[WebNN EP] Support Dropout op (#21586)

### Description
WebNN only supports test mode, so we don't care about other inputs or
attributes about training mode, use WebNN's identity op to implement the
Dropout op directly.

js/web/docs/webnn-operators.md

@@ -25,6 +25,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | ConvTranspose | ai.onnx(7-10, 11+) | convTranspose2d | ✓ | ✓ | Only supports 3-D or 4-D input and 'W' (weight). WebNN CPU backend only supports default dilations and group |
 | Cos | ai.onnx(7+) | cos | ✓ | ✓ | |
 | Div | ai.onnx(7-12, 13, 14+) | div | ✓ | ✓ | |
+| Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | ✓ | ✓ | Only supports test mode |
 | Elu | ai.onnx(7+) | elu | ✓ | ✓ | WebNN CPU backend only supports 'alpha' value is 1.0 |
 | Equal | ai.onnx(7-10, 11-12, 13-18, 19+) | equal | ✓ | ✓ | |
 | Erf | ai.onnx(7-9, 10-12, 13+) | erf | ✗ | ✓ | |

onnxruntime/core/providers/webnn/builders/helper.h

@@ -171,6 +171,7 @@ static const InlinedHashMap<std::string, WebnnOpInfo> op_map = {
     {"Cos", {"cos", true}},
     {"Div", {"div", true}},
     {"DequantizeLinear", {"dequantizeLinear", false}},
+    {"Dropout", {"identity", true}},
     {"DynamicQuantizeLinear", {"dynamicQuantizeLinear", false}},
     {"Elu", {"elu", true}},
     {"Equal", {"equal", true}},

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

@@ -0,0 +1,101 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Copyright (c) Intel Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/providers/common.h"
+#include "core/providers/shared/utils/utils.h"
+#include "core/providers/webnn/builders/helper.h"
+#include "core/providers/webnn/builders/model_builder.h"
+#include "core/providers/webnn/builders/op_builder_factory.h"
+
+#include "base_op_builder.h"
+
+namespace onnxruntime {
+namespace webnn {
+
+class DropoutOpBuilder : public BaseOpBuilder {
+  // Add operator related.
+ public:
+  void AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const override;
+
+ private:
+  Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
+                               const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
+
+  // Operator support related.
+ private:
+  bool IsOpSupportedImpl(const InitializedTensorSet& initializers, const Node& node,
+                         const WebnnDeviceType /* device_type */, const logging::Logger& logger) const override;
+};
+
+// Add operator related.
+
+void DropoutOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
+  // Skip ratio and training_mode if present.
+  for (size_t i = 1; i < node.InputDefs().size(); i++) {
+    const auto input_name = node.InputDefs()[i]->Name();
+    model_builder.AddInitializerToSkip(input_name);
+    model_builder.AddInputToSkip(input_name);
+  }
+}
+
+Status DropoutOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
+                                               const Node& node,
+                                               const logging::Logger& logger) const {
+  const auto& input_defs = node.InputDefs();
+  const auto& output_defs = node.OutputDefs();
+  emscripten::val input = model_builder.GetOperand(input_defs[0]->Name());
+  emscripten::val options = emscripten::val::object();
+  options.set("label", node.Name());
+
+  // WebNN EP only supports test mode. So we don't need to care about other inputs or
+  // attributes about training mode. Simply use WebNN's identity op to copy the input.
+  emscripten::val output = model_builder.GetBuilder().call<emscripten::val>("identity", input, options);
+
+  model_builder.AddOperand(output_defs[0]->Name(), std::move(output));
+
+  // If mask output is requested as output it will contain all ones (bool tensor).
+  if (output_defs.size() > 1) {
+    std::vector<int64_t> mask_shape;
+    ORT_RETURN_IF_NOT(GetShape(*output_defs[1], mask_shape, logger), "Cannot get mask output's shape");
+    std::vector<uint32_t> dims = GetVecUint32FromVecInt64(mask_shape);
+
+    emscripten::val desc = emscripten::val::object();
+    desc.set("dataType", "uint8");
+    desc.set("dimensions", emscripten::val::array(dims));
+    const auto num_elements = narrow<uint32_t>(Product(mask_shape));
+    emscripten::val ones_buffer = emscripten::val::global("Uint8Array").new_(num_elements);
+    ones_buffer.call<void>("fill", 1);
+
+    emscripten::val mask_output = model_builder.GetBuilder().call<emscripten::val>("constant", desc, ones_buffer);
+
+    emscripten::val options = emscripten::val::object();
+    options.set("label", output_defs[1]->Name() + "_identity");
+    // Add additional identity op in case the mask is the output of a WebNN graph,
+    // beacuse WebNN does not support a constant operand as output.
+    mask_output = model_builder.GetBuilder().call<emscripten::val>("identity", mask_output, options);
+    model_builder.AddOperand(output_defs[1]->Name(), std::move(mask_output));
+  }
+  return Status::OK();
+}
+
+// Operator support related.
+bool DropoutOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers,
+                                         const Node& node,
+                                         const WebnnDeviceType /* device_type */,
+                                         const logging::Logger& logger) const {
+  const auto& input_defs = node.InputDefs();
+  std::vector<int64_t> input_shape;
+  if (!GetShape(*input_defs[0], input_shape, logger))
+    return false;
+
+  return true;
+}
+
+void CreateDropoutOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
+  op_registrations.builders.push_back(std::make_unique<DropoutOpBuilder>());
+  op_registrations.op_builder_map.emplace(op_type, op_registrations.builders.back().get());
+}
+
+}  // namespace webnn
+}  // namespace onnxruntime

onnxruntime/core/providers/webnn/builders/op_builder_factory.cc

@@ -81,6 +81,10 @@ static OpBuilderRegistrations CreateOpBuilderRegistrations() {
     CreateConcatOpBuilder("Concat", op_registrations);
   }
 
+  {  // Dropout
+    CreateDropoutOpBuilder("Dropout", op_registrations);
+  }
+
   {  // Quantize/Dequantize
     CreateDynamicQuantizeLinearOpBuilder("DynamicQuantizeLinear", op_registrations);
     CreateDequantizeLinearOpBuilder("DequantizeLinear", op_registrations);

onnxruntime/core/providers/webnn/builders/op_builder_factory.h

@@ -26,6 +26,7 @@ void CreateCastOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_
 void CreateClipOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateConvOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateConcatOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
+void CreateDropoutOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateDynamicQuantizeLinearOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateDequantizeLinearOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateExpandOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);