Official code repository of the paper Linear Transformers Are Secretly Fast Weight Programmers.

Last update: Dec 19, 2022

Overview

Linear Transformers Are Secretly Fast Weight Programmers

This repository contains the code accompanying the paper Linear Transformers Are Secretly Fast Weight Programmers which is published at ICML'21. It also contains the logs of all synthetic experiments.

Synthetic Experiments

Requirements

$ cat req.txt 
jupyter==1.0.0
pandas==1.0.1
seaborn==0.10.0
torch==1.6.0
matplotlib==3.1.3
numpy==1.17.2

pip3 install -r req.txt

Rerun Experiments

Logs are provided in the synthetic/logs folder. The files in that folder are a result of running the following commands:

Setting 1 (capacity):

python3 main.py --begin=20 --end=600 --step=20 --attn_name=softmax --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=linear --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=dpfp --attn_arg=1 --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=dpfp --attn_arg=2 --update_rule=sum

python3 main.py --begin=20 --end=600 --step=20 --attn_name=dpfp --attn_arg=3 --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=favor --attn_arg=64 --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=favor --attn_arg=128 --update_rule=sum
python3 main.py --begin=20 --end=600 --step=20 --attn_name=favor --attn_arg=512 --update_rule=sum

Setting 2 (update rule):

python3 main.py --begin=20 --end=200 --step=20 --attn_name=dpfp --attn_arg=1 --update_rule=sum --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=dpfp --attn_arg=1 --update_rule=ours --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=tanh --update_rule=fwm --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=dpfp --attn_arg=1 --update_rule=fwm --replace

python3 main.py --begin=20 --end=200 --step=20 --attn_name=dpfp --attn_arg=2 --update_rule=ours --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=linear --update_rule=ours --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=favor --attn_arg=64 --update_rule=ours --replace
python3 main.py --begin=20 --end=200 --step=20 --attn_name=favor --attn_arg=128 --update_rule=ours --replace

Generate figures from the logs using the following notebooks:

synthetic/setting1_generate_figure.ipynb
synthetic/setting2_generate_figure.ipynb

Language Modelling & Machine Translation

The toolkit and scripts for language modeling experiments can be found at IDSIA/lmtool-fwms.

For machine translation experiments, we ported the different attention functions implemented in the language modeling toolkit to the multi-head attention implementation in FAIRSEQ.

Citation

@inproceedings{schlag2021linear,
      title={Linear Transformers Are Secretly Fast Weight Programmers}, 
      author={Imanol Schlag and Kazuki Irie and J\"urgen Schmidhuber},
      booktitle={Proc. Int. Conf. on Machine Learning (ICML)},
      address = {Virtual only},
      month = jul,
      year={2021}
}

Official code repository of the paper Linear Transformers Are Secretly Fast Weight Programmers.

Related tags

Overview

Linear Transformers Are Secretly Fast Weight Programmers

Synthetic Experiments

Requirements

Rerun Experiments

Language Modelling & Machine Translation

Citation

Owner

Imanol Schlag

Search Git commits in natural language

Code for using and evaluating SpanBERT.

Nystromformer: A Nystrom-based Algorithm for Approximating Self-Attention

無料で使える中品質なテキスト読み上げソフトウェア、VOICEVOXの音声合成エンジン

Code for "Finetuning Pretrained Transformers into Variational Autoencoders"

Perform sentiment analysis and keyword extraction on Craigslist listings

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

✨Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.

[EMNLP 2021] LM-Critic: Language Models for Unsupervised Grammatical Error Correction

Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

Indonesia spellchecker with python

A linter to manage all your python exceptions and try/except blocks (limited only for those who like dinosaurs).

ConferencingSpeech2022; Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge

Command Line Text-To-Speech using Google TTS

profile tools for pytorch nn models

Train GPT-3 model on V100(16GB Mem) Using improved Transformer.

Question and answer retrieval in Turkish with BERT

Chatbot for the Chatango messaging platform

Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

Telegram AI chat bot written in Python using Pyrogram