onnx-runtime-ir 0.1.0-dev.0

Graph IR for the ORT 2.0 runtime: types, symbolic shapes, strided layouts, device placement, and a mutable graph model
Documentation

onnx-runtime-ir

The Graph intermediate representation (IR) for the ORT 2.0 runtime.

This crate is the stable contract that every downstream runtime crate (onnx-runtime-loader, onnx-runtime-ep-api, onnx-runtime-session, …) builds against. It is intentionally pure, safe Rust with no FFI and no device dependencies so it compiles standalone on any target.

It is a Rust port of the design captured in docs/ORT2.md §3 (Graph IR), §5 (Striding & Layout) and §11 (Dynamic Shape), itself inspired by the Python onnx-ir package.

What lives here

Concept Type
Element type [DataType]
Symbolic / static shapes [Shape], [Dim], [SymbolConstraints]
Physical strided layout [TensorLayout], [MemoryFormat]
Device placement [DeviceType], [DeviceId]
Graph values (SSA edges) [Value], [ValueId]
Graph operations [Node], [NodeId], [Attribute]
Constant / weight storage [TensorData], [SparseTensorData], [WeightRef]
The graph itself [Graph]
Errors [IrError], [GraphError]

Design guarantees

  • SSA-like: every [Value] has at most one producer [Node]; node outputs are unique.
  • First-class layout & device: every value carries a [TensorLayout] and an optional [DeviceId], unlike upstream ONNX / onnx-ir.
  • Mutable during optimization: the [Graph] mutation API keeps producer/consumer edges consistent so optimization passes can rewrite it, then it is shared immutably via Arc once frozen.

Deep algorithms whose full implementation belongs to a later task (e.g. per-op shape inference) are represented here only by their data model; the Graph operations that are cheap and foundational (topological ordering, validation, edge rewiring, broadcasting, stride arithmetic) are fully implemented and unit-tested so downstream crates compile against a stable, working surface.