-
Notifications
You must be signed in to change notification settings - Fork 3.2k
[WebNN] Always execute decomposed *SimplifiedLayerNormalization in FP32 #24437
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
base: main
Are you sure you want to change the base?
Conversation
3cd14fd
to
d14f09d
Compare
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
👀
onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
Outdated
Show resolved
Hide resolved
onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
Outdated
Show resolved
Hide resolved
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.
d14f09d
to
9d390da
Compare
@fdwr, thanks for your comments, fixed in new commit, PTAL again. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
👍
/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 |
/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 |
/azp run Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Win_TRT_Minimal_CUDA_Test_CI |
/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 |
Azure Pipelines successfully started running 1 pipeline(s). |
Azure Pipelines successfully started running 2 pipeline(s). |
Azure Pipelines successfully started running 3 pipeline(s). |
1 similar comment
Azure Pipelines successfully started running 3 pipeline(s). |
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.