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opus-train

This is a set of scripts to ease automation of downloading and training NMT models.

All the scripts are configured and tested for CSD3, to use it on Cirrus or elsewhere, please change the #SBATCH parameters at the beggining of the Slurm scripts or overwrite them with desired cli parameters. Also, SLURM modules loaded at the begining of the scripts would be different.

Installation

# Multilingual TED sets are required, so clone revursively
git clone --recursive --depth 1 https://github.com/paracrawl/opus-train
cd opus-train
module load python/3.7
python3.7 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Compile Marian (optional)

By default training script points to a compiled marian on ~/rds/rds-t2-cs119/cs-zara1/marian-dev/build version 1.9.0, so you don't need to compile it. If you want to compile your own Marian, place this into your .bashrc on a login-gpu node:

# User specific aliases and functions
#Upgrade to CUDA 9.2 (the default is 8.0)
module switch cuda/8.0 cuda/10.1
#Upgrade to a newer cmake
module add cmake-3.12.3-gcc-5.4.0-rgjxm2x
#Add MPI support for multi-node training
module add openmpi-2.1.1-gcc-5.4.0-wtt3gne
#Makes CPU version of Marian compile correctly.
module add intel/mkl/2019.3
#Set your compiler to optimize by default
export CFLAGS="-O3 -march=native -pipe"
export CXXFLAGS="-O3 -march=native -pipe"
#tcmalloc makes Marian faster
export INCLUDE=/home/cs-zara1/rds/rds-t2-cs119/cs-zara1/perftools/include${INCLUDE:+:$INCLUDE}
export LIB=/home/cs-zara1/rds/rds-t2-cs119/cs-zara1/perftools/lib${LIB:+:$LIB}
export CPATH=/home/cs-zara1/rds/rds-t2-cs119/cs-zara1/perftools/include${CPATH:+:$CPATH}
export LIBRARY_PATH=/home/cs-zara1/rds/rds-t2-cs119/cs-zara1/perftools/lib${LIBRARY_PATH:+:$LIBRARY_PATH}
export LD_LIBRARY_PATH=/home/cs-zara1/rds/rds-t2-cs119/cs-zara1/perftools/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}
#If you want to compile Marian with SentencePiece (-DUSE_SENTENCEPIECE=on)
module add protobuf-3.4.0-gcc-5.4.0-zkpendv

and build (marian is included as submodule)

cd marian-dev
git submodule update --init
mkdir build
cd build
cmake .. -DUSE_SENTENCEPIECE=ON -DCMAKE_CXX_STANDARD_LIBRARIES="-liconv" # -liconv option to allow building on CSD3
make -j24

Download corpora

sbatch -J download-mt download.slurm en mt

IMPORTANT: for downloading use always ParaCrawl convention for source-target. For example: en ?? or es ??. Otherwise it won't find ParaCrawl files. After downloading, languages are kept in separated files to allow training in both directions easily.

Training models

sbatch -J train-mt train.slurm mt en opus

That will train Transformer base model with Maletese-English OPUS corpora. Logs are located at logs/{train,valid}.opus.base.log, model files at models/mten.opus.base.npz* and vocab file at models/vocab.mten.opus.base.spm.

Download step also creates OPUS+ParaCrawl training file, to train with it simply use:

sbatch -J train-mt train.slurm mt en opus-paracrawlv7.1

Running test

To run a test with a model and obtain BLEU score:

Run tests (OPUS and TED) with a model
Usage: sbatch [params] test.slurm <lang1> <lang2> <modelname>
modelname must follow the pattern <corpus_train>.<model_config>

run

sbatch -J test-mt test.slurm mt en opus.base

Assuming you want to test Transformer base model trained on OPUS Maltese to English that has trained on the previous step. To test OPUS+ParaCrawl do the same but with opus-paracrawlv7.1.base instead of opus.base.

Checking overlap sentences with test

There's a script to count overlap sentences with two files, it will print to stderr the found sentences and to stdout the % of the file passed as parameter that are on the stdin:

zcat data/en-mt/opus.en.gz | ./scripts/overlap.py data/en-mt/test-opus.en

Train/dev/test split policy

The download script will use the smaller corpora (less than 250k words on source side) to create the dev/test sets from OPUS. Also, sentences that overlap are explictily removed from the training set. Despite of that, take into account that parapgraph overlap is still present, especially for low-resourced languages. That's why Multilingual TED is also used. For the development, the TED data is concatenated to the development set of OPUS and for the test, it is kept as a separated test file. Both, the held-out test from OPUS and the TED test, will be run in the test.slurm script.