[MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel by hariharans29 · Pull Request #26753 · microsoft/onnxruntime

hariharans29 · 2025-12-08T15:47:52Z

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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onnxruntime/contrib_ops/cpu/activations.h

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Comment

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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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onnxruntime/core/mlas/lib/eltwise.cpp

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

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Pull request overview

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onnxruntime/contrib_ops/cpu/activations.h

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

tianleiwu · 2026-01-14T23:42:23Z

The PR optimizes the QuickGelu activation in the CPU Execution Provider, specifically improving the performance for the SiLU (Sigmoid Linear Unit) case where alpha_ is 1.0. It also introduces a new MLAS function MlasEltwiseMul for vectorized element-wise multiplication.

Key Changes

1. `QuickGelu` Optimization (`activations.h`)

Condition for alpha_: Added a check for alpha_ == 1.0f.
- Previous behavior: Always multiplied input by alpha_ in a loop, then computed Logistic, then element-wise multiplied input and output.
- New behavior:
  - If alpha_ != 1.0f: Maintains the original logic (scaling input before Logistic).
  - If alpha_ == 1.0f (SiLU): Skips the scaling loop and directly computes Logistic on the input.
Vectorization: Replaced the final scalar loop p_output[i] = p_input[i] * p_output[i] with a call to the new MlasEltwiseMul, enabling SIMD acceleration.

2. New MLAS Function: `MlasEltwiseMul` (`mlas.h`, `eltwise.cpp`)

Interface: void MlasEltwiseMul(const T* left, const T* right, T* output, size_t N)
Implementation (Float):
- Uses MLAS_FLOAT32X4 intrinsics to process 4 elements at a time.
- Includes a scalar fallback for remaining elements.
- This allows for efficient vectorization (SSE/AVX depending on the platform) of the final multiplication step in QuickGelu.

3. Testing (`test_eltwise.cpp`)

Added MlasEltwiseMulTest to verify the correctness of the new MlasEltwiseMul function.
Tests with random inputs and compares against a reference scalar implementation.
Covers both QuickGelu scaling use case (implied) and general element-wise multiplication.

Code Review Comments

Performance: The changes correctly identify and optimize the SiLU case by removing one pass of memory access (reading/writing for alpha_ scaling). The introduction of MlasEltwiseMul further improves performance by vectorizing the final multiplication.
Correctness: The logic for splitting the alpha_ cases preserves the mathematical equivalence. The vectorization logic in eltwise.cpp correctly handles constraints and tail elements.
Maintainability: The changes are localized and clean. The new MLAS function is reusable for other operators that might need element-wise multiplication.

Conclusion

The PR looks solid. It effectively optimizes a common activation function (SiLU) and adds a useful primitive to MLAS. The added tests ensure correctness.

Status: Approved (Pending CI results)

…QuickGelu CPU kernel (microsoft#26753) ### 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 (https://github.com/microsoft/onnxruntime/blob/f98c756b45b81520c6e2a09c370575a013f02cce/onnxruntime/test/contrib_ops/activation_op_test.cc#L126) **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 --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

…QuickGelu CPU kernel (#26753) ### 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 (https://github.com/microsoft/onnxruntime/blob/f98c756b45b81520c6e2a09c370575a013f02cce/onnxruntime/test/contrib_ops/activation_op_test.cc#L126) **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 --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> (cherry picked from commit 2d2ba6b)

