[mlas] Speed up tanhf activation function

[r-devulap]

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

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[yufenglee]

/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

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 7 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[r-devulap]

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? https://github.com/microsoft/onnxruntime/blob/69cfcba38a60d65498f94cde30cb9c2030f7255b/onnxruntime/test/onnx/microbenchmark/activation.cc#L342-L344

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 https://github.com/microsoft/onnxruntime/pull/20612/commits/c6c93092f333650f126d0a83bce3340a4a179eb4

[yufenglee]

/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

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 7 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[yufenglee]

/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

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 7 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 8 pipeline(s).

[yufenglee]

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

[r-devulap]

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

Yup, working on it.

[yufenglee]

/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

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 7 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[yufenglee]

/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

[azure-pipelines[bot]]

Azure Pipelines successfully started running 8 pipeline(s).

[r-devulap]

@yufenglee Couple of questions I need help with:

1. I'm unable to replicate the Windows CI pipeline run fail locally (fail log here). The test LSTMTest.BackwardCompute passes for me. Any pointer on why its behaving different?

2. The other failure in Windows GPU CI Pipeline (see log) fails a test onnxruntime_test_all -> ModelTests/ModelTest.Run/fp16_coreml_FNS_Candy_opset7_CPU. But my local build doesn't contain the ModelTests* set at all. How do I build these tests?

[yufenglee]

@yufenglee Couple of questions I need help with:

1. I'm unable to replicate the Windows CI pipeline run fail locally (fail log here). The test LSTMTest.BackwardCompute passes for me. Any pointer on why its behaving different?
2. The other failure in Windows GPU CI Pipeline (see log) fails a test onnxruntime_test_all -> ModelTests/ModelTest.Run/fp16_coreml_FNS_Candy_opset7_CPU. But my local build doesn't contain the ModelTests* set at all. How do I build these tests?

The 1st failure is with DML EP on. Did you build with dml ep enabled? For the 2nd issue, @snnn , is it possible that we can share the model?

[r-devulap]

The 1st failure is with DML EP on. Did you build with dml ep enabled?

Nope, let me try building with DirectML.

For the 2nd issue, @snnn , is it possible that we can share the model?

You probably don't need to if we can just help figure out why this commit https://github.com/microsoft/onnxruntime/pull/20612/commits/5329f70d3b71f9261add1b1307f6c99364b8b624 isn't being applied to this model ModelTests/ModelTest.Run/fp16_coreml_FNS_Candy_opset7_CPU.

[snnn]

For the 2nd issue, @snnn , is it possible that we can share the model?

Sorry, we cannot. We may consider removing these models from our build pipelines if there is no better way to handle them.