An implementation of Fastformer: Additive Attention Can Be All You Need in TensorFlow

Last update: Dec 28, 2022

Overview

Fast Transformer

This repo implements Fastformer: Additive Attention Can Be All You Need by Wu et al. in TensorFlow. Fast Transformer is a Transformer variant based on additive attention that can handle long sequences efficiently with linear complexity. Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance.

Installation

Run the following to install:

pip install fast-transformer

Developing fast-transformer

To install fast-transformer, along with tools you need to develop and test, run the following in your virtualenv:

git clone https://github.com/Rishit-dagli/Fast-Transformer.git
# or clone your own fork

cd fast-transformer
pip install -e .[dev]

Usage

import tensorflow as tf
from fast_transformer import FastTransformer

mask = tf.ones([1, 4096], dtype=tf.bool)
model = FastTransformer(
    num_tokens = 20000,
    dim = 512,
    depth = 2,
    max_seq_len = 4096,
    absolute_pos_emb = True, # Absolute positional embeddings
    mask = mask
)
x = tf.experimental.numpy.random.randint(0, 20000, (1, 4096))

logits = model(x) # (1, 4096, 20000)

Want to Contribute 🙋‍♂️ ?

Awesome! If you want to contribute to this project, you're always welcome! See Contributing Guidelines. You can also take a look at open issues for getting more information about current or upcoming tasks.

Want to discuss? 💬

Have any questions, doubts or want to present your opinions, views? You're always welcome. You can start discussions.

Citation

@misc{wu2021fastformer,
    title   = {Fastformer: Additive Attention is All You Need}, 
    author  = {Chuhan Wu and Fangzhao Wu and Tao Qi and Yongfeng Huang},
    year    = {2021},
    eprint  = {2108.09084},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

Yannic Kilcher's video was super helpful while building this.

License

Copyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

You might also like...

Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

11 Mar 14, 2022

The tool under this branch fork can be used to crack devices above A12 and up to A15. After cracking, you can also use SSH channel strong opening tool to open SSH channel and activate it with Demo or Shell script. The file can be extracted from my Github homepage, and the SSH channel opening tool can be extracted from Dr238 account.

Welcome to C0xy-A12-A15-Attack-Tool The tool under this branch fork can be used to crack devices above A12 and up to A15. After cracking, you can also

13 Dec 23, 2022

PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."

PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick." [Project page] [Paper

59 Sep 25, 2022

Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

134 Dec 30, 2022

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

6.1k Jan 5, 2023

Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

41 Jan 7, 2023

Neural network-based build time estimation for additive manufacturing

Neural network-based build time estimation for additive manufacturing Oh, Y., Sharp, M., Sprock, T., & Kwon, S. (2021). Neural network-based build tim

1 Nov 15, 2021

Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

79 Dec 28, 2022

Code for "Diffusion is All You Need for Learning on Surfaces"

Source code for "Diffusion is All You Need for Learning on Surfaces", by Nicholas Sharp Souhaib Attaiki Keenan Crane Maks Ovsjanikov NOTE: the linked

247 Dec 28, 2022

Comments

Implement Additive Attention
Implement Additive Attention as a TensorFlow layer:

[x] Figure out using rotary embeddings

[x] Add masking functionality

[x] Relative Position embeddings

[x] Calculate query attention logits

[x] Calculate Global Query tokens

[x] Calculate key attention logits

[x] Calculate Global Key tokens

[x] Add queries as residuals
opened by Rishit-dagli 0

Releases(v0.2.0)

v0.2.0(Jan 16, 2022)
✅ Bug Fixes / Improvements

Unit Tests for output rank and shape

Looser dependency requirements (now supports all TensorFlow versions >= 2.5.0)

Source code(tar.gz)
Source code(zip)
v0.1.0(Sep 3, 2021)
This is the initial release of Fast Transformer and implements Fast Transformer as a subclassed TensorFlow model.

