[Feature Request] [web/webgpu] Support Non-symmetrical Padding In Conv

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Feature Request: Enhancing ONNX Runtime Web with WebGPU Support for Non-Symmetrical Padding in Conv

Introduction

The ONNX Runtime Web with WebGPU is a powerful tool for executing machine learning models in the browser. However, it currently has a limitation when it comes to convolutional (Conv) operations with non-symmetrical padding. In this article, we will discuss the importance of supporting non-symmetrical padding in Conv and propose a feature request to enhance the ONNX Runtime Web with WebGPU.

Describe the Feature Request

The current implementation of ONNX Runtime Web with WebGPU expects padding in Conv to be totally symmetrical. This means that the padding on both sides of the input tensor must be equal. However, this is not always the case in real-world models. For instance, some models may require asymmetric padding to achieve better performance or to accommodate specific input shapes.

The issue arises when models that need Conv with non-symmetrical padding are executed using the ONNX Runtime Web with WebGPU. The error message indicates that the padding is not symmetrical, and the execution fails.

To overcome this limitation, a possible workaround for users might involve factoring out the padding into a separate Pad op. However, this approach can be tricky to implement, especially for models with pre-trained weights. The weights are typically optimized for a specific padding scheme, and modifying the padding can affect the overall performance of the model.

Describe Scenario Use Case

The need for supporting non-symmetrical padding in Conv is essential for executing any model that requires convolutions with non-symmetrical padding under ONNX Runtime Web with WebGPU. One example of such a model is TransnetV2, a popular model for image classification tasks.

TransnetV2 uses a combination of convolutional and transformer layers to achieve state-of-the-art performance on various image classification benchmarks. However, the model requires non-symmetrical padding in its convolutional layers, which makes it incompatible with the current implementation of ONNX Runtime Web with WebGPU.

Benefits of Supporting Non-Symmetrical Padding in Conv

Supporting non-symmetrical padding in Conv would have several benefits for the ONNX Runtime Web with WebGPU community:

  • Increased model compatibility: By supporting non-symmetrical padding, more models can be executed using the ONNX Runtime Web with WebGPU, including popular models like TransnetV2.
  • Improved performance: Non-symmetrical padding can be used to achieve better performance in certain models, especially those that require specific input shapes or padding schemes.
  • Simplified model development: Supporting non-symmetrical padding would simplify the development process for models that require this feature, as users would not need to resort to workarounds like factoring out the padding into a separate Pad op.

Implementation Details

To implement support for non-symmetrical padding in Conv, the following changes can be made to the ONNX Runtime Web with WebGPU:

  • Modify the Conv op: The Conv op should be modified to accept non-symmetrical padding as an input. This can be achieved by adding new parameters to the Conv op that specify the padding on each side of the input tensor.
  • Update the padding logic: The padding logic should be updated to handle non-symmetrical padding. This can be done by using a flexible padding scheme that allows for different padding values on each side of the input tensor.
  • Test and validate: The updated Conv op should be thoroughly tested and validated to ensure that it works correctly with non-symmetrical padding.

Conclusion

Supporting non-symmetrical padding in Conv is a crucial feature for the ONNX Runtime Web with WebGPU community. By implementing this feature, more models can be executed using the ONNX Runtime Web with WebGPU, including popular models like TransnetV2. The benefits of supporting non-symmetrical padding include increased model compatibility, improved performance, and simplified model development. We hope that this feature request will be considered and implemented in the future to enhance the ONNX Runtime Web with WebGPU.

Future Work

Future work on this feature request can include:

  • Extending the Conv op: The Conv op can be extended to support more advanced padding schemes, such as dynamic padding or padding with learnable parameters.
  • Optimizing the padding logic: The padding logic can be optimized to improve performance and reduce memory usage.
  • Adding more test cases: More test cases can be added to ensure that the updated Conv op works correctly with non-symmetrical padding.

By continuing to work on this feature request, we can further enhance the ONNX Runtime Web with WebGPU and make it an even more powerful tool for machine learning model development.
Q&A: Enhancing ONNX Runtime Web with WebGPU Support for Non-Symmetrical Padding in Conv

Introduction

In our previous article, we discussed the importance of supporting non-symmetrical padding in Conv operations for the ONNX Runtime Web with WebGPU. We proposed a feature request to enhance the ONNX Runtime Web with WebGPU to support non-symmetrical padding in Conv. In this article, we will answer some frequently asked questions (FAQs) related to this feature request.

Q: What is non-symmetrical padding in Conv?

A: Non-symmetrical padding in Conv refers to the use of different padding values on each side of the input tensor. This is in contrast to symmetrical padding, where the padding values on both sides of the input tensor are equal.

Q: Why is non-symmetrical padding important?

A: Non-symmetrical padding is important because it allows for more flexibility in model design. Some models may require non-symmetrical padding to achieve better performance or to accommodate specific input shapes.

Q: What are the benefits of supporting non-symmetrical padding in Conv?

A: The benefits of supporting non-symmetrical padding in Conv include increased model compatibility, improved performance, and simplified model development.

Q: How will supporting non-symmetrical padding in Conv affect the ONNX Runtime Web with WebGPU?

A: Supporting non-symmetrical padding in Conv will enhance the ONNX Runtime Web with WebGPU by allowing it to execute more models, including popular models like TransnetV2.

Q: What changes will be made to the ONNX Runtime Web with WebGPU to support non-symmetrical padding in Conv?

A: The following changes will be made to the ONNX Runtime Web with WebGPU to support non-symmetrical padding in Conv:

  • Modify the Conv op to accept non-symmetrical padding as an input.
  • Update the padding logic to handle non-symmetrical padding.
  • Test and validate the updated Conv op.

Q: What are the potential challenges of supporting non-symmetrical padding in Conv?

A: The potential challenges of supporting non-symmetrical padding in Conv include:

  • Complexity of implementing non-symmetrical padding.
  • Potential impact on performance and memory usage.
  • Need for additional testing and validation.

Q: How will the community be involved in the development of this feature?

A: The community will be involved in the development of this feature through:

  • Providing feedback and suggestions.
  • Participating in testing and validation.
  • Contributing to the development of the feature.

Q: What is the expected timeline for the development and release of this feature?

A: The expected timeline for the development and release of this feature is:

  • Initial development: 2-3 months.
  • Testing and validation: 1-2 months.
  • Release: 1-2 months.

Q: What are the next steps for this feature request?

A: The next steps for this feature request are:

  • Finalize the design and implementation plan.
  • Begin development of the feature.
  • Conduct testing and validation.

By answering these FAQs, we hope to provide more clarity and understanding of the feature request to support non-symmetrical padding in Conv for the ONNX Runtime Web with WebGPU.