RMS Norm Optimization

[aris134]

Description

Fixes # (16527)

RMSNorm falls back to general kernel implementation on several DeepSeek and Qwen shapes, causing poor performance. These shapes have been registered with the tuned kernel cache, and a performance benchmark for RMSNorm has been added.

Additionally, a fallback warning is printed the first time at which a tuned config is not found for a requested kernel. For example:

in function getKernel: Falling back to general normalization kernel because no tuned kernel is available for this config. hidden_size=128, wtype=bf16, itype=bf16, otype=bf16, ctype=fp32

E2E TFLOPS/s/GPU for proxy models (Previous -> Current with RMSNorm tuning) :

Qwen:
bf16: 369.4 -> 374.7
fp8: 352.1 ->358.2

Deepseek:
bf16: 501.4 -> 529.4
fp8: 463.9 -> 511.4

Also added matching tuned configs for LayerNorm.

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

Please list the changes introduced in this PR:

• Change A
• Change B

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes