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https://github.com/IBM/pytorch-seq2seq

An open source framework for seq2seq models in PyTorch.
https://github.com/IBM/pytorch-seq2seq

deeplearning pytorch seq2seq

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An open source framework for seq2seq models in PyTorch.

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README 

        
# pytorch-seq2seq



[![Build Status](https://travis-ci.org/IBM/pytorch-seq2seq.svg?branch=master)](https://travis-ci.org/IBM/pytorch-seq2seq)

[![Join the chat at https://gitter.im/pytorch-seq2seq/Lobby](https://badges.gitter.im/pytorch-seq2seq/Lobby.svg)](https://gitter.im/pytorch-seq2seq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)



**[Documentation](https://ibm.github.io/pytorch-seq2seq/public/index.html)**



This is a framework for sequence-to-sequence (seq2seq) models implemented in [PyTorch](http://pytorch.org).  The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc.  This is an alpha release. We appreciate any kind of feedback or contribution.



# What's New in 0.1.6



* Compatible with PyTorch 0.4

* Added support for pre-trained word embedding



# Roadmap

Seq2seq is a fast evolving field with new techniques and architectures being published frequently.  The goal of this library is facilitating the development of such techniques and applications.  While constantly improving the quality of code and documentation, we will focus on the following items:



* Evaluation with benchmarks such as WMT machine translation, COCO image captioning, conversational models, etc;

* Provide more flexible model options, improving the usability of the library;

* Adding latest architectures such as the CNN based model proposed by [Convolutional Sequence to Sequence Learning](https://arxiv.org/abs/1705.03122) and the transformer model proposed by [Attention Is All You Need](https://arxiv.org/abs/1706.03762);

* Support features in the new versions of PyTorch.



# Installation

This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).  



### Prerequisites



* Numpy: `pip install numpy` (Refer [here](https://github.com/numpy/numpy) for problem installing Numpy).

* PyTorch: Refer to [PyTorch website](http://pytorch.org/) to install the version w.r.t. your environment.



### Install from source

Currently we only support installation from source code using setuptools.  Checkout the source code and run the following commands:



    pip install -r requirements.txt

    python setup.py install



If you already had a version of PyTorch installed on your system, please verify that the active torch package is at least version 0.1.11.



# Get Started

### Prepare toy dataset



	# Run script to generate the reverse toy dataset

    # The generated data is stored in data/toy_reverse by default

	scripts/toy.sh



### Train and play

	TRAIN_PATH=data/toy_reverse/train/data.txt

	DEV_PATH=data/toy_reverse/dev/data.txt

	# Start training

    python examples/sample.py --train_path $TRAIN_PATH --dev_path $DEV_PATH



It will take about 3 minutes to train on CPU and less than 1 minute with a Tesla K80.  Once training is complete, you will be prompted to enter a new sequence to translate and the model will print out its prediction (use ctrl-C to terminate).  Try the example below!



    Input:  1 3 5 7 9

	Expected output: 9 7 5 3 1 EOS



### Checkpoints

Checkpoints are organized by experiments and timestamps as shown in the following file structure



    experiment_dir

	+-- input_vocab

	+-- output_vocab

	+-- checkpoints

	|  +-- YYYY_mm_dd_HH_MM_SS

	   |  +-- decoder

	   |  +-- encoder

	   |  +-- model_checkpoint



The sample script by default saves checkpoints in the `experiment` folder of the root directory.  Look at the usages of the sample code for more options, including resuming and loading from checkpoints.



# Benchmarks



* WMT Machine Translation (Coming soon)



# Troubleshoots and Contributing

If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/IBM/pytorch-seq2seq/issues/new) on Github.  For live discussions, please go to our [Gitter lobby](https://gitter.im/pytorch-seq2seq/Lobby).



We appreciate any kind of feedback or contribution.  Feel free to proceed with small issues like bug fixes, documentation improvement.  For major contributions and new features, please discuss with the collaborators in corresponding issues.  



### Development Cycle

We are using 4-week release cycles, where during each cycle changes will be pushed to the `develop` branch and finally merge to the `master` branch at the end of each cycle.



### Development Environment

We setup the development environment using [Vagrant](https://www.vagrantup.com/).  Run `vagrant up` with our 'Vagrantfile' to get started.



The following tools are needed and installed in the development environment by default:

* Git

* Python

* Python packages: nose, mock, coverage, flake8



### Test

The quality and the maintainability of the project is ensured by comprehensive tests.  We encourage writing unit tests and integration tests when contributing new codes.



Locally please run `nosetests` in the package root directory to run unit tests.  We use TravisCI to require that a pull request has to pass all unit tests to be eligible to merge.  See [travis configuration](https://github.com/IBM/pytorch-seq2seq/blob/master/.travis.yml) for more information.



### Code Style

We follow [PEP8](https://www.python.org/dev/peps/pep-0008/) for code style.  Especially the style of docstrings is important to generate documentation.



* *Local*: Run the following commands in the package root directory

```

# Python syntax errors or undefined names

flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

# Style checks

flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

```

* *Github*: We use [Codacy](https://www.codacy.com) to check styles on pull requests and branches.