[js/webgpu] Optimize InstanceNorm in some shapes (microsoft#22637)

BUG microsoft#22031

Optimize below two situations:
1. Increase workgroupSize if only one workgroup is dispatched.
2. Avoid transpose if not necessary.

The overall time of demucs model becomes 106.36 ms from 154.60 ms on my
dGPUs with this PR and PR microsoft#22577

js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts

@@ -36,7 +36,10 @@ const computeChannelScaleShift = (
   const f32Type = components === 1 ? 'f32' : `vec${components}f`;
   const wgType = components === 1 ? 'vec2f' : `mat2x${components}f`;
   const unitsOfWork = n * c;
-
+  let workgroupSize = 64;
+  if (unitsOfWork === 1) {
+    workgroupSize = 256;
+  }
   const inputShape = [n, c, h / components];
   const outputShape = [n, c, 2];
   const inputDependencies: ProgramInputTensorInfoDependency[] = ['rank', 'type', 'type'];
@@ -49,7 +52,6 @@ const computeChannelScaleShift = (
     const b = inputVariable('bias', bias.dataType, bias.dims);
     const output = outputVariable('output', DataType.float, 3, 2);
     const variables = [x, s, b, output];
-    const workgroupSize = 64;
     return `
   var<workgroup> workgroup_shared : array<${wgType}, ${workgroupSize}>;
   const workgroup_size = ${workgroupSize}u;
@@ -91,7 +93,7 @@ const computeChannelScaleShift = (
     {
       name: 'InstanceNormComputeChannelScaleShift',
       // TODO: use epsilon as uniform. Currently epsilon as uniform fails test_instancenorm_epsilon.
-      shaderCache: { hint: `${components};${epsilon}`, inputDependencies },
+      shaderCache: { hint: `${components};${epsilon};${workgroupSize}`, inputDependencies },
       getRunData: () => ({
         outputs: [{ dims: outputShape, dataType: DataType.float }],
         dispatchGroup: { x: unitsOfWork },
@@ -187,14 +189,21 @@ const createInstanceNormNHWCProgramInfo = (
   const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type'];
 
   // 1. transpose x from NHWC to NCHW
+  let needTranspose = false;
   const transposedXPerm = [0, xShape.length - 1];
   for (let i = 0; i < xShape.length - 2; i++) {
+    needTranspose = needTranspose || xShape[i + 1] !== 1;
     transposedXPerm.push(i + 1);
   }
-  const transposedX = context.compute(createTransposeProgramInfo(context.inputs[0], transposedXPerm), {
-    inputs: [context.inputs[0]],
-    outputs: [-1],
-  })[0];
+
+  needTranspose = needTranspose && xShape[xShape.length - 1] !== 1;
+
+  const transposedX = needTranspose
+    ? context.compute(createTransposeProgramInfo(context.inputs[0], transposedXPerm), {
+        inputs: [context.inputs[0]],
+        outputs: [-1],
+      })[0]
+    : context.inputs[0].reshape(Array.from({ length: xShape.length }, (_, i) => xShape[transposedXPerm[i]]));
   // 2. compute channel scale and channel shift.
   const channelScaleShift = computeChannelScaleShift(
     context,