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Modular configuration design

pytorch/pytorch
Based on 3 comments
Other

Configuration systems should be designed with modularity and clarity in mind. When creating configuration classes or mechanisms: 1. **Separate generic from specific configurations**: Organize configuration options by their scope and applicability. Use class hierarchies or namespaces to isolate domain-specific settings from generic ones.

Configurations Other

Reviewer Prompt

Configuration systems should be designed with modularity and clarity in mind. When creating configuration classes or mechanisms:

  1. Separate generic from specific configurations: Organize configuration options by their scope and applicability. Use class hierarchies or namespaces to isolate domain-specific settings from generic ones.
// Good practice - clear separation of concerns
class AcceleratorAllocatorConfig {
  // Common settings applicable to all accelerators
};

class CUDAAllocatorConfig : public AcceleratorAllocatorConfig {
  // CUDA-specific settings
};
  1. Use descriptive, consistent naming conventions: Configuration option names should clearly indicate their purpose and scope.
    • Avoid device-specific terms in generic interfaces (e.g., use device_malloc not cudamalloc)
    • Use consistent prefixes to group related options (e.g., pinned_ for pinned memory settings)
  2. Document all configuration options: Each option should have clear documentation explaining its purpose, valid values, and implications.

  3. Design for extensibility: Implement hook mechanisms that allow domain-specific configurations to be integrated with generic ones.

  4. Propagate build-time configurations: When generating configuration files (e.g., from .cmake.in templates), ensure build-time choices are properly encoded for downstream consumers.

These practices improve maintainability by making configuration systems more intuitive, better documented, and easier to extend for new devices or backends.

3
Comments Analyzed
Other
Primary Language
Configurations
Category

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