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.
Requires Python: >=3.6
Keywords: python,adamix,peft

# 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

<p>
<img src="https://github.com/microsoft/AdaMix/raw/main/NLG/figures/Results.png" width="800" >
</p>

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](https://huggingface.co/).
There are several directories in this repo:
* [src/](src) contains the source code used for data processing, training, and decoding.
* [eval/](eval) contains the code for task-specific evaluation scripts.
* [data/](data) contains the raw data we used in our experiments.
* [vocab/](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 ../..
    ```