FLOATER

This is the official implementation of Learning to encode position for transformer with continuous dynamical model in ICML 2020

The codebase is modified upon fairseq.

Install the dependencies

In what follows I assume PyTorch(>=1.3) is installed.

# Install torchdiffeq
pip install [email protected]:xuanqing94/torchdiffeq.git

# Install fairseq
git clone https://github.com/xuanqing94/FLOATER.git
cd FLOATER
pip install -e .

NOTE Do not install the official torchdiffeq, I made several fixes to work with FLOATER.

Run WMT14 En-De and En-Fr

First, download and preprocess the dataset:

# Download & bpe tokenize
bash examples/translation/prepare-wmt14en2de.sh --icml17
bash examples/translation/prepare-wmt14en2fr.sh

Then binarize the dataset:

TEXT=ende32k_wmt14
fairseq-preprocess \
    --trainpref $TEXT/train \
    --validpref $TEXT/valid \
    --testpref $TEST/test \
    --source-lang en --target-lang de \
    --destdir data-bin/tokenized.en-de \
    --joined-dictionary \
    --workers 20

# Binarize En-Fr similarly.

You can choose to train FLOATER from scratch but it will take a pretty long time until convergence. So, I recommend to follow the pretraining and finetuning approach discussed in the paper:

# Pretraining
fairseq-train \
    data-bin/wmt14.en-de/ \
    --arch transformer_wmt_en_de --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr 7e-4 --min-lr 1.0e-9 \
    --lr-scheduler inverse_sqrt \
    --weight-decay 0.0 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 2000 --save-dir ./checkpoints/base-flow \
    --warmup-init-lr 1.0e-7 --warmup-updates 4000 \
    --find-unused-parameters \
    --update-freq 2 \
    --reset-optimizer \
    --max-epoch 30 \

# initialize checkpoint_last.pt with original transformer model
python model_migration.py

# fine-tune FLOATER model for 10 more epochs
fairseq-train \
    data-bin/wmt14.en-de/ \
    --arch flow_transformer_wmt_en_de --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr 3.5e-4 --min-lr 1.0e-9 \
    --lr-scheduler inverse_sqrt \
    --weight-decay 0.0 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 2000 --save-dir ./checkpoints/base-flow \
    --warmup-init-lr 1.0e-7 --warmup-updates 4000 \
    --find-unused-parameters \
    --update-freq 2 \
    --save-interval-updates 2000 \
    --reset-optimizer \
    --max-epoch 10 \

# evaluate on test set
folder=base-flow
nckpt=5
python scripts/average_checkpoints.py \
    --inputs ./checkpoints/${folder}/ \
    --output ./checkpoints/${folder}/averaged.pt \
    --num-update-checkpoints ${nckpt} \

 fairseq-generate data-bin/tokenized.en-de \
       --path checkpoints/${folder}/averaged.pt \
       --beam 4 --batch-size 64 --remove-bpe  --lenpen 0.6 > tmp_out

bash scripts/compound_split_bleu.sh tmp_out