Awesome

staged-training

In our paper Staged Training for Transformer Language Models, we propose a staged training setup that begins with a small model and incrementally increases the amount of compute used for training by applying a "growth operator" to increase the model depth and width. By initializing each stage with the output of the previous one, the training process effectively re-uses the compute from prior stages and becomes more efficient.

We release the reproducible code for the growth operator and evaluation scripts here.

Setup

The scripts in this repository require Python 3.7 or newer. Once you have a suitable Python environment, first install PyTorch v1.9.0 according the official instructions. Then run

pip install -r requirements.txt

Growth Operator

Our growth operators (width/depth) each take as input the entire training state (including model parameters, optimizer state, learning rate schedule, etc.) and output a new training state from which training continues.

Please see the scripts/cheatsheet.txt for more examples on how to use the corresponding scripts.

For example, you can apply the width operator with:

CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/gpt_pretrain.py \
  --save_prefix final_gpt2_large_div2_width_check_bs512_lr0.0020_warmup3k_seqlen1024_debug \
  --gpu_count -1 \
  --model gpt2  \
  --tokenizer gpt2 \
  --batch_size 4 \
  --grad_accum 32  \
  --lr 0.002006911598778545  \
  --warmup_steps 3000 \  \
  --train_steps 250000  \
  --val_every 50  \
  --val_batches 50 \
  --fp16 \
  --seqlen 1024 \
  --log_rate 10 \
  --num_workers 4 \
  --size GPT2_large_div2_width \
  --random \
  --resume final_runs/final_gpt2_large_div2_width_check_bs512_lr0.0021_warmup3k_seqlen1024_debug/checkpoint-xxx.ckpt \
  --doubling weights

Or the depth operator with:

CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/gpt_pretrain.py \
  --save_prefix final_gpt2_large_div2_depthx2_check_bs512_lr0.0020_warmup3k_seqlen1024_debug \
  --gpu_count -1 \
  --model gpt2  \
  --tokenizer gpt2 \
  --batch_size 4 \
  --grad_accum 32 \
  --lr 0.002006911598778545 \
  --warmup_steps 3000 \
  --train_steps 250000 \
  --val_every 50 \
  --val_batches 50 \
  --fp16 \
  --seqlen 1024 \
  --log_rate 10 \
  --num_workers 4 \
  --size GPT2_large_div2_depth \
  --random \
  --resume final_runs/final_gpt2_large_div2_depth_check_bs512_lr0.0020_warmup3k_seqlen1024_debug/checkpoint-epoch=0-step=6499.ckpt \
  --doubling layers

Evaluation

Use evaluation/eval_wikitext.py or evaluation/eval_lambada.py to evaluate GPT-2 on one of the supported datasets. For example:

python evaluation/eval_wikitext.py

Or using Docker:

docker build -t evaluation:latest .
docker run --rm --gpus all evaluation:latest evaluation/eval_wikitext.py

Reference

If you use staged training in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

@misc{shen2022staged,
    title={Staged Training for Transformer Language Models},
    author={Sheng Shen and Pete Walsh and Kurt Keutzer and Jesse Dodge and Matthew Peters and Iz Beltagy},
    year={2022},
    eprint={2203.06211},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}