Sparsity on Onnx Model

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

Actually I want to do sparsity of onnx model and then convert it to tensorrt engine but somehow after enable sparsity tensorrt version 10.0.01 didn’t do any sparsity on layer it says some layers are to be sparse but spares layerd 0. Please help me with this

4

If you are encountering issues with enabling sparsity on your ONNX model and converting it to a TensorRT engine using version 10.0.01, here are some insights and potential solutions to consider:

Common Issues with Enabling Sparsity

  1. Incorrect Configuration:

    • Ensure that you have enabled sparsity correctly in your ONNX model. Check if the appropriate parameters are set in your model and confirm that you are using the correct commands for enabling sparsity during model conversion.

  2. Unsupported Layers:

    • Some layers in the ONNX model may not support sparsity. Review the documentation for TensorRT and the list of supported operations to confirm whether the layers you are trying to mark as sparse can actually be sparsified.

  3. Sparsity Formats:

    • Make sure that the sparsity format you are using in the ONNX model is compatible with TensorRT’s sparsity formats. If there’s a mismatch between formats, TensorRT may not mark any layers as sparse.

  4. Sparse Training:

    • If the model was not trained with sparsity in mind (e.g., using techniques like Weight Pruning or Sparse Regularization), it might not yield effective sparsity when you attempt to enable it.

Solutions to Consider

  1. Validate Sparsity Configuration:

    • Review and double-check the settings related to sparsity in your configuration for both the ONNX model and the TensorRT conversion command. Make sure that the layers intended to be sparse are properly defined.

  2. Use Supported Layers:

    • Confirm the supported layers for sparsity within the context of TensorRT by checking the TensorRT documentation or the operator’s support matrix. If any critical layers cannot be sparsified, consider modifying your model architecture.

  3. Sparsity Format Adjustment:

    • Convert the ONNX model to ensure compatibility with TensorRT’s sparsity format. If necessary, apply pruning techniques or adjustments to the sparse format you are implementing.

  4. Sparsity Algorithms:

    • If the model architecture allows it, apply a sparsity training algorithm or fine-tune the model for sparsity. Training with techniques like L1 regularization or structured sparsity may yield better results.

  5. Upgrade to Newer Versions:

    • If feasible, consider upgrading your TensorRT version to a later release (e.g., TensorRT 10.2 or higher) that might offer improved support for sparsity and better conversion capabilities. This may help overcome existing limitations in the version you are using.

  6. Profile Memory Usage:

    • Use profiling tools to observe memory utilization during the conversion process, as this can help you identify whether the model is running into memory constraints leading to failed sparsity applications.

  7. Consult NVIDIA Documentation:

    • Review the official NVIDIA TensorRT documentation for any recent updates or changes concerning sparsity optimization. Additionally, check the release notes for version 10.0.01 for any noted limitations or issues related to sparsity.

By thoroughly investigating these areas and applying the recommended solutions, you should be able to address the issue of enabling sparsity on your ONNX model and converting it to a TensorRT engine. If challenges persist, Please let us know.