Seq2Seq in PyTorch

This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train and infer using them.

Using this code you can train:

Neural-machine-translation (NMT) models
Language models
Image to caption generation
Skip-thought sentence representations

And more...

Installation

git clone --recursive https://github.com/eladhoffer/seq2seq.pytorch
cd seq2seq.pytorch; python setup.py develop

Models

Models currently available:

Simple Seq2Seq recurrent model
Recurrent Seq2Seq with attentional decoder
Google neural machine translation (GNMT) recurrent model
Transformer - attention-only model from "Attention Is All You Need"

Datasets

Datasets currently available:

WMT16
WMT17
OpenSubtitles 2016
COCO image captions
Conceptual captions

All datasets can be tokenized using 3 available segmentation methods:

Character based segmentation
Word based segmentation
Byte-pair-encoding (BPE) as suggested by bpe with selectable number of tokens.

After choosing a tokenization method, a vocabulary will be generated and saved for future inference.

Training methods

The models can be trained using several methods:

Basic Seq2Seq - given encoded sequence, generate (decode) output sequence. Training is done with teacher-forcing.
Multi Seq2Seq - where several tasks (such as multiple languages) are trained simultaneously by using the data sequences as both input to the encoder and output for decoder.
Image2Seq - used to train image to caption generators.

Usage

Example training scripts are available in scripts folder. Inference examples are available in examples folder.

example for training a transformer on WMT16 according to original paper regime:


DATASET=${1:-"WMT16_de_en"}
DATASET_DIR=${2:-"./data/wmt16_de_en"}
OUTPUT_DIR=${3:-"./results"}

WARMUP="4000" LR0="512**(-0.5)"

python main.py \ --save transformer \ --dataset ${DATASET} \ --dataset-dir ${DATASET_DIR} \ --results-dir ${OUTPUT_DIR} \ --model Transformer \ --model-config "{'num_layers': 6, 'hidden_size': 512, 'num_heads': 8, 'inner_linear': 2048}" \ --data-config "{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}" \ --b 128 \ --max-length 100 \ --device-ids 0 \ --label-smoothing 0.1 \ --trainer Seq2SeqTrainer \ --optimization-config "[{'step_lambda': \"lambda t: { \ 'optimizer': 'Adam', \ 'lr': ${LR0} * min(t * -0.5, t ${WARMUP} ** -1.5), \ 'betas': (0.9, 0.98), 'eps':1e-9}\" }]"


* example for training attentional LSTM based model with 3 layers in both encoder and decoder:

python main.py \ --save de_en_wmt17 \ --dataset ${DATASET} \ --dataset-dir ${DATASET_DIR} \ --results-dir ${OUTPUT_DIR} \ --model RecurrentAttentionSeq2Seq \ --model-config "{'hidden_size': 512, 'dropout': 0.2, \ 'tie_embedding': True, 'transfer_hidden': False, \ 'encoder': {'num_layers': 3, 'bidirectional': True, 'num_bidirectional': 1, 'context_transform': 512}, \ 'decoder': {'num_layers': 3, 'concat_attention': True,\ 'attention': {'mode': 'dot_prod', 'dropout': 0, 'output_transform': True, 'output_nonlinearity': 'relu'}}}" \ --data-config "{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}" \ --b 128 \ --max-length 80 \ --device-ids 0 \ --trainer Seq2SeqTrainer \ --optimization-config "[{'epoch': 0, 'optimizer': 'Adam', 'lr': 1e-3}, {'epoch': 6, 'lr': 5e-4}, {'epoch': 8, 'lr':1e-4}, {'epoch': 10, 'lr': 5e-5}, {'epoch': 12, 'lr': 1e-5}]" \