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fairseq/examples/backtranslation/prepare-wmt18en2de.sh 0 → 100644
View file @ 18d27e00
#!/bin/bash
# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://github.com/rsennrich/subword-nmt.git
SCRIPTS=mosesdecoder/scripts
TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
CLEAN=$SCRIPTS/training/clean-corpus-n.perl
NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
BPEROOT=subword-nmt/subword_nmt
BPE_TOKENS=32000
URLS=(
"http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
"http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
"http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
"http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
"http://data.statmt.org/wmt17/translation-task/dev.tgz"
"http://statmt.org/wmt14/test-full.tgz"
)
FILES=(
"training-parallel-europarl-v7.tgz"
"training-parallel-commoncrawl.tgz"
"training-parallel-nc-v13.tgz"
"rapid2016.tgz"
"dev.tgz"
"test-full.tgz"
)
CORPORA=(
"training/europarl-v7.de-en"
"commoncrawl.de-en"
"training-parallel-nc-v13/news-commentary-v13.de-en"
"rapid2016.de-en"
)
if [ ! -d "$SCRIPTS" ]; then
echo "Please set SCRIPTS variable correctly to point to Moses scripts."
exit 1
fi
OUTDIR=wmt18_en_de
src=en
tgt=de
lang=en-de
prep=$OUTDIR
tmp=$prep/tmp
orig=orig
mkdir -p $orig $tmp $prep
cd $orig
for ((i=0;i<${#URLS[@]};++i)); do
file=${FILES[i]}
if [ -f $file ]; then
echo "$file already exists, skipping download"
else
url=${URLS[i]}
wget "$url"
if [ -f $file ]; then
echo "$url successfully downloaded."
else
echo "$url not successfully downloaded."
exit 1
fi
if [ ${file: -4} == ".tgz" ]; then
tar zxvf $file
elif [ ${file: -4} == ".tar" ]; then
tar xvf $file
fi
fi
done
cd ..
echo "pre-processing train data..."
for l in $src $tgt; do
rm $tmp/train.tags.$lang.tok.$l
for f in "${CORPORA[@]}"; do
cat $orig/$f.$l | \
perl $NORM_PUNC $l | \
perl $REM_NON_PRINT_CHAR | \
perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
done
done
echo "pre-processing test data..."
for l in $src $tgt; do
if [ "$l" == "$src" ]; then
t="src"
else
t="ref"
fi
grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
sed -e 's/<seg id="[0-9]*">\s*//g' | \
sed -e 's/\s*<\/seg>\s*//g' | \
sed -e "s/\’/\'/g" | \
perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l
echo ""
done
echo "splitting train and valid..."
for l in $src $tgt; do
awk '{if (NR%100 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l
awk '{if (NR%100 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l
done
TRAIN=$tmp/train.de-en
BPE_CODE=$prep/code
rm -f $TRAIN
for l in $src $tgt; do
cat $tmp/train.$l >> $TRAIN
done
echo "learn_bpe.py on ${TRAIN}..."
python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE
for L in $src $tgt; do
for f in train.$L valid.$L test.$L; do
echo "apply_bpe.py to ${f}..."
python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f
done
done
perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250
perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250
for L in $src $tgt; do
cp $tmp/bpe.test.$L $prep/test.$L
done
fairseq/examples/backtranslation/sacrebleu.sh 0 → 100644
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#!/bin/bash
if [ $# -ne 5 ]; then
echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]"
exit
fi
DATASET=$1
LANGPAIR=$2
DATABIN=$3
BPECODE=$4
MODEL=$5
SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1)
TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2)
BPEROOT=examples/backtranslation/subword-nmt/subword_nmt
if [ ! -e $BPEROOT ]; then
BPEROOT=subword-nmt/subword_nmt
if [ ! -e $BPEROOT ]; then
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://github.com/rsennrich/subword-nmt.git
fi
fi
sacrebleu -t $DATASET -l $LANGPAIR --echo src \
| sacremoses tokenize -a -l $SRCLANG -q \
| python $BPEROOT/apply_bpe.py -c $BPECODE \
| fairseq-interactive $DATABIN --path $MODEL \
-s $SRCLANG -t $TGTLANG \
--beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \
| grep ^H- | cut -f 3- \
| sacremoses detokenize -l $TGTLANG -q \
| sacrebleu -t $DATASET -l $LANGPAIR
fairseq/examples/backtranslation/tokenized_bleu.sh 0 → 100644
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#!/bin/bash
if [ $# -ne 5 ]; then
echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]"
exit
fi
DATASET=$1
LANGPAIR=$2
DATABIN=$3
BPECODE=$4
MODEL=$5
SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1)
TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2)
