Introduction

This repository contains the code for Instance-wise Ordered Transformer (IOT), which is introduced in the ICLR2021 paper IOT: Instance-wise Layer Reordering for Transformer Structures.

If you find this work helpful in your research, please cite as:

@inproceedings{
zhu2021iot,
title={{\{}IOT{\}}: Instance-wise Layer Reordering for Transformer Structures},
author={Jinhua Zhu and Lijun Wu and Yingce Xia and Shufang Xie and Tao Qin and Wengang Zhou and Houqiang Li and Tie-Yan Liu},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=ipUPfYxWZvM}
}

Requirements and Installation

PyTorch version == 1.0.0
Python version >= 3.5

To install IOT:

git clone https://github.com/instance-wise-ordered-transformer/IOT
cd IOT
pip install --editable .

Getting Started

Take IWSLT14 De-En translation as an example.

Data Preprocessing

cd examples/translation/
bash prepare-iwslt14.sh
cd ../..

TEXT=examples/translation/iwslt14.tokenized.de-en
python preprocess.py --source-lang de --target-lang en \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/iwslt14.tokenized.de-en --joined-dictionary

Training

Encoder order is set to be the default one without reordering (ENCODER_MAX_ORDER=1), since the paper finds that both reordering encoder and decoder is not good as reordering decoder only.

#!/bin/bash
export CUDA_VISIBLE_DEVICES=${1:-0}
nvidia-smi

ENCODER_MAX_ORDER=1
DECODER_MAX_ORDER=3
DECODER_ORDER="0 3 5"
DIVERSITY=0.1
GS_MAX=20
GS_MIN=2
GS_R=0
GS_UF=5000
KL=0.01
CLAMPVAL=0.05

DECODER_ORDER_NAME=`echo $DECODER_ORDER | sed 's/ //g'`
SAVE_DIR=checkpoints/dec_${DECODER_MAX_ORDER}_order_${DECODER_ORDER_NAME}_div_${DIVERSITY}_gsmax_${GS_MAX}_gsmin_${GS_MIN}_gsr_${GS_R}_gsuf_${GS_UF}_kl_${KL}_clampval_${CLAMPVAL}
mkdir -p ${SAVE_DIR}

python -u train.py data-bin/iwslt14.tokenized.de-en -a transformer_iwslt_de_en \
--optimizer adam --lr 0.0005 -s de -t en --label-smoothing 0.1 --dropout 0.3 --max-tokens 4000 \
--min-lr 1e-09 --lr-scheduler inverse_sqrt --weight-decay 0.0001 --criterion label_smoothed_cross_entropy \
--max-update 100000 --warmup-updates 4000 --warmup-init-lr 1e-07 --adam-betas '(0.9,0.98)' \
--save-dir $SAVE_DIR --share-all-embeddings  --gs-clamp --decoder-orders $DECODER_ORDER  \
--encoder-max-order $ENCODER_MAX_ORDER  --decoder-max-order $DECODER_MAX_ORDER  --diversity $DIVERSITY \
--gumbel-softmax-max $GS_MAX  --gumbel-softmax-min $GS_MIN --gumbel-softmax-tau-r $GS_R  --gumbel-softmax-update-freq $GS_UF \
--kl $KL --clamp-value $CLAMPVAL | tee -a ${SAVE_DIR}/train.log

Evaluation

#!/bin/bash
set -x
set -e

pip install -e . --user
export CUDA_VISIBLE_DEVICES=${1:-0}
nvidia-smi

ENCODER_MAX_ORDER=1
DECODER_MAX_ORDER=3
DECODER_ORDER="0 3 5"
DIVERSITY=0.1
GS_MAX=20
GS_MIN=2
GS_R=0
GS_UF=5000
KL=0.01
CLAMPVAL=0.05

DECODER_ORDER_NAME=`echo $DECODER_ORDER | sed 's/ //g'`
SAVE_DIR=checkpoints/dec_${DECODER_MAX_ORDER}_order_${DECODER_ORDER_NAME}_div_${DIVERSITY}_gsmax_${GS_MAX}_gsmin_${GS_MIN}_gsr_${GS_R}_gsuf_${GS_UF}_kl_${KL}_clampval_${CLAMPVAL}

python generate.py data-bin/iwslt14.tokenized.de-en \
  --path $SAVE_DIR/checkpint_best.pt \
  --batch-size 128 --beam 5 --remove-bpe --quiet --num-ckts $DECODER_MAX_ORDER

IOT: Instance-wise Layer Reordering for Transformer Structures

Related tags

Overview

Introduction

Requirements and Installation

Getting Started

Data Preprocessing

Training

Evaluation

Owner

IOT

Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.

Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Transformers based fully on MLPs

PyTorch Implementation of Backbone of PicoDet

Patches desktop steam to look like the new steamdeck ui.

This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Rax is a Learning-to-Rank library written in JAX

Dynamic Capacity Networks using Tensorflow

GitHub repository for "Improving Video Generation for Multi-functional Applications"

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

Pretrained models for Jax/Haiku; MobileNet, ResNet, VGG, Xception.

Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

Official Python implementation of the FuzionCoin protocol

A deep learning model for style-specific music generation.

Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capability)

Tutorials, assignments, and competitions for MIT Deep Learning related courses.