### Description This PR cherry-picks the following changes for the 1.24.0 release. ### Cherry-picked Commits | Commit | Commit Title | Author | |---|---|---| | 744e7fe | Add type definitions, registration, utilities for INT2/UINT2 support (#26824) | vraspar | | 530a1fb | [QNN EP] Add BFloat16 dtype support in QNN EP (#26987) | tirupath-qti | | 8e050d1 | Implement new experimental lookup-based matrix multiplication method(TMAC) (#26695) | vraspar | | 2d2ba6b | [MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel (#26753) | Hariharan Seshadri | | 1c02b79 | [QNN EP] Add support for handling 0-dimension for Concat Op (#27000) | Ashwath Shankarnarayan | | cc2b01b | Fix ClipQuantFusion crash when Clip has multiple input edges (#27016) | Edward Chen | | bbd3850 | [QNN EP] Support quantized BatchNorm with per-channel DQ params on QNN HTP (#26959) | qti-yuduo | | d8f0318 | Add API to get ep graph partitioning info (#26781) | Adrian Lizarraga | | b912b18 | [OVEP] OpenVINO EP Features and bug-fixes for ORT-1.24 - Follow up (#27007) | Preetha Veeramalai | | ba11af4 | [QNN-EP] Add MatMulNBits translation for GPU (#26340) | quic-tirupath | | c03c419 | [MLAS/NEON] Add dedicated kernel for depthwise convolution for ARM64 using NEON intrinsics (#26688) | Hariharan Seshadri | | e7dfd69 | [QNN-EP] Support alternate Layernorm fusion pattern in QNN preprocess (#26060) | qti-mattsinc | | 4013dc1 | Implement multithreading in qgemm_kleidi (#26301) | Melike Kaptan | | 9f06181 | [CXX] Enable users to specify custom OrtSyncStream via RunOptions (#26988) | Dmitri Smirnov | | cfccd64 | Added support for QMX kernels in MLAS (#26849) | qti-vaiskv | | 29d9b2f | Tweak external resource importer handle structs (#27040) | Scott McKay | | 9d108d0 | [QNN EP] Add QuickGELU operator support for QNN provider (#27034) | tirupath-qti | | b35688f | Add INT2 and UINT2 support for QDQ, transpose and cast ops (#27022) | vraspar | | 6d34aba | Introducing BF16 Pointwise NCHWc Convolution for Arm64 (#26838) | Rohanjames1997 | | 36017ad | [EP ABI] Add CreateCustomOpDomains() API for plugin EP to register custom ops (#27050) | Chi Lo | | 50a03e4 | Add a new pipeline for CUDA 13 nuget builds (#27023) | eserscor | | a0d4439 | [EP ABI] Update Graph_GetGraphView() implementation (#26711) | Chi Lo | | 34bb209 | [webgpu] Fix a bug for im2col (#27069) | Wenqin Yang | | 46e8d45 | [QNN EP] Add FusedMatMul operator support (#27044) | tirupath-qti | | 5e7e7a3 | Disable Float32_2Bits_Asymmetric_256x256 test (#27046) | vraspar | | 39f966e | Fix Doxygen documentation build error in onnxruntime_c_api.h (#27083) | Nick Eubanks | | 8a7a797 | Print tensor for new packed type of 2 bits (#27064) | Tianlei Wu | | 01f40e6 | Fix GPU JAR testing on Linux (#27011) | eserscor | | b6ed7f3 | Fix warning around ununsed code in QNN Android Emulator builds by clang (#27026) | Hariharan Seshadri | | d7daa45 | Raise the timeout for the ios simulator job (#27045) | Hariharan Seshadri | | 7e1d818 | upgrade emsdk to 4.0.23 (#27029) | Yulong Wang | | 347b990 | Fix failing mainline build on Arm64 linux (#27101) | Rohanjames1997 | | f481b17 | Add dedicated API to support extracting compatibility string from model metadata (#27015) | adrastogi | --------- Signed-off-by: Liqun Fu <liqun.fu@microsoft.com> Signed-off-by: bfilipek <bartlomiej.filipek@intel.com> Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: Jonathan Clohessy <jonathan.clohessy@arm.com> Signed-off-by: Christian Bourjau <christian.bourjau@quantco.com> Signed-off-by: melkap01 <melike.kaptan@arm.com> Co-authored-by: vraspar <vrajang@outlook.com> Co-authored-by: tirupath-qti <tirupath@qti.qualcomm.com> Co-authored-by: Ashwath Shankarnarayan <ashwshan@qti.qualcomm.com> Co-authored-by: Liqun Fu <liqun.fu@microsoft.com> Co-authored-by: carzh <wolfivyaura@gmail.com> Co-authored-by: Hector Li <hecli@microsoft.com> Co-authored-by: carzh <carolinezhu@microsoft.com> Co-authored-by: Vrajang Parikh <vrparikh@microsoft.com> Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Yuduo Wu <yuduow@qti.qualcomm.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com> Co-authored-by: jatinwadhwa921 <110383850+jatinwadhwa921@users.noreply.github.com> Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com> Co-authored-by: saurabh <saurabh1.kale@intel.com> Co-authored-by: Ankit Maheshkar <ankit.maheshkar@intel.com> Co-authored-by: sfatimar <sahar.fatima@intel.com> Co-authored-by: Javier Martinez <javier.e.martinez@intel.com> Co-authored-by: Bartlomiej Filipek <bartlomiej.filipek@intel.com> Co-authored-by: bopeng1234 <bo.peng@intel.com> Co-authored-by: Eric Crawford 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hariharans29 added 4 commits December 8, 2025 02:44

