Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

Related tags

Deep Learningmemorytransformersartificial-intelligenceattention-mechanism
Overview

Memformer - Pytorch

Implementation of Memformer, a Memory-augmented Transformer, in Pytorch. It includes memory slots, which are updated with attention, learned efficiently through Memory-Replay BackPropagation (MRBP) through time.

Install

$ pip install memformer

Usage

Full encoder / decoder, as in the paper

import torch
from memformer import Memformer

model = Memformer(
    dim = 512,
    enc_num_tokens = 256,
    enc_depth = 2,
    enc_heads = 8,
    enc_max_seq_len = 1024,
    dec_num_tokens = 256,
    dec_depth = 2,
    dec_heads = 8,
    dec_max_seq_len = 1024,
    num_memory_slots = 128
)

src_seg_1 = torch.randint(0, 256, (1, 1024))
src_seg_2 = torch.randint(0, 256, (1, 1024))
src_seg_3 = torch.randint(0, 256, (1, 1024))

tgt = torch.randint(0, 256, (1, 1024))

enc_out1, mems1,    _ = model(src_seg_1) # (1, 1024, 512), (1, 128, 512), _
enc_out2, mems2,    _ = model(src_seg_2, mems = mems1)
enc_out3, mems3, loss = model(src_seg_3, tgt, mems = mems2)

loss.backward()

Encoder only

import torch
from memformer import Memformer

model = Memformer(
    dim = 512,
    enc_num_tokens = 256,
    enc_heads = 8,
    enc_depth = 2,
    enc_max_seq_len = 1024,
    num_memory_slots = 128,
    num_mem_updates = 2,
    encoder_only = True       # only use encoder, in which output is encoded output
)

src1 = torch.randint(0, 256, (1, 1024))
src2 = torch.randint(0, 256, (1, 1024))

enc1, mems1 = model(src1) # (1, 1024, 512), (1, 128, 512)
enc2, mems2 = model(src2, mems = mems1)

Memory Replay Back-Propagation

import torch
from memformer import Memformer, memory_replay_backprop

model = Memformer(
    dim = 512,
    num_memory_slots = 128,
    enc_num_tokens = 256,
    enc_depth = 2,
    enc_max_seq_len = 1024,
    dec_num_tokens = 256,
    dec_depth = 2,
    dec_max_seq_len = 1024
).cuda()

seq = torch.randint(0, 256, (1, 8192)).cuda()
seq_mask = torch.ones_like(seq).bool().cuda()

tgt = torch.randint(0, 256, (1, 512)).cuda()
tgt_mask = torch.ones_like(tgt).bool().cuda()

# will automatically split the source sequence to 8 segments
memory_replay_backprop(
    model,
    src = seq,
    tgt = tgt,
    src_mask = seq_mask,
    tgt_mask = tgt_mask
)

Citations

@inproceedings{
    anonymous2021memformer,
    title={Memformer: The Memory-Augmented Transformer},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=_adSMszz_g9},
    note={under review}
}
You might also like...
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Phil Wang 12.6k Jan 9, 2023
Styled Augmented Translation
Styled Augmented Translation

SAT Style Augmented Translation Introduction By collecting high-quality data, we were able to train a model that outperforms Google Translate on 6 dif

null 139 Dec 29, 2022
TANL: Structured Prediction as Translation between Augmented Natural Languages

TANL: Structured Prediction as Translation between Augmented Natural Languages Code for the paper "Structured Prediction as Translation between Augmen

null 98 Dec 15, 2022
A neuroanatomy-based augmented reality experience powered by computer vision. Features 3D visuals of the Atlas Brain Map slices.

