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Contributing to NNCF

Contributions are accepted in the form of:

  • Submitting issues against the current code to report bugs or request features
  • Extending NNCF functionality with important features (e.g. to address community requests, improve usability, implement a recently published compression algorithm, etc.)
  • Adding example scripts to showcase NNCF usage in real training pipelines and provide the means to reproduce the reported compression results
  • Providing recipes (specific NNCF configurations and training hyperparameters) to obtain state-of-the-art compression using NNCF for existing models
  • Adding well-defined patches that integrate NNCF into third-party repositories
  • Reducing performance overhead of NNCF compression by writing specialized CUDA kernels for compression operations or improving existing ones.

The latter forms are accepted as pull requests from your own forks of the NNCF repository.

Any contributions must not violate the repository's LICENSE requirements.

Installation

(Experimental) ONNXRuntime-OpenVINO

Install the package and its dependencies by running the following in the repository root directory:

make install-onnx-dev

Testing

After your pull request is submitted, the maintainer will launch a scope of CI tests against it. The scope will depend on the impact of the commit, but will at least be equal to pre-commit scope checks.

The pre-commit scope may be run locally by executing the pytest command (without any additional arguments) in the root repository folder. Please run the pre-commit testing scope locally before submitting your PR and ensure that it passes to conserve your own time and that of the reviewing maintainer.

New feature pull requests should include all the necessary testing code. Testing is done using the pytest framework. The test files should be located inside the tests directory and start with test_ so that the pytest is able to discover them. Any additional data that is required for tests (configuration files, mock datasets, etc.) must be stored within the tests/data folder. The test files themselves may be grouped in arbitrary directories according to their testing purpose and common sense.

Any additional tests in the tests directory will be automatically added into the pre-commit CI scope. If your testing code is more extensive than unit tests (in terms of test execution time), or would be more suited to be executed on a nightly/weekly basis instead of for each future commit, please inform the maintainers in your PR discussion thread so that our internal testing pipelines could be adjusted accordingly.

Preset command for testing

You can launch appropriate tests against the framework by running the following command:

  • (Experimental) ONNXRuntime-OpenVINO
test-onnx

Code style

Changes to NNCF Python code should conform to Python Style Guide

Pylint is used throughout the project to ensure code cleanliness and quality. A Pylint run is also done as part of the pre-commit scope - the pre-commit pytest scope will not be run if your code fails the Pylint checks. The Pylint rules and exceptions for this repository are described in the standard .pylintrc format - make sure your local linter uses these.

Preset command for linting

You can launch appropriate linting against the framework by running the following command:

  • (Experimental) ONNXRuntime-OpenVINO
pylint-onnx

Binary files

Please refrain from adding huge binary files into the repository. If binary files have to be added, mark these to use Git LFS via the .gitattributes file.

Model identifiers

When adding model configs and checkpoints to be showcased in NNCF's sample script, follow the format for naming these files:

  1. The base name must be the same for the NNCF config file, AC config file, checkpoint file (PT/ONNX/OV) or checkpoint folder (TF), and other associated artifacts.
  2. This name should be composed with the following format: {model_name}_{dataset_name} for FP32 models, {topology_name}_{dataset_name}_{compression_algorithms_applied}. The format may be extended if there are multiple models with the same topology, dataset and compression algos applied, which only differ in something else such as exact value of achieved sparsity. Align the naming of the new checkpoints with the existing ones.
  3. Additional human-readable information on the model such as expected metrics and compression algorithm specifics (e.g. level of pruning/sparsity, per-tensor/per-channel quantizer configuration etc.) should be stored in a registry file (tests/torch/sota_checkpoints_eval.json for PT, tests/tensorflow/sota_checkpoints_eval.json for TF)