PyTorch Contribution Guide
==========================

PyTorch is a GPU-accelerated Python tensor computation package for
building deep neural networks built on tape-based autograd systems.

The PyTorch Contribution Process
--------------------------------

The PyTorch organization is governed by :doc:`PyTorch
Governance <governance>`.

The PyTorch development process involves a healthy amount of open
discussions between the core development team and the community.

PyTorch operates similar to most open source projects on GitHub.
However, if you've never contributed to an open source project before,
here is the basic process.

-  **Figure out what you're going to work on.** The majority of open
   source contributions come from people scratching their own itches.
   However, if you don't know what you want to work on, or are just
   looking to get more acquainted with the project, here are some tips
   for how to find appropriate tasks:

   -  Look through the `issue
      tracker <https://github.com/pytorch/pytorch/issues/>`__ and see if
      there are any issues you know how to fix. Issues that are
      confirmed by other contributors tend to be better to investigate.
      We also maintain some labels for issues which are likely to be
      good for new people, e.g., **bootcamp** and **1hr**, although
      these labels are less well maintained.
   -  Join us on Slack and let us know you're interested in getting to
      know PyTorch. We're very happy to help out researchers and
      partners get up to speed with the codebase.

-  **Figure out the scope of your change and reach out for design
   comments on a GitHub issue if it's large.** The majority of pull
   requests are small; in that case, no need to let us know about what
   you want to do, just get cracking. But if the change is going to be
   large, it's usually a good idea to get some design comments about it
   first.

   -  If you don't know how big a change is going to be, we can help you
      figure it out! Just post about it on issues or Slack.
   -  Some feature additions are very standardized; for example, lots of
      people add new operators or optimizers to PyTorch. Design
      discussion in these cases boils down mostly to, “Do we want this
      operator/optimizer?” Giving evidence for its utility, e.g., usage
      in peer reviewed papers, or existence in other frameworks, helps a
      bit when making this case.

      - **Adding operators / algorithms from recently-released research**
        is generally not accepted, unless there is overwhelming evidence that
        this newly published work has ground-breaking results and will eventually
        become a standard in the field. If you are not sure where your method falls,
        open an issue first before implementing a PR.

   -  Core changes and refactors can be quite difficult to coordinate,
      as the pace of development on PyTorch master is quite fast.
      Definitely reach out about fundamental or cross-cutting changes;
      we can often give guidance about how to stage such changes into
      more easily reviewable pieces.

-  **Code it out!**

   -  See the technical guide for advice for working with PyTorch in a
      technical form.

-  **Open a pull request.**

   -  If you are not ready for the pull request to be reviewed, tag it
      with [WIP]. We will ignore it when doing review passes. If you are
      working on a complex change, it's good to start things off as WIP,
      because you will need to spend time looking at CI results to see
      if things worked out or not.
   -  Find an appropriate reviewer for your change. We have some folks
      who regularly go through the PR queue and try to review
      everything, but if you happen to know who the maintainer for a
      given subsystem affected by your patch is, feel free to include
      them directly on the pull request. You can learn more about this
      structure at PyTorch Subsystem Ownership.

-  **Iterate on the pull request until it's accepted!**

   -  We'll try our best to minimize the number of review roundtrips and
      block PRs only when there are major issues. For the most common
      issues in pull requests, take a look at `Common Mistakes <#common-mistakes-to-avoid>`__.
   -  Once a pull request is accepted and CI is passing, there is
      nothing else you need to do; we will merge the PR for you.

Getting Started
---------------

Proposing new features
~~~~~~~~~~~~~~~~~~~~~~

New feature ideas are best discussed on a specific issue. Please include
as much information as you can, any accompanying data, and your proposed
solution. The PyTorch team and community frequently reviews new issues
and comments where they think they can help. If you feel confident in
your solution, go ahead and implement it.

Reporting Issues
~~~~~~~~~~~~~~~~

If you've identified an issue, first search through the `list of
existing issues <https://github.com/pytorch/pytorch/issues>`__ on the
repo. If you are unable to find a similar issue, then create a new one.
Supply as much information you can to reproduce the problematic
behavior. Also, include any additional insights like the behavior you
expect.

Implementing Features or Fixing Bugs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you want to fix a specific issue, it's best to comment on the
individual issue with your intent. However, we do not lock or assign
issues except in cases where we have worked with the developer before.
It's best to strike up a conversation on the issue and discuss your
proposed solution. The PyTorch team can provide guidance that saves you
time.

