Contributing Guide

Welcome to the Hetu-Galvatron community! We’re excited to have you contribute to advancing automatic distributed training for large-scale AI models.

Full Contributing Guide: For the complete contributing guide with detailed setup instructions, coding standards, and community information, please see our CONTRIBUTING.md file.

How to Contribute

Code Contributions

We welcome all types of code contributions:

High-Impact Areas

New Parallelism Strategies: Implement novel parallel training methods
Hardware Support: Add support for new GPU/TPU architectures
Performance Optimization: Improve training efficiency and memory usage
New Architecture Models: Such as multi-modal models, extending support beyond language models

Beginner-Friendly Tasks

Documentation: Improve code comments and user guides
Bug Fixes: Resolve issues labeled as good first issue
Testing: Add unit tests and integration tests
Examples: Create tutorials and example scripts
Hardware and Model Profiling: Add profile data for new hardware and models

Non-Code Contributions

Your expertise is valuable beyond coding:

Documentation Translation: Help make Galvatron accessible globally
Community Support: Answer questions in issues and discussions
Tutorial Creation: Write blog posts, videos, or workshops
Testing & Feedback: Try new features and report your experience
Evangelism: Present Galvatron at conferences or meetups

Quick Start Guide

Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/Hetu-Galvatron.git
cd Hetu-Galvatron

# Set up development environment
conda create -n galvatron-dev python=3.8
conda activate galvatron-dev

# Install in development mode
pip install -r requirements.txt
pip install -e .

Making Your First Contribution

# Create a new branch for your feature
git checkout -b feature/your-awesome-feature

# Make your changes
# ... edit files ...

# Test your changes
python -m pytest tests/

# Commit with clear message
git add .
git commit -m "[Runtime] feat: add awesome new feature"

# Push and create PR
git push origin feature/your-awesome-feature

Code Standards

Commit Messages

Similar to Conventional Commits:

[Modified Module]<type>(<scope>): <description>

Modified Module: Runtime, Search Engine, Profiler, Misc
Types: feat, fix, docs, style, refactor, test, chore
Example: feat(profiler): add GPU memory profiling support

Testing

Write tests for new features
Maintain test coverage above 80%
Use pytest for testing framework
Mock external dependencies

Newcomer’s Guide - Try Hardware and Model Profiling

In the models folder, we provide some example models and provide the profiling information of the model’s computation and memory, as well as the recommended parallel strategies in the configs folder. However, it is unrealistic to measure the corresponding profiling data for all models and hardware devices, so we encourage you to measure different hardware and models and submit PRs. The specific profiling method can be referred to the Profiling with Galvatron section.

Documentation Guidelines

Documentation Types

API Documentation: Docstrings for all public functions
User Guides: Step-by-step tutorials
Developer Guides: Technical implementation details
Examples: Complete working code samples

Building Documentation Locally

# English documentation
cd docs/en
make html
open _build/html/index.html

# Chinese documentation
cd docs/zh_CN
make html
open _build/html/index.html

Writing Style

Use clear, concise language
Include code examples with expected output
Add diagrams for complex concepts
Keep Chinese and English versions synchronized

Reporting Issues

Before Reporting

Check existing issues
Search discussions
Try the latest version from main branch

Issue Templates

Mainly includes Bug Report and Feature Request templates, please refer to the issue submission interface.