[WebNN] Support AveragePool with count_include_pad == 1 (#24465)

### Description
WebNN doesn't support AveragePool with count_include_pad == 1.



### Motivation and Context
Support it by adding a pad and calling averagePool2D with pads as 0's.

Author: Honry

Diff for: js/web/docs/webnn-operators.md

@@ -18,7 +18,7 @@ platforms. Check the [WebNN status](https://webmachinelearning.github.io/webnn-s
 | And | ai.onnx(7+) | logicalAnd | |
 | ArgMax | ai.onnx(7-10, 11, 12, 13+) | argMax | |
 | ArgMin | ai.onnx(7-10, 11, 12, 13+) | argMin | |
-| AveragePool | ai.onnx(7-9, 10, 11, 12-18, 19+) | averagePool2d | Only supports 4-D input, 2-D 'kernel_shape', 'count_include_pad' value is 0 |
+| AveragePool | ai.onnx(7-9, 10, 11, 12-18, 19+) | averagePool2d | Only supports 4-D input, 2-D 'kernel_shape' |
 | BatchNormalization | ai.onnx(7-8, 9-13, 14, 15+) | batchNormalization | Only supports 'training_mode' value is 0, one output |
 | Cast | ai.onnx(7-8, 9-12, 13-18, 19-20, 21+) | cast | |
 | Ceil | ai.onnx(7-12, 13+) | ceil | |

Diff for: onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc

@@ -60,16 +60,17 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   emscripten::val options = emscripten::val::object();
   options.set("label", node.Name());
   NodeAttrHelper helper(node);
+  const bool is_nhwc = model_builder.GetPreferredLayout() == DataLayout::NHWC;
 
-  const auto kernel_shape = helper.Get("kernel_shape", std::vector<int32_t>{0, 0});
+  const auto onnx_kernel_shape = helper.Get("kernel_shape", std::vector<int32_t>{0, 0});
   if (!is_global_pooling) {
-    options.set("windowDimensions", emscripten::val::array(kernel_shape));
+    options.set("windowDimensions", emscripten::val::array(onnx_kernel_shape));
   }
   const auto strides = helper.Get("strides", std::vector<int32_t>{1, 1});
   options.set("strides", emscripten::val::array(strides));
   const auto dilations = helper.Get("dilations", std::vector<int32_t>{1, 1});
   options.set("dilations", emscripten::val::array(dilations));
-  if (model_builder.GetPreferredLayout() == DataLayout::NHWC) {
+  if (is_nhwc) {
     options.set("layout", emscripten::val("nhwc"));
   } else {
     options.set("layout", emscripten::val("nchw"));
@@ -78,7 +79,6 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   // Add Padding.
   // Usually using autopadding is more efficient than using explicit padding.
   // Try to see if we can map explicit padding to auto padding.
-  const auto onnx_kernel_shape = helper.Get("kernel_shape", std::vector<int64_t>{0, 0});
   const auto onnx_strides = helper.Get("strides", std::vector<int64_t>{1, 1});
   const auto onnx_pads = helper.Get("pads", std::vector<int64_t>{0, 0, 0, 0});
   auto pads = helper.Get("pads", std::vector<uint32_t>{0, 0, 0, 0});
@@ -93,7 +93,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
                                       helper.Get("dilations", std::vector<int64_t>{1, 1}),
                                       auto_pad_type,
                                       pads_out,
-                                      model_builder.GetPreferredLayout() == DataLayout::NCHW));
+                                      !is_nhwc));
     pads = GetNarrowedIntfromInt64<uint32_t>(pads_out);
   }
   // Permute the ONNX's pads, which is [beginning_height, beginning_width, ending_height, ending_width],
@@ -105,6 +105,23 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   options.set("roundingType", ceil_mode == 0 ? emscripten::val("floor")
                                              : emscripten::val("ceil"));
 
+  // WebNN doesn't support AveragePool with count_include_pad == 1, emulate it by pad + averagePool2d.
+  if (op_type == "AveragePool" && helper.Get("count_include_pad", 0) == 1) {
+    std::vector<uint32_t> beginning_padding{0, 0, pads[0], pads[1]};
+    std::vector<uint32_t> ending_padding{0, 0, pads[2], pads[3]};
+    // Unset padding option, because we will use pad op instead.
+    options.set("padding", emscripten::val::array(std::vector<uint32_t>{0, 0, 0, 0}));
+    if (is_nhwc) {
+      beginning_padding = {0, pads[0], pads[1], 0};
+      ending_padding = {0, pads[2], pads[3], 0};
+    }
+
+    emscripten::val pad_options = emscripten::val::object();
+    pad_options.set("label", node.Name() + "_pad");
+    input = model_builder.GetBuilder().call<emscripten::val>("pad", input, emscripten::val::array(beginning_padding),
+                                                             emscripten::val::array(ending_padding), pad_options);
+  }
+
   emscripten::val output = model_builder.GetBuilder().call<emscripten::val>(webnn_op_name.c_str(), input, options);
   model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
   return Status::OK();
@@ -138,13 +155,6 @@ bool PoolOpBuilder::IsOpSupportedImpl(const GraphViewer&,
     }
   }
 
-  if (op_type == "AveragePool") {
-    if (helper.Get("count_include_pad", 0) != 0) {
-      LOGS(logger, VERBOSE) << "AveragePool only supports count_include_pad == 0";
-      return false;
-    }
-  }
-
   if (op_type == "MaxPool") {
     if (helper.Get("storage_order", 0) == 1) {
       LOGS(logger, VERBOSE) << "MaxPool storage_order == 1 is not supported";