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Contributing

DeepSpeed welcomes your contributions!

Prerequisites

DeepSpeed uses pre-commit to ensure that formatting is consistent across DeepSpeed. First, ensure that pre-commit is installed from either installing DeepSpeed or pip install pre-commit. Next, the pre-commit hooks must be installed once before commits can be made:

pre-commit install

Afterwards, our suite of formatting tests run automatically before each git commit. You can also run these manually:

pre-commit run --all-files

If a formatting test fails, it will fix the modified code in place and abort the git commit. After looking over the changes, you can git add <modified files> and then repeat the previous git commit command.

Testing

DeepSpeed tracks two types of tests: unit tests and more costly model convergence tests. The model convergence tests train DeepSpeedExamples and measure end-to-end convergence and related metrics. Unit tests are found in tests/unit/ and the model convergence tests are found in tests/model/.

Unit Tests

PyTest is used to execute tests. PyTest can be installed from PyPI via pip install pytest. Simply invoke pytest --forked to run the unit tests:

pytest --forked tests/unit/

You can also provide the -v flag to pytest to see additional information about the tests. Note that pytest-forked and the --forked flag are required to test CUDA functionality in distributed tests.

Model Tests

To execute model tests, first install DeepSpeed. The DeepSpeedExamples repository is cloned as part of this process. Next, execute the model test driver:

cd tests/model/
pytest run_sanity_check.py

Note that the --forked flag is not necessary for the model tests.

Contributor License Agreement

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

New Feature Contribution Guidelines

Unlike bug fix or improving existing feature (where users usually directly submit a PR and we review it), adding a new feature to DeepSpeed requires several steps: (1) proposal and discussion, (2) implementation and verification, (3) release and maintenance. This general guideline applies to all new feature contributions. Core DeepSpeed team member contributions may complete step 1 internally.

Step 1: proposal and discussion

We ask users to first post your intended feature in an issue. This issue needs to include:

  • A description of the proposed feature.
  • A motivation of why it will be useful to DeepSpeed users.
  • A rough design of how you implement the feature inside DeepSpeed.
  • (Important) Results or planned experiments to demonstrate the effectiveness and correctness of the feature.
    • If this is a general feature applicable to different tasks, we require testing it on at least one CV task (e.g., CIFAR) and one NLP task (e.g., SQuAD). If this is a feature for one kind of task only, it is fine to just test on the specific task.
    • If the feature only affects performance and does not affect training convergence, we require testing on a fraction of training to demonstrate that the training/validation loss are consistent with baseline, and that the performance is better than baseline.
    • If the feature does affect training convergence, we require testing the whole training to demonstrate that the feature achieves better/on-par final model quality and training performance compared to baseline.

Based on the issue we shall discuss the merit of the new feature and decide whether accept or decline the proposal. Once accepted and after we confirm the design and implementation plan, we are ready for step 2.

Step 2: implementation and verification

Contributor will go ahead and implement the feature, and the DeepSpeed team will provide guidance/helps as needed. The required deliverables include:

  • A PR to microsoft/DeepSpeed including (1) the feature implementation (2) unit tests (3) documentation (4) tutorial
  • A PR to microsoft/DeepSpeedExamples or microsoft/Megatron-DeepSpeed including the examples of how to use the feature (this is related to the planned testing experiments in proposal)
  • In the implementation (code, documentation, tutorial), we require the feature author to record their GitHub username as a contact method for future questions/maintenance.

After receiving the PRs, we will review them and merge them after necessary tests/fixes.

Step 3: release and maintenance

After the PRs are merged, we will announce the feature on our website (with credit to the feature author). We ask the feature author to commit to the maintenance of the feature.