Implementation of a Transformer using ReLA (Rectified Linear Attention)

Related tags

Deep Learningrela-transformer
Overview

ReLA (Rectified Linear Attention) Transformer

Implementation of a Transformer using ReLA (Rectified Linear Attention). It will also contain an attempt to combine the feedforward into the ReLA layer as memory key / values, as proposed in All Attention, suggestion made by Charles Foster.

Install

$ pip install rela-transformer

Usage

import torch
from rela_transformer.rela_transformer import ReLATransformer

model = ReLATransformer(
    num_tokens = 20000,
    dim = 512,
    depth = 8,
    max_seq_len = 1024,
    dim_head = 64,
    heads = 8,
    causal = True
)

x = torch.randint(0, 20000, (1, 1024))
logits = model(x) # (1, 1024, 20000)

Enwik8

$ python train.py

Citations

@misc{zhang2021sparse,
    title   = {Sparse Attention with Linear Units},
    author  = {Biao Zhang and Ivan Titov and Rico Sennrich},
    year    = {2021},
    eprint  = {2104.07012},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}
You might also like...
Attention for PyTorch with Linear Memory Footprint
Attention for PyTorch with Linear Memory Footprint

Attention for PyTorch with Linear Memory Footprint Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+

null 11 Jan 9, 2022
Episodic Transformer (E.T.) is a novel attention-based architecture for vision-and-language navigation. E.T. is based on a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions.
Episodic Transformer (E.T.) is a novel attention-based architecture for vision-and-language navigation. E.T. is based on a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions.

Episodic Transformers (E.T.) Episodic Transformer for Vision-and-Language Navigation Alexander Pashevich, Cordelia Schmid, Chen Sun Episodic Transform

Alex Pashevich 62 Dec 24, 2022
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

Jiezhang Cao 225 Nov 13, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers

hierarchical-transformer-1d Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers In Progress!! 2021.

MyungHoon Jin 7 Nov 6, 2022
Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones
Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

Phil Wang 189 Nov 22, 2022
Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification
Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

STAM - Pytorch Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in

Phil Wang 109 Dec 28, 2022
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 7, 2022
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 5, 2023
Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

cosFormer Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention Update log 2022/2/28 Add core code License This

null 120 Dec 15, 2022
Comments
  • LayerNorm/GatedRMS inconsistency

    LayerNorm/GatedRMS inconsistency

    Hi! looking through pipeline it seems there are some inconsistencies with normalisation

    # ReLA
input to GRMSNorm
# att code
output: Linear(inner_dim, dim) + GRMSNorm
# next in FF module 
input to LayerNorm

    here we have problem with double norm since we have last layer GRMSNorm in att and first layer LayerNorm in FF.

    looking at the paper it seems that in ReLA GRMSNorm is applied to result of mult(attn, v) before output projection not after projection like in this code. I also confused about usage of LayerNorm in FF should it be GRMSNorm instead? not clear from the paper as well

    opened by inspirit 6
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
Stochastic Scene-Aware Motion Prediction

Stochastic Scene-Aware Motion Prediction [Project Page] [Paper] Description This repository contains the training code for MotionNet and GoalNet of SA

Mohamed Hassan 31 Dec 09, 2022
Code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"

SEW (Squeezed and Efficient Wav2vec) The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speec

ASAPP Research 67 Dec 01, 2022
Adversarial Attacks are Reversible via Natural Supervision

Adversarial Attacks are Reversible via Natural Supervision ICCV2021 Citation @InProceedings{Mao_2021_ICCV, author = {Mao, Chengzhi and Chiquier

Computer Vision Lab at Columbia University 20 May 22, 2022
TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

Keras implementation of PSPNet(caffe) Implemented Architecture of Pyramid Scene Parsing Network in Keras. For the best compability please use Python3.

VladKry 386 Dec 29, 2022
An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

EquivariantSelfAttention An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astro

2 Nov 09, 2021
Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

forward-thinking-pytorch Pytorch implementation of Forward Thinking: Building and Training Neural Networks One Layer at a Time Requirements Python 2.7

Kim Heecheol 65 Oct 06, 2022
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022

PyCRE Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed

<a href=[email protected]"> 7 May 06, 2022
Python implementation of a live deep learning based age/gender/expression recognizer

TUT live age estimator Python implementation of a live deep learning based age/gender/smile/celebrity twin recognizer. All components use convolutiona

Heikki Huttunen 80 Nov 21, 2022
YOLOX + ROS(1, 2) object detection package

YOLOX + ROS(1, 2) object detection package

Ar-Ray 158 Dec 21, 2022
EfficientNetv2 TensorRT int8

EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu：18.04 cuda：11.0 cudnn：8.0 tensorrt：7

34 Apr 24, 2022
tf2-keras implement yolov5

YOLOv5 in tesnorflow2.x-keras yolov5数据增强jupyter示例 Bilibili视频讲解地址: 《yolov5 解读,训练,复现》 Bilibili视频讲解PPT文件: yolov5_bilibili_talk_ppt.pdf Bilibili视频讲解PPT文件:

yangcheng 254 Jan 08, 2023
A LiDAR point cloud cluster for panoptic segmentation

Divide-and-Merge-LiDAR-Panoptic-Cluster A demo video of our method with semantic prior: More information will be coming soon! As a PhD student, I don'

YimingZhao 65 Dec 22, 2022
Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

Christoph Reich 122 Dec 12, 2022
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Python Outlier Detection (PyOD) Deployment & Documentation & Stats Build Status & Coverage & Maintainability & License PyOD is a comprehensive and sca

Yue Zhao 6.6k Jan 03, 2023
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

2s-AGCN Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19 Note PyTorch version should be 0.3! For PyTor

LShi 547 Dec 26, 2022
Retina blood vessel segmentation with a convolutional neural network

Retina blood vessel segmentation with a convolution neural network (U-net) This repository contains the implementation of a convolutional neural netwo

Orobix 1.2k Jan 06, 2023
Fuzzy Overclustering (FOC)

Fuzzy Overclustering (FOC) In real-world datasets, we need consistent annotations between annotators to give a certain ground-truth label. However, in

2 Nov 08, 2022
MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

MarcoPolo is a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering Overview MarcoPolo

Chanwoo Kim 13 Dec 18, 2022
Active window border replacement for window managers.

xborder Active window border replacement for window managers. Usage git clone https://github.com/deter0/xborder cd xborder chmod +x xborders ./xborder

deter 250 Dec 30, 2022
Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Official Implementation of SimIPU SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations Since

Zhyever 37 Dec 01, 2022