DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.
This project has been identified as having known security escapes.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.

Training BERT with Compute/Time (Academic) Budget

This repository contains scripts for pre-training and finetuning BERT-like models with limited time and compute budget. The code is based on the work presented in the following paper:

Peter Izsak, Moshe Berchansky, Omer Levy, How to Train BERT with an Academic Budget (EMNLP 2021).

Installation

The pre-training and finetuning scripts are based on Deepspeed and HuggingFace Transformers libraries.

Preliminary Installation

We recommend creating a virtual environment with python 3.6+, PyTorch and apex.

Installation Requirements

pip install -r requirements.txt

We suggest running Deepspeed's utility ds_report and verify Deepspeed components can be compiled (JIT).

Dataset

The dataset directory includes scripts to pre-process the datasets we used in our experiments (Wikipedia, Bookcorpus). See dedicated README for full details.

Pretraining

Pretraining script: run_pretraining.py

For all possible pretraining arguments see: python run_pretraining.py -h

We highly suggest reviewing the various training features we provide within the library.

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 \
  --fp16

Time-based Training

Pretraining can be limited to a time-based value by defining --total_training_time=24.0 (24 hours for example).

Time-based Learning Rate Scheduling

The learning rate can be scheduled to change according to the configured total training time. The argument --total_training_time controls the total time assigned for the trainer to run, and must be specified in order to use time-based learning rate scheduling.

To select time-based learning rate scheduling, define --lr_schedule time, and define a shape for for the annealing curve (--curve=linear for example, as seen in the figure). The warmup phase of the learning rate is define by specifying a proportion (--warmup_proportion) which accounts for the time-budget proportion available in the training session (as defined by --total_training_time). For example, for a 24 hour training session, warmup_proportion=0.1 would account for 10% of 24 hours, that is, 2.4 hours (or 144 minutes) to reach peak learning rate. The learning rate will then be scheduled to reach 0 at the end of the time budget. We refer to the provided figure for an example.

Checkpoints and Finetune Checkpoints

There are 2 types of checkpoints that can be enabled:

• Training checkpoint - saves model weights, optimizer state and training args. Defined by --num_epochs_between_checkpoints.
• Finetuning checkpoint - saves model weights and configuration to be used for finetuning later on. Defined by --finetune_time_markers.

finetune_time_markers can be assigned multiple points in the training time-budget by providing a list of time markers of the overall training progress. For example --finetune_time_markers=0.5 will save a finetuning checkpoint when reaching 50% of training time budget. For multiple finetuning checkpoints, use commas without space 0.5,0.6,0.9.

Validation Scheduling

Enable validation while pre-training with --do_validation

Control the number of epochs between validation runs with --validation_epochs=<num>

To control the amount of validation runs in the beginning and end (running more that validation_epochs) use validation_begin_proportion and validation_end_proportion to specify the proportion of time and, validation_epochs_begin and validation_epochs_end to control the custom values accordingly.

Mixed Precision Training

Mixed precision is supported by adding --fp16. Use --fp16_backend=ds to use Deepspeed's mixed precision backend and --fp16_backend=apex for apex (--fp16_opt controls optimization level).

Finetuning

Use 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

Example for finetuning on MRPC:

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 50

Generating Pretraining Commands

We provide a useful script for generating multiple (or single) pretraining commands by using python generate_training_commands.py.

python generate_training_commands.py -h

	--param_file PARAM_FILE Hyperparameter and configuration yaml
  	--job_name JOB_NAME   job name
 	--init_cmd INIT_CMD   initialization command (deepspeed or python directly)

A parameter yaml must be defined with 2 main keys: hyperparameters with argument values defined as a list of possible values, and default_parameters as default values. Each generated command will be a possible combination of the various arguments specified in the hyperparameters section.

Example:

hyperparameters:
  param1: [val1, val2]
  param2: [val1, val2]

default_parameters:
  param3: 0.0

will result in:

deepspeed run_pretraining.py --param1=val1 --param2=val1 --param3=0.0
deepspeed run_pretraining.py --param1=val1 --param2=val2 --param3=0.0
deepspeed run_pretraining.py --param1=val2 --param2=val1 --param3=0.0
deepspeed run_pretraining.py --param1=val2 --param2=val2 --param3=0.0

Citation

If you find this paper or this code useful, please cite this paper:

@inproceedings{izsak-etal-2021-train,
    title = "How to Train {BERT} with an Academic Budget",
    author = "Izsak, Peter  and
      Berchansky, Moshe  and
      Levy, Omer",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.831",
}