ONNX IR
An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation.
Features ✨
- Full ONNX spec support: all valid models representable by ONNX protobuf, and a subset of invalid models (so you can load and fix them).
- Low memory footprint: mmap'ed external tensors; unified interface for ONNX TensorProto, Numpy arrays and PyTorch Tensors etc. No tensor size limitation. Zero copies.
- Straightforward access patterns: Access value information and traverse the graph topology at ease.
- Robust mutation: Create as many iterators as you like on the graph while mutating it.
- Speed: Performant graph manipulation, serialization/deserialization to Protobuf.
- Pythonic and familiar APIs: Classes define Pythonic apis and still map to ONNX protobuf concepts in an intuitive way.
- No protobuf dependency: The IR does not require protobuf once the model is converted to the IR representation, decoupling from the serialization format.
Code Organization 🗺️
_protocols.py: Interfaces defined for all entities in the IR._core.py: Implementation of the core entities in the IR, includingModel,Graph,Node,Value, and others._enums.py: Definition of the type enums that correspond to theDataTypeandAttributeTypeinonnx.proto._name_authority.py: The authority for giving names to entities in the graph, used internally._linked_list.py: The data structure as the node container in the graph that supports robust iteration and mutation. Internal._metadata.py: Metadata store for all entities in the IR.
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