Contributing#

We welcome contributions to TorchWM! This guide covers how to get started.

Development Setup#

Clone the repository

git clone https://github.com/ParamThakkar123/torchwm.git
cd torchwm

Install in development mode
```
uv sync --dev
```
Install pre-commit hooks
```
pre-commit install
```

Code Style#

We use:

Black for code formatting
Ruff for linting
MyPy for type checking
Pre-commit for automated checks

Run checks:

# Format code
black .

# Lint
ruff check .

# Type check
mypy .

# All checks
pre-commit run --all-files

Testing#

Run the test suite:

# All tests
pytest

# Specific tests
pytest tests/test_operators.py

# With coverage
pytest --cov=world_models --cov-report=html

Documentation#

Build docs locally:

cd docs
sphinx-build -b html source build/html

Open docs/build/html/index.html in your browser.

Adding New Features#

1. New Operators#

from world_models.inference.operators.base import OperatorABC

class NewOperator(OperatorABC):
    def process(self, inputs):
        # Your preprocessing logic
        return processed_tensors

Add to __init__.py and create tests.

2. New Models#

Create model class in world_models/models/
Add config class in world_models/configs/
Add operator in world_models/inference/operators/
Update training scripts
Add documentation and tests

3. New Environments#

Implement environment wrapper in world_models/envs/
Add to environment registry
Update documentation

Pull Request Process#

Fork the repository
Create a feature branch
```
git checkout -b feature/my-feature
```
Make your changes
Run tests and checks
```
pre-commit run --all-files
pytest
```
Update documentation if needed

Commit your changes

git commit -m "Add feature: my feature"

Push to your fork
```
git push origin feature/my-feature
```
Create a Pull Request

Commit Messages#

Use conventional commit format:

feat: New features
fix: Bug fixes
docs: Documentation
test: Testing
refactor: Code refactoring
chore: Maintenance

Examples:

feat: add VLA operator support
fix: resolve memory leak in dreamer training
docs: update operators guide

Issue Reporting#

Bug reports: Use the bug report template
Feature requests: Use the feature request template
Questions: Use discussions

Code of Conduct#

Please follow our code of conduct in all interactions.

License#

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