Prefix-Tuning: Optimizing Continuous Prompts for Generation

Overview

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}
}
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
Learning to Predict Gradients for Semi-Supervised Continual Learning

Learning to Predict Gradients for Semi-Supervised Continual Learning Code for project: "Learning to Predict Gradients for Semi-Supervised Continual Le

Yan Luo 2 Mar 05, 2022
The original weights of some Caffe models, ported to PyTorch.

pytorch-caffe-models This repo contains the original weights of some Caffe models, ported to PyTorch. Currently there are: GoogLeNet (Going Deeper wit

Katherine Crowson 9 Nov 04, 2022
TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Sayak Paul 67 Dec 20, 2022
AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-

AI Necromancer 299 Dec 17, 2022
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

MLP Mixer Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. Give us a star if you like this repo. Author: Github: bangoc123 Emai

Ngoc Nguyen Ba 86 Dec 10, 2022
Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Sara Fridovich-Keil 81 Dec 25, 2022
An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

Xingdong Zuo 12 Dec 02, 2022
Image classification for projects and researches

This is a tool to help you quickly solve classification problems including: data analysis, training, report results and model explanation.

Nguyễn Trường Lâu 2 Dec 27, 2021
Methods to get the probability of a changepoint in a time series.

Bayesian Changepoint Detection Methods to get the probability of a changepoint in a time series. Both online and offline methods are available. Read t

Johannes Kulick 554 Dec 30, 2022
Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

federated is the source code for the Bachelor's Thesis Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federat

Dilawar Mahmood 25 Nov 30, 2022
This repository will be a summary and outlook on all our open, medical, AI advancements.

medical by LAION This repository will be a summary and outlook on all our open, medical, AI advancements. See the medical-general channel in the medic

LAION AI 18 Dec 30, 2022
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

CV Newbie 28 Dec 13, 2022
EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures

SCICAP: Scientific Figures Dataset This is the Github repo of the EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures (Hsu

Edward 26 Nov 21, 2022
The official repository for "Score Transformer: Generating Musical Scores from Note-level Representation" (MMAsia '21)

Score Transformer This is the official repository for "Score Transformer": Score Transformer: Generating Musical Scores from Note-level Representation

22 Dec 22, 2022
Einshape: DSL-based reshaping library for JAX and other frameworks.

Einshape: DSL-based reshaping library for JAX and other frameworks. The jnp.einsum op provides a DSL-based unified interface to matmul and tensordot o

DeepMind 62 Nov 30, 2022
Streaming over lightweight data transformations

Description Data augmentation libarary for Deep Learning, which supports images, segmentation masks, labels and keypoints. Furthermore, SOLT is fast a

Research Unit of Medical Imaging, Physics and Technology 256 Jan 08, 2023
[CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis [arxiv|pdf|v

Yinan He 78 Dec 22, 2022
Taming Transformers for High-Resolution Image Synthesis

Taming Transformers for High-Resolution Image Synthesis CVPR 2021 (Oral) Taming Transformers for High-Resolution Image Synthesis Patrick Esser*, Robin

CompVis Heidelberg 3.5k Jan 03, 2023
Predictive Maintenance LSTM

Predictive-Maintenance-LSTM - Predictive maintenance study for Complex case study, we've obtained failure causes by operational error and more deeply by design mistakes.

Amir M. Sadafi 1 Dec 31, 2021