adamix-gpt2 0.0.2

PyTorch implementation of low-rank adaptation (LoRA) and Adamix, a parameter-efficient approach to adapt a large pre-trained deep learning model which obtains performance better than full fine-tuning.

Adapting GPT-2 using LoRA, Adapters and Adamix

This folder contains the implementation of LoRA, Adamix Adapter and Adamix LoRA in GPT-2 using the modiifed Python package lora and steps to replicate the results in our recent paper

This repo reproduces our experiments on GPT-2 Medium.

Repository Overview

Our implementation is based on the fine-tuning code for GPT-2 in Hugging Face. There are several directories in this repo:

• src/ contains the source code used for data processing, training, and decoding.
• eval/ contains the code for task-specific evaluation scripts.
• data/ contains the raw data we used in our experiments.
• vocab/ contains the GPT-2 vocabulary files.

Getting Started

1. You can start with the following docker image: nvcr.io/nvidia/pytorch:20.03-py3 on a GPU-capable machine, but any generic PyTorch image should work.

docker run -it nvcr.io/nvidia/pytorch:20.03-py3

2. Clone the repo and install dependencies using the provided setup script in a virtual environment (remove sudo wherever necessary if running in docker container):

bash setup.sh

Now we are ready to replicate the results in our paper.

Replicating Our Results on GPT-2 Medium

(see our paper for hyperparameters for GPT-2 Medium)

1. Train GPT-2 Medium on E2E NLG Challenge dataset

• LoRA

  python -m torch.distributed.launch --nproc_per_node=1 src/gpt2_ft.py \
  --train_data ./data/e2e/train.jsonl \
  --valid_data ./data/e2e/valid.jsonl \
  --train_batch_size 8 \
  --grad_acc 1 \
  --valid_batch_size 4 \
  --seq_len 512 \
  --model_card gpt2.md \
  --init_checkpoint ./pretrained_checkpoints/gpt2-medium-pytorch_model.bin \
  --platform local \
  --clip 0.0 \
  --lr 0.0002 \
  --weight_decay 0.01 \
  --correct_bias \
  --adam_beta2 0.999 \
  --scheduler linear \
  --warmup_step 500 \
  --max_epoch 5 \
  --save_interval 1000 \
  --lora_dim 4 \
  --lora_alpha 32 \
  --lora_dropout 0.1 \
  --label_smooth 0.1 \
  --work_dir ./trained_models/GPT2_M/e2e/lora_only \
  --random_seed 110 \
  --lora_only 1
  

• Adapter with Adamix

  python -m torch.distributed.launch --nproc_per_node=1 src/gpt2_ft.py \
  --train_data ./data/e2e/train.jsonl \
  --valid_data ./data/e2e/valid.jsonl \
  --train_batch_size 8 \
  --grad_acc 1 \
  --valid_batch_size 4 \
  --seq_len 512 \
  --model_card gpt2.md \
  --init_checkpoint ./pretrained_checkpoints/gpt2-medium-pytorch_model.bin \
  --platform local \
  --clip 0.0 \
  --lr 0.0002 \
  --weight_decay 0.01 \
  --correct_bias \
  --adam_beta2 0.999 \
  --scheduler linear \
  --warmup_step 2000 \
  --max_epoch 20 \
  --eval_interval 5000 \
  --save_interval 5000 \
  --lora_dim 4 \
  --lora_alpha 32 \
  --lora_dropout 0.1 \
  --label_smooth 0.1 \
  --work_dir ./trained_models/GPT2_M/e2e/adapter_adamix \
  --random_seed 110 \
  --adamix_only 1 \
  --n_experts 8 \
  --share_A 0 \
  --share_B 1
  

