support integer types in fast_tanh and fast_exp

[jikechao]

Fix #18767.

This PR fixes the issue by adding explicit type casting in fast_tanh and fast_exp to convert integer inputs to float32.

[gemini-code-assist]

Summary of Changes

Hello @jikechao, 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 enhances the fast_tanh function by introducing a mechanism to automatically convert integer input tensors to float32. This change addresses a reported bug where the function failed to process integer types correctly, thereby broadening its compatibility and ensuring reliable operation across various numerical inputs without requiring manual type conversion from the user.

Highlights

• Integer Type Support: Added explicit type casting within the fast_tanh function to convert integer (int and uint) input tensors to float32.
• Bug Fix: Resolved an issue where fast_tanh did not correctly handle integer input types, preventing potential errors and improving function robustness.

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Changelog

• python/tvm/topi/math.py
  • Added explicit type casting for integer and unsigned integer inputs to float32 in the fast_tanh function.

Activity

• No specific activity (comments, reviews, progress updates) has been recorded for this pull request yet.

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[gemini-code-assist]

Code Review

The pull request correctly adds support for integer types in fast_tanh by casting them to float32, which aligns with the behavior of other mathematical functions in the library. The implementation is sound. I have a minor suggestion to improve the conciseness of the code.

[gemini-code-assist]

This condition can be made more concise. The startswith method can accept a tuple of strings to check for multiple prefixes.

Suggested change

	if x.dtype.startswith('int') or x.dtype.startswith('uint'):
	if x.dtype.startswith(('int', 'uint')):