Classes

FastAttention: Implements additive attention as a TensorFlow Keras layer, and supports using relative positional encodings.

PreNorm: Normalize the activations of the previous layer for each given example in a batch independently and apply some function to it, implemented as a TensorFlow Keras Layer.

FeedForward: Create a FeedForward neural net with two Dense layers and GELU activation, implemented as a TensorFlow Keras Layer.

FastTransformer: Implements the FastTransformer model using all the other classes, allows using rotary embeddings, weight tie projections, and converts to logits. Implemented as a TensorFlow Keras Model.

Source code(tar.gz)
Source code(zip)

Owner

Rishit Dagli

High School,TEDx,2xTED-Ed speaker | International Speaker | Microsoft Student Ambassador | Mentor, @TFUGMumbai | Organize @KotlinMumbai

GitHub Repository

Malware Env for OpenAI Gym

Malware Env for OpenAI Gym Citing If you use this code in a publication please cite the following paper: Hyrum S. Anderson, Anant Kharkar, Bobby Fila

563 Dec 29, 2022

Feature extraction made simple with torchextractor

torchextractor: PyTorch Intermediate Feature Extraction Introduction Too many times some model definitions get remorselessly copy-pasted just because

89 Oct 31, 2022

The AWS Certified SysOps Administrator

The AWS Certified SysOps Administrator – Associate (SOA-C02) exam is intended for system administrators in a cloud operations role who have at least 1 year of hands-on experience with deployment, man

32 Dec 11, 2022

Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

13 Dec 14, 2022

Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an

152 Dec 23, 2022

Evolution Strategies in PyTorch

Evolution Strategies This is a PyTorch implementation of Evolution Strategies. Requirements Python 3.5, PyTorch = 0.2.0, numpy, gym, universe, cv2 Wh

333 Nov 14, 2022

a dnn ai project to classify which food people are eating on audio recordings

Deep Learning - EAT Challenge About This project is part of an AI challenge of the DeepLearning course 2021 at the University of Augsburg. The objecti

1 Oct 24, 2021

[ICCV 2021] Released code for Causal Attention for Unbiased Visual Recognition

CaaM This repo contains the codes of training our CaaM on NICO/ImageNet9 dataset. Due to my recent limited bandwidth, this codebase is still messy, wh

66 Dec 31, 2022

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

145 Jan 01, 2023

Official implementation of "An Image is Worth 16x16 Words, What is a Video Worth?" (2021 paper)

An Image is Worth 16x16 Words, What is a Video Worth? paper Official PyTorch Implementation Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor DAMO Academy, Al

213 Nov 12, 2022

Numerical Methods with Python, Numpy and Matplotlib

Numerical Bric-a-Brac Collections of numerical techniques with Python and standard computational packages (Numpy, SciPy, Numba, Matplotlib ...). Diffe

10 Dec 20, 2021

Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Pano3D A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation Pano3D is a new benchmark for depth estimation from spherical panoramas. We

50 Dec 29, 2022

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021.

UniRE Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021. Requirements python: 3.7.6 pytorch: 1.8.1 transformers:

109 Nov 29, 2022

A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video

45 Nov 29, 2022

load .txt to train YOLOX, same as Yolo others

YOLOX train your data you need generate data.txt like follow format (per line- one image). prepare one data.txt like this: img_path1 x1,y1,x2,y2,clas

18 Aug 18, 2022

Mixup for Supervision, Semi- and Self-Supervision Learning Toolbox and Benchmark

OpenSelfSup News Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes). 'GaussianBlur' is

332 Jan 03, 2023

Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

19 Dec 12, 2022

RID-Noise: Towards Robust Inverse Design under Noisy Environments

This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B

2 Nov 23, 2022

Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

39 Dec 29, 2022

PyElastica is the Python implementation of Elastica, an open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.

PyElastica PyElastica is the python implementation of Elastica: an open-source project for simulating assemblies of slender, one-dimensional structure

105 Jan 09, 2023