ptgnn: A PyTorch GNN Library

This is a library containing pyTorch code for creating graph neural network (GNN) models. The library provides some sample implementations.

If you are interested in using this library, please read about its architecture and how to define GNN models or follow this tutorial.

Note that

ptgnn

nn.Module

AbstractNeuralModel

nn.Module

The library is mainly engineered to be fast for sparse graphs. For example, for the Graph2Class task (discussed below) on a V100 with the default hyperparameters and architecture

ptgnn

Implemented Tasks

All task implementations can be found in the

ptgnn.implementations

• PPI The PPI task as described by Zitnik and Leskovec, 2017.
• VarMisuse This is a re-implementation of the VariableMisuse task of Allamanis et al., 2018.
• Graph2Sequence This is re-implementation of the GNN->GRU model of Fernandes et. al., 2019.
• Graph2Class Classify (Label) a subset of the input nodes into classes similar to Graph2Class in Typilus.

The tutorial gives a step-by-step example for coding the Graph2Class model.

Installation

This code was tested with PyTorch 1.4 and depends on

pytorch-scatter

pytorch-scatter

1. To install PyTorch 1.4, use the up-to-date command from PyTorch Get Started, selecting the appropriate options, e.g. for Linux, pip, and CUDA 10.1 it's currently:

   bash
   pip install torch torchvision
   

2. To install

   pytorch-scatter

   , follow the instructions from the GitHub repo, choosing the appropriate CUDA option, e.g., for CUDA 10.1:

   bash
   pip install torch-scatter==2.0.4+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
   

3. To install

   ptgnn

   from pypi, including all other dependencies:

   bash
   pip install ptgnn
   

   If you want to use ptgnn sampels with Azure ML (e.g. the

   --aml

   flag in the implementation CLIs), install with

   bash
   pip install ptgnn[aml]
   

   or directly from the sources,

   cd

   into the root directory of the project and run

   bash
   pip install -e .
   

   To check that installation was successful and run the unit tests:

   python
   python setup.py test
   

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Developing ptgnn

To contribute to this library, first follow the next steps to setup your development environment: * Install the library requirements. * Install the pre-commit hooks: * Run

pip3 install pre-commit

pre-commit install

Using Conda

If you are using conda, then download the correct torch-scatter wheel. If using

torch==1.5.0

$ conda env create -f environment.yml
$ conda activate ptgnn-env
$ pip install torch_scatter-2.0.4+cu102-cp37-cp37m-linux_x86_64.whl
$ pip install -e .
$ python setup.py test
$ pip install pre-commit
$ pre-commit install

Releasing to PyPi

To create a PyPi release push a tag in the form

v1.3.4