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Contributing to RustyGradients

Thank you for your interest in contributing to RustyGradients! This document provides guidelines for contributing.

Getting Started

Prerequisites

  • Rust 1.70+ (stable)
  • Git

Setup

# Clone the repository
git clone https://github.com/Xzdes/RustyGradients.git
cd RustyGradients

# Build with default features
cargo build --features "cpu serialization"

# Run tests
cargo test --lib --features "cpu serialization"

Development Workflow

Branch Strategy

  • master - stable release branch
  • Feature branches: feature/your-feature-name
  • Bug fixes: fix/issue-description

Making Changes

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/your-feature
  3. Make your changes
  4. Run tests: cargo test --lib --features "cpu serialization"
  5. Run clippy: cargo clippy --features "cpu serialization"
  6. Format code: cargo fmt
  7. Commit with a clear message
  8. Push and create a Pull Request

Commit Messages

Follow conventional commits:

feat: add new activation function
fix: correct gradient calculation in matmul
docs: update README with CUDA examples
test: add tests for tokenization module
refactor: simplify backend trait

Code Style

Rust Guidelines

  • Use cargo fmt before committing
  • Run cargo clippy and address warnings
  • Write documentation for public APIs
  • Add tests for new functionality

Documentation

  • Use /// for public API documentation
  • Use //! for module-level documentation
  • Include examples in doc comments when helpful

Testing

Running Tests

# Unit tests only (fastest)
cargo test --lib --features "cpu serialization"

# All tests including integration
cargo test --features "cpu serialization"

# Specific test
cargo test test_name --features "cpu serialization"

Writing Tests

  • Place unit tests in the same file using #[cfg(test)] module
  • Use descriptive test names: test_matmul_2d_shapes
  • Test edge cases and error conditions

Feature Flags

Available features:

Feature Description
cpu CPU backend with rayon parallelization (default)
cpu-blas BLAS acceleration for matrix operations
cuda NVIDIA GPU support via cuBLAS
metal-backend Apple Silicon GPU support
serialization Safetensors model format
tokenization BPE and HuggingFace tokenizers
huggingface HuggingFace Hub integration

Project Structure

src/
├── backend/       # Multi-backend abstraction (CPU, CUDA, Metal)
├── core/          # Autograd and computational graph
├── nn/            # Neural network layers
├── ops/           # Tensor operations
├── models/        # Model implementations (GPT)
├── serialization/ # Model save/load (Safetensors)
├── tokenization/  # Tokenizers (BPE, HuggingFace)
└── error.rs       # Error types

Pull Request Process

  1. Ensure all tests pass
  2. Update documentation if needed
  3. Add entry to CHANGELOG (if applicable)
  4. Request review from maintainers

PR Checklist

  • Tests pass locally
  • Code is formatted (cargo fmt)
  • No clippy warnings (cargo clippy)
  • Documentation updated (if needed)
  • Commit messages follow convention

Areas for Contribution

High Priority

  1. CUDA Backend - Optimize custom kernels
  2. Metal Backend - Apple Silicon support
  3. Model Zoo - Add LLaMA, Mistral, BERT
  4. Documentation - Tutorials and examples

Good First Issues

Look for issues labeled good first issue on GitHub.

Questions?

  • Open an issue on GitHub
  • Check existing documentation in /docs

License

By contributing, you agree that your contributions will be licensed under the MIT License.