Decoding library based on SGNMT: https://github.com/ucam-smt/sgnmt. See their docs for setting up a fairseq model to work with the library.
Clone library and submodules
git clone --recurse-submodules https://github.com/rycolab/uid-decoding.git
Dependencies
fairseq==0.10.1
scipy==1.5.4
numpy==1.19.4
Cython==0.29.21
sortedcontainers==2.3.0
subword-nmt==0.3.7
To compile the datastructure classes, run:
pip install -e .
To compile the statistics classes, navigate to the runstats submodule:
cd runstats
python setup.py install
We recommend starting with the pretrained models available from fairseq. Download any of the models from, e.g., their NMT examples, unzip, and place model checkpoints in data/ckpts. You'll have to preprocess the dictionary files to a format that the library expects. Using the pre-trained convolutional English-French WMT‘14 model an example:
curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf -
cat wmt14.en-fr.fconv-py/dict.en.txt | awk 'BEGIN{print "<epsilon> 0\n<s> 1\n</s> 2\n<unk> 3"}{print $1" "(NR+3)}' > wmap.en
cat wmt14.en-fr.fconv-py/dict.fr.txt | awk 'BEGIN{print "<epsilon> 0\n<s> 1\n</s> 2\n<unk> 3"}{print $1" "(NR+3)}' > wmap.fr
Tokenization (for input) and detokenization (for output) should be performed with the mosesdecoder library. If the model uses BPE, you'll have to preprocess the input file to put words in byte pair format. Given your bpecodes listed in e.g., file bpecodes, the entire pre-processing of input file input_file.txt in English (en) can be done as follows. Again using the convolutional English-French WMT‘14 model with the newstest test set as an example input file:
grep '<seg id' test-full/newstest2014-fren-src.en.sgm | \
sed -e 's/<seg id="[0-9]*">\s*//g' | \
sed -e 's/\s*<\/seg>\s*//g' | \
sed -e "s/\’/\'/g" > newstest_cleaned.txt
cat newstest_cleaned.txt | perl mosesdecoder/scripts/tokenizer/tokenizer.perl -threads 8 -l en > out
subword-nmt apply-bpe -c wmt14.en-fr.fconv-py/bpecodes -i out -o newstest_bpe.txt
Alternatively, one can play around with the toy model in the test scripts. Outputs are not meaningful but it is deterministic and useful for debugging.
Basic beam search can be performed on a fairseq model translating from English to French on the WMT'14 dataset as follows:
python decode.py --fairseq_path wmt14.en-fr.fconv-py/model.pt --fairseq_lang_pair en-fr --src_wmap wmap.en --trg_wmap wmap.fr --input_file newstest_bpe.txt --preprocessing word --postprocessing bpe@@ --decoder beam --beam 10
A faster version, best first beam search, simply changes the decoder:
python decode.py --fairseq_path wmt14.en-fr.fconv-py/model.pt --fairseq_lang_pair en-fr --src_wmap wmap.en --trg_wmap wmap.fr --input_file
newstest_bpe.txt --preprocessing word --postprocessing bpe@@ --decoder dijkstra_ts --beam 10
By default, both decoders only return the best solution. Set --early_stopping False if you want the entire set.
A basic example of outputs can be seen when using the test suite:
python test.py --decoder beam --beam 10
Additionally, you can run
python decode.py --help
to see descriptions of all available arguments.
The following flags may be used to include UID-regularized decoding:
--greedy_regularizer <lambda>
--local_variance_regularizer <lambda>
--variance_regularizer <lambda>
--max_regularizer <lambda>
--square_regularizer <lambda>
where <lambda> should be a (positive) float specifying the strength of the penalty. Note that it is not recommended to use early_stopping with the variance or local variance regularizers.
To use length normalized scores, add the --length_normalization flag.
To see all outputs, set --num_log <n> for however many outputs (per input) you'd like to see. To write all outputs to files, set --outputs nbest_sep --output_path <path_prefix>. You'll then get a file of samples for each position (not each input!). To just write the first/best output to a file, use --outputs text --output_path <output_file_name>
Scoring is not integrated into the library but can be performed afterwards using, e.g., the sacrebleu package. Make sure you use the arguments --outputs text --output_path <output_file_name> during decoding and then detokenize the text using the mosesdecoder detokenizer script. Given a (detokenized) baseline, you can then run sacrebleu to calculate BLEU. For example:
cat <output_file_name> | perl mosesdecoder/scripts/tokenizer/detokenizer.perl -threads 8 -l en | sacrebleu reference.txt