• LoRA with Adamix

  python -m torch.distributed.launch --nproc_per_node=1 src/gpt2_ft.py \
  --train_data ./data/e2e/train.jsonl \
  --valid_data ./data/e2e/valid.jsonl \
  --train_batch_size 8 \
  --grad_acc 1 \
  --valid_batch_size 4 \
  --seq_len 512 \
  --model_card gpt2.md \
  --init_checkpoint ./pretrained_checkpoints/gpt2-medium-pytorch_model.bin \
  --platform local \
  --clip 0.0 \
  --lr 0.0002 \
  --weight_decay 0.01 \
  --correct_bias \
  --adam_beta2 0.999 \
  --scheduler linear \
  --warmup_step 2000 \
  --max_epoch 20 \
  --eval_interval 5000 \
  --save_interval 5000 \
  --lora_dim 4 \
  --lora_alpha 32 \
  --lora_dropout 0.1 \
  --label_smooth 0.1 \
  --work_dir ./trained_models/GPT2_M/e2e/lora_adamix \
  --random_seed 110 \
  --n_experts 8 \
  --share_A 0 \
  --share_B 1
  

2. Generate outputs from the trained model using beam search (LoRA with Adamix):

   python -m torch.distributed.launch --nproc_per_node=1 src/gpt2_beam.py \
   --data ./data/e2e/test.jsonl \
   --batch_size 1 \
   --seq_len 128 \
   --eval_len 64 \
   --model_card gpt2.md \
   --init_checkpoint ./trained_models/GPT2_M/e2e/lora_adamix/model.final.pt \
   --platform local \
   --lora_dim 4 \
   --lora_alpha 32 \
   --beam 10 \
   --length_penalty 0.8 \
   --no_repeat_ngram_size 4 \
   --repetition_penalty 1.0 \
   --eos_token_id 628 \
   --work_dir ./trained_models/GPT2_M/e2e/lora_adamix \
   --output_file predict.jsonl \
   --n_experts 8 \
   --share_A 0 \
   --share_B 1
   

3. Decode outputs from step (2)

   python src/gpt2_decode.py \
   --vocab ./vocab \
   --sample_file ./trained_models/GPT2_M/e2e/lora_adamix/predict.jsonl \
   --input_file ./data/e2e/test_formatted.jsonl \
   --output_ref_file e2e_ref.txt \
   --output_pred_file e2e_pred.txt
   

4. Run evaluation on E2E test set

   python eval/e2e/measure_scores.py e2e_ref.txt e2e_pred.txt -p
   

Replicating Our Result on WebNLG

1. Follow steps 1 and 2 from E2E pipeline by replacing references to E2E with webnlg (see our paper for hyperparameters)

2. Decode outputs from beam search (step 2 above)

   python src/gpt2_decode.py \
       --vocab ./vocab \
       --sample_file ./trained_models/GPT2_M/webnlg/lora_adamix/predict.jsonl \
       --input_file ./data/webnlg_challenge_2017/test_formatted.jsonl \
       --ref_type webnlg \
       --ref_num 6 \
       --output_ref_file eval/GenerationEval/data/references_webnlg \
       --output_pred_file eval/GenerationEval/data/hypothesis_webnlg \
       --tokenize --lower
   

3. Run evaluation on WebNLG test set

   cd ./eval/GenerationEval/
   python eval.py \
       -R data/references_webnlg/reference \
       -H data/hypothesis_webnlg \
       -nr 6 \
       -m bleu,meteor,ter 
   cd ../..
   

Replicating Our Result on DART

1. Follow steps 1 and 2 from E2E pipeline by replacing references to E2E with dart (see our paper for hyperparameters)

2. Decode outputs from beam search (step 2 above)

   python src/gpt2_decode.py \
           --vocab ./vocab \
           --sample_file ./trained_models/GPT2_M/dart/lora_adamix/predict.jsonl \
           --input_file ./data/dart/test_formatted.jsonl \
           --ref_type dart \
           --ref_num 6 \
           --output_ref_file eval/GenerationEval/data/references_dart \
           --output_pred_file eval/GenerationEval/data/hypothesis_dart \
           --tokenize --lower
   

3. Run evaluation on Dart test set

   cd ./eval/GenerationEval/
   python eval.py \
       -R data/references_dart/reference \
       -H data/hypothesis_dart \
       -nr 6 \
       -m bleu,meteor,ter 
   cd ../..