Design Principles

Note

This page lists the design principles summarized by AI (https://deepwiki.com/search/what-are-the-design-principles_aa6206f7-8f38-41df-b4a3-268776b3766d) which I (@justinchuby) find reasonable.

Note

When designing and implementing features, The Flax philosophy is also a good resource and food for thought.

1. Full ONNX Specification Support with Flexibility

The system supports all valid ONNX models and even a subset of invalid models to enable loading and fixing them README.md:31 . This principle ensures comprehensive compatibility while providing tools for model repair.

2. Memory Efficiency and Zero-Copy Operations

The architecture prioritizes low memory footprint through memory-mapped external tensors and a unified interface for different tensor types README.md:32 . The system supports zero-copy operations without tensor size limitations, enabling efficient handling of large models.

3.Protocol-Based Design for Extensibility

The system employs protocol-based interfaces to define contracts between components while allowing flexible implementations _protocols.py:14-18 . This approach enables type safety, extensibility, and performance optimization without requiring inheritance from specific base classes.

4. Separation of Concerns

The IR maintains strict separation between the in-memory representation and serialization formats _core.py:6-9 . The core IR module is kept protobuf-free, decoupling the representation from the serialization format README.md:37 .

5. Robust Mutation Support

The system enables safe concurrent iteration during graph mutations through specialized data structures like DoublyLinkedSet README.md:34 . This allows creating multiple iterators on graphs while performing modifications.

6. Balanced Mutability Architecture

The design carefully balances immutability for safety with efficient mutation capabilities. Core entities like Node have immutable structure but mutable membership, while Value objects are immutable with usage tracking _core.py:1619-1631 .

7. Pythonic and Intuitive APIs

The classes provide Pythonic APIs that still map intuitively to ONNX protobuf concepts README.md:36 . This principle ensures familiar usage patterns while maintaining conceptual alignment with the ONNX specification.

8. Performance-Oriented Graph Manipulation

The system emphasizes performant graph manipulation and serialization/deserialization to protobuf README.md:35 , enabling efficient model transformations and analysis passes.

Notes

The design principles are implemented through a layered architecture with clear separation between the public API layer, core IR layer, type system, infrastructure components, and serialization layer. The metadata system supports both serializable properties and transient analysis data, enabling flexible annotation of IR entities for various use cases.