[js/webgpu] Optimize ConvTranspose (Continue) #23429
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This needs to add padding or recalculate the correct offset to get the right result. fix errors
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/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline |
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/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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Azure Pipelines successfully started running 1 pipeline(s). |
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/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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Azure Pipelines could not run because the pipeline triggers exclude this branch/path. |
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/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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Azure Pipelines successfully started running 1 pipeline(s). |
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Azure Pipelines could not run because the pipeline triggers exclude this branch/path. |
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BUG #23273
This PR does below optimizations:
inputChannelsPerGroupintoinputChannelsPerGroupIntandinputChannelsRemainderparts so that we can always access 4 data forinputChannelsPerGroupInt.With this PR, ConvTranspose becomes 3.7s from 8.4s on Intel Meteor Lake.
On NV RTX 2000 Ada, it becomes 1.6s from 2.7s.