YOLOv8 Fails On RyzenAI 1.6: Troubleshooting Guide

by Alex Johnson 51 views

Introduction

Are you experiencing issues with the YOLOv8 object detection sample on RyzenAI 1.6 in Windows? You're not alone. Many users have encountered a frustrating error where the quicktest.py script passes successfully, but the YOLOv8 object detection tutorial sample fails with a perplexing "cannot find producer" error. This article delves into this specific problem, offering insights, potential solutions, and guidance to help you get your YOLOv8 application running smoothly on your RyzenAI-powered system.

This comprehensive guide aims to address the common challenges faced when deploying YOLOv8 models on the RyzenAI 1.6 platform within a Windows environment. We'll explore the error in detail, investigate potential causes, and provide step-by-step troubleshooting methods to help you resolve the issue. By the end of this article, you should have a clearer understanding of the problem and the necessary steps to overcome it. This guide is tailored for both novice and experienced users, offering a blend of fundamental concepts and advanced techniques to ensure a successful deployment.

Understanding the Issue

The core problem lies in the discrepancy between the successful execution of the basic quicktest.py script and the failure of the YOLOv8 object detection sample. The error message "cannot find producer" typically indicates an issue within the computational graph of the ONNX model, specifically with an operation named split_with_sizes_24_split_0. This operation is a part of the YOLOv8 model's architecture, responsible for splitting tensors into smaller chunks. The error suggests that the RyzenAI toolchain is unable to locate the source or producer of this specific operation within the graph. This can stem from several factors, including inconsistencies in ONNX graph structure, unsupported operations, or compatibility issues with the RyzenAI runtime environment. Understanding the intricacies of the error message and its potential causes is crucial for effective troubleshooting and resolution.

Error Details

The error manifests as a check failure: node != nullptrcannot find producer. onnx_node_arg_name=split_with_sizes_24_split_0 message. This error occurs during the model loading or compilation phase, where the RyzenAI toolchain attempts to optimize and partition the ONNX model for execution on the NPU. The inability to find the producer for the split_with_sizes_24_split_0 operation halts the process, preventing the model from being deployed to the NPU. The error message is indicative of a deeper issue within the graph's dependency structure, where the toolchain cannot trace the origin of a specific tensor or operation. This typically points to a misalignment between the model's architecture and the toolchain's parsing capabilities, necessitating a closer examination of the model's ONNX representation.

Reproducing the Error

To reproduce the error, users often follow these steps:

  1. Set up a Python environment (e.g., using conda or venv) with the required packages, including ryzen-ai-lt, ryzenai_onnx_utils, onnx, onnxruntime-vitisai, ultralytics, torch, and torchvision. Ensure that the versions match those recommended by the RyzenAI SDK documentation.
  2. Download the YOLOv8 model in ONNX format (e.g., yolov8m_BF16.onnx).
  3. Obtain a test image (e.g., test_image.jpg).
  4. Run the run_inference.py script with the appropriate arguments, specifying the model path, input image path, output image path, and device (npu-bf16).

The error typically occurs during the execution of the run_inference.py script, specifically when the RyzenAI toolchain attempts to load and compile the ONNX model for NPU execution. The consistent recurrence of the error across different setups indicates a systemic issue, rather than an isolated incident. This repeatability is essential for troubleshooting, as it allows for controlled experimentation and validation of potential solutions.

Potential Causes

Several factors might contribute to the "cannot find producer" error. Let's explore the most common causes:

