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CONTRIBUTING.md

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Development

To build ONNX from source please follow the instructions listed here.

Then, after you have made changes to Python and C++ files:

  • Python files: the changes are effective immediately in your installation. You don't need to install these again.
  • C++ files: you need to install these again to trigger the native extension build.

Assuming build succeed in the initial step, simply running

pip install -e .

from onnx root dir should work.

Folder structure

  • onnx/: the main folder that all code lies under
    • onnx.proto: the protobuf that contains all the structures
    • checker.py: a utility to check whether a serialized ONNX proto is legal
    • shape_inference.py: a utility to infer types and shapes for ONNX models
    • version_converter.py: a utility to upgrade or downgrade version for ONNX models
    • parser.py: a utility to create an ONNX model or graph from a textual representation
    • hub.py: a utility for downloading models from ONNX Model Zoo
    • compose.py: a utility to merge ONNX models
    • helper.py: tools for graph operation
    • defs/: a subfolder that defines the ONNX operators
    • test/: test files

Generated operator documentation

Operator docs in Operators.md are automatically generated based on C++ operator definitions and backend Python snippets. To refresh these docs, run the following commands from the repo root and commit the results. Note ONNX_ML=0 updates Operators.md whereas ONNX_ML=1 updates Operators-ml.md:

# Windows
set ONNX_ML=0
# UNIX
# export ONNX_ML=0
pip install setup.py
python onnx/defs/gen_doc.py

Adding a new operator

ONNX is an open standard, and we encourage developers to contribute high quality operators to ONNX specification. Before proposing a new operator, please read the tutorial.

Code style

We use flake8, mypy, and clang-format for checking code format.

To run the checks locally, install lintrunner and the linters with

pip install lintrunner lintrunner-adapters
lintrunner init

Then lint with

lintrunner

format with

lintrunner -a

Run lintrunner --help and see the .lintrunner.toml file for more usage examples, as well as how to create new linters.

Testing

ONNX uses pytest as a test driver. To run tests, you'll first need to install pytest:

pip install pytest nbval

After installing pytest, run from the root of the repo:

pytest

to begin the tests.

You'll need to regenerate test coverage too, by running this command from the root of the repo:

python onnx\backend\test\stat_coverage.py

Cpp tests (googletest)

Some functionalities are tested with googletest. Those tests are listed in test/cpp, and include tests for shape inference, data propagation, parser, and others.

To run them, first build ONNX with -DONNX_BUILD_TESTS=1 or ONNX_BUILD_TESTS=1 pip install -e ..

Linux and MacOS

The cpp tests require dynamically linking to built libraries.

export LD_LIBRARY_PATH="./.setuptools-cmake-build/:$LD_LIBRARY_PATH"
.setuptools-cmake-build/onnx_gtests

Windows

# If you set DEBUG=1, use `.setuptools-cmake-build\Debug\onnx_gtests.exe` instead
.setuptools-cmake-build\Release\onnx_gtests.exe

CI Pipelines

Every PR needs to pass CIs before merge. CI pipelines details are here.

Other developer documentation

License

Apache License v2.0

Code of Conduct

ONNX Open Source Code of Conduct