BPEROOT=examples/backtranslation/subword-nmt/subword_nmt
if [ ! -e $BPEROOT ]; then
BPEROOT=subword-nmt/subword_nmt
if [ ! -e $BPEROOT ]; then
echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
git clone https://github.com/rsennrich/subword-nmt.git
fi
fi
TMP_REF=$(mktemp)
sacrebleu -t $DATASET -l $LANGPAIR --echo ref -q \
| sacremoses normalize -l $TGTLANG -q \
| sacremoses tokenize -a -l $TGTLANG -q \
> $TMP_REF
sacrebleu -t $DATASET -l $LANGPAIR --echo src -q \
| sacremoses normalize -l $SRCLANG -q \
| sacremoses tokenize -a -l $SRCLANG -q \
| python $BPEROOT/apply_bpe.py -c $BPECODE \
| fairseq-interactive $DATABIN --path $MODEL \
-s $SRCLANG -t $TGTLANG \
--beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \
| grep ^H- | cut -f 3- \
| fairseq-score --ref $TMP_REF
rm -f $TMP_REF
fairseq/examples/bart/README.glue.md 0 → 100644
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# Fine-tuning BART on GLUE tasks
### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:
```bash
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
python download_glue_data.py --data_dir glue_data --tasks all
```
### 2) Preprocess GLUE task data (same as RoBERTa):
```bash
./examples/roberta/preprocess_GLUE_tasks.sh glue_data <glue_task_name>
```
`glue_task_name` is one of the following:
`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}`
Use `ALL` for preprocessing all the glue tasks.
### 3) Fine-tuning on GLUE task:
Example fine-tuning cmd for `RTE` task
```bash
TOTAL_NUM_UPDATES=2036 # 10 epochs through RTE for bsz 16
WARMUP_UPDATES=61 # 6 percent of the number of updates
LR=1e-05 # Peak LR for polynomial LR scheduler.
NUM_CLASSES=2
MAX_SENTENCES=16 # Batch size.
BART_PATH=/path/to/bart/model.pt
CUDA_VISIBLE_DEVICES=0,1 fairseq-train RTE-bin/ \
--restore-file $BART_PATH \
--batch-size $MAX_SENTENCES \
--max-tokens 4400 \
--task sentence_prediction \
--add-prev-output-tokens \
--layernorm-embedding \
--share-all-embeddings \
--share-decoder-input-output-embed \
--reset-optimizer --reset-dataloader --reset-meters \
--required-batch-size-multiple 1 \
--init-token 0 \
--arch bart_large \
--criterion sentence_prediction \
--num-classes $NUM_CLASSES \
--dropout 0.1 --attention-dropout 0.1 \
--weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 \
--clip-norm 0.0 \
--lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
--fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
--max-epoch 10 \
--find-unused-parameters \
--best-checkpoint-metric accuracy --maximize-best-checkpoint-metric;
```
For each of the GLUE task, you will need to use following cmd-line arguments:
Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
---|---|---|---|---|---|---|---|---
`--num-classes` | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1
`--lr` | 5e-6 | 1e-5 | 1e-5 | 1e-5 | 5e-6 | 2e-5 | 2e-5 | 2e-5
`bsz` | 128 | 32 | 32 | 32 | 128 | 64 | 64 | 32
`--total-num-update` | 30968 | 33112 | 113272 | 1018 | 5233 | 1148 | 1334 | 1799
`--warmup-updates` | 1858 | 1986 | 6796 | 61 | 314 | 68 | 80 | 107
For `STS-B` additionally add `--regression-target --best-checkpoint-metric loss` and remove `--maximize-best-checkpoint-metric`.
**Note:**
a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--batch-size=32/64/128` depending on the task.
b) Above cmd-args and hyperparams are tested on Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
### Inference on GLUE task
After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
```python
from fairseq.models.bart import BARTModel
bart = BARTModel.from_pretrained(
'checkpoints/',
checkpoint_file='checkpoint_best.pt',
data_name_or_path='RTE-bin'
)
label_fn = lambda label: bart.task.label_dictionary.string(
[label + bart.task.label_dictionary.nspecial]
)
ncorrect, nsamples = 0, 0
bart.cuda()
bart.eval()
with open('glue_data/RTE/dev.tsv') as fin:
fin.readline()
for index, line in enumerate(fin):
tokens = line.strip().split('\t')
sent1, sent2, target = tokens[1], tokens[2], tokens[3]
tokens = bart.encode(sent1, sent2)
prediction = bart.predict('sentence_classification_head', tokens).argmax().item()
prediction_label = label_fn(prediction)
ncorrect += int(prediction_label == target)
nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
```
fairseq/examples/bart/README.md 0 → 100644
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# BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
[https://arxiv.org/pdf/1910.13461.pdf]
## Introduction
BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details.
