Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

This repo contains the implementation of our paper:

Replication

Python environment

pip install -e . # under DSLP directory
pip install tensorflow tensorboard sacremoses nltk Ninja omegaconf
pip install 'fuzzywuzzy[speedup]'
pip install hydra-core==1.0.6
pip install sacrebleu==1.5.1
pip install git+https://github.com/dugu9sword/lunanlp.git
git clone --recursive https://github.com/parlance/ctcdecode.git
cd ctcdecode && pip install .

Dataset

We downloaded the distilled data from FairSeq

Preprocessed by

TEXT=wmt14_ende_distill
python3 fairseq_cli/preprocess.py --source-lang en --target-lang de \
   --trainpref $TEXT/train.en-de --validpref $TEXT/valid.en-de --testpref $TEXT/test.en-de \
   --destdir data-bin/wmt14.en-de_kd --workers 40 --joined-dictionary

Or you can download all the binarized files here.

Hyperparameters

	EN<->RO	EN<->DE
--validate-interval-updates	300	500
number of tokens per batch	32K	128K
--dropout	0.3	0.1

Note:

We found that label smoothing for CTC-based models are not useful (at least not with our implementation), it is suggested to keep --label-smoothing as 0 for them.
Dropout rate plays a significant role for GLAT, CMLM, and the Vanilla NAT. On WMT'14 EN->De, for example, the Vanilla NAT with dropout 0.1 reaches 21.18 BLEU; but only gives 19.68 BLEU with dropout 0.3.

Training:

We provide the scripts for replicating the results on WMT'14 EN->DE task. For other tasks, you need to adapt the binary path, --source-lang, --target-lang, and some other hyperparameters accordingly.

GLAT with DSLP

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_glat --criterion glat_loss --arch glat_sd --noise full_mask \ 
   --concat-yhat --concat-dropout 0.0  --label-smoothing 0.1 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192 --glat-mode glat \ 
   --length-loss-factor 0.1 --pred-length-offset

CMLM with DSLP

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch glat_sd --noise full_mask \ 
   --concat-yhat --concat-dropout 0.0  --label-smoothing 0.1 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192 \
   --length-loss-factor 0.1 --pred-length-offset

Vanilla NAT with DSLP

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch nat_sd --noise full_mask \ 
   --concat-yhat --concat-dropout 0.0  --label-smoothing 0.1 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192 \
   --length-loss-factor 0.1 --pred-length-offset

Vanilla NAT with DSLP and Mixed Training:

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch nat_sd --noise full_mask \ 
   --concat-yhat --concat-dropout 0.0  --label-smoothing 0.1 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192  --ss-ratio 0.3 --fixed-ss-ratio --masked-loss \ 
   --length-loss-factor 0.1 --pred-length-offset

CTC with DSLP:

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch nat_ctc_sd --noise full_mask \ 
   --src-upsample-scale 2 --use-ctc-decoder --ctc-beam-size 1  --concat-yhat --concat-dropout 0.0  --label-smoothing 0.0 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192

CTC with DSLP and Mixed Training:

python3 train.py data-bin/wmt14.en-de_kd --source-lang en --target-lang de  --save-dir checkpoints  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \ 
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch nat_ctc_sd_ss --noise full_mask \ 
   --src-upsample-scale 2 --use-ctc-decoder --ctc-beam-size 1  --concat-yhat --concat-dropout 0.0  --label-smoothing 0.0 \ 
   --activation-fn gelu --dropout 0.1  --max-tokens 8192 --ss-ratio 0.3 --fixed-ss-ratio

Evaluation

Average the last best 5 checkpoints with scripts/average_checkpoints.py, our results are based on either the best checkpoint or the averaged checkpoint, depending on their valid set BLEU.

fairseq-generate data-bin/wmt14.en-de_kd  --path PATH_TO_A_CHECKPOINT \
    --gen-subset test --task translation_lev --iter-decode-max-iter 0 \
    --iter-decode-eos-penalty 0 --beam 1 --remove-bpe --print-step --batch-size 100

Note: 1) Add --plain-ctc --model-overrides '{"ctc_beam_size": 1, "plain_ctc": True}' if it is CTC based; 2) Change the task to translation_glat if it is GLAT based.

Output

We in addition provide the output of CTC w/ DSLP, CTC w/ DSLP & Mixed Training, Vanilla NAT w/ DSLP, Vanilla NAT w/ DSLP with Mixed Training, GLAT w/ DSLP, and CMLM w/ DSLP for review purpose.

Model	Reference	Hypothesis
CTC w/ DSLP	ref	hyp
CTC w/ DSLP & Mixed Training	ref	hyp
Vanilla NAT w/ DSLP	ref	hyp
Vanilla NAT w/ DSLP & Mixed Training	ref	hyp
GLAT w/ DSLP	ref	hyp
CMLM w/ DSLP	ref	hyp

Note: The output is on WMT'14 EN-DE. The references are paired with hypotheses for each model.

Training Efficiency

We show the training efficiency of our DSLP model based on vanilla NAT model. Specifically, we compared the BLUE socres of vanilla NAT and vanilla NAT with DSLP & Mixed Training on the same traning time (in hours).

As we observed, our DSLP model achieves much higher BLUE scores shortly after the training started (~3 hours). It shows that our DSLP is much more efficient in training, as our model ahieves higher BLUE scores with the same amount of training cost.

We run the experiments with 8 Tesla V100 GPUs. The batch size is 128K tokens, and each model is trained with 300K updates.

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

Related tags

Overview

Replication

Python environment

Dataset

Hyperparameters

Training:

Evaluation

Output

Training Efficiency

Owner

Chenyang Huang

LSTM model - IMDB review sentiment analysis

A high-level Python library for Quantum Natural Language Processing

Torchrecipes provides a set of reproduci-able, re-usable, ready-to-run RECIPES for training different types of models, across multiple domains, on PyTorch Lightning.

Every Google, Azure & IBM text to speech voice for free

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Fast topic modeling platform

Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

FireFlyer Record file format, writer and reader for DL training samples.

Machine learning models from Singapore's NLP research community

Searching keywords in PDF file folders

topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API

Code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".

A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

The ability of computer software to identify words and phrases in spoken language and convert them to human-readable text

Silero Models: pre-trained speech-to-text, text-to-speech models and benchmarks made embarrassingly simple

FB ID CLONER WUTHOT CHECKPOINT, FACEBOOK ID CLONE FROM FILE

Extract rooms type, door, neibour rooms, rooms corners nad bounding boxes, and generate graph from rplan dataset

Open source code for AlphaFold.