  1. ONNX Graph Incompatibilities: The generated ONNX graph might contain operations or structures that are not fully supported by the RyzenAI 1.6 toolchain. This can occur due to variations in ONNX operator sets, custom operators, or unsupported graph patterns. The toolchain's ability to parse and optimize the ONNX graph is contingent upon its adherence to a specific set of standards and conventions. Deviations from these norms can lead to parsing errors, including the inability to resolve dependencies between operations.
  2. ONNX Export Settings: Incorrect settings during the ONNX export process from PyTorch or other frameworks can lead to an improperly formed graph. This includes using incompatible opset versions, failing to include necessary operators, or generating a graph with inconsistent data types. The ONNX export process is a critical step in model deployment, as it translates the model's architecture from a framework-specific representation to a standardized format. Errors during this stage can propagate through the deployment pipeline, manifesting as runtime failures.
  3. RyzenAI Toolchain Bugs: There might be an undiscovered bug within the RyzenAI 1.6 toolchain that prevents it from correctly handling certain YOLOv8 model architectures or operations. Software systems, particularly those involving complex model transformations and optimizations, are prone to bugs. The RyzenAI toolchain, despite rigorous testing, may still contain latent defects that surface under specific conditions, such as when processing certain YOLOv8 models.
  4. Version Mismatch: Inconsistencies between the versions of ultralytics, torch, torchvision, and the RyzenAI SDK can lead to compatibility issues. Different versions of these libraries may have conflicting dependencies or introduce changes that affect ONNX export and runtime behavior. Maintaining a consistent and compatible set of library versions is crucial for ensuring the stability and reliability of the deployment process.
  5. Missing Custom Operators: The YOLOv8 model might rely on custom operators that are not registered or correctly loaded by the RyzenAI runtime. Custom operators extend the functionality of standard ONNX operators, allowing for specialized computations. If these operators are not properly integrated into the runtime environment, the model's graph may be incomplete, leading to errors during execution.

Troubleshooting Steps

To effectively tackle the "cannot find producer" error, follow these systematic troubleshooting steps:

  1. Verify ONNX Model Integrity: Use tools like Netron to visually inspect the ONNX graph and identify any potential issues, such as missing connections or unsupported operations. Netron provides a graphical representation of the model's architecture, allowing you to trace the flow of data between operations and identify potential bottlenecks or inconsistencies. Analyzing the graph's structure can provide valuable clues about the root cause of the error.
  2. Check ONNX Export Settings: Review the export settings used when converting the YOLOv8 model to ONNX. Ensure that you're using a compatible opset version (e.g., opset 16 or 17) and that all necessary operators are included. The opset version specifies the set of ONNX operators that are supported by the model. Choosing an appropriate opset version is crucial for ensuring compatibility with the RyzenAI toolchain. Additionally, verifying that all custom operators and required transformations are included in the export settings can prevent potential errors.
  3. Update RyzenAI SDK: Ensure you're using the latest version of the RyzenAI SDK, as newer versions often include bug fixes and performance improvements. The RyzenAI SDK is the primary interface for deploying models on the RyzenAI platform. Keeping it up-to-date ensures that you have access to the latest features, bug fixes, and performance enhancements.
  4. Downgrade Ultralytics: Try downgrading the ultralytics package to a known working version, as newer versions might introduce changes that affect ONNX export. The ultralytics package provides a high-level interface for training and deploying YOLOv8 models. Compatibility issues between different versions of ultralytics and the RyzenAI SDK can lead to deployment errors. Downgrading to a known stable version can help isolate the problem.
  5. Examine the Model Graph: Use ONNX utilities to examine the model graph programmatically and identify the producer of the split_with_sizes_24_split_0 operation. The ONNX library provides APIs for programmatically manipulating and inspecting ONNX models. Using these APIs, you can trace the inputs and outputs of operations, identify dependencies, and pinpoint the source of the error. This approach allows for a more detailed analysis of the model's structure compared to visual inspection.
  6. Simplify the Model: If possible, try simplifying the model architecture or removing potentially problematic operations to see if the error persists. Model simplification can help isolate the root cause of the error by reducing the complexity of the graph. Removing specific operations or layers can reveal whether a particular component is causing the issue. This technique is particularly useful when dealing with complex models that contain a large number of operations and dependencies.
  7. Contact AMD Support: If all else fails, reach out to AMD support for assistance. Provide them with detailed information about your environment, the steps you've taken, and the error messages you're encountering. AMD support has access to specialized knowledge and resources that can help diagnose and resolve complex issues. Providing them with comprehensive information about your setup and troubleshooting efforts will expedite the support process.