## Pre-trained models
Model | Description | # params | Download
---|---|---|---
`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz)
`bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz)
`bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz)
`bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz)
`bart.large.xsum` | `bart.large` finetuned on `Xsum` | 400M | [bart.large.xsum.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz)
## Results
**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)**
_(dev set, single model, single-task finetuning)_
Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
---|---|---|---|---|---|---|---|---
`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
`bart.large` | 89.9 | 94.9 | 92.5 | 87.0 | 96.6 | 90.4 | 62.8 | 91.2
**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)**
_(dev set, no additional data used)_
Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
---|---|---
`roberta.large` | 88.9/94.6 | 86.5/89.4
`bart.large` | 88.8/94.6 | 86.1/89.2
**[CNN/Daily Mail](http://nlpprogress.com/english/summarization.html)**
_(test set, no additional data used)_
Model | R1 | R2 | RL
---|---|---|---
`BERTSUMEXTABS` | 42.13 | 19.60 | 39.18
`bart.large` | 44.16 | 21.28 | 40.90
## Example usage
##### Load BART from torch.hub (PyTorch >= 1.1):
```python
import torch
bart = torch.hub.load('pytorch/fairseq', 'bart.large')
bart.eval() # disable dropout (or leave in train mode to finetune)
```
##### Load BART (for PyTorch 1.0 or custom models):
```python
# Download bart.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz
tar -xzvf bart.large.tar.gz
# Load the model in fairseq
from fairseq.models.bart import BARTModel
bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt')
bart.eval() # disable dropout (or leave in train mode to finetune)
```
##### Apply Byte-Pair Encoding (BPE) to input text:
```python
tokens = bart.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
bart.decode(tokens) # 'Hello world!'
```
##### Extract features from BART:
```python
# Extract the last layer's features
last_layer_features = bart.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])
# Extract all layer's features from decoder (layer 0 is the embedding layer)
all_layers = bart.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 13
assert torch.all(all_layers[-1] == last_layer_features)
```
##### Use BART for sentence-pair classification tasks:
```python
# Download BART already finetuned for MNLI
bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')
bart.eval() # disable dropout for evaluation
# Encode a pair of sentences and make a prediction
tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.')
bart.predict('mnli', tokens).argmax() # 0: contradiction
# Encode another pair of sentences
tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.')
bart.predict('mnli', tokens).argmax() # 2: entailment
```
##### Register a new (randomly initialized) classification head:
```python
bart.register_classification_head('new_task', num_classes=3)
logprobs = bart.predict('new_task', tokens)
```
##### Batched prediction:
```python
import torch
from fairseq.data.data_utils import collate_tokens
bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')
bart.eval()
batch_of_pairs = [
['BART is a seq2seq model.', 'BART is not sequence to sequence.'],
['BART is denoising autoencoder.', 'BART is version of autoencoder.'],
]
batch = collate_tokens(
[bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
)
logprobs = bart.predict('mnli', batch)
print(logprobs.argmax(dim=1))
# tensor([0, 2])
```
##### Using the GPU:
```python
bart.cuda()
bart.predict('new_task', tokens)
```
#### Evaluating the `bart.large.mnli` model:
Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set.
```python
label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
ncorrect, nsamples = 0, 0
bart.cuda()
bart.eval()
with open('glue_data/MNLI/dev_matched.tsv') as fin:
fin.readline()
for index, line in enumerate(fin):
tokens = line.strip().split('\t')
sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
tokens = bart.encode(sent1, sent2)
prediction = bart.predict('mnli', tokens).argmax().item()
prediction_label = label_map[prediction]
ncorrect += int(prediction_label == target)
nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
# Expected output: 0.9010
```
#### Evaluating the `bart.large.cnn` model:
Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample.
```python
bart = torch.hub.load('pytorch/fairseq', 'bart.large.cnn')
bart.cuda()
bart.eval()
bart.half()
count = 1
bsz = 32
with open('test.source') as source, open('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()
```
Install `files2rouge` from [here](https://github.com/pltrdy/files2rouge).
```bash
export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar
# Tokenize hypothesis and target files.
cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized
cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target
files2rouge test.hypo.tokenized test.hypo.target
# Expected output: (ROUGE-2 Average_F: 0.21238)
```
## Finetuning
- [Finetuning on GLUE](README.glue.md)
- [Finetuning on CNN-DM](README.summarization.md)
## Citation
```bibtex
@article{lewis2019bart,
title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural
Language Generation, Translation, and Comprehension},
author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and
Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov
and Luke Zettlemoyer },
journal={arXiv preprint arXiv:1910.13461},
year = {2019},
}
```
fairseq/examples/bart/README.summarization.md 0 → 100644
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# Fine-tuning BART on CNN-Dailymail summarization task
### 1) Download the CNN and Daily Mail data and preprocess it into data files with non-tokenized cased samples.
Follow the instructions [here](https://github.com/abisee/cnn-dailymail) to download the original CNN and Daily Mail datasets. To preprocess the data, refer to the pointers in [this issue](https://github.com/pytorch/fairseq/issues/1391) or check out the code [here](https://github.com/artmatsak/cnn-dailymail).
Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to download the original Extreme Summarization datasets, or check out the code [here](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset), Please keep the raw dataset and make sure no tokenization nor BPE on the dataset.
### 2) BPE preprocess:
```bash
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'
TASK=cnn_dm
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 "$TASK/$SPLIT.$LANG" \
--outputs "$TASK/$SPLIT.bpe.$LANG" \
--workers 60 \
--keep-empty;
done
done
```
### 3) Binarize dataset:
```bash
fairseq-preprocess \
--source-lang "source" \
--target-lang "target" \
--trainpref "${TASK}/train.bpe" \
--validpref "${TASK}/val.bpe" \
--destdir "${TASK}-bin/" \
--workers 60 \
--srcdict dict.txt \
--tgtdict dict.txt;
```
### 4) Fine-tuning on CNN-DM summarization task:
Example fine-tuning CNN-DM
```bash
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 fairseq-train 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`.
Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task
### 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:
```python
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()
```
Use beam=6, lenpen=1.0, max_len_b=60, min_len=10 for Xsum Generation
fairseq/examples/byte_level_bpe/README.md 0 → 100644
View file @ 18d27e00
# Neural Machine Translation with Byte-Level Subwords
https://arxiv.org/abs/1909.03341
We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as
example.
## Data
Get data and generate fairseq binary dataset:
```bash
bash ./get_data.sh
```
## Model Training
Train Transformer model with Bi-GRU embedding contextualization (implemented in `gru_transformer.py`):
```bash
# VOCAB=bytes
# VOCAB=chars
VOCAB=bbpe2048
# VOCAB=bpe2048
# VOCAB=bbpe4096
# VOCAB=bpe4096
# VOCAB=bpe16384
```
```bash
fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
--arch gru_transformer --encoder-layers 2 --decoder-layers 2 --dropout 0.3 --share-all-embeddings \
--optimizer adam --adam-betas '(0.9, 0.98)' \
--lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
--log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \
--batch-size 100 --max-update 100000 --update-freq 2
```
## Generation
`fairseq-generate` requires bytes (BBPE) decoder to convert byte-level representation back to characters:
```bash
# BPE=--bpe bytes
# BPE=--bpe characters
BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model
# BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model
```
```bash
fairseq-generate "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
--source-lang fr --gen-subset test --sacrebleu --path "checkpoints/${VOCAB}/checkpoint_last.pt" \
--tokenizer moses --moses-target-lang en ${BPE}
```
When using `fairseq-interactive`, bytes (BBPE) encoder/decoder is required to tokenize input data and detokenize model predictions:
```bash
fairseq-interactive "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
--path "checkpoints/${VOCAB}/checkpoint_last.pt" --input data/test.fr --tokenizer moses --moses-source-lang fr \
--moses-target-lang en ${BPE} --buffer-size 1000 --max-tokens 10000
```
## Results
| Vocabulary | Model | BLEU |
|:-------------:|:-------------:|:-------------:|
| Joint BPE 16k ([Kudo, 2018](https://arxiv.org/abs/1804.10959)) | 512d LSTM 2+2 | 33.81 |
| Joint BPE 16k | Transformer base 2+2 (w/ GRU) | 36.64 (36.72) |
| Joint BPE 4k | Transformer base 2+2 (w/ GRU) | 35.49 (36.10) |
| Joint BBPE 4k | Transformer base 2+2 (w/ GRU) | 35.61 (35.82) |
| Joint BPE 2k | Transformer base 2+2 (w/ GRU) | 34.87 (36.13) |
| Joint BBPE 2k | Transformer base 2+2 (w/ GRU) | 34.98 (35.43) |
| Characters | Transformer base 2+2 (w/ GRU) | 31.78 (33.30) |
| Bytes | Transformer base 2+2 (w/ GRU) | 31.57 (33.62) |
## Citation
```
@misc{wang2019neural,
title={Neural Machine Translation with Byte-Level Subwords},
author={Changhan Wang and Kyunghyun Cho and Jiatao Gu},
year={2019},
eprint={1909.03341},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contact
Changhan Wang ([changhan@fb.com](mailto:changhan@fb.com)),
Kyunghyun Cho ([kyunghyuncho@fb.com](mailto:kyunghyuncho@fb.com)),
Jiatao Gu ([jgu@fb.com](mailto:jgu@fb.com))
fairseq/examples/byte_level_bpe/get_bitext.py 0 → 100644
View file @ 18d27e00
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as op
from collections import namedtuple
from multiprocessing import cpu_count
from typing import List, Optional
import sentencepiece as sp
from fairseq.data.encoders.byte_bpe import ByteBPE
from fairseq.data.encoders.byte_utils import byte_encode
from fairseq.data.encoders.bytes import Bytes