Issues that are labeled first-new-issue, low, or medium priority provide
the best entrance point are great places to start.

Adding Tutorials
~~~~~~~~~~~~~~~~

A great deal of the tutorials on `pytorch.org <https://pytorch.org/>`__
come from the community itself and we welcome additional contributions.
To learn more about how to contribute a new tutorial you can learn more
here: `PyTorch.org Tutorial Contribution Guide on
Github <https://github.com/pytorch/tutorials/#contributing>`__

Improving Documentation & Tutorials
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We aim to produce high quality documentation and tutorials. On rare
occasions that content includes typos or bugs. If you find something you
can fix, send us a pull request for consideration.

Take a look at the `Documentation <#on-documentation>`__ section to learn how our system
works.

Participating in online discussions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can find active discussions happening on the PyTorch Discussion
`forum <https://discuss.pytorch.org/>`__.

Submitting pull requests to fix open issues
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can view a list of all open issues
`here <https://github.com/pytorch/pytorch/issues>`__. Commenting on an
issue is a great way to get the attention of the team. From here you can
share your ideas and how you plan to resolve the issue.

For more challenging issues, the team will provide feedback and
direction for how to best solve the issue.

If you're not able to fix the issue itself, commenting and sharing
whether you can reproduce the issue can be useful for helping the team
identify problem areas.

Reviewing open pull requests
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We appreciate your help reviewing and commenting on pull requests. Our
team strives to keep the number of open pull requests at a manageable
size, we respond quickly for more information if we need it, and we
merge PRs that we think are useful. However, due to the high level of
interest, additional eyes on pull requests is appreciated.

Improving code readability
~~~~~~~~~~~~~~~~~~~~~~~~~~

Improve code readability helps everyone. It is often better to submit a
small number of pull requests that touch few files versus a large pull
request that touches many files. Starting a discussion in the PyTorch
forum `here <https://discuss.pytorch.org/>`__ or on an issue related to
your improvement is the best way to get started.

Adding test cases to make the codebase more robust
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Additional test coverage is appreciated.

Promoting PyTorch
~~~~~~~~~~~~~~~~~

Your use of PyTorch in your projects, research papers, write ups, blogs,
or general discussions around the internet helps to raise awareness for
PyTorch and our growing community. Please reach out to
`pytorch-marketing@fb.com <http://mailto:pytorch-marketing@fb.com/>`__
for marketing support.

Triaging issues
~~~~~~~~~~~~~~~

If you feel that an issue could benefit from a particular tag or level
of complexity comment on the issue and share your opinion. If an you
feel an issue isn't categorized properly comment and let the team know.

About open source development
-----------------------------

If this is your first time contributing to an open source project, some
aspects of the development process may seem unusual to you.

-  **There is no way to “claim” issues.** People often want to “claim”
   an issue when they decide to work on it, to ensure that there isn't
   wasted work when someone else ends up working on it. This doesn't
   really work too well in open source, since someone may decide to work
   on something, and end up not having time to do it. Feel free to give
   information in an advisory fashion, but at the end of the day, we
   will take running code and rough consensus.
-  **There is a high bar for new functionality that is added.** Unlike
   in a corporate environment, where the person who wrote code
   implicitly “owns” it and can be expected to take care of it in the
   beginning of its lifetime, once a pull request is merged into an open
   source project, it immediately becomes the collective responsibility
   of all maintainers on the project. When we merge code, we are saying
   that we, the maintainers, are able to review subsequent changes and
   make a bugfix to the code. This naturally leads to a higher standard
   of contribution.

Common Mistakes To Avoid
------------------------

-  **Did you add tests?** (Or if the change is hard to test, did you
   describe how you tested your change?)

   -  We have a few motivations for why we ask for tests:

      1. to help us tell if we break it later
      2. to help us tell if the patch is correct in the first place
         (yes, we did review it, but as Knuth says, “beware of the
         following code, for I have not run it, merely proven it
         correct”)

   -  When is it OK not to add a test? Sometimes a change can't be
      conveniently tested, or the change is so obviously correct (and
      unlikely to be broken) that it's OK not to test it. On the
      contrary, if a change is seems likely (or is known to be likely)
      to be accidentally broken, it's important to put in the time to
      work out a testing strategy.

