[webgpu] Fix batch-norm for ort-web-tests

[jchen10]

'global_idx' should be used to calculate the output indices.

[jchen10]

@xhcao @fs-eire PTAL
The fix should be right per my understanding and local verification. However, the JS operator has the same problem. I am not clear yet why it can pass the same test. @qjia7 It seems these code lines were originally from you. Please help double check this, thanks!

[fs-eire]

I think this makes sense to me. The definition of "offset" is a little bit different between JSEP and WebGPUEP. In JSEP, "offset" is defined on the "tensor data". In WebGPU EP, "offset" is defined on the "WGSL input/output data". take the following example:

we have a tensor x of shape [2, 3, 4], components = 4.

tensor's element is float32.
the corresponding input/output element in WGSL is vec4.

The JSEP's offset range is [0, 24), and WebGPUEP's offset range is [0, 6).

[fs-eire]

/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows GPU Doc Gen CI Pipeline,ONNX Runtime Web CI Pipeline,Win_TRT_Minimal_CUDA_Test_CI,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline

[fs-eire]

/azp run Linux QNN CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline,Linux DNNL CI Pipeline,Linux MIGraphX CI Pipeline,Linux ROCm CI Pipeline

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[jchen10]

I think this makes sense to me. The definition of "offset" is a little bit different between JSEP and WebGPUEP. In JSEP, "offset" is defined on the "tensor data". In WebGPU EP, "offset" is defined on the "WGSL input/output data". take the following example:

we have a tensor x of shape [2, 3, 4], components = 4.

tensor's element is float32. the corresponding input/output element in WGSL is vec4.

The JSEP's offset range is [0, 24), and WebGPUEP's offset range is [0, 6).

Got it, thanks for the clarification.

[fs-eire]

/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows GPU Doc Gen CI Pipeline,ONNX Runtime Web CI Pipeline,Win_TRT_Minimal_CUDA_Test_CI,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline

[fs-eire]

/azp run Linux QNN CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline,Linux DNNL CI Pipeline,Linux MIGraphX CI Pipeline,Linux ROCm CI Pipeline

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Azure Pipelines successfully started running 5 pipeline(s).

[azure-pipelines]

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