[WebNN EP] Support local attention feature for GQA (#26565)

### Description
<!-- Describe your changes. -->
Support the `local_window_size` attribute in **GroupQueryAttention**
Operator, which is designed for sliding window attention and may
influence the attention mask pattern.

For local window size not equal to -1, new attention mask pattern will
be created as follows for applying sliding window.
```
     condition_1 (old attn_mask) ---> CumSum (axis=3, exclusive=true, reversed=true)
          |                             |
          |                           Lesser <--- local_window_size
          |                             |
      LogicalAnd <----------------- condition_2
          |
    new attn_mask
```


### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

Author: peishenyan

onnxruntime/core/providers/webnn/builders/impl/gqa_op_builder.cc

@@ -107,6 +107,7 @@ Status GroupQueryAttentionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_b
   ORT_RETURN_IF_NOT(GetShape(*input_defs[3], input_past_k_shape, logger), "Cannot get past_key shape");
 
   NodeAttrHelper helper(node);
+  const int32_t local_window_size = helper.Get("local_window_size", -1);
   const uint32_t kv_num_heads = helper.Get("kv_num_heads", 0);
   const uint32_t num_heads = helper.Get("num_heads", 0);
 
@@ -290,18 +291,17 @@ Status GroupQueryAttentionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_b
           |                                                                 |
           +-------------------------------> Lesser <---------------------Transpose (1,0)
                                                 |
-                                      1 ---> Where <--- finfo_min (minimum value of FP32)
+                                      1 ---> Where (attn_mask) <--- finfo_min (minimum value of FP32)
                                                 |
                                           attention_bias
   */
-  const std::vector<int32_t> mask_shape_ones_shape(batch_size * num_heads * qkv_sequence_length * past_sequence_length,
-                                                   1);
-  std::string mask_shape_ones_shape_name = "webnn_GQA_left_constant_of_scatter_indices_" + std::to_string(batch_size) +
-                                           "_" + std::to_string(num_heads) + "_" + std::to_string(qkv_sequence_length) +
-                                           "_" + std::to_string(past_sequence_length);
-  emscripten::val mask_shape_ones_shape_constant = model_builder.CreateOrGetConstant<int32_t>(
-      ONNX_NAMESPACE::TensorProto_DataType_INT32, mask_shape_ones_shape_name, mask_shape_ones_shape,
-      std::vector<uint32_t>({batch_size, num_heads, qkv_sequence_length, past_sequence_length}));
+  emscripten::val value_int_one_constant =
+      model_builder.CreateOrGetConstant<int>(ONNX_NAMESPACE::TensorProto_DataType_INT32, 1, {1});
+
+  std::vector<uint32_t> mask_shape_ones_shape = {batch_size, num_heads, qkv_sequence_length, past_sequence_length};
+  common_options.set("label", node.Name() + "_/GQA/GQA_mask_shape_ones/expand");
+  emscripten::val mask_shape_ones_shape_constant = model_builder.GetBuilder().call<emscripten::val>(
+      "expand", value_int_one_constant, emscripten::val::array(mask_shape_ones_shape), common_options);
 
   emscripten::val cumsum_options = emscripten::val::object();
   cumsum_options.set("label", node.Name() + "_range_of_mask_shape");
@@ -315,7 +315,7 @@ Status GroupQueryAttentionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_b
   std::iota(pre_neq_right_data_range.begin(), pre_neq_right_data_range.end(), 1);
 
   std::string pre_neq_right_data_range_name =
-      "webnn_GQA_left_constant_of_scatter_indices_" + std::to_string(qkv_sequence_length);
+      "webnn_GQA_pre_neq_right_data_range_" + std::to_string(qkv_sequence_length);
   emscripten::val pre_neq_right_data_range_constant = model_builder.CreateOrGetConstant<int32_t>(
       ONNX_NAMESPACE::TensorProto_DataType_INT32, pre_neq_right_data_range_name, pre_neq_right_data_range,
       std::vector<uint32_t>({qkv_sequence_length}));
@@ -333,10 +333,46 @@ Status GroupQueryAttentionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_b
   emscripten::val neq_right =
       model_builder.GetBuilder().call<emscripten::val>("transpose", expanded_neq_right, transpose_options);
 
-  common_options.set("label", node.Name() + "_/GQA/attn_mask/condition");
-  emscripten::val condition =
+  common_options.set("label", node.Name() + "_/GQA/attn_mask/condition_1");
+  emscripten::val condition_1 =
       model_builder.GetBuilder().call<emscripten::val>("lesser", neq_left, neq_right, common_options);
 
+  emscripten::val condition = condition_1;
+  // For local window size not equal to -1, new attention mask pattern for applying sliding window
+  /*
+     condition_1 (old attn_mask) ---> CumSum (axis=3, exclusive=true, reversed=true)
+          |                             |
+          |                           Lesser <--- local_window_size
+          |                             |
+      LogicalAnd <----------------- condition_2
+          |
+    new attn_mask
+  */
+  if (local_window_size != -1) {
+    // Cast condition
+    common_options.set("label", node.Name() + "_/GQA/attn_mask/condition_2/cast");
+    emscripten::val casted_condition_1 =
+        model_builder.GetBuilder().call<emscripten::val>("cast", condition_1, emscripten::val("int32"), common_options);
+
+    cumsum_options = emscripten::val::object();
+    cumsum_options.set("label", node.Name() + "_/GQA/attn_mask/condition_2/cumsum");
+    cumsum_options.set("exclusive", true);
+    cumsum_options.set("reversed", true);
+    emscripten::val neq_left_2 = model_builder.GetBuilder().call<emscripten::val>(
+        "cumulativeSum", casted_condition_1, gsl::narrow<uint32_t>(3), cumsum_options);
+
+    emscripten::val local_window_size_constant =
+        model_builder.CreateOrGetConstant<int>(ONNX_NAMESPACE::TensorProto_DataType_INT32, local_window_size, {1});
+
+    common_options.set("label", node.Name() + "_/GQA/attn_mask/condition_2");
+    emscripten::val condition_2 =
+        model_builder.GetBuilder().call<emscripten::val>("lesser", neq_left_2, local_window_size_constant, common_options);
+
+    common_options.set("label", node.Name() + "_/GQA/attn_mask/condition/and");
+    condition = model_builder.GetBuilder().call<emscripten::val>(
+        "logicalAnd", condition_1, condition_2, common_options);
+  }
+
   emscripten::val value_one_constant =
       model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, 1, {1});