-  **Is your PR too long?**

   -  It's easier for us to review and merge small PRs. Difficulty of
      reviewing a PR scales nonlinearly with its size.
   -  When is it OK to submit a large PR? It helps a lot if there was a
      corresponding design discussion in an issue, with sign off from
      the people who are going to review your diff. We can also help
      give advice about how to split up a large change into individually
      shippable parts. Similarly, it helps if there is a complete
      description of the contents of the PR: it's easier to review code
      if we know what's inside!

-  **Comments for subtle things?** In cases where behavior of your code
   is nuanced, please include extra comments and documentation to allow
   us to better understand the intention of your code.
-  **Did you add a hack?** Sometimes a hack is the right answer. But
   usually we will have to discuss it.
-  **Do you want to touch a very core component?** In order to prevent
   major regressions, pull requests that touch core components receive
   extra scrutiny. Make sure you've discussed your changes with the team
   before undertaking major changes.
-  **Want to add a new feature?** If you want to add new features,
   comment your intention on the related issue. Our team tries to
   comment on and provide feedback to the community. It's better to have
   an open discussion with the team and the rest of the community prior
   to building new features. This helps us stay aware of what you're
   working on and increases the chance that it'll be merged.
-  **Did you touch unrelated code to the PR?** To aid in code review,
   please only include files in your pull request that are directly
   related to your changes.

Frequently asked questions
--------------------------

-  **How can I contribute as a reviewer?** There is lots of value if
   community developer reproduce issues, try out new functionality, or
   otherwise help us identify or troubleshoot issues. Commenting on
   tasks or pull requests with your environment details is helpful and
   appreciated.
-  **CI tests failed, what does it mean?** Maybe you need to merge with
   master or rebase with latest changes. Pushing your changes should
   re-trigger CI tests. If the tests persist, you'll want to trace
   through the error messages and resolve the related issues.
-  **What are the most high risk changes?** Anything that touches build
   configuration is an risky area. Please avoid changing these unless
   you've had a discussion with the team beforehand.
-  **Hey, a commit showed up on my branch, what's up with that?**
   Sometimes another community member will provide a patch or fix to
   your pull request or branch. This is often needed for getting CI tests
   to pass.

On Documentation
----------------

Python Docs
~~~~~~~~~~~

PyTorch documentation is generated from python source using
`Sphinx <https://www.sphinx-doc.org/en/master/>`__. Generated HTML is
copied to the docs folder in the master branch of
`pytorch.github.io <https://github.com/pytorch/pytorch.github.io/tree/master/docs>`__,
and is served via GitHub pages.

-  Site: https://pytorch.org/docs
-  GitHub: https://github.com/pytorch/pytorch/tree/master/docs
-  Served from:
   `https://github.com/pytorch/pytorch.github.io/tree/master/doc <https://github.com/pytorch/pytorch.github.io/tree/master/docs>`__

C++ Docs
~~~~~~~~

For C++ code we use Doxygen to generate the content files. The C++ docs
are built on a special server and the resulting files are copied to the
https://github.com/pytorch/cppdocs repo, and are served from GitHub
pages.

-  Site: https://pytorch.org/cppdocs
-  GitHub: https://github.com/pytorch/pytorch/tree/master/docs/cpp
-  Served from: https://github.com/pytorch/cppdocs

Tutorials
---------

PyTorch tutorials are documents used to help understand using PyTorch to
accomplish specific tasks or to understand more holistic concepts.
Tutorials are built using
`Sphinx-Gallery <https://sphinx-gallery.readthedocs.io/en/latest/index.html>`__
from executable python sources files, or from restructured-text (rst)
files.

-  Site: https://pytorch.org/tutorials
-  GitHub: https://github.com/pytorch/tutorials

Tutorials Build Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For tutorials, `pull
requests <https://github.com/pytorch/tutorials/pulls>`__ trigger a
rebuild the entire site using CircleCI to test the effects of the
change. This build is sharded into 9 worker builds and takes around 40
minutes total. At the same time, we do a Netlify build using *make
html-noplot*, which builds the site without rendering the notebook
output into pages for quick review.

After a PR is accepted, the site is rebuilt and deployed from CircleCI.

Contributing a new Tutorial
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`PyTorch.org Tutorial Contribution
Guide <https://github.com/pytorch/tutorials/#contributing>`__
