large-uncased \
--hidden_act gelu \
--hidden_size 1024 \
--num_hidden_layers 24 \
--num_attention_heads 16 \
--intermediate_size 4096 \
--hidden_dropout_prob 0.1 \
--attention_probs_dropout_prob 0.1 \
--encoder_ln_mode pre-ln \
--lr 1e-3 \
--train_batch_size 4096 \
--train_micro_batch_size_per_gpu 32 \
--lr_schedule time \
--curve linear \
--warmup_proportion 0.06 \
--gradient_clipping 0.0 \
--optimizer_type adamw \
--weight_decay 0.01 \
--adam_beta1 0.9 \
--adam_beta2 0.98 \
--adam_eps 1e-6 \
--total_training_time 24.0 \
--early_exit_time_marker 24.0 \
--dataset_path <dataset path> \
--output_dir /tmp/training-out \
--print_steps 100 \
--num_epochs_between_checkpoints 10000 \
--job_name pretraining_experiment \
--project_name budget-bert-pretraining \
--validation_epochs 3 \
--validation_epochs_begin 1 \
--validation_epochs_end 1 \
--validation_begin_proportion 0.05 \
--validation_end_proportion 0.01 \
--validation_micro_batch 16 \
--deepspeed \
--data_loader_type dist \
--do_validation \
--use_early_stopping \
--early_stop_time 180 \
--early_stop_eval_loss 6 \
--seed 42 \
--fp16Use run_glue.py to run finetuning for a saved checkpoint on GLUE tasks.
The finetuning script is identical to the one provided by Huggingface with the addition of our model.
For all possible pretraining arguments see: python run_glue.py -h
python run_glue.py \
--model_name_or_path <path to model> \
--task_name MRPC \
--max_seq_length 128 \
--output_dir /tmp/finetuning \
--overwrite_output_dir \
--do_train --do_eval \
--evaluation_strategy steps \
--per_device_train_batch_size 32 --gradient_accumulation_steps 1 \
--per_device_eval_batch_size 32 \
--learning_rate 5e-5 \
--weight_decay 0.01 \
--eval_steps 50 --evaluation_strategy steps \
--max_grad_norm 1.0 \
--num_train_epochs 5 \
--lr_scheduler_type polynomial \
--warmup_steps 50If you find this repository useful, you may cite our paper as:
@inproceedings{extreme-bert,
title={ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT},
author={Rui Pan and Shizhe Diao and Jianlin Chen and Tong Zhang},
year={2022},
eprint={2211.17201},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2211.17201},
}
A significant portion of the code is based on How to Train BERT with an Academic Budget licensed under Apache 2.0.
For help or issues using this package, please submit a GitHub issue.
For personal communication related to this package, please contact Rui Pan ([email protected]) and Shizhe Diao ([email protected]).
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