Train GPT-3 model on V100(16GB Mem) Using improved Transformer.

Related tags

Text Data & NLPgpt
Overview

Pytorch GPT-X

My Own Pytorch GPT-X

1. Abstract

Train GPT-3 model on V100(16GB Mem) Using improved Transformer.

2. Model

Transformer

Additional Module

① Rezero

Rezero Is All You Need link

② Explicit Sparse Transformer

Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection link

③ Macaron Architecture

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View link

④ RealFormer, Residual Attention

RealFormer link

Train

DeepSpeed

TODO

  • ReZero
  • RealFormer, Residual Attention
  • Macaron architectures
  • Macaron architectures - layer Scale 0.5
  • Explicit Sparse Transformer
  • torch lightning
  • Deepspeed train on single GPU
  • Deepspeed parallel trainig on 2 V100 GPU with 16GB Memory

Parameter For Few-shot

The 175B parameter model is very large, but a large model is needed for Few-Shot Learning. So this repository try to use DeepSpeed for training extremely big model.

GPT-3 Config

model_name n_params n_layer d_model n_heads d_head batch_size learning_rate
GPT-3 175B 175B 96 12288 96 128 3.2M 0.6 x 10^-4
GPT-3 13B 13B 40 5140 40 128 2M 1.0 x 10^-4
GPT-3 6.7B 6.7B 32 4096 32 128 2M 1.2 x 10^-4
GPT-3 2.7B 2.7B 32 25560 32 80 1M 1.6 x 10^-4

References

Transformer

DeepSpeed

ReZero

Explicit Sparse Transformer

Macaron Architecrue

Owner
Seonghwan Kim
Seonghwan Kim
Code for ACL 2022 main conference paper "STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation".

STEMM: Self-learning with Speech-Text Manifold Mixup for Speech Translation This is a PyTorch implementation for the ACL 2022 main conference paper ST

ICTNLP 29 Oct 16, 2022
Proquabet - Convert your prose into proquints and then you essentially have Vogon poetry

Proquabet Turn your prose into a constant stream of encrypted and meaningless-so

Milo Fultz 2 Oct 10, 2022
T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets

T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets (product titles, images, comments, etc.).

55 Nov 22, 2022
使用pytorch+transformers复现了SimCSE论文中的有监督训练和无监督训练方法

SimCSE复现 项目描述 SimCSE是一种简单但是很巧妙的NLP对比学习方法,创新性地引入Dropout的方式,对样本添加噪声,从而达到对正样本增强的目的。 该框架的训练目的为:对于batch中的每个样本,拉近其与正样本之间的距离,拉远其与负样本之间的距离,使得模型能够在大规模无监督语料(也可以

58 Dec 20, 2022
本插件是pcrjjc插件的重置版,可以独立于后端api运行

pcrjjc2 本插件是pcrjjc重置版,不需要使用其他后端api,但是需要自行配置客户端 本项目基于AGPL v3协议开源,由于项目特殊性,禁止基于本项目的任何商业行为 配置方法 环境需求:.net framework 4.5及以上 jre8 别忘了装jre8 别忘了装jre8 别忘了装jre8

132 Dec 26, 2022
DeepSpeech - Easy-to-use Speech Toolkit including SOTA ASR pipeline, influential TTS with text frontend and End-to-End Speech Simultaneous Translation.

(简体中文|English) Quick Start | Documents | Models List PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks i

5.6k Jan 03, 2023
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoenc

Venelin Valkov 1.8k Dec 31, 2022
A2T: Towards Improving Adversarial Training of NLP Models (EMNLP 2021 Findings)

A2T: Towards Improving Adversarial Training of NLP Models This is the source code for the EMNLP 2021 (Findings) paper "Towards Improving Adversarial T

QData 17 Oct 15, 2022
NLP, Machine learning

Netflix-recommendation-system NLP, Machine learning About Recommendation algorithms are at the core of the Netflix product. It provides their members

Harshith VH 6 Jan 12, 2022
A framework for cleaning Chinese dialog data

A framework for cleaning Chinese dialog data

Yida 136 Dec 20, 2022
Code for the paper "Language Models are Unsupervised Multitask Learners"

Status: Archive (code is provided as-is, no updates expected) gpt-2 Code and models from the paper "Language Models are Unsupervised Multitask Learner

OpenAI 16.1k Jan 08, 2023
Pipeline for chemical image-to-text competition

BMS-Molecular-Translation Introduction This is a pipeline for Bristol-Myers Squibb – Molecular Translation by Vadim Timakin and Maksim Zhdanov. We got

Maksim Zhdanov 7 Sep 20, 2022
Winner system (DAMO-NLP) of SemEval 2022 MultiCoNER shared task over 10 out of 13 tracks.

KB-NER: a Knowledge-based System for Multilingual Complex Named Entity Recognition The code is for the winner system (DAMO-NLP) of SemEval 2022 MultiC

116 Dec 27, 2022
NLTK Source

Natural Language Toolkit (NLTK) NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting

Natural Language Toolkit 11.4k Jan 04, 2023
A simple version of DeTR

DeTR-Lite A simple version of DeTR Before you enjoy this DeTR-Lite The purpose of this project is to allow you to learn the basic knowledge of DeTR. P

Jianhua Yang 11 Jun 13, 2022
A Chinese to English Neural Model Translation Project

ZH-EN NMT Chinese to English Neural Machine Translation This project is inspired by Stanford's CS224N NMT Project Dataset used in this project: News C

Zhenbang Feng 29 Nov 26, 2022
Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Low-resource-Machine-Translation This repository contains the code for the project relative to the course Deep Natural Language Processing. The goal o

Andrea Cavallo 3 Jun 22, 2022
The FinQA dataset from paper: FinQA: A Dataset of Numerical Reasoning over Financial Data

Data and code for EMNLP 2021 paper "FinQA: A Dataset of Numerical Reasoning over Financial Data"

Zhiyu Chen 114 Dec 29, 2022
Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

1.3k Dec 26, 2022
ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体,包括上市公司所属行业关系、行业上级关系、产品上游原材料关系、产品下游产品关系、公司主营产品、产品小类共6大类。 上市公司4,654家,行业511个,产品95,559条、上游材料56,824条,上级行业480条,下游产品390条,产品小类52,937条,所属行业3,946条。

liuhuanyong 415 Jan 06, 2023