[MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel

[hariharans29]

Description

The Silu activation is basically the same as QuickGelu but with the scaling factor (alpha) as 1. In cusomer models containing Silu, the graph optimizer suite correctly fuses the nodes into a QuickGelu with alpha = 1. This optimizes the implementation of QuickGelu when alpha = 1 by avoiding the scaling and vectorizes the subsequent elementwise multiplication.

Tests:
There are already tests for QuickGelu with alpha = 1 and there are no new tests necessary (

onnxruntime/onnxruntime/test/contrib_ops/activation_op_test.cc

Line 126 in f98c756

// Silu = x*sigmoid(x), i.e., alpha = 1.0f.

)

Performance improvements measured:
Gives about 2.5% throughput boost for a customer model that has a lot of Silu activations.

Motivation and Context

Some low hanging fruit perf improvements that give instant easy perf wins

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[Copilot]

Pull request overview

This PR optimizes the CPU implementation of the QuickGelu activation function for the special case where alpha=1.0 (equivalent to the Silu activation). The optimization avoids unnecessary scaling operations and adds a vectorized element-wise multiplication function to improve performance.

• Adds vectorized MlasEltwiseMul function for efficient element-wise multiplication
• Optimizes QuickGelu computation by skipping scaling when alpha=1.0
• Replaces scalar multiplication loop with vectorized MlasEltwiseMul call

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
onnxruntime/core/mlas/lib/eltwise.cpp	Implements vectorized element-wise multiplication function MlasEltwiseMul<float> following the pattern of existing MlasEltwiseAdd
onnxruntime/core/mlas/inc/mlas.h	Adds template declaration for MlasEltwiseMul function
onnxruntime/contrib_ops/cpu/activations.h	Modifies QuickGelu kernel to branch on alpha value, avoiding scaling for alpha=1.0 and using vectorized multiplication for final step

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