[AdvancedCompiler]Optimize argmax

[AdvancedCompiler]

PR Category

Operator

Type of Change

Performance Optimization

Description

Issue

Progress

• Change is properly reviewed (1 reviewer required, 2 recommended).
• Change is responded to an issue.
• Change is fully covered by a UT.

Performance

[gemini-code-assist]

Summary of Changes

Hello @AdvancedCompiler, 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 refactors the argmax operator implementation to improve its performance, particularly by introducing specialized kernels and dynamic heuristic configurations based on input tensor dimensions.

Highlights

• Specialized Argmax Kernels: The existing argmax_kernel has been refactored into two distinct kernels: argmax_kernel_non_inner for general cases (K > 1) and argmax_kernel_inner for specific cases where K = 1.
• Dynamic Kernel Dispatch: The main argmax function now intelligently dispatches to the appropriate kernel (argmax_kernel_non_inner or argmax_kernel_inner) based on the value of the K dimension, allowing for more optimized execution paths.
• Enhanced Heuristic Configurations: New and more granular heuristic functions have been introduced for TILE_K, TILE_N, ONE_TILE_PER_CTA, and num_warps for both argmax_non_inner and argmax_inner kernels, enabling better tuning for performance.
• Improved Kernel Logic: The kernel implementations have been updated to handle data types more robustly (e.g., float16/bfloat16 to float32 for intermediate calculations) and to manage loop iterations and remainders more efficiently for better performance.

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[gemini-code-assist]

Code Review

This pull request introduces significant performance optimizations for the argmax operator by implementing two specialized Triton kernels: one for reductions along the inner dimension (argmax_kernel_inner) and another for non-inner dimensions (argmax_kernel_non_inner). This is a solid approach that yields impressive performance gains as shown in the benchmarks. My review includes a critical fix for accessing tensor types within a Triton kernel, a suggestion to improve numerical stability, and recommendations for enhancing code maintainability by reducing code duplication and replacing magic numbers with named constants.