[BugFix] Fix topk_softmax assert

[ProExpertProg]

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Fix the assert (only compiled in debug mode) and change it to a TORCH_CHECK. Introduced in #18434.

Test Plan

CI

Test Result

(Optional) Documentation Update

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[gemini-code-assist]

Summary of Changes

Hello @ProExpertProg, 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 addresses a bug by enhancing the robustness of the topk_softmax kernel. My change ensures that a critical input tensor's data type is validated at runtime across all build configurations, thereby improving the stability and reliability of the MoE (Mixture of Experts) component.

Highlights

• Bug Fix & Runtime Safety: I've replaced a debug-only assert with a TORCH_CHECK in csrc/moe/topk_softmax_kernels.cu. This ensures that the type validation for topk_indices is performed at runtime in all builds (debug and release), preventing potential issues that might arise from incorrect tensor types in production.
• Type Consistency: The TORCH_CHECK now explicitly validates that topk_indices is of at::ScalarType::Long. This change from at::ScalarType::Int64 aligns with common PyTorch type naming conventions for 64-bit integers, improving clarity and consistency.

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[gemini-code-assist]

Code Review

This pull request fixes an assertion in topk_softmax by replacing it with TORCH_CHECK and using at::ScalarType::Long, ensuring type validation in all builds.

[varun-sundar-rabindranath]

Thanks for the fix @ProExpertProg 🙌

[DarkLight1337]

Can you merge from main to see if the CI failures can be resolved?