Design Principles¶
Project Philosophy
TorchEBM is built on a set of core design principles that guide its development. Understanding these principles will help you contribute in a way that aligns with the project's vision.
Core Philosophy¶
TorchEBM aims to be:
-
Performant
High-performance implementations that leverage PyTorch's capabilities and CUDA acceleration.
-
Modular
Components that can be easily combined, extended, and customized.
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Intuitive
Clear, well-documented APIs that are easy to understand and use.
-
Educational
Serves as both a practical tool and a learning resource for energy-based modeling.
Key Design Patterns¶
Composable Base Classes¶
TorchEBM is built around a set of extensible base classes that provide common interface:
This design allows for:
- Composition: Combining energy functions via addition, multiplication, etc.
- Extension: Creating new energy functions by subclassing
- Integration: Using energy functions with any sampler that follows the interface
Factory Methods¶
Factory methods create configured instances with sensible defaults:
Configuration through Constructor¶
Classes are configured through their constructor rather than setter methods:
This approach:
- Makes the configuration explicit and clear
- Encourages immutability of key parameters
- Simplifies object creation and usage
Method Chaining¶
Methods return the object itself when appropriate to allow method chaining:
Lazily-Evaluated Operations¶
Computations are performed lazily when possible to avoid unnecessary work:
Architecture Principles¶
Separation of Concerns¶
Components have clearly defined responsibilities:
- Energy Functions: Define the energy landscape
- Samplers: Generate samples from energy functions
- Losses: Train energy functions from data
- Models: Parameterize energy functions using neural networks
- Utils: Provide supporting functionality
Minimizing Dependencies¶
Each module has minimal dependencies on other modules:
- Core modules (e.g.,
core
,samplers
) don't depend on higher-level modules - Utility modules are designed to be used by all other modules
- CUDA implementations are separated to allow for CPU-only usage
Consistent Error Handling¶
Error handling follows consistent patterns:
- Use descriptive error messages that suggest solutions
- Validate inputs early with helpful validation errors
- Provide debug information when operations fail
Consistent API Design¶
APIs are designed consistently across the library:
- Similar operations have similar interfaces
- Parameters follow consistent naming conventions
- Return types are consistent and well-documented
Progressive Disclosure¶
Simple use cases are simple, while advanced functionality is available but not required:
Implementation Principles¶
PyTorch First¶
TorchEBM is built on PyTorch and follows PyTorch patterns:
- Use PyTorch's tensor operations whenever possible
- Follow PyTorch's model design patterns (e.g.,
nn.Module
) - Leverage PyTorch's autograd for gradient computation
- Support both CPU and CUDA execution
Vectorized Operations¶
Operations are vectorized where possible for efficiency:
CUDA Optimization¶
Performance-critical operations are optimized with CUDA when appropriate:
- CPU implementations as fallback
- CUDA implementations for performance
- Automatic selection based on available hardware
Type Annotations¶
Code uses type annotations for clarity and static analysis:
Testing Principles¶
- Unit Testing: Individual components are thoroughly tested
- Integration Testing: Component interactions are tested
- Property Testing: Properties of algorithms are tested
- Numerical Testing: Numerical algorithms are tested for stability and accuracy
Documentation Principles¶
Documentation is comprehensive and includes:
- API Documentation: Clear documentation of all public APIs
- Tutorials: Step-by-step guides for common tasks
- Examples: Real-world examples of using the library
- Theory: Explanations of the underlying mathematical concepts
Future Compatibility¶
TorchEBM is designed with future compatibility in mind:
- API Stability: Breaking changes are minimized and clearly documented
- Feature Flags: Experimental features are clearly marked
- Deprecation Warnings: Deprecated features emit warnings before removal
Contributing Guidelines¶
When contributing to TorchEBM, adhere to these design principles:
- Make sure new components follow existing patterns
- Keep interfaces consistent with the rest of the library
- Write thorough tests for new functionality
- Document public APIs clearly
- Optimize for readability and maintainability
Design Example: Adding a New Sampler
When adding a new sampler:
- Subclass the
Sampler
base class - Implement required methods (
sample
,sample_chain
) - Follow the existing parameter naming conventions
- Add comprehensive documentation
- Write tests that verify the sampler's properties
- Optimize performance-critical sections
-
Project Structure
Understand how the project is organized.
-
Core Components
Learn about the core components in detail.
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Code Style
Follow the project's coding standards.