[js/webgpu] Support capture and replay for jsep

[qjia7]

Description

This PR expands the graph capture capability to JS EP, which is similar to #16081. But for JS EP, we don't use the CUDA Graph, instead, we records all gpu commands and replay them, which removes most of the cpu overhead to avoid the the situation that gpu waiting for cpu.

mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from 4.58ms on Intel A770.

All limitations are similar with CUDA EP:

1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not supported.
2. Usage of graph capture is limited to models where-in all ops in the model can be partitioned to the JS EP or CPU EP and no memory copy between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.

The usage is like below:
Method 1: specify outputs buffers explicitly.

    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);
   
    // prepare the inputBuffer/outputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   const fetches = {
       'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
   };

   let results = await session.run(feeds, fetches);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds, fetches);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();

Method 2: Don't specify outputs buffers explicitly. Internally, when graph capture is enabled, it will set all outputs location to 'gpu-buffer'.

    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   let results = await session.run(feeds);  // The first run will begin to capture the graph.
   
   // update inputBuffer content
  ... ...
   results = = await session.run(feeds);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();

[qjia7]

@skottmckay @fs-eire @guschmue Please take a look, thanks!

[qjia7]

@fs-eire Please take a another look, thanks.

[fs-eire]

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

[fs-eire]

/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-python-checks-ci-pipeline,onnxruntime-binary-size-checks-ci-pipeline,Android CI Pipeline

[fs-eire]

/azp run iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

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