ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 17 : FAIL : Unsupported operator

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 17 : FAIL : Unsupported operator

Understanding ONNX Runtime

ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices. By supporting a wide range of hardware and software environments, ONNX Runtime enables developers to optimize their models for both speed and efficiency.

Identifying the Symptom

When working with ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 17 : FAIL : Unsupported operator. This error indicates that the model you are trying to run includes an operator that is not supported by ONNX Runtime.

Exploring the Issue

What Causes This Error?

The error arises because ONNX Runtime does not support every operator available in the ONNX specification. This can occur if the model was exported from a framework that uses custom or less common operators.

Understanding Operator Support

ONNX Runtime supports a wide range of operators, but it is essential to verify that all operators used in your model are supported. You can find the list of supported operators in the ONNX Runtime Operator Documentation.

Steps to Resolve the Issue

Step 1: Identify Unsupported Operators

First, identify which operators in your model are unsupported. You can do this by examining the error message or by using tools like ONNX's model checker to validate your model.

import onnxfrom onnx import checkermodel = onnx.load('your_model.onnx')checker.check_model(model)

This will help you pinpoint the unsupported operators.

Step 2: Modify the Model

Once you have identified the unsupported operators, you need to modify your model to replace them with supported alternatives. This may involve re-training your model or using a different export method from your original framework.

For example, if you are using PyTorch, you might need to replace certain layers with equivalent ones that are supported by ONNX Runtime.

Step 3: Re-export the Model

After making the necessary changes, re-export your model to the ONNX format. Ensure that you specify the correct opset version that matches the operators you are using.

import torch# Assuming 'model' is your PyTorch modeltorch.onnx.export(model, dummy_input, 'updated_model.onnx', opset_version=11)

Conclusion

By following these steps, you can resolve the "Unsupported operator" error in ONNX Runtime. Always ensure that your model's operators are compatible with the ONNX Runtime version you are using. For further reading, check the ONNX Runtime Documentation for more details on supported operators and troubleshooting tips.