This problem uses recurrent neural network to do language translation.

• Python 3.6
• CUDA 9.0
• PyTorch 1.0.1
• sacrebleu

• nvidia-docker
• pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime container

To setup the environment on Ubuntu 16.04 (16 CPUs, one P100, 100 GB disk), you can use these commands. This may vary on a different operating system or graphics card.

# Install docker
sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository    "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"
sudo apt update
# sudo apt install docker-ce -y
sudo apt install docker-ce=18.03.0~ce-0~ubuntu -y --allow-downgrades

# Install nvidia-docker2
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt install nvidia-docker2 -y


sudo tee /etc/docker/daemon.json <<EOF
{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}
EOF
sudo pkill -SIGHUP dockerd

sudo apt install -y bridge-utils
sudo service docker stop
sleep 1;
sudo iptables -t nat -F
sleep 1;
sudo ifconfig docker0 down
sleep 1;
sudo brctl delbr docker0
sleep 1;
sudo service docker start

Download the data using the following command:

bash download_dataset.sh

Verify data with:

bash verify_dataset.sh

cd pytorch
sudo docker build . --rm -t gnmt:latest
SEED=1
NOW=`date "+%F-%T"`
sudo nvidia-docker run -it --rm --ipc=host \
  -v $(pwd)/../data:/data \
  gnmt:latest "./run_and_time.sh" $SEED |tee benchmark-$NOW.log

sudo NV_GPU=0 nvidia-docker run -it --rm --ipc=host \
  -v $(pwd)/../data:/data \
  gnmt:latest "./run_and_time.sh" $SEED |tee benchmark-$NOW.log

We use WMT16 English-German for training.

Script uses subword-nmt package to segment text into subword units (BPE), by default it builds shared vocabulary of 32,000 tokens. Preprocessing removes all pairs of sentences that can't be decoded by latin-1 encoder.

Vocabulary is generated by the following lines from the download_dataset.sh script:

# Create vocabulary file for BPE
cat "${OUTPUT_DIR}/train.tok.clean.bpe.${merge_ops}.en" "${OUTPUT_DIR}/train.tok.clean.bpe.${merge_ops}.de" | \
  ${OUTPUT_DIR}/subword-nmt/get_vocab.py | cut -f1 -d ' ' > "${OUTPUT_DIR}/vocab.bpe.${merge_ops}"

Vocabulary is stored to the rnn_translator/data/vocab.bpe.32000 plain text file. Tokens are separated with a newline character, one token per line. The vocabulary file doesn't contain special tokens like for example BOS (begin-of-string) or EOS (end-of-string) tokens.

Here are first 10 lines from the rnn_translator/data/vocab.bpe.32000 file:

,
.
the
in
of
and
die
der
to
und

The download_dataset.sh script automatically creates training, validation and test datasets. Datasets are stored as plain text files. Sentences are separated with a newline character, and tokens within each sentence are separated with a single space character.

Training data:

• source language (English): rnn_translator/data/train.tok.clean.bpe.32000.en
• target language (German): rnn_translator/data/train.tok.clean.bpe.32000.de

Validation data:

• source language (English): rnn_translator/data/newstest_dev.tok.clean.bpe.32000.en
• target language (German): rnn_translator/data/newstest_dev.tok.clean.bpe.32000.de

Test data:

• source language (English): rnn_translator/data/newstest2014.tok.bpe.32000.en
• target language (German): rnn_translator/data/newstest2014.de
  • notice that the newstest2014.de file isn't tokenized, BLEU evaluation is performed by the sacrebleu package and sacrebleu expects plain text raw data (tokenization is performed internally by sacrebleu)

Here are first 5 lines from the rnn_translator/data/train.tok.clean.bpe.32000.en file:

Res@@ um@@ ption of the session
I declare resumed the session of the European Parliament ad@@ jour@@ ned on Friday 17 December 1999 , and I would like once again to wish you a happy new year in the hope that you enjoyed a pleasant fes@@ tive period .
Although , as you will have seen , the d@@ read@@ ed ' millenn@@ ium bug ' failed to materi@@ alise , still the people in a number of countries suffered a series of natural disasters that truly were d@@ read@@ ful .
You have requested a debate on this subject in the course of the next few days , during this part-session .
In the meantime , I should like to observe a minute ' s silence , as a number of Members have requested , on behalf of all the victims concerned , particularly those of the terrible stor@@ ms , in the various countries of the European Union .

And here are first 5 lines from the rnn_translator/data/train.tok.clean.bpe.32000.de file:

Wiederaufnahme der Sitzungsperiode
Ich erkläre die am Freitag , dem 17. Dezember unterbro@@ ch@@ ene Sitzungsperiode des Europäischen Parlaments für wieder@@ aufgenommen , wünsche Ihnen nochmals alles Gute zum Jahres@@ wechsel und hoffe , daß Sie schöne Ferien hatten .
Wie Sie feststellen konnten , ist der ge@@ für@@ ch@@ tete " Mill@@ en@@ i@@ um-@@ Bu@@ g " nicht eingetreten . Doch sind Bürger einiger unserer Mitgliedstaaten Opfer von schrecklichen Naturkatastrophen geworden .
Im Parlament besteht der Wunsch nach einer Aussprache im Verlauf dieser Sitzungsperiode in den nächsten Tagen .
Heute möchte ich Sie bitten - das ist auch der Wunsch einiger Kolleginnen und Kollegen - , allen Opfern der St@@ ür@@ me , insbesondere in den verschiedenen Ländern der Europäischen Union , in einer Schwei@@ ge@@ minute zu ge@@ denken .