from fairseq.data.encoders.characters import Characters
from fairseq.data.encoders.moses_tokenizer import MosesTokenizer
from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE
SPLITS = ["train", "valid", "test"]
def _convert_xml(in_path: str, out_path: str):
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
ss = s.strip()
if not ss.startswith("<seg"):
continue
ss = ss.replace("</seg>", "").split('">')
assert len(ss) == 2
f_o.write(ss[1].strip() + "\n")
def _convert_train(in_path: str, out_path: str):
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
ss = s.strip()
if ss.startswith("<"):
continue
f_o.write(ss.strip() + "\n")
def _get_bytes(in_path: str, out_path: str):
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
f_o.write(Bytes.encode(s.strip()) + "\n")
def _get_chars(in_path: str, out_path: str):
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
f_o.write(Characters.encode(s.strip()) + "\n")
def pretokenize(in_path: str, out_path: str, src: str, tgt: str):
Args = namedtuple(
"Args",
[
"moses_source_lang",
"moses_target_lang",
"moses_no_dash_splits",
"moses_no_escape",
],
)
args = Args(
moses_source_lang=src,
moses_target_lang=tgt,
moses_no_dash_splits=False,
moses_no_escape=False,
)
pretokenizer = MosesTokenizer(args)
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
f_o.write(pretokenizer.encode(s.strip()) + "\n")
def _convert_to_bchar(in_path_prefix: str, src: str, tgt: str, out_path: str):
with open(out_path, "w") as f_o:
for lang in [src, tgt]:
with open(f"{in_path_prefix}.{lang}") as f:
for s in f:
f_o.write(byte_encode(s.strip()) + "\n")
def _get_bpe(in_path: str, model_prefix: str, vocab_size: int):
arguments = [
f"--input={in_path}",
f"--model_prefix={model_prefix}",
f"--model_type=bpe",
f"--vocab_size={vocab_size}",
"--character_coverage=1.0",
"--normalization_rule_name=identity",
f"--num_threads={cpu_count()}",
]
sp.SentencePieceTrainer.Train(" ".join(arguments))
def _apply_bbpe(model_path: str, in_path: str, out_path: str):
Args = namedtuple("Args", ["sentencepiece_model_path"])
args = Args(sentencepiece_model_path=model_path)
tokenizer = ByteBPE(args)
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
f_o.write(tokenizer.encode(s.strip()) + "\n")
def _apply_bpe(model_path: str, in_path: str, out_path: str):
Args = namedtuple("Args", ["sentencepiece_model"])
args = Args(sentencepiece_model=model_path)
tokenizer = SentencepieceBPE(args)
with open(in_path) as f, open(out_path, "w") as f_o:
for s in f:
f_o.write(tokenizer.encode(s.strip()) + "\n")
def _concat_files(in_paths: List[str], out_path: str):
with open(out_path, "w") as f_o:
for p in in_paths:
with open(p) as f:
for r in f:
f_o.write(r)
def preprocess_iwslt17(
root: str,
src: str,
tgt: str,
bpe_size: Optional[int],
need_chars: bool,
bbpe_size: Optional[int],
need_bytes: bool,
):
# extract bitext
in_root = op.join(root, f"{src}-{tgt}")
for lang in [src, tgt]:
_convert_train(
op.join(in_root, f"train.tags.{src}-{tgt}.{lang}"),
op.join(root, f"train.{lang}"),
)
_convert_xml(
op.join(in_root, f"IWSLT17.TED.dev2010.{src}-{tgt}.{lang}.xml"),
op.join(root, f"valid.{lang}"),
)
_convert_xml(
op.join(in_root, f"IWSLT17.TED.tst2015.{src}-{tgt}.{lang}.xml"),
op.join(root, f"test.{lang}"),
)
# pre-tokenize
for lang in [src, tgt]:
for split in SPLITS:
pretokenize(
op.join(root, f"{split}.{lang}"),
op.join(root, f"{split}.moses.{lang}"),
src,
tgt,
)
# tokenize with BPE vocabulary
if bpe_size is not None:
# learn vocabulary
concated_train_path = op.join(root, "train.all")
_concat_files(
[op.join(root, "train.moses.fr"), op.join(root, "train.moses.en")],
concated_train_path,
)
bpe_model_prefix = op.join(root, f"spm_bpe{bpe_size}")
_get_bpe(concated_train_path, bpe_model_prefix, bpe_size)
os.remove(concated_train_path)
# apply
for lang in [src, tgt]:
for split in SPLITS:
_apply_bpe(
bpe_model_prefix + ".model",
op.join(root, f"{split}.moses.{lang}"),
op.join(root, f"{split}.moses.bpe{bpe_size}.{lang}"),
)
# tokenize with bytes vocabulary
if need_bytes:
for lang in [src, tgt]:
for split in SPLITS:
_get_bytes(
op.join(root, f"{split}.moses.{lang}"),
op.join(root, f"{split}.moses.bytes.{lang}"),
)
# tokenize with characters vocabulary
if need_chars:
for lang in [src, tgt]:
for split in SPLITS:
_get_chars(
op.join(root, f"{split}.moses.{lang}"),
op.join(root, f"{split}.moses.chars.{lang}"),
)
# tokenize with byte-level BPE vocabulary
if bbpe_size is not None:
# learn vocabulary
bchar_path = op.join(root, "train.bchar")
_convert_to_bchar(op.join(root, "train.moses"), src, tgt, bchar_path)
