STT-tensorflow/tensorflow/python/autograph/CONTRIBUTING.md

How to contribute

We'd love to have your patches and contributions! Here are some guidelines. In general, we follow the TensorFlow contributing guidelines, but have some AutoGraph-specific style guidelines. More details below.

Note to active contributors

In preparation for TF 2.0, we moved the code base of AutoGraph from tensorflow/contrib/autograph to tensorflow/python/autograph. The move does not impact functionality, and AutoGraph will remain accessible under tensorflow.contrib.autograph until tensorflow.contrib is retired.

TensorFlow Code of Conduct

Please review and follow the TensorFlow Code of Conduct.

Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you've already submitted one (even if it was for a different project), you probably don't need to do it again.

Code reviews

All submissions, including submissions by project members, require review. We use GitHub pull requests for this purpose. Consult GitHub Help for more information on using pull requests.

After a pull request is approved, we merge it. Note our merging process differs from GitHub in that we pull and submit the change into an internal version control system. This system automatically pushes a git commit to the GitHub repository (with credit to the original author) and closes the pull request.

Style

See the AutoGraph style guide.

Unit tests

Please include unit tests when contributing new features (example here), as they help to a) prove that your code works correctly, and b) guard against future breaking changes to lower the maintenance cost. It's also helpful to check that any changes you propose do not break existing unit tests. You can run tests using the command,

bazel test --config=opt --copt=-O3 --copt=-march=native \
  //tensorflow/contrib/autograph/...

from the root of the tensorflow repository. For more details see the main TensorFlow Contributing File

Developer info

Module structure

The graph below describes the dependencies between AutoGraph modules (not to be mistaken with the directory structure for these modules, which is flat):

digraph d_modules {
  autograph [style=filled];
  converters;
  core;
  impl;
  lang;
  operators;

  autograph -> impl
  autograph -> lang

  impl -> converters
  impl -> core
  impl -> operators

  lang -> operators

  converters -> core
  converters -> lang
}

autograph is the sole user-visible module.

A short description of the modules:

• autograph: the main module imported by the user and by the generated code; only contains declarations
• impl: high level code and the implementation of the api frontend
• core: base classes for the AutoGraph source code transformation logic; see in particular converter.py
• lang: special user-visible functions that serve as extensions to the Python language
• converters: collection of source code transformation modules specialized for particular AutoGraph features
• operators: collection of operators that AutoGraph overloads; these correspond to Python operators as well as Python syntactic structures, like control flow

There are two additional modules, pyct and utils. These are independent of AutoGraph:

• pyct: a general purpose Python source code transformation library
• utils: the kitchen sync; deprecated

Note: we have a long term plan to factor out an implementation of impl and converters that is independent of autograph, into a general purpose Python operator overloading library.