Training uses WMT16 English-German dataset, validation is on concatenation of newstest2015 and newstest2016, BLEU evaluation is done on newstest2014.

Training is executed only on pairs of sentences which satisfy the following equation:

    min_len <= src sentence sequence length <= max_len AND
    min_len <= tgt sentence sequence length <= max_len

min_len is set to 0, max_len is set to 50. Source and target sequence lengths include special BOS (begin-of-sentence) and EOS (end-of-sentence) tokens.

Filtering is implemented in pytorch/seq2seq/data/dataset.py, class LazyParallelDataset.

Training script does bucketing by sequence length. Bucketing algorithm uses 5 equal-width buckets (num_buckets = 5). Pairs of training sentences are assigned to buckets by the value of max(src_sentence_len // bucket_width, tgt_sentence_len // bucket_width), where bucket_width = (max_len + num_buckets - 1) // num_buckets. Before each training epoch batches are randomly sampled from the buckets (last incomplete batches are dropped for each bucket), then all batches are reshuffled.

Bucketing is implemented in pytorch/seq2seq/data/sampler.py, class BucketingSampler.

Implemented model is similar to the one from Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper.

Most important difference is in the attention mechanism. This repository implements gnmt_v2 attention: output from first LSTM layer of decoder goes into attention, then re-weighted context is concatenated with inputs to all subsequent LSTM layers in decoder at current timestep.

The same attention mechanism is also implemented in default GNMT-like models from tensorflow/nmt and NVIDIA/OpenSeq2Seq.

• general:
  • encoder and decoder are using shared embeddings
  • data-parallel multi-gpu training
  • trained with label smoothing loss (smoothing factor 0.1)
• encoder:
  • 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest of layers are unidirectional
  • with residual connections starting from 3rd LSTM layer
  • uses standard pytorch nn.LSTM layer
  • dropout is applied on input to all LSTM layers, probability of dropout is set to 0.2
  • hidden state of LSTM layers is initialized with zeros
  • weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1) distribution
• decoder:
  • 4-layer unidirectional LSTM with hidden size 1024 and fully-connected classifier
  • with residual connections starting from 3rd LSTM layer
  • uses standard pytorch nn.LSTM layer
  • dropout is applied on input to all LSTM layers, probability of dropout is set to 0.2
  • hidden state of LSTM layers is initialized with zeros
  • weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1) distribution
  • weights and bias of fully-connected classifier is initialized with uniform(-0.1, 0.1) distribution
• attention:
  • normalized Bahdanau attention
  • model uses gnmt_v2 attention mechanism
  • output from first LSTM layer of decoder goes into attention, then re-weighted context is concatenated with the input to all subsequent LSTM layers in decoder at the current timestep
  • linear transform of keys and queries is initialized with uniform(-0.1, 0.1), normalization scalar is initialized with 1.0 / sqrt(1024), normalization bias is initialized with zero
• inference:
  • beam search with beam size of 5
  • with coverage penalty and length normalization, coverage penalty factor is set to 0.1, length normalization factor is set to 0.6 and length normalization constant is set to 5.0
  • BLEU computed by sacrebleu

Implementation:

• base Seq2Seq model: pytorch/seq2seq/models/seq2seq_base.py, class Seq2Seq
• GNMT model: pytorch/seq2seq/models/gnmt.py, class GNMT
• encoder: pytorch/seq2seq/models/encoder.py, class ResidualRecurrentEncoder
• decoder: pytorch/seq2seq/models/decoder.py, class ResidualRecurrentDecoder
• attention: pytorch/seq2seq/models/attention.py, class BahdanauAttention
• inference (including BLEU evaluation and detokenization): pytorch/seq2seq/inference/inference.py, class Translator
• beam search: pytorch/seq2seq/inference/beam_search.py, class SequenceGenerator

Cross entropy loss with label smoothing (smoothing factor = 0.1), padding is not considered part of the loss.

Loss function is implemented in pytorch/seq2seq/train/smoothing.py, class LabelSmoothing.

Adam optimizer with learning rate 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon = 1e-8 and no weight decay. Network is trained with gradient clipping, max L2 norm of gradients is set to 5.0.

Optimizer is implemented in pytorch/seq2seq/train/fp_optimizers.py, class Fp32Optimizer.

Model is trained with exponential learning rate warmup for 200 steps and with step learning rate decay. Decay is started after 2/3 of training steps, decays for a total of 4 times, at regularly spaced intervals, decay factor is 0.5.

Learning rate scheduler is implemented in pytorch/seq2seq/train/lr_scheduler.py, class WarmupMultiStepLR.

Uncased BLEU score on newstest2014 en-de dataset. BLEU scores reported by sacrebleu package (version 1.2.10). Sacrebleu is executed with the following flags: --score-only -lc --tokenize intl.

Uncased BLEU score of 24.00.

Evaluation of BLEU score is done after every epoch.

Evaluation uses all of newstest2014.en (3003 sentences).