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

Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

51 Dec 29, 2022

Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

7 Jan 24, 2022

Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch This is WORK IN PROGRESS, please feel free to contribute vi

94 Nov 22, 2022

A Convolutional Transformer for Keyword Spotting

☢️ Audiomer ☢️ Audiomer: A Convolutional Transformer for Keyword Spotting [ arXiv ] [ Previous SOTA ] [ Model Architecture ] Results on SpeechCommands

49 Jan 27, 2022

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

985 Jan 08, 2023

Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

19 Dec 07, 2022

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

443 Jan 06, 2023

A collection of loss functions for medical image segmentation

3.1k Jan 03, 2023

Create UIs for prototyping your machine learning model in 3 minutes

Note: We just launched Hosted, where anyone can upload their interface for permanent hosting. Check it out! Welcome to Gradio Quickly create customiza

11.7k Jan 07, 2023

A pyparsing-based library for parsing SOQL statements

CONTRIBUTORS WANTED!! Installation pip install python-soql-parser or, with poetry poetry add python-soql-parser Usage from python_soql_parser import p

0 Jun 07, 2022

Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

227 Dec 31, 2022

This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing described in paper Discontinuous Grammar as a Foreign Language.

Discontinuous Grammar as a Foreign Language This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing

2 Apr 07, 2022

Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN in PyTorch Official implementation of StyleCariGAN:Caricature Generation via StyleGAN Feature Map Modulation in PyTorch Requirements PyTo

49 Oct 31, 2022

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval - ICCV2021

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval Pytorch implementation of SPQ Accepted to ICCV 2021 - paper Young Kyun Jang

71 Dec 27, 2022

Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022

Official Datasets and Implementation from our Paper "Video Class Agnostic Segmentation in Autonomous Driving".

Video Class Agnostic Segmentation [Method Paper] [Benchmark Paper] [Project] [Demo] Official Datasets and Implementation from our Paper "Video Class A

26 Oct 24, 2022

Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

12 Nov 23, 2022

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) We consider how a user of a web servi

20 Aug 21, 2022

OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

428 Dec 24, 2022

Masked regression code - Masked Regression

Masked Regression MR - Python Implementation This repositery provides a python implementation of MR (Masked Regression). MR can efficiently synthesize

1 Dec 23, 2021