Principle:Onnx Onnx Opset Version Conversion

Knowledge Sources	ONNX Version Converter ONNX Versioning
Domains	Versioning, Model_Transformation
Last Updated	2026-02-10 00:00 GMT

Overview

A model transformation mechanism that converts ONNX operators between different opset versions by applying registered version adapters.

Description

Opset version conversion transforms an ONNX model from one opset version to another within the default domain (ai.onnx). When operators change between opset versions (new parameters, different semantics, renamed operators), the version converter applies registered adapters that rewrite affected nodes to conform to the target opset's semantics. This enables forward and backward compatibility: models created with one opset version can be converted to work with runtimes that support a different version.

The conversion is implemented as a C++ engine with a registry of per-operator version adapters. Each adapter knows how to transform a specific operator from one version to the next (or previous), and the converter chains these adapters as needed to reach the target version.

Usage

Use this principle when you need to deploy an ONNX model to a runtime that supports a different opset version than the model was created with, or when standardizing multiple models to a common opset version. The conversion operates only on the default domain (ai.onnx); ML and training domain operators are not converted.

Theoretical Basis

Version conversion applies a sequence of version adapters:

$convert (M, v_{target}) = A_{v_{n - 1} \to v_{n}} \circ \dots \circ A_{v_{1} \to v_{2}} \circ A_{v_{0} \to v_{1}} (M)$

Where each $A_{v_{i} \to v_{i + 1}}$ is an adapter that transforms operators from version $v_{i}$ to $v_{i + 1}$ . For downgrade conversions, the adapters are applied in reverse.

Pseudo-code:

# Abstract version conversion
def convert(model, target_version):
    current = model.opset_version
    while current != target_version:
        direction = 1 if target_version > current else -1
        next_version = current + direction
        for node in model.graph.nodes:
            adapter = find_adapter(node.op_type, current, next_version)
            if adapter:
                node = adapter.adapt(node)
        current = next_version
    model.opset_version = target_version

Related Pages

Implemented By

Implementation:Onnx_Onnx_Version_Converter_Convert_Version

Uses Heuristic

Heuristic:Onnx_Onnx_Opset_Version_Selection

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