Add Silu implementation in MLAS

7cb7475

Fix

db4fd88

Hook-up CPU EP op with MLAS code

abfd829

Fix

17144ec

hariharans29 changed the title ~~WIP: [MLAS] [DO NOT REVIEW] Implement vectorized Silu operation~~ WIP: [MLAS] [DO NOT REVIEW] Implement vectorized fused Silu operation Dec 9, 2025

hariharans29 added 2 commits December 8, 2025 23:25

Expt

6c5ebd1

Revert separate Silu opration

0836eac

hariharans29 changed the title ~~WIP: [MLAS] [DO NOT REVIEW] Implement vectorized fused Silu operation~~ WIP: [MLAS] Improve performance of Silu activation path within the QuickGelu CPU kernel Dec 10, 2025

hariharans29 added 2 commits December 9, 2025 22:02

Add vectorized elementwise operation

4736b8f

hook up the kernel

5f3478e

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Remove unnecessary lines of code

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hariharans29 changed the title ~~WIP: [MLAS] Improve performance of Silu activation path within the QuickGelu CPU kernel~~ WIP: [MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel Dec 10, 2025

hariharans29 added 2 commits December 9, 2025 23:33

Bug fix

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Merge remote-tracking branch 'origin/main' into hari/mlas_silu

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hariharans29 added 2 commits December 10, 2025 18:22

Update activations.h

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onnxruntime/core/mlas/lib/eltwise.cpp Outdated Show resolved Hide resolved

hariharans29 changed the title ~~WIP: [MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel~~ [MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel Dec 11, 2025

hariharans29 and others added 3 commits December 10, 2025 18:26

Update onnxruntime/core/mlas/lib/eltwise.cpp

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hariharans29 deleted the hari/mlas_silu branch January 15, 2026 20:16

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1.24.0 release cherry-pick round 1 #27103

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[MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel#26753

[MLAS/CPU EP] Improve performance of Silu activation path within the QuickGelu CPU kernel#26753
hariharans29 merged 17 commits intomainfrom
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hariharans29 commented Dec 8, 2025 •

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tianleiwu commented Jan 14, 2026

Key Changes

1. QuickGelu Optimization (activations.h)

2. New MLAS Function: MlasEltwiseMul (mlas.h, eltwise.cpp)

3. Testing (test_eltwise.cpp)

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hariharans29 commented Dec 8, 2025 •

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1. `QuickGelu` Optimization (`activations.h`)

2. New MLAS Function: `MlasEltwiseMul` (`mlas.h`, `eltwise.cpp`)

3. Testing (`test_eltwise.cpp`)