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
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

CottonWeeds Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems. requirements pytorch torchsumma

Dong Chen 8 Jun 07, 2022
Main Results on ImageNet with Pretrained Models

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure

Microsoft 151 Dec 14, 2022
Code for ICCV 2021 paper: ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators..

ARAPReg Code for ICCV 2021 paper: ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators.. Installation The cod

Bo Sun 132 Nov 28, 2022
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Adversarially-Robust-Periphery Code + Data from the paper "Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks" by A

Anne Harrington 2 Feb 07, 2022
Python Multi-Agent Reinforcement Learning framework

- Please pay attention to the version of SC2 you are using for your experiments. - Performance is *not* always comparable between versions. - The re

whirl 1.3k Jan 05, 2023
IJCAI2020 & IJCV 2020 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

Seg_Uncertainty In this repo, we provide the code for the two papers, i.e., MRNet：Unsupervised Scene Adaptation with Memory Regularization in vivo, IJ

Zhedong Zheng 348 Jan 05, 2023
AI Toolkit for Healthcare Imaging

Medical Open Network for AI MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its am

Project MONAI 3.7k Jan 07, 2023
A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing"

A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf 2021). Abstract In this work we propose Pathfind

Benedek Rozemberczki 49 Dec 01, 2022
[RSS 2021] An End-to-End Differentiable Framework for Contact-Aware Robot Design

DiffHand This repository contains the implementation for the paper An End-to-End Differentiable Framework for Contact-Aware Robot Design (RSS 2021). I

Jie Xu 60 Jan 04, 2023
Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

Edwin Chen 928 Dec 30, 2022
Facilitates implementing deep neural-network backbones, data augmentations

Introduction Nowadays, the training of Deep Learning models is fragmented and unified. When AI engineers face up with one specific task, the common wa

40 Dec 29, 2022
Educational 2D SLAM implementation based on ICP and Pose Graph

slam-playground Educational 2D SLAM implementation based on ICP and Pose Graph How to use: Use keyboard arrow keys to navigate robot. Press 'r' to vie

Kirill 19 Dec 17, 2022
Lolviz - A simple Python data-structure visualization tool for lists of lists, lists, dictionaries; primarily for use in Jupyter notebooks / presentations

lolviz By Terence Parr. See Explained.ai for more stuff. A very nice looking javascript lolviz port with improvements by Adnan M.Sagar. A simple Pytho

Terence Parr 785 Dec 30, 2022
Code for Discriminative Sounding Objects Localization (NeurIPS 2020)

Discriminative Sounding Objects Localization Code for our NeurIPS 2020 paper Discriminative Sounding Objects Localization via Self-supervised Audiovis

51 Dec 11, 2022
Pytorch implementation of Deep Recursive Residual Network for Super Resolution (DRRN)

DRRN-pytorch This is an unofficial implementation of "Deep Recursive Residual Network for Super Resolution (DRRN)", CVPR 2017 in Pytorch. [Paper] You

yun_yang 192 Dec 12, 2022
Towards Fine-Grained Reasoning for Fake News Detection

FinerFact This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Ar

Ahren_Jin 15 Dec 15, 2022
Simple reference implementation of GraphSAGE.

Reference PyTorch GraphSAGE Implementation Author: William L. Hamilton Basic reference PyTorch implementation of GraphSAGE. This reference implementat

William L Hamilton 861 Jan 06, 2023
Energy consumption estimation utilities for Jetson-based platforms

This repository contains a utility for measuring energy consumption when running various programs in NVIDIA Jetson-based platforms. Currently TX-2, NX, and AGX are supported.

OpenDR 10 Jun 17, 2022
This program was designed to detect whether someone is wearing a facemask through a live video stream.

This program was designed to detect whether someone is wearing a facemask through a live video stream. A custom lightweight CNN trained with TensorFlow on a public dataset provided by Kaggle is used

0 Apr 02, 2022