Export dynamic batch size ONNX using ONNX's DeformConv

[itskyf]

This PR replaces the usage of deform_conv2d_onnx_exporter with the native DeformConv operator available in ONNX opset 19. The exported ONNX model now supports dynamic batch sizes.

Notes

• The symbolic_deform_conv_19() function was generated using OpenAI o1. It works in my testing, but let me know if there are any special requirements to consider.
• Resolves Request: Support for deform_conv2d from an alternate repository to enable better ONNX export with dynamic_shape #127.

[ZhengPeng7]

Thanks a lot! I'll take time to look at it tomorrow, which really helps.
BTW, could you update the PR with no output and minimal modification in the notebook? That would be very nice for me to read and test the updated part in a clear way.

[itskyf]

Sure, I've updated the notebook to reduce the modifications.

[alfausa1]

Thank you so much, @itskyf, for your contribution! Have you had a chance to test whether the execution works with ONNX Runtime?

[ZhengPeng7]

Hi, @itskyf. Did you successfully export the ONNX model? I tried it but met this problem. I tried both `PyTorch==2.0.1+onnxruntime-gpu==1.18.1` and `PyTorch==2.5.1+onnxruntime-gpu==1.20.1`).

[alfausa1]

Hi @ZhengPeng7,

I believe the issue arises because ONNX has implemented the DeformConv operator, but unfortunately, ONNX Runtime does not currently support it. As a result, any code that includes this operator cannot be executed within a Runtime Session. :/

[itskyf]

@ZhengPeng7 ah, I forgot to mention that we need to also update the onnx package for opset 19.
@alfausa1 I faced the same problem. The dynamic batched model can only be used after TensorRT conversion. But since DefirmConv is an ONNX operator, I hope it will be supported in ONNXRuntime soon.

[alfausa1]

@itskyf Could you please provide the code in which you have converted the dynamic batched model to TensorRT? Thanks in advance!

[jhwei]

Thanks for @itskyf 's PR. This is exactly what I tested, and it worked.

I have a question about this PR for @itskyf

When I tested in this way, I found the result trt engine will work as expected when the batch size used when generating is different from batch size used for inferencing. And I figured out #166 this change should be made. Do you find the same issue ?