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[mlas] Speed up tanhf activation function #20612

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[mlas] Speed up tanhf activation function #20612

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Description

New faster algorithm for tanhf activation function using Intel SVML.

Motivation and Context

Improves performance of tanhf by nearly 40%. The newer algorithm also fixes a bug in the current tanhf algorithm which goes out of bounds [-1, 1]. Example: for x = +0x1.06417ep+003, tanhf= +0x1.000002p+000.

Benchmark                                                 Time             CPU      Time Old      Time New       CPU Old       CPU New
--------------------------------------------------------------------------------------------------------------------------------------
[BM_Tanh vs. BM_Tanh]/40000/real_time                  -0.3822         -0.3825         15059          9304         15035          9283
[BM_Tanh vs. BM_Tanh]/80000/real_time                  -0.3845         -0.3844         30055         18499         29998         18467
[BM_Tanh vs. BM_Tanh]/160000/real_time                 -0.3146         -0.3144         17803         12203         17762         12178
[BM_Tanh vs. BM_Tanh]/320000/real_time                 -0.3495         -0.3491         32840         21362         32724         21300
[BM_Tanh vs. BM_Tanh]/640000/real_time                 -0.3563         -0.3568         62902         40487         62754         40361
[BM_Tanh vs. BM_Tanh]/1280000/real_time                -0.3326         -0.3333        128536         85780        128102         85408
OVERALL_GEOMEAN                                        -0.3538         -0.3539             0             0             0             0

@r-devulap
[mlas]: Speed up tanhf activation function
c6c9309 
Use Intel SVML tanhf function which speeds up tanhf computation by up to ~38%.
The algorithm has a max ULP error of 1536. Benchmark numbers comparison v/s
main branch is provided below (generated on TigerLake Dell XPS laptop using:
https://github.com/google/benchmark/blob/main/tools/compare.py)

|-----------------+---------+---------+----------+----------+---------+---------|
| Benchmark       | Time    | CPU     | Time Old | Time New | CPU Old | CPU New |
|-----------------+---------+---------+----------+----------+---------+---------|
| BM_Tanh/40000   | -0.3822 | -0.3825 | 15059    | 9304     | 15035   | 9283    |
| BM_Tanh/80000   | -0.3845 | -0.3844 | 30055    | 18499    | 29998   | 18467   |
| BM_Tanh/160000  | -0.3146 | -0.3144 | 17803    | 12203    | 17762   | 12178   |
| BM_Tanh/320000  | -0.3495 | -0.3491 | 32840    | 21362    | 32724   | 21300   |
| BM_Tanh/640000  | -0.3563 | -0.3568 | 62902    | 40487    | 62754   | 40361   |
| BM_Tanh/1280000 | -0.3326 | -0.3333 | 128536   | 85780    | 128102  | 85408   |
|-----------------+---------+---------+----------+----------+---------+---------|
| OVERALL_GEOMEAN | -0.3538 | -0.3539 | 0        | 0        | 0       | 0       |
|-----------------+---------+---------+----------+----------+---------+---------|
@r-devulap r-devulap requested a review from a team as a code owner May 8, 2024 20:39
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yihonglyu
yihonglyu reviewed May 9, 2024
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Please add a benchmark for the tanh activation function in onnxruntime/test/mlas/bench/. Once you've done that, make sure to record the performance number both with and without your patch in the commit message.

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Please add a benchmark for the tanh activation function in onnxruntime/test/mlas/bench/.

There is already a benchmark for tanhf BM_Tanh. Is this not sufficient?

onnxruntime/onnxruntime/test/onnx/microbenchmark/activation.cc

Lines 342 to 344 in 69cfcba

static void BM_Tanh(benchmark::State& state) {
RunSingleNode<Tanh<float>>("Tanh", "", {}, state, -2.0f, 2.0f);
}

Once you've done that, make sure to > record the performance number both with and without your patch in the commit message.

The performance numbers of BM_Tanh before and after have already been included in the commit message: See c6c9309

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yufenglee
yufenglee reviewed May 13, 2024
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onnxruntime/core/mlas/lib/tanh.cpp

size_t count = 0;
while (count < N) {
if (N - count >= 4) {
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there needs a check for each iteration with this change. If N is large, the previous version can save a significant amount of instructions

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@r-devulap r-devulap May 13, 2024 •
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For large values of N, the CPU branch predictor should be able to predict this branch pretty easily. It will only miss at the very last iteration for the tail but when N is large, one single branch miss should hardly matter in terms of performance. It does bring the benefit of processing the entire array contained in a single loop.

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yufenglee
yufenglee previously approved these changes May 17, 2024
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  1. Removed if (provider_name == "cpu") for fp16_coreml_FNS test filter
  2. Added relative and absolute error tolerance for LSTM.BackwardCompute test.

Hoping it will fix it, I still am not able to reproduce the failure locally though :/

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snnn commented May 23, 2024

/azp run ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux GPU CI Pipeline,orttraining-amd-gpu-ci-pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

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snnn commented May 23, 2024

/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Android CI Pipeline

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You need sign the license/cla agreement to move on.

CLA shows up as signed now.

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snnn commented May 30, 2024

/azp run ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux GPU CI Pipeline,orttraining-amd-gpu-ci-pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

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snnn commented May 30, 2024

/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Android CI Pipeline

@snnn snnn requested review from yufenglee and yihonglyu May 30, 2024 05:12
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snnn commented May 31, 2024

/azp run ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux GPU CI Pipeline,orttraining-amd-gpu-ci-pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

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snnn commented May 31, 2024

/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Android CI Pipeline

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snnn commented Jun 1, 2024

@yufenglee , please help review

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snnn commented Jun 1, 2024

/azp run Linux Android Emulator QNN CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows x64 QNN CI Pipeline

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/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Android CI Pipeline

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