[WebNN] QDQ's axis should be used for broadcasting #22721
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For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
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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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/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 successfully started running 4 pipeline(s). |
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Azure Pipelines successfully started running 3 pipeline(s). |
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Azure Pipelines successfully started running 9 pipeline(s). |
onnxruntime/core/providers/webnn/builders/impl/qdq_op_builder.cc
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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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/azp run Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline |
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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 successfully started running 2 pipeline(s). |
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Azure Pipelines successfully started running 3 pipeline(s). |
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Azure Pipelines successfully started running 4 pipeline(s). |
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Azure Pipelines successfully started running 9 pipeline(s). |
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.
For per-axis quantization/dequantization, WebNN requires the scale and zero_point inputs to be broadcastable. Axis should be used for reshape these two inputs.