BANG

This repo provides the code for reproducing the experiments in BANG.
In the paper, we propose a new pre-trained language model called BANG for sequence-to-sequence learning, which considers autoregressive, non-autoregressive and semi-autoregressive generation as its pretraining tasks.

Pretrained Models:

BANG base
Pretrained on 16GB English corpus, Wikipedia and BookCorpus.

Dependency

pip install torch==1.3.0
pip install fairseq==v0.9.0
pip install tensorboardX==1.7

How to use

The procedure includes 1) Tokenize, 2) Binarize, 3) Finetune, 4) Inference.
BANG is implemented on base of Fairseq, which you can refer to Fairseq Mannual.

Tokenize. Prepare your train.src, train.tgt, and valid, test sets. Input and output of one sample are placed in the .src and .tgt file with one line.
Use bert-uncased tokenizer to tokenize your data into word piece.

from transformers import BertTokenizer


def bert_uncased_tokenize(fin, fout):
    fin = open(fin, 'r', encoding='utf-8')
    fout = open(fout, 'w', encoding='utf-8')
    tok = BertTokenizer.from_pretrained('bert-base-uncased')
    for line in fin:
        word_pieces = tok.tokenize(line.strip())
        new_line = " ".join(word_pieces)
        fout.write('{}\n'.format(new_line))
bert_uncased_tokenize('train.src', 'tokenized_train.src')
bert_uncased_tokenize('train.tgt', 'tokenized_train.tgt')
bert_uncased_tokenize('valid.src', 'tokenized_valid.src')
bert_uncased_tokenize('valid.tgt', 'tokenized_valid.tgt')
bert_uncased_tokenize('test.src', 'tokenized_test.src')
bert_uncased_tokenize('test.tgt', 'tokenized_test.tgt')

Binirize it with fairseq-preprocess

fairseq-preprocess \
--user-dir ./bang/bang \
--task translation_bang \
--source-lang src --target-lang tgt \
--trainpref tokenized_train --validpref tokenized_valid --testpref tokenized_test \
--destdir processed_data --srcdict ./bang/vocab.txt --tgtdict ./bang/vocab.txt \
--workers 20

Fine tune with fairseq-train.

Autoregressive Generation

Set these parameters:
--disable-ngram-loss：please set True for AR finetuning
--ngram: please set 1 for AR finetuning
--nar-ratio: please set 0.0 for AR finetuning
--fp16: if your GPU device supports, set True to accelerate training

DATA_DIR=processed_data
ARCH=bang_ar_nar_mixed_base
CRITERION=ngram_language_loss_NAR_mixed
SAVE_DIR=models/model_ar
TENSORBOARD_LOGDIR=models/logs_ar
PRETRAINED_MODEL=checkpoint_base_9gram_ck35.pt
NAR_RATIO=0.0

fairseq-train $DATA_DIR \
--user-dir ./bang/bang  \
--task translation_bang --arch $ARCH \
--optimizer adam --adam-betas '(0.9, 0.999)' --clip-norm 0.1 \
--lr 0.0001 --min-lr 1e-09 --nar-ratio ${NAR_RATIO} --ngram 1 --disable-ngram-loss \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 1000 \
--dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
--criterion $CRITERION --label-smoothing 0.1 \
--update-freq 1  --max-tokens 3072 \
--num-workers 8  \
--load-from-pretrained-model $PRETRAINED_MODEL \
--ddp-backend=no_c10d --max-epoch 10 \
--max-source-positions 512 --max-target-positions 512 \
--truncate-source \
--save-dir $SAVE_DIR \
--keep-last-epochs 10  --save-interval 1 \
--tensorboard-logdir $TENSORBOARD_LOGDIR \

Inference with fairseq-generate to generate targets for given processed test files. Or you can fairseq-interactive to generate answers for your typed-in text (which should also been tokenized).

BEAM=4
LENPEN=1.2
CHECK_POINT=models/model_ar/checkpoint8.pt
SUFFIX=_ar_pelt${LENPEN}_test_beam${BEAM}
OUTPUT_FILE=outputs/output$SUFFIX.txt

PYTHONIOENCODING=utf-8 fairseq-generate ./processed_data --path $CHECK_POINT --user-dir ./bang/bang --task translation_bang --batch-size 36 --gen-subset train --beam $BEAM --num-workers 4 --lenpen $LENPEN 2>&1 > $OUTPUT_FILE
grep ^H $OUTPUT_FILE | cut -c 3- | sort -n | cut -f3- | sed "s/ ##//g" > outputs/sort_hypo$SUFFIX.txt
grep ^H $OUTPUT_FILE | cut -c 3- | sort -n | cut -f3-  > outputs/sort_hypo$SUFFIX.txt.tokenized

Non-autoregressive Generation

--nar-ratio: please set 1.0 for NAR finetuning
--fp16: if your GPU device supports, set True to accelerate training