bbpe_model_prefix = op.join(root, f"spm_bbpe{bbpe_size}")
_get_bpe(bchar_path, bbpe_model_prefix, bbpe_size)
os.remove(bchar_path)
# apply
for lang in [src, tgt]:
for split in SPLITS:
_apply_bbpe(
bbpe_model_prefix + ".model",
op.join(root, f"{split}.moses.{lang}"),
op.join(root, f"{split}.moses.bbpe{bbpe_size}.{lang}"),
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--root", type=str, default="data")
parser.add_argument(
"--bpe-vocab",
default=None,
type=int,
help="Generate tokenized bitext with BPE of size K."
"Default to None (disabled).",
)
parser.add_argument(
"--bbpe-vocab",
default=None,
type=int,
help="Generate tokenized bitext with BBPE of size K."
"Default to None (disabled).",
)
parser.add_argument(
"--byte-vocab",
action="store_true",
help="Generate tokenized bitext with bytes vocabulary",
)
parser.add_argument(
"--char-vocab",
action="store_true",
help="Generate tokenized bitext with chars vocabulary",
)
args = parser.parse_args()
preprocess_iwslt17(
args.root,
"fr",
"en",
args.bpe_vocab,
args.char_vocab,
args.bbpe_vocab,
args.byte_vocab,
)
if __name__ == "__main__":
main()
fairseq/examples/byte_level_bpe/get_data.sh 0 → 100644
View file @ 18d27e00
#!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
PY_BIN_ROOT=
# PyPI dependency
${PY_BIN_ROOT}pip install sentencepiece sacremoses
# Get data
if [ ! -d "data" ]; then
mkdir data
fi
if [ ! -f "data/fr-en.tgz" ]; then
wget https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz -P data
tar xvf data/fr-en.tgz -C data
fi
${PY_BIN_ROOT}python get_bitext.py --bpe-vocab 16384 --byte-vocab --char-vocab
for VOCAB_SIZE in 2048 4096; do
${PY_BIN_ROOT}python get_bitext.py --bpe-vocab ${VOCAB_SIZE} --bbpe-vocab ${VOCAB_SIZE}
done
rm -r data/fr-en data/fr-en.tgz
# Generate binary dataset
${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bpe16384 --joined-dictionary \
--workers "$(nproc)" --trainpref data/train.moses.bpe16384 --validpref data/valid.moses.bpe16384 \
--testpref data/test.moses.bpe16384
${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bytes --joined-dictionary \
--workers "$(nproc)" --trainpref data/train.moses.bytes --validpref data/valid.moses.bytes \
--testpref data/test.moses.bytes
${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_chars --joined-dictionary \
--workers "$(nproc)" --trainpref data/train.moses.chars --validpref data/valid.moses.chars \
--testpref data/test.moses.chars
for VOCAB_SIZE in 2048 4096; do
for TYPE in bbpe bpe; do
${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir "data/bin_${TYPE}${VOCAB_SIZE}" \
--joined-dictionary --workers "$(nproc)" --trainpref "data/train.moses.${TYPE}${VOCAB_SIZE}" \
--validpref "data/valid.moses.${TYPE}${VOCAB_SIZE}" --testpref "data/test.moses.${TYPE}${VOCAB_SIZE}"
done
done
fairseq/examples/byte_level_bpe/gru_transformer.py 0 → 100644
View file @ 18d27e00
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerEncoder, TransformerModel
@register_model("gru_transformer")
class GRUTransformerModel(TransformerModel):
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return GRUTransformerEncoder(args, src_dict, embed_tokens)
class GRUTransformerEncoder(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
self.emb_ctx = nn.GRU(
input_size=embed_tokens.embedding_dim,
hidden_size=embed_tokens.embedding_dim // 2,
num_layers=1,
bidirectional=True,
)
def forward_embedding(self, src_tokens):
# embed tokens and positions
x = embed = self.embed_scale * self.embed_tokens(src_tokens)
if self.embed_positions is not None:
x = embed + self.embed_positions(src_tokens)
# contextualize embeddings
x = x.transpose(0, 1)
x = self.dropout_module(x)
x, _ = self.emb_ctx.forward(x)
x = x.transpose(0, 1)
if self.layernorm_embedding is not None:
x = self.layernorm_embedding(x)
x = self.dropout_module(x)
return x, embed
@register_model_architecture("gru_transformer", "gru_transformer")
def gru_transformer_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.no_cross_attention = getattr(args, "no_cross_attention", False)
args.cross_self_attention = getattr(args, "cross_self_attention", False)
args.layer_wise_attention = getattr(args, "layer_wise_attention", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
@register_model_architecture("gru_transformer", "gru_transformer_big")
def gru_transformer_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
gru_transformer_base_architecture(args)
fairseq/examples/camembert/README.md 0 → 100644
View file @ 18d27e00
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a pretrained language model trained on 138GB of French text based on RoBERTa.
