DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

Overview

DSEE

Codes for [Preprint] DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Chen, Zhangyang Wang, Ahmed Hassan Awadallahp

License: MIT

Overview

TBD

Requirements

We use conda to create virtual environments.

conda create -f environment.yml
conda activate dsee

Command

Unstructured DSEE

Step 0.

cd non-GPT-2
pip install -e .
cd ..

Step 1. Pre-training

Take SST-2 as example:

OUTPUT_DIR='./sst2_rank16_s1_64'
num_gpus=4
python -m torch.distributed.launch \
    --nproc_per_node=$num_gpus \
    --master_port=12345 non-GPT-2/examples/pytorch/text-classification/run_glue.py \
    --save_total_limit 10 \
    --model_name_or_path bert-base-uncased \ 
    --task_name sst2 \
    --output_dir ${OUTPUT_DIR} \
    --do_train \
    --do_eval \
    --num_train_epochs 3 \
    --save_steps 50 \
    --seed 1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --max_seq_length 128 \
    --overwrite_output_dir \
    --logging_steps 50 \
    --load_best_model_at_end True \
    --metric_for_best_model eval_accuracy \
    --apply_lora \
    --lora_r 16 \
    --apply_sparse \
    --num_sparse 64  \
    --learning_rate 2e-4 \
    --evaluation_strategy steps 

Step 2. Pruning & Fine-tuning

OUTPUT_DIR='./sst2_rank16_s1_64_prune_0.5'
num_gpus=4
python -m torch.distributed.launch \
    --nproc_per_node=$num_gpus \
    --master_port=12335 \
    non-GPT-2/examples/pytorch/text-classification/run_glue_prune_tune.py \
    --save_total_limit 10 \
    --model_name_or_path sst2_rank16_s1_64 \
    --task_name sst2 \
    --output_dir ${OUTPUT_DIR} \
    --do_train \
    --do_eval \
    --num_train_epochs 3 \
    --save_steps 50 \
    --seed 1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --max_seq_length 128 \
    --overwrite_output_dir \
    --logging_steps 50 \
    --load_best_model_at_end True \
    --metric_for_best_model eval_accuracy \
    --apply_lora \
    --lora_r 16 \
    --apply_sparse \
    --num_sparse 64 \
    --learning_rate 2e-4 \
    --pruning_ratio 0.5 \
    --evaluation_strategy steps

TODO

  • Codes for Unstructured DSEE on GPT-2
  • Codes for Structured DSEE

Acknowledgement

  1. The Huggingface's Transformers (https://github.com/huggingface/transformers)
Owner
VITA
Visual Informatics Group @ University of Texas at Austin
VITA
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
Code and data for "TURL: Table Understanding through Representation Learning"

TURL This Repo contains code and data for "TURL: Table Understanding through Representation Learning". Environment and Setup Data Pretraining Finetuni

SunLab-OSU 63 Nov 23, 2022
Massively parallel Monte Carlo diffusion MR simulator written in Python.

Disimpy Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of dat

Leevi 16 Nov 11, 2022
Liver segmentation using MONAI and pytorch

Machine Learning use case in the field of Healthcare. In this project MONAI and pytorch frameworks are used for 3D Liver segmentation.

Abhishek Gajbhiye 2 May 30, 2022
3D position tracking for soccer players with multi-camera videos

This repo contains a full pipeline to support 3D position tracking of soccer players, with multi-view calibrated moving/fixed video sequences as inputs.

Yuchang Jiang 72 Dec 27, 2022
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021 Spotlight

PCAN for Multiple Object Tracking and Segmentation This is the offical implementation of paper PCAN for MOTS. We also present a trailer that consists

ETH VIS Group 328 Dec 29, 2022
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations This repo contains official code for the NeurIPS 2021 paper Imi

Jiayao Zhang 2 Oct 18, 2021
2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup)智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境: python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Astitva Veer Garg 1 Jan 13, 2022
A package for "Procedural Content Generation via Reinforcement Learning" OpenAI Gym interface.

Readme: Illuminating Diverse Neural Cellular Automata for Level Generation This is the codebase used to generate the results presented in the paper av

Sam Earle 27 Jan 05, 2023
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem Liang Xin, Wen Song, Zhiguang

xinliangedu 33 Dec 27, 2022
METER: Multimodal End-to-end TransformER

METER Code and pre-trained models will be publicized soon. Citation @article{dou2021meter, title={An Empirical Study of Training End-to-End Vision-a

Zi-Yi Dou 257 Jan 06, 2023
ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm

ManipulaTHOR: A Framework for Visual Object Manipulation Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha

AI2 65 Dec 30, 2022
A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

AMAZ3DSim AMAZ3DSim is a lightweight python-based 3D network multi-agent simulator. It uses a cell-based congestion model. It calculates risk, battery

Daniel Hirsch 13 Nov 04, 2022
Simulate genealogical trees and genomic sequence data using population genetic models

msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis

Tskit developers 150 Dec 14, 2022
A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

About This repository contains the code of a PaddlePaddle implementation of STGCN based on the paper Spatio-Temporal Graph Convolutional Networks: A D

Tianjian Li 1 Jan 11, 2022
A deep learning model for style-specific music generation.

DeepJ: A model for style-specific music generation https://arxiv.org/abs/1801.00887 Abstract Recent advances in deep neural networks have enabled algo

Henry Mao 704 Nov 23, 2022
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
Preprocessed Datasets for our Multimodal NER paper

Unified Multimodal Transformer (UMT) for Multimodal Named Entity Recognition (MNER) Two MNER Datasets and Codes for our ACL'2020 paper: Improving Mult

76 Dec 21, 2022
MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

Front-end View Backend View Table of Contents Description Prerequisites Running Basic Information Measurements User Interface Feedback and usage Descr

Center for Computational Imaging and Personalized Diagnostics 58 Dec 02, 2022