DATA_DIR=processed_data
ARCH=bang_ar_nar_mixed_base
CRITERION=ngram_language_loss_NAR_mixed
SAVE_DIR=models/model_nar
TENSORBOARD_LOGDIR=models/logs_nar
PRETRAINED_MODEL=checkpoint_base_9gram_ck35.pt
NAR_RATIO=1.0

fairseq-train $DATA_DIR \
--user-dir ./bang/bang  \
--task translation_bang --arch $ARCH \
--optimizer adam --adam-betas '(0.9, 0.999)' --clip-norm 0.1 \
--lr 0.0001 --min-lr 1e-09 --nar-ratio $NAR_RATIO --ngram 1 --disable-ngram-loss \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 1000 \
--dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
--criterion $CRITERION --label-smoothing 0.1 \
--update-freq 1  --max-tokens 3072 \
--num-workers 8  \
--load-from-pretrained-model $PRETRAINED_MODEL \
--ddp-backend=no_c10d --max-epoch 50 \
--max-source-positions 512 --max-target-positions 512 \
--truncate-source \
--save-dir $SAVE_DIR \
--keep-last-epochs 10  --save-interval 5 \
--tensorboard-logdir $TENSORBOARD_LOGDIR \

Inference with fairseq-generate to generate targets for given processed test files. Or you can fairseq-interactive to generate answers for your typed-in text (which should also been tokenized).

SUFFIX=_nar
CHECK_POINT=models/model_nar/checkpoint40.pt
OUTPUT_FILE=outputs/output${SUFFIX}.txt

PYTHONIOENCODING=utf8 fairseq-generate processed_data  --user-dir ./bang/bang --path ${CHECK_POINT} --truncate-source --max-source-positions 512 --task translation_bang_nar --batch-size 36 --beam 1 --gen-subset test  2>&1 > ${OUTPUT_FILE}

grep ^H $OUTPUT_FILE | cut -c 3- | sort -n | cut -f3- > outputs/sort_hypo${SUFFIX}.txt
python post_processed_nar.py outputs_v1/sort_hypo${SUFFIX}.txt outputs/sort_hypo${SUFFIX}.txt.dedup

TIPS:

1, Autoregressive needs fewer finetuning steps, while Non-autoregressive needs longtime finetuning to get good performance.
2, We highly recommend you use sequence distillation before NAR finetuning.
3, If you met problems to run fairseq-preprocess, fairseq-train and other commands, or if you want to modify the workflow/inference pipeline, it's a good choice to download fairseq git repo, checkout v0.9.0, and merge our codes. Then, modify their preprocess.py, train.py or generate.py, to run your new pipeline.

Repo Reference

This repo is referred to Fairseq-v0.9.0 and ProphetNet.

How to Cite

If you extend or use this work, please cite the paper where it was introduced:

@inproceedings{qi2021bang,
  title={Bang: Bridging autoregressive and non-autoregressive generation with large scale pretraining},
  author={Qi, Weizhen and Gong, Yeyun and Jiao, Jian and Yan, Yu and Chen, Weizhu and Liu, Dayiheng and Tang, Kewen and Li, Houqiang and Chen, Jiusheng and Zhang, Ruofei and others},
  booktitle={International Conference on Machine Learning},
  pages={8630--8639},
  year={2021},
  organization={PMLR}
}

Microsoft Open Source Code of Conduct

--	B1	B2	D1	D2
My reproduction	30.69	19.27	2.1	23.0
Your Result	39.82	30.72	1.9	14.2

microsoft / bang Goto Github PK

bang's Introduction

BANG

Pretrained Models:

Dependency

How to use

Autoregressive Generation

Non-autoregressive Generation

TIPS:

Repo Reference

How to Cite

bang's People

Contributors

Stargazers

Watchers

Forkers

bang's Issues

Bang is a great paper. But I have some problems when i try to get the scores in the paper. First, the BLUE-4 and Rouge-L in Squad question generation by Mass pretrained model is different with the paper with beam size as 5，and get BLEU4 = 22.43, Rouge as:

1 ROUGE-1 Average_R: 0.48431 (95%-conf.int. 0.47986 - 0.48875) 1 ROUGE-1 Average_P: 0.54315 (95%-conf.int. 0.53853 - 0.54740) 1 ROUGE-1 Average_F: 0.49817 (95%-conf.int. 0.49411 - 0.50238)

1 ROUGE-2 Average_R: 0.26775 (95%-conf.int. 0.26311 - 0.27234) 1 ROUGE-2 Average_P: 0.29883 (95%-conf.int. 0.29365 - 0.30367) 1 ROUGE-2 Average_F: 0.27436 (95%-conf.int. 0.26965 - 0.27884)

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Recommend Org

Bang is a great paper. But I have some problems when i try to get the scores in the paper.
First, the BLUE-4 and Rouge-L in Squad question generation by Mass pretrained model is different with the paper with beam size as 5，and get BLEU4 = 22.43, Rouge as:

1 ROUGE-1 Average_R: 0.48431 (95%-conf.int. 0.47986 - 0.48875)
1 ROUGE-1 Average_P: 0.54315 (95%-conf.int. 0.53853 - 0.54740)
1 ROUGE-1 Average_F: 0.49817 (95%-conf.int. 0.49411 - 0.50238)

1 ROUGE-2 Average_R: 0.26775 (95%-conf.int. 0.26311 - 0.27234)
1 ROUGE-2 Average_P: 0.29883 (95%-conf.int. 0.29365 - 0.30367)
1 ROUGE-2 Average_F: 0.27436 (95%-conf.int. 0.26965 - 0.27884)