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Fine-tuning BART on CNN-Dailymail summarization task

1) Follow instructions here to download and process into data-files with non-tokenized cased samples.

2) BPE preprocess:

wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'

for SPLIT in train val
do
  for LANG in source target
  do
    python -m examples.roberta.multiprocessing_bpe_encoder \
    --encoder-json encoder.json \
    --vocab-bpe vocab.bpe \
    --inputs "cnn_dm/$SPLIT.$LANG" \
    --outputs "cnn_dm/$SPLIT.bpe.$LANG" \
    --workers 60 \
    --keep-empty;
  done
done

3) Binarize dataset:

fairseq-preprocess \
  --source-lang "source" \
  --target-lang "target" \
  --trainpref "cnn_dm/train.bpe" \
  --validpref "cnn_dm/val.bpe" \
  --destdir "cnn_dm-bin/" \
  --workers 60 \
  --srcdict dict.txt \
  --tgtdict dict.txt;

4) Fine-tuning on CNN-DM summarization task:

Example fine-tuning cmd

TOTAL_NUM_UPDATES=20000  
WARMUP_UPDATES=500      
LR=3e-05
MAX_TOKENS=2048
UPDATE_FREQ=4
BART_PATH=/path/to/bart/model.pt

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py cnn_dm-bin \
    --restore-file $BART_PATH \
    --max-tokens $MAX_TOKENS \
    --task translation \
    --source-lang source --target-lang target \
    --truncate-source \
    --layernorm-embedding \
    --share-all-embeddings \
    --share-decoder-input-output-embed \
    --reset-optimizer --reset-dataloader --reset-meters \
    --required-batch-size-multiple 1 \
    --arch bart_large \
    --criterion label_smoothed_cross_entropy \
    --label-smoothing 0.1 \
    --dropout 0.1 --attention-dropout 0.1 \
    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \
    --clip-norm 0.1 \
    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
    --fp16 --update-freq $UPDATE_FREQ \
    --skip-invalid-size-inputs-valid-test \
    --find-unused-parameters;

Above is expected to run on 1 node with 8 32gb-V100. Expected training time is about 5 hours. Training time can be reduced with distributed training on 4 nodes and --update-freq 1.

Inference for CNN-DM test data using above trained checkpoint.

After training the model as mentioned in previous step, you can perform inference with checkpoints in checkpoints/ directory using following python code snippet:

import torch
from fairseq.models.bart import BARTModel

bart = BARTModel.from_pretrained(
    'checkpoints/',
    checkpoint_file='checkpoint_best.pt',
    data_name_or_path='cnn_dm-bin'
)

bart.cuda()
bart.eval()
bart.half()
count = 1
bsz = 32
with open('cnn_dm/test.source') as source, open('cnn_dm/test.hypo', 'w') as fout:
    sline = source.readline().strip()
    slines = [sline]
    for sline in source:
        if count % bsz == 0:
            with torch.no_grad():
                hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)

            for hypothesis in hypotheses_batch:
                fout.write(hypothesis + '\n')
                fout.flush()
            slines = []

        slines.append(sline.strip())
        count += 1
    if slines != []:
        hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
        for hypothesis in hypotheses_batch:
            fout.write(hypothesis + '\n')
            fout.flush()