Also available in [github.com/huggingface/transformers](https://github.com/huggingface/transformers/).
## Pre-trained models
| Model | #params | Download | Arch. | Training data |
|--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------|
| `camembert` / `camembert-base` | 110M | [camembert-base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz) | Base | OSCAR (138 GB of text) |
| `camembert-large` | 335M | [camembert-large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz) | Large | CCNet (135 GB of text) |
| `camembert-base-ccnet` | 110M | [camembert-base-ccnet.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz) | Base | CCNet (135 GB of text) |
| `camembert-base-wikipedia-4gb` | 110M | [camembert-base-wikipedia-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz) | Base | Wikipedia (4 GB of text) |
| `camembert-base-oscar-4gb` | 110M | [camembert-base-oscar-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz) | Base | Subsample of OSCAR (4 GB of text) |
| `camembert-base-ccnet-4gb` | 110M | [camembert-base-ccnet-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz) | Base | Subsample of CCNet (4 GB of text) |
## Example usage
### fairseq
##### Load CamemBERT from torch.hub (PyTorch >= 1.1):
```python
import torch
camembert = torch.hub.load('pytorch/fairseq', 'camembert')
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Load CamemBERT (for PyTorch 1.0 or custom models):
```python
# Download camembert model
wget https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz
tar -xzvf camembert.tar.gz
# Load the model in fairseq
from fairseq.models.roberta import CamembertModel
camembert = CamembertModel.from_pretrained('/path/to/camembert')
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks:
```python
masked_line = 'Le camembert est <mask> :)'
camembert.fill_mask(masked_line, topk=3)
# [('Le camembert est délicieux :)', 0.4909118115901947, ' délicieux'),
# ('Le camembert est excellent :)', 0.10556942224502563, ' excellent'),
# ('Le camembert est succulent :)', 0.03453322499990463, ' succulent')]
```
##### Extract features from Camembert:
```python
# Extract the last layer's features
line = "J'aime le camembert !"
tokens = camembert.encode(line)
last_layer_features = camembert.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 10, 768])
# Extract all layer's features (layer 0 is the embedding layer)
all_layers = camembert.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 13
assert torch.all(all_layers[-1] == last_layer_features)
```
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
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# (Vectorized) Lexically constrained decoding with dynamic beam allocation
This page provides instructions for how to use lexically constrained decoding in Fairseq.
Fairseq implements the code described in the following papers:
* [Fast Lexically Constrained Decoding With Dynamic Beam Allocation](https://www.aclweb.org/anthology/N18-1119/) (Post & Vilar, 2018)
* [Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://www.aclweb.org/anthology/N19-1090/) (Hu et al., 2019)
## Quick start
Constrained search is enabled by adding the command-line argument `--constraints` to `fairseq-interactive`.
Constraints are appended to each line of input, separated by tabs. Each constraint (one or more tokens)
is a separate field.
The following command, using [Fairseq's WMT19 German--English model](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md),
translates the sentence *Die maschinelle Übersetzung ist schwer zu kontrollieren.* with the constraints
"hard" and "to influence".
echo -e "Die maschinelle Übersetzung ist schwer zu kontrollieren.\thard\ttoinfluence" \
| normalize.py | tok.py \
| fairseq-interactive /path/to/model \
--path /path/to/model/model1.pt \
--bpe fastbpe \
--bpe-codes /path/to/model/bpecodes \
--constraints \
-s de -t en \
--beam 10
(tok.py and normalize.py can be found in the same directory as this README; they are just shortcuts around Fairseq's WMT19 preprocessing).
This will generate the following output:
[snip]
S-0 Die masch@@ in@@ elle Über@@ setzung ist schwer zu kontrollieren .
