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Contributing to PyTorch

If you are interested in contributing to PyTorch, your contributions will fall into two categories:

  1. You want to propose a new Feature and implement it
    • post about your intended feature, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
  2. You want to implement a feature or bug-fix for an outstanding issue
    • Look at the outstanding issues here: https://github.com/pytorch/pytorch/issues
    • Especially look at the Low Priority and Medium Priority issues
    • Pick an issue and comment on the task that you want to work on this feature
    • If you need more context on a particular issue, please ask and we shall provide.

Once you finish implementing a feature or bugfix, please send a Pull Request to https://github.com/pytorch/pytorch

If you are not familiar with creating a Pull Request, here are some guides:

Developing locally with PyTorch

To locally develop with PyTorch, here are some tips:

  1. Uninstall all existing pytorch installs
conda uninstall pytorch
pip uninstall torch
pip uninstall torch # run this command twice
  1. Locally clone a copy of PyTorch from source:
git clone https://github.com/pytorch/pytorch
cd pytorch
  1. Install PyTorch in build develop mode:

A full set of instructions on installing PyTorch from Source are here: https://github.com/pytorch/pytorch#from-source

The change you have to make is to replace

python setup.py install

with

python setup.py build develop

This is especially useful if you are only changing Python files.

This mode will symlink the python files from the current local source tree into the python install.

Hence, if you modify a python file, you do not need to reinstall pytorch again and again.

For example:

  • Install local pytorch in build develop mode
  • modify your python file torch/__init__.py (for example)
  • test functionality
  • modify your python file torch/__init__.py
  • test functionality
  • modify your python file torch/__init__.py
  • test functionality

You do not need to repeatedly install after modifying python files.

If you are working on C files, you will need to build your changes:

(rm -rf build; python setup.py develop)

We recommend removing the build directory before rebuilding because setup.py's recompilation checking is not great, and it will often fail to rebuild C code after you modify code in a generic directory.

Managing multiple build trees

One downside to using python setup.py develop is that your development version of pytorch will be installed globally on your account (e.g., if you run import torch anywhere else, the development version will be used.

If you want to manage multiple builds of PyTorch, you can make use of conda environments to maintain separate Python package environments, each of which can be tied to a specific build of PyTorch. To set one up:

conda create -n pytorch-myfeature
source activate pytorch-myfeature
# if you run python now, torch will NOT be installed
python setup.py build develop

C++ Development tips

When you are developing on the C++ side of things, the environment variables DEBUG and NO_CUDA are helpful.

  • DEBUG=1 will enable debug builds (-g -O0)
  • NO_CUDA=1 will disable compiling CUDA (in case you are developing on something not CUDA related), to save compile time.

For example:

NO_CUDA=1 DEBUG=1 python setup.py build develop

Also, if you are developing a lot, using ccache is a real time-saver. By default, ccache does not properly support CUDA stuff, so here are the instructions for installing a custom ccache fork that has CUDA support:

# install and export ccache
if ! ls ~/ccache/bin/ccache
then
    sudo apt-get update
    sudo apt-get install -y automake autoconf
    sudo apt-get install -y asciidoc
    mkdir -p ~/ccache
    pushd /tmp
    rm -rf ccache
    git clone https://github.com/colesbury/ccache -b ccbin
    pushd ccache
    ./autogen.sh
    ./configure
    make install prefix=~/ccache
    popd
    popd

    mkdir -p ~/ccache/lib
    mkdir -p ~/ccache/cuda
    ln -s ~/ccache/bin/ccache ~/ccache/lib/cc
    ln -s ~/ccache/bin/ccache ~/ccache/lib/c++
    ln -s ~/ccache/bin/ccache ~/ccache/lib/gcc
    ln -s ~/ccache/bin/ccache ~/ccache/lib/g++
    ln -s ~/ccache/bin/ccache ~/ccache/cuda/nvcc

    ~/ccache/bin/ccache -M 25Gi
fi

export PATH=~/ccache/lib:$PATH
export CUDA_NVCC_EXECUTABLE=~/ccache/cuda/nvcc

Hope this helps, and thanks for considering to contribute.