https://github.com/onnx/ir-py

Efficient in-memory representation for ONNX, in Python
https://github.com/onnx/ir-py

computation-graph intermediate-representation large-language-models machine-learning onnx

Last synced: 6 days ago
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Efficient in-memory representation for ONNX, in Python

• Host: GitHub
• URL: https://github.com/onnx/ir-py
• Owner: onnx
• License: apache-2.0
• Created: 2025-05-14T03:03:53.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-08-08T15:49:52.000Z (5 months ago)
• Last Synced: 2025-08-08T16:09:52.439Z (5 months ago)
• Topics: computation-graph, intermediate-representation, large-language-models, machine-learning, onnx
• Language: Python
• Homepage: http://onnx.ai/ir-py/
• Size: 810 KB
• Stars: 22
• Watchers: 6
• Forks: 9
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: CODEOWNERS
  • Security: SECURITY.md

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An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation.

## Getting Started

[onnx-ir documentation](https://onnx.ai/ir-py/)

### Installation

Via pip:

```

pip install onnx-ir

```

Or from source:

```

pip install git+https://github.com/onnx/ir-py.git

```

## 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.

## Concept Diagram

![Concept Diagram](docs/resource/onnx-ir-entities.svg)

## Code Organization 🗺️

- [`_protocols.py`](src/onnx_ir/_protocols.py): Interfaces defined for all entities in the IR.

- [`_core.py`](src/onnx_ir/_core.py): Implementation of the core entities in the IR, including `Model`, `Graph`, `Node`, `Value`, and others.

- [`_enums.py`](src/onnx_ir/_enums.py): Definition of the type enums that correspond to the `DataType` and `AttributeType` in `onnx.proto`.

- [`_name_authority.py`](src/onnx_ir/_name_authority.py): The authority for giving names to entities in the graph, used internally.

- [`_linked_list.py`](src/onnx_ir/_linked_list.py): The data structure as the node container in the graph that supports robust iteration and mutation. Internal.

- [`_metadata.py`](src/onnx_ir/_metadata.py): Metadata store for all entities in the IR.