Specific Solutions and Workarounds

Based on the information available, here are some specific solutions and workarounds that might help:

  1. ONNX Opset Version: Try exporting the YOLOv8 model with a different ONNX opset version. Some users have reported success using opset 16 or 17. The ONNX opset version defines the set of operators and their semantics that are used in the model. Different opset versions may have varying levels of support within the RyzenAI toolchain. Experimenting with different opset versions can help identify a compatible configuration.
  2. Custom Operator Registration: Ensure that any custom operators used by the YOLOv8 model are correctly registered with the ONNX runtime. Custom operators extend the functionality of standard ONNX operators, allowing for specialized computations. If the RyzenAI runtime is not aware of these custom operators, it will be unable to execute the model correctly. Registering custom operators involves providing the runtime with the necessary information about their implementation and semantics.
  3. Model Partitioning: Experiment with different model partitioning strategies. The RyzenAI toolchain partitions the ONNX model into subgraphs that can be executed on different hardware resources, such as the NPU and CPU. Modifying the partitioning strategy can sometimes resolve issues related to operator placement and dependency resolution. However, this approach requires a deep understanding of the toolchain's internals and should be undertaken with caution.
  4. BF16 Support: Verify that the NPU and RyzenAI toolchain fully support BF16 (BFloat16) data types. BF16 is a reduced-precision floating-point format that offers significant performance advantages on certain hardware platforms. However, not all hardware and software components fully support BF16. Ensuring compatibility between the model's data types and the hardware's capabilities is crucial for optimal performance and stability. If BF16 is not fully supported, consider converting the model to FP16 or FP32.

Answering the Questions

Let's address the specific questions raised in the original post:

  1. Is this a known issue with run_inference.py and YOLOv8 BF16 models on RyzenAI 1.6 (Windows)?

    While not officially documented as a widespread issue, the error pattern suggests a potential compatibility problem between the RyzenAI 1.6 toolchain and certain YOLOv8 models, particularly those using BF16 data types. The fact that quicktest.py passes while the YOLOv8 sample fails indicates that the fundamental RyzenAI setup is functional, but there's a specific issue with the model or the inference script's interaction with the toolchain.

  2. Does this error suggest there is something wrong with the generated yolov8m_BF16.onnx graph (e.g., missing producer for split_with_sizes_24_split_0), or is it expected to be handled inside the RyzenAI toolchain?

    The error strongly suggests an issue with the generated ONNX graph, specifically the absence of a producer for the split_with_sizes_24_split_0 operation. While the RyzenAI toolchain is responsible for optimizing and partitioning the graph, it relies on the graph being well-formed and containing all necessary information. The toolchain is designed to handle standard ONNX operations and graph patterns, but if the graph is missing critical dependencies, it will be unable to process it correctly. Therefore, the error likely stems from an issue in the ONNX export process or the model's architecture itself.

  3. Are there recommended ONNX export settings / opset / ultralytics versions for YOLOv8 models to avoid this “cannot find producer” error?

    Based on community experiences and best practices, the following recommendations can help avoid the "cannot find producer" error:

    • ONNX Opset Version: Use opset 16 or 17 for exporting the model.
    • Ultralytics Version: Downgrade to a known stable version of ultralytics if you're using the latest version.
    • Data Types: Ensure that the data types used in the model are compatible with the RyzenAI toolchain and NPU. If BF16 is causing issues, try using FP16 or FP32.
    • Operator Inclusion: Verify that all necessary operators, including custom operators, are included during the ONNX export process.

Conclusion

The "cannot find producer" error when running YOLOv8 object detection on RyzenAI 1.6 in Windows can be a challenging issue to resolve. However, by understanding the potential causes, following the troubleshooting steps outlined in this article, and applying the specific solutions and workarounds, you can increase your chances of successfully deploying your YOLOv8 model. Remember to systematically investigate the problem, verify your environment and settings, and leverage the resources available, including AMD support and online communities. This error often stems from inconsistencies in the ONNX graph or compatibility issues between software components. By carefully addressing these factors, you can overcome the hurdle and unlock the potential of your RyzenAI-powered system for object detection tasks.

For further resources on RyzenAI and ONNX, visit the AMD Ryzen AI Documentation.