W-0 1.844 seconds
C-0 hard
C-0 influence
H-0 -1.5333266258239746 Mach@@ ine trans@@ lation is hard to influence .
D-0 -1.5333266258239746 Machine translation is hard to influence .
P-0 -0.5434 -0.1423 -0.1930 -0.1415 -0.2346 -1.8031 -0.1701 -11.7727 -0.1815 -0.1511
By default, constraints are generated in the order supplied, with any number (zero or more) of tokens generated
between constraints. If you wish for the decoder to order the constraints, then use `--constraints unordered`.
Note that you may want to use a larger beam.
## Implementation details
The heart of the implementation is in `fairseq/search.py`, which adds a `LexicallyConstrainedBeamSearch` instance.
This instance of beam search tracks the progress of each hypothesis in the beam through the set of constraints
provided for each input sentence. It does this using one of two classes, both found in `fairseq/token_generation_contstraints.py`:
* OrderedConstraintState: assumes the `C` input constraints will be generated in the provided order
* UnorderedConstraintState: tries to apply `C` (phrasal) constraints in all `C!` orders
## Differences from Sockeye
There are a number of [differences from Sockeye's implementation](https://awslabs.github.io/sockeye/inference.html#lexical-constraints).
* Generating constraints in the order supplied (the default option here) is not available in Sockeye.
* Due to an improved beam allocation method, there is no need to prune the beam.
* Again due to better allocation, beam sizes as low as 10 or even 5 are often sufficient.
* [The vector extensions described in Hu et al.](https://github.com/edwardjhu/sockeye/tree/trie_constraints) (NAACL 2019) were never merged
into the main Sockeye branch.
## Citation
The paper first describing lexical constraints for seq2seq decoding is:
```bibtex
@inproceedings{hokamp-liu-2017-lexically,
title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search",
author = "Hokamp, Chris and
Liu, Qun",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1141",
doi = "10.18653/v1/P17-1141",
pages = "1535--1546",
}
```
The fairseq implementation uses the extensions described in
```bibtex
@inproceedings{post-vilar-2018-fast,
title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation",
author = "Post, Matt and
Vilar, David",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N18-1119",
doi = "10.18653/v1/N18-1119",
pages = "1314--1324",
}
```
and
```bibtex
@inproceedings{hu-etal-2019-improved,
title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting",
author = "Hu, J. Edward and
Khayrallah, Huda and
Culkin, Ryan and
Xia, Patrick and
Chen, Tongfei and
Post, Matt and
Van Durme, Benjamin",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1090",
doi = "10.18653/v1/N19-1090",
pages = "839--850",
}
```
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#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
from sacremoses.normalize import MosesPunctNormalizer
def main(args):
normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn)
for line in sys.stdin:
print(normalizer.normalize(line.rstrip()), flush=True)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--lang", "-l", default="en")
parser.add_argument("--penn", "-p", action="store_true")
args = parser.parse_args()
main(args)
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#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import sacremoses
def main(args):
"""Tokenizes, preserving tabs"""
mt = sacremoses.MosesTokenizer(lang=args.lang)
def tok(s):
return mt.tokenize(s, return_str=True)
for line in sys.stdin:
parts = list(map(tok, line.split("\t")))
print(*parts, sep="\t", flush=True)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--lang", "-l", default="en")
parser.add_argument("--penn", "-p", action="store_true")
parser.add_argument("--fields", "-f", help="fields to tokenize")
args = parser.parse_args()
main(args)
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# Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
## Pre-trained models
Description | Dataset | Model | Test set(s)
---|---|---|---
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2)
## Example usage
See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and
WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures.
## Citation
```bibtex
@inproceedings{gehring2017convs2s,
title = {Convolutional Sequence to Sequence Learning},
author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N},
booktitle = {Proc. of ICML},
year = 2017,
}
```
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#!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
SPM_ENCODE=flores/scripts/spm_encode.py
DATA=data_tmp
SPM_MODEL=criss_checkpoints/sentence.bpe.model
DICT=criss_checkpoints/dict.txt
git clone https://github.com/facebookresearch/LASER
mkdir -p data_tmp
declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn")
for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do
lang_tatoeba=${lang_tatoeba_map[$lang]}
echo $lang_tatoeba
datadir=$DATA/${lang}-en_XX-tatoeba
rm -rf $datadir
mkdir -p $datadir
TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba
python $SPM_ENCODE \
--model ${SPM_MODEL} \
--output_format=piece \
--inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \
--outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX
# binarize data
fairseq-preprocess \
--source-lang ${lang} --target-lang en_XX \
--testpref $datadir/test.bpe.${lang}-en_XX \
--destdir $datadir \
--srcdict ${DICT} \
--joined-dictionary \
--workers 4
done
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