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CONTRIBUTING.md

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Contributing guidelines

If you have improvements to the oneDNN code, please send us your pull requests! To get started, see the GitHub howto.

You can:

  • Submit your changes directly with a pull request
  • Log a bug or feedback with an issue

See also: Contributor Covenant code of conduct.

Pull request checklist

Before sending your pull requests, please make sure that you followed this list.

Library functionality guidelines

oneDNN focuses on functionality that satisfies all of the following criteria:

  1. Performance: the functionality has material impact on a workload level. In other words, this means that for a new primitive it should be demonstrated that it brings visible performance improvement to some workload.

  2. Generality: the functionality is useful in a wide range of deep learning applications. This implies that when introducing a new primitive, its API needs to be general enough to be integrated into multiple deep learning frameworks that have similar functionality.

  3. Complexity: it is not trivial to implement the functionality directly in a deep learning application.

RFC pull requests

Significant library changes (new primitives, library architecture changes, API modifications, etc) require approval from oneDNN maintainers before opening a Pull Request with such implementation. For that we use the Request For Comments (RFC) process, which consists of opening, discussing, and accepting (promoting) RFC pull requests.

More information about the process can be found in the dedicated rfcs branch.

Code contribution guidelines

The code must be:

  • Tested: oneDNN uses gtests for lightweight functional testing and benchdnn for functionality that requires both performance and functional testing.

  • Documented: oneDNN uses Doxygen for inline comments in public header files that is used to build reference manual and markdown (also processed by Doxygen) for user guide.

  • Portable: oneDNN supports different operating systems, CPU and GPU architectures, compilers, and run-times. The new code should be compliant with the System Requirements.

Coding style

The general principle is to follow the style of existing / surrounding code.

Particularly:

  • Use 4-space indentation.
  • Limit line length to 80 columns.
  • Do put spaces after if, for, switch; otherwise, do not put spaces around braces, parenthesis, square or angle brackets.
  • Do put spaces around binary arithmetic operators.
  • Avoid trailing and double spaces (unless used for indentation).
  • Do not indent namespaces, private:, public:, protected: and case labels.
  • Keep opening brace on the same line as the statement or function.

If in doubt, use the clang-format:

clang-format -style=file -i foo.cpp

This will format code using the _clang_format file found in the oneDNN top level directory.

Coding style is secondary to the general code design.

Unit tests

oneDNN uses gtests for lightweight functional testing and benchdnn for performance and functional testing.

Be sure to extend the existing tests when fixing an issue.

Developing new benchdnn tests can be hard, so it is a good idea to start with gtests first.