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[WebNN] Always execute decomposed *SimplifiedLayerNormalization in FP32 #24437

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@Honry Honry commented Apr 16, 2025

Decomposed [Skip]SimplifiedLayerNormalization will lose precision in FP16, we'd like to add cast (to: fp32) ops around it in WebNN EP to ensure its precision rather than manually add cast nodes in each model file.

@Honry Honry force-pushed the cast-to-fp32-sim-layernorm branch from 3cd14fd to d14f09d Compare April 16, 2025 00:55
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Honry commented Apr 16, 2025

@fdwr, @guschmue, PTAL, thanks!

@guschmue guschmue added the ep:WebNN WebNN execution provider label Apr 16, 2025
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guschmue previously approved these changes Apr 16, 2025
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👀

Honry added 2 commits April 21, 2025 10:38
Decomposed [Skip]SimplifiedLayerNormalization will lose precision in FP16,
we'd like to add cast (to: fp32) ops around it in WebNN EP to ensure its
precision rather than manually add cast nodes in each model file.
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Honry commented Apr 21, 2025

@fdwr, thanks for your comments, fixed in new commit, PTAL again.

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👍

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fdwr commented Apr 21, 2025

/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline,Windows GPU WebGPU CI Pipeline,Windows OpenVINO CI Pipeline

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fdwr commented Apr 21, 2025

/azp run Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline

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fdwr commented Apr 21, 2025

/azp run Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Win_TRT_Minimal_CUDA_Test_CI

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fdwr commented Apr 21, 2025

/azp run Windows GPU TensorRT CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,Windows x64 QNN CI Pipeline,Big Models

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