Prefix Tuning

Files:

.
├── gpt2                          # Code for GPT2 style autoregressive LM
│   ├── train_e2e.py              # high-level scripts to train.
│   ├── train_control.py          # code that implements prefix-tuning.
│   ├── trainer_prefix.py         # trainer code for the training loop. 
│   ├── run_language_modeling.py  # training code (contains data loading, model loading, and calls trainer)
│   ├── gen.py                    # high-level scripts to decode. 
│   └── run_generation.py         # decoding code. 
│
├── seq2seq                       # Code for encoder-decoder architecture
│   ├── train_bart.py             # high-level scripts to train.
│   ├── prefixTuning.py           # code that implements prefix-tuning.
│   ├── finetune.py               # training code (contains data loading, model loading, and calls trainer)   
│   ├── lightning_base.py         # helper code
│   ├── utils.py                  # helper code
│   └── callbacks.py              # helper code
└── ...

To run the code for GPT2 style autoregressive LM, the code is in gpt2/. This corresponds to the table-to-text experiments in the paper.

To run the code for encoder-decoder architecture like BART, the code is in seq2seq. This corresponds to the summarization experiments in the paper.

The two primary scripts I used to run my codes are gpt2/train_e2e.py (for table-to-text) and seq2seq/train_bart.py(for summarization). they are set to default of good hyperparameters, and can be used to tune hyperparameter :)

Setup:

cd transformer; pip install -e .

Train via prefix-tuning:

cd gpt2;

python train_e2e.py --optim_prefix yes --preseqlen 5 --epoch 5 --learning_rate 0.00005 --mode webnlg --bsz 5 --seed 101

cd seq2seq; 

python train_bart.py --mode xsum --preseqlen 200 --do_train yes --fp16 yes --bsz 16  --epoch 30  --gradient_accumulation_step 3 --learning_rate 0.00005  --mid_dim 800

Other baseline approaches

cd gpt2;

python train_e2e.py --tuning_mode {finetune/adaptertune} --epoch 5 --learning_rate 0.00005 --mode webnlg --bsz 5 --seed 101

cd seq2seq;

python train_e2e.py --tuning_mode finetune --epoch 5 --learning_rate 0.00005 --mode webnlg --bsz 5 --seed 101

Decode:

cd gpt2;

python gen.py {data2text/webnlg/...} yes test {checkpoint_path} no

cd seq2seq; 

python train_bart.py --mode xsum --do_train no --prefix_model_path {checkpoint_path} --preseqlen {same as training} --mid_dim {same as training}

For details of the methods and results, please refer to our paper.

@misc{li2021prefixtuning,
      title={Prefix-Tuning: Optimizing Continuous Prompts for Generation}, 
      author={Xiang Lisa Li and Percy Liang},
      year={2021},
      eprint={2101.00190},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Related tags

Overview

Prefix Tuning

Files:

Setup:

Train via prefix-tuning:

Decode:

Owner

Invariant Causal Prediction for Block MDPs

DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.

Boundary-aware Transformers for Skin Lesion Segmentation

Unofficial implementation of One-Shot Free-View Neural Talking Head Synthesis

Research code of ICCV 2021 paper "Mesh Graphormer"

Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

Controlling the MicriSpotAI robot from scratch

Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

使用yolov5训练自己数据集(详细过程)并通过flask部署

Norm-based Analysis of Transformer

SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker

Code for the paper: Sketch Your Own GAN

Sparse-dense operators implementation for Paddle

Code for the paper A Theoretical Analysis of the Repetition Problem in Text Generation

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction