Sionnx: Automatic Unit Test Generator for ONNX Conformance

TL;DR

Sionnx is an automatic unit test generator designed to verify ONNX operator compliance, using a new specification language and LLVM TableGen to achieve high coverage and cross-framework support.

Contribution

It introduces a novel Operator Specification Language and leverages LLVM TableGen to automatically generate comprehensive conformance tests for ONNX operators.

Findings

Enables automated verification of ONNX operator compliance.

Provides high coverage testing across multiple frameworks.

Open-sourced implementation available for community use.

Abstract

Open Neural Network Exchange (ONNX) is an open format to represent AI models and is supported by many machine learning frameworks. While ONNX defines unified and portable computation operators across various frameworks, the conformance tests for those operators are insufficient, which makes it difficult to verify if an operator's behavior in an ONNX backend implementation complies with the ONNX standard. In this paper, we present the first automatic unit test generator named Sionnx for verifying the compliance of ONNX implementation. First, we propose a compact yet complete set of rules to describe the operator's attributes and the properties of its operands. Second, we design an Operator Specification Language (OSL) to provide a high-level description for the operator's syntax. Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage. Sionnx is lightweight and flexible to support cross-framework verification. The Sionnx framework is open-sourced in the github repository (https://github.com/alibaba/Sionnx).