Summary of Changes

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This pull request addresses a critical bug in the Relax framework's HardSigmoid operator where NaN inputs were not handled correctly, leading to an erroneous output of 1.0. The changes ensure that HardSigmoid now propagates NaN values as expected, improving the numerical stability and correctness of computations involving undefined or unrepresentable floating-point numbers. The fix is accompanied by a dedicated test case to validate the new behavior.

Highlights

• HardSigmoid NaN Handling: The HardSigmoid operator in relax.frontend.onnx has been updated to correctly propagate NaN (Not a Number) values. Previously, NaN inputs would incorrectly result in an output of 1.0.
• Implementation Details: The fix involves computing the standard clipped result, then explicitly checking for NaN inputs using relax.op.isnan. If an input is NaN, relax.op.where is used to return a NaN value (generated by 0.0 / 0.0) at that position, otherwise the clipped value is returned.
• New Test Case: A new test, test_hardsigmoid_nan, has been added to tests/python/relax/test_frontend_onnx.py to verify that HardSigmoid correctly preserves NaN values in its output when given NaN inputs.

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