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FlagGems Contributor's Guide

Thank you for your interest in FlagGems! We use github to host code, manage issues and pull requests. Before contributing, please read the following guidelines.

1. Bug Reports

Please report bugs using Github's issues. When reporting bugs, please provide

  • a brief summary,
  • steps to reproduce,
  • and be specific!
  • Some sample codes will be helpful too.

2. Code Contribution

In pull requests, contributor should describe what changed and why. Please also provide test cases if applicable. Pull requests require approvals from one members before merging. Additionally, they must pass continuous integration checks.

Currently, continuous integration checks include four pipelines:

2.1 Code Format Check

Using pre-commit git hooks with FlagGems, you can format source Python code and perform basic code pre-checks when calling the git commit command

pip install pre-commit
pre-commit install
pre-commit

2.2 Op Unit Test

Operator Unit Tests check the correctness of operators. If new operators are added, you need to add test cases in the corresponding file under the tests directory. If new test files are added, you should also add the test commands to the cmd variable in the tools/coverage.sh file.

2.3 Model Test

Model Tests check the correctness of models. Adding a new model follows a process similar to adding a new operator.

2.4 Python Coverage

Python Coverage checks the coverage of the new code added in the current PR. It depends on the successful execution of both Op Unit Test and Model Tests; otherwise, it will be skipped. Code coverage is represented as:

Lines added in the PR and covered by the tests / Total lines of new code in the PR

Note: Since Triton JIT functions do not actually run, they will be excluded from the coverage calculation.

The code merging requirement is a coverage rate of 90% or above. Detailed information about code coverage can be viewed in the log.

To reproduce locally, you need to install tools like lcov, coverage and PyGithub.

cd $FlagGemsROOT
PR_ID=your_pr_id
bash tools/op-unit-test.sh
bash tools/model-test.sh
tools/code_coverage/coverage.sh PR_ID

Currently, the pipeline does not check the performance of operators. You can write performance tests in the benchmark directory to evaluate your optimization results.

3. Project Structure

FlagGems
├── src: source code for library
│   ├──flag_gems
│   │   ├──utils: utilities for automatic code generation
│   │   ├──ops: single operators
│   │   ├──fused: fused operators
│   │   ├──__init__.py
├── tests: accuracy test files
├── benchmark: performance test files
├── examples: model test files
├── LICENSE
├── README.md
├── README_cn.md
├── OperatorList.md
├── CONTRIBUTING.md
└── pyproject.toml

4. License

Any contributions you make will be under the Apache License.