Getting warning message [TensorRT

Issue Overview

Users have reported encountering a series of warning messages while running a modified version of the sample.py code from the TensorRT samples directory, specifically for INT8 calibration. The warnings indicate missing scale and zero-point values for various tensors in the neural network layers, leading to a fallback to non-INT8 implementations. The issue arises during the execution of inference tasks, where users expect optimized performance from the INT8 calibration but instead receive warnings that suggest a potential degradation in efficiency.

Symptoms:

• Warning messages during execution:
  • [TensorRT] WARNING: Missing scale and zero-point for tensor (Unnamed Layer* 0) [Convolution]_output
  • Similar warnings for multiple unnamed layers.
• Performance may be impacted due to fallback to non-INT8 implementations.

Context:

• Occurs when running the modified sample.py script located in /usr/src/tensorrt/samples/python/network_api_pytorch_mnist/.
• Users are attempting to implement INT8 calibration for improved inference speed and efficiency.

Hardware/Software Specifications:

• Nvidia Jetson Orin Nano Dev board.
• TensorRT framework utilized for deep learning inference.

Frequency:

• The issue appears consistently when the specific conditions are met (i.e., during INT8 calibration).

Impact:

• The presence of these warnings suggests that users may not be achieving the expected performance improvements from their models, potentially affecting application responsiveness and efficiency.

Possible Causes

• Missing Calibration Cache: The warnings are triggered by missing scale and zero-point values, which are crucial for INT8 quantization.

• Improper File Management: Users may inadvertently copy existing calibration files that contain outdated or incorrect data.

• Configuration Errors: Misconfigurations in the setup or code modifications may lead to improper calibration file handling.

• Driver Issues: Outdated or incompatible drivers could potentially interfere with TensorRT’s operation.

Troubleshooting Steps, Solutions & Fixes

1. Identify Warning Messages:

   • Review the warning messages carefully to determine which layers are affected.
   • Note any patterns in the warnings related to specific operations (e.g., Convolution, Pooling).

2. Check Calibration Cache:

   • Navigate to the directory where the calibration cache is stored.
   • Verify if there is an existing mnist_calibration.cache file present.

3. Remove Existing Cache:

   • If an old calibration cache file exists, remove it using the following command:

     rm /usr/src/tensorrt/samples/python/network_api_pytorch_mnist/mnist_calibration.cache
     

4. Create a New Calibration Cache:

   • Run your modified sample.py script again. This will create a new empty calibration cache file automatically.
   • Confirm that the warning messages no longer appear after this step.

5. Validate Code Modifications:

   • Ensure that any changes made to sample.py do not inadvertently affect how tensors are handled.
   • Review TensorRT documentation for best practices on INT8 calibration.

6. Check Driver Versions:

   • Ensure that you are using compatible versions of TensorRT and any associated drivers.
   • Update drivers if necessary using your package manager or Nvidia’s official resources.

7. Test with Different Configurations:

   • If issues persist, consider testing with different hardware configurations or reverting to a known working state of your code.

8. Consult Documentation:

   • Refer to Nvidia’s official TensorRT documentation for additional guidance on INT8 calibration and troubleshooting common issues.

9. Best Practices for Future Use:

   • Regularly check for updates to TensorRT and related libraries.
   • Maintain backup copies of working configurations and calibration files to avoid similar issues in the future.

By following these steps, users should be able to resolve the warning messages related to missing scale and zero-point values effectively.