[Relax][Onnx] Support Multi Input Ops with Multidirectional Broadcasting

[locnd182644]

This PR support Multi Input Ops with Multidirectional Broadcasting

Description

• Support Multi Input Ops with Multidirectional Broadcasting (Min, Max, Mean, Sum)
• Edit handle workflow for MultiInputBase:
  • Compute target shape for Multidirectional Broadcasting
  • Broadcast_to with target shape
  • Stack op
  • Reduce ops with axis same stack op

Expected

• Example target shape:

Reference

• Multidirectional Broadcasting: https://onnx.ai/onnx/repo-docs/Broadcasting.html
• Fixed: [Bug] Importing ONNX Min with broadcasting fails #18592

[gemini-code-assist]

Summary of Changes

Hello @locnd182644, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the ONNX frontend for Relax by introducing robust support for multidirectional broadcasting in multi-input operators. This ensures that models leveraging operations like Min, Max, Sum, and Mean with inputs of different but broadcast-compatible shapes are correctly translated and executed within the Relax framework, improving compatibility and functionality for a wider range of ONNX models.

Highlights

• Multidirectional Broadcasting Support: Implemented support for multidirectional broadcasting in ONNX multi-input operators such as Min, Max, Sum, and Mean, allowing these operations to correctly handle inputs with varying shapes that require broadcasting.
• New Broadcasting Utility: Added a new utility function, compute_broadcast_shape, to determine the target shape for multidirectional broadcasting between two input shapes.
• Updated MultiInputBase Conversion: Modified the MultiInputBase conversion logic to first compute the target broadcast shape for all inputs, then apply relax.op.broadcast_to to each input before stacking them, and finally performing the reduction operation.
• Comprehensive Test Cases: Introduced new parameterized test cases (test_multi_input_broadcasting) to validate the correct behavior of multidirectional broadcasting for multi-input operators across various complex input shape combinations.

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[gemini-code-assist]

Code Review

This pull request adds support for multi-input ops (Min, Max, Mean, Sum) with multidirectional broadcasting in the ONNX frontend. The changes include a new helper function to compute the broadcast shape and an update to the MultiInputBase operator converter to handle broadcasting before stacking and reducing the inputs. The accompanying tests have been updated to cover various broadcasting scenarios, ensuring the correctness of the implementation. The changes look good and correctly implement the desired functionality. I have one minor suggestion to improve code conciseness.

[locnd182644]

@tvm-bot rerun

[tlopex]

LGTM! Thank you @locnd182644

[locnd182644]

@tlopex Thank you !