SwinV2 Traced Inference Error on Nvidia Jetson Orin

Issue Overview

Users are experiencing an error when attempting to run a traced inference of the SwinV2 model on the Nvidia Jetson Orin platform. The issue occurs specifically when using CUDA, while CPU inference works without problems. The error manifests after one inference step, suggesting a potential compatibility issue between the traced model and the Jetson Orin’s CUDA implementation.

The specific error message is:

RuntimeError: The size of tensor a (3) must match the size of tensor b (32) at non-singleton dimension 1

This error occurs during the matrix multiplication operation in the attention mechanism of the SwinV2 model. The issue appears to be platform-specific, as the same code works correctly on x86 systems.

Possible Causes

1. Platform-specific CUDA implementation differences: The Jetson Orin’s CUDA implementation may handle certain operations differently compared to desktop GPUs, leading to tensor size mismatches.

2. PyTorch version incompatibility: Different PyTorch versions between the tracing environment and the inference environment could lead to unexpected behavior.

3. Model architecture mismatch: The traced model might not be fully compatible with the Jetson Orin’s architecture, causing tensor dimension misalignments.

4. CUDA version mismatch: Differences in CUDA versions between the tracing and inference environments might cause compatibility issues.

5. Incorrect model loading or initialization: The model might not be loaded or initialized correctly on the Jetson Orin platform.

Troubleshooting Steps, Solutions & Fixes

1. Verify PyTorch and CUDA versions:
   Ensure that the PyTorch and CUDA versions are compatible with the Jetson Orin platform. Use the following commands to check:

   python -c "import torch; print(torch.__version__)"
   python -c "import torch; print(torch.version.cuda)"
   

2. Try tracing on Jetson Orin:
   Attempt to trace the model directly on the Jetson Orin platform to ensure compatibility. If this fails, note the error message for further investigation.

3. Use a pre-built container:
   Utilize the NVIDIA-provided PyTorch container for Jetson:

   docker pull nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
   

   This container is optimized for Jetson platforms and may resolve compatibility issues.

4. Check input tensor dimensions:
   Verify that the input tensor dimensions match the model’s expected input size:

   print(inputs.shape)
   

   Ensure it matches the expected (1, 3, 256, 256) shape.

5. Investigate attention mechanism:
   The error occurs in the attention calculation. Examine the SwinTransformerBlock implementation in the timm library for any potential issues with tensor shapes.

6. Try different PyTorch versions:
   Test the model with different PyTorch versions, such as 1.12.1, to identify if the issue is version-specific:

   pip install torch==1.12.1
   

7. Use JIT compilation instead of tracing:
   Try using torch.jit.script instead of torch.jit.trace to capture the model’s logic:

   jit_model = torch.jit.script(model)
   

8. Gradual model simplification:
   Simplify the model architecture gradually to isolate the problematic component. Start with a basic version of SwinV2 and add complexity incrementally.

9. Memory profiling:
   Use NVIDIA’s memory profiling tools to check for any memory-related issues:

   nvprof python your_script.py
   

10. Update NVIDIA drivers and L4T:
    Ensure that the Jetson Orin is running the latest NVIDIA drivers and L4T (Linux for Tegra) version.

11. Community support:
    If the issue persists, consider reaching out to the PyTorch community forums or NVIDIA’s developer forums with detailed information about your setup and the steps you’ve taken.

12. Custom CUDA kernels:
    As a last resort, you may need to implement custom CUDA kernels for the problematic operations, tailored specifically for the Jetson Orin architecture.

Remember to test each solution thoroughly and document the results for future reference. If a particular fix works consistently, consider sharing it with the NVIDIA developer community to help others facing similar issues.