Brain Augmented Reality (AR) A neuroanatomy-based augmented reality experience powered by computer vision that features 3D visuals of the Atlas Brain

Yasmeen Brain 10 Oct 6, 2022
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments [Project website] [Paper] This project is a PyTorch

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 49 Nov 28, 2022
A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)
A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

Library | Paper | Slack We released two versions of OAG-BERT in CogDL package. OAG-BERT is a heterogeneous entity-augmented academic language model wh

THUDM 58 Dec 17, 2022
DrQ-v2: Improved Data-Augmented Reinforcement Learning
DrQ-v2: Improved Data-Augmented Reinforcement Learning

DrQ-v2: Improved Data-Augmented RL Agent Method DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ,

Facebook Research 234 Jan 1, 2023
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Yu Meng 60 Dec 30, 2022
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and

Salesforce 72 Dec 5, 2022
Comments
  • WIP - MemformerEncoder

    WIP - MemformerEncoder

    I´m always trying all your awesome work on transformers. My problem is NER on very large texts, with few examples.

    Memformer is the first one so far to converge faster and wield better accuracy than RNN encoders as LSTM, SRU and IndRNN It is ridiculously better than everything else I tested, congratulations @lucidrains 🥳

    I need to use the transformer as a Encoder in my pipeline, to feed a CRF layer. So I modified the code to accept an already embedded input, and to only do the Encode step.

    TODO:

    • [ ] Support Mask
    • [ ] Re-utilize code with Memformer class

    Is this within the scope of the project?

    opened by bratao 10
  • ETA on complete examples

    ETA on complete examples

    @lucidrains As I asked about the feedback-transformer, I was also wondering about this memformer implementation as I would love to try it. Any eta on any complete examples here? They will be much appreciated. Thanks.

    And similarly, I would love to see a simple example for custom line-by-line TXT datasets as well.

    Thank you again :)

    opened by asigalov61 0
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
Code for the paper "A Study of Face Obfuscation in ImageNet"

A Study of Face Obfuscation in ImageNet Code for the paper: A Study of Face Obfuscation in ImageNet Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng,

35 Oct 04, 2022
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
It is the assignment for COMP 576 in Rice University

COMP-576 It is the assignment for COMP 576 in Rice University There are two programming assignments and one Final Project. Assignment 1: It is a MLP a

Maojie Tang 1 Nov 25, 2021
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Epistasis Lab at UPenn 8.9k Dec 30, 2022
LSTM model trained on a small dataset of 3000 names written in PyTorch

LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no

Sahil Lamba 1 Dec 20, 2021
This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

Soomin Park 28 Dec 16, 2022
DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:

Kuang-Jui Hsu 139 Dec 22, 2022
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

Tobias Hinz 181 Dec 27, 2022
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

CM-NAS Official Pytorch code of paper CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification in ICCV2021. Vis

JDAI-CV 40 Nov 25, 2022
This repository provides an efficient PyTorch-based library for training deep models.

s3sec Test AWS S3 buckets for read/write/delete access This tool was developed to quickly test a list of s3 buckets for public read, write and delete

Bytedance Inc. 123 Jan 05, 2023
Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

aventau 102 Dec 26, 2022
the code for paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration"

EOW-Softmax This code is for the paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration". Accepted by ICCV21. Usage Commnd exa

Yezhen Wang 36 Dec 02, 2022
On Evaluation Metrics for Graph Generative Models

On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic

13 Jan 07, 2023
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
A python script to dump all the challenges locally of a CTFd-based Capture the Flag.

A python script to dump all the challenges locally of a CTFd-based Capture the Flag. Features Connects and logins to a remote CTFd instance. Dumps all

Podalirius 77 Dec 07, 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
SMIS - Semantically Multi-modal Image Synthesis(CVPR 2020)

Semantically Multi-modal Image Synthesis Project page / Paper / Demo Semantically Multi-modal Image Synthesis(CVPR2020). Zhen Zhu, Zhiliang Xu, Anshen

316 Dec 01, 2022
"Inductive Entity Representations from Text via Link Prediction" @ The Web Conference 2021

Inductive entity representations from text via link prediction This repository contains the code used for the experiments in the paper "Inductive enti

Daniel Daza 45 Jan 09, 2023
Assessing syntactic abilities of BERT

BERT-Syntax Assesing the syntactic abilities of BERT. What Evaluate Google's BERT-Base and BERT-Large models on the syntactic agreement datasets from

Yoav Goldberg 147 Aug 02, 2022
PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

PyTorch-Style-Transfer This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included

Hang Zhang 906 Jan 04, 2023