ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 39 : FAIL : Model graph cycle detected

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 39 : FAIL : Model graph cycle detected

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 a variety of platforms and devices. ONNX Runtime supports a wide range of models and provides optimizations for both CPU and GPU execution.

Identifying the Symptom

When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 39 : FAIL : Model graph cycle detected. This error indicates a problem with the structure of your model graph.

What You Observe

During the model loading or execution phase, the process fails, and the above error message is displayed. This prevents the model from being used for inference.

Understanding the Issue

The error message Model graph cycle detected suggests that the model graph contains a cycle. In graph theory, a cycle is a path of edges and vertices wherein a vertex is reachable from itself. In the context of ONNX models, cycles are not allowed because they can lead to infinite loops during model execution.

Why Cycles Occur

Cycles can occur due to incorrect model design or conversion issues from other formats to ONNX. It is crucial to ensure that the graph is acyclic to maintain the integrity and functionality of the model.

Steps to Fix the Issue

To resolve the cycle detection error, follow these steps:

1. Visualize the Model Graph

Use tools like Netron to visualize the model graph. This will help you identify any cycles present in the graph. Load your ONNX model into Netron and inspect the connections between nodes.

2. Identify and Remove Cycles

Once you have visualized the graph, look for any cycles. A cycle might appear as a loop or a back edge in the graph. Modify the model to remove these cycles. This might involve redesigning parts of the model or adjusting the conversion process if the model was converted from another format.

3. Validate the Model

After making changes, validate the model to ensure that it is acyclic. You can use ONNX Runtime's validation tools to check the integrity of the model.

4. Test the Model

Reload the model into ONNX Runtime and test it to ensure that the error is resolved. Run inference tests to confirm that the model behaves as expected.

Conclusion

By following these steps, you can resolve the "Model graph cycle detected" error in ONNX Runtime. Ensuring that your model graph is acyclic is crucial for successful model deployment and inference. For more information on ONNX and ONNX Runtime, visit the official ONNX website.