Support ReduceMax, ReduceMean

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

@@ -54,6 +54,8 @@ static const InlinedHashMap<std::string, std::string> op_map = {
     {"GlobalMaxPool", "maxPool2d"},
     {"AveragePool", "averagePool2d"},
     {"MaxPool", "maxPool2d"},
+    {"ReduceMax", "reduceMax"},
+    {"ReduceMean", "reduceMean"},
     {"Reshape", "reshape"},
     {"Resize", "resample2d"},
     {"Transpose", "transpose"}};

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

@@ -0,0 +1,137 @@
+// 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 "core/optimizer/initializer.h"
+
+#include "base_op_builder.h"
+#include "builder_utils.h"
+
+namespace onnxruntime {
+namespace webnn {
+
+class ReductionOpBuilder : public BaseOpBuilder {
+  // Add operator related.
+ public:
+  void AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const override;
+
+  // Add operator related.
+ 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 logging::Logger& logger) const override;
+};
+
+// Add operator related.
+void ReductionOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
+  const auto& input_defs = node.InputDefs();
+  if (input_defs.size() > 1) {
+    model_builder.AddInitializerToSkip(input_defs[1]->Name());  // axes
+  }
+}
+
+// Add operator related.
+
+Status ReductionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
+                                                 const Node& node,
+                                                 const logging::Logger& logger) const {
+  const auto& input_defs = node.InputDefs();
+  emscripten::val input = model_builder.GetOperand(input_defs[0]->Name());
+  std::vector<int64_t> input_shape;
+  ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get shape");
+  const auto input_rank = input_shape.size();
+
+  NodeAttrHelper helper(node);
+  const auto keep_dims = helper.Get("keepdims", 1);
+  emscripten::val options = emscripten::val::object();
+  options.set("keepDimensions", keep_dims == 1);
+  std::vector<int32_t> axes_data;
+
+  emscripten::val output = emscripten::val::object();
+
+  const auto opset = node.SinceVersion();
+  if (opset >= 18) {
+    // Since opset 18, axes is an optional input.
+    const auto noop_with_empty_axes = helper.Get("noop_with_empty_axes", 0);
+    if (input_defs.size() > 1) {
+      // Optional input axes is provided, use axes initializer data.
+      const auto& initializers(model_builder.GetInitializerTensors());
+      const auto& axes_tensor = *initializers.at(input_defs[1]->Name());
+      Initializer axes_initializer(axes_tensor);
+      const auto axes_data_span = axes_initializer.DataAsSpan<int64_t>();
+      std::transform(
+          axes_data_span.begin(), axes_data_span.end(), std::back_inserter(axes_data),
+          [input_rank](int64_t axis) -> int32_t { return HandleNegativeAxis(axis, input_rank); });
+    } else {
+      if (noop_with_empty_axes) {
+        // When axes is empty and this attribute is set to true, input tensor will not be reduced.
+        output = input;
+        model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
+        return Status::OK();
+      }
+    }
+  } else {
+    if (helper.HasAttr("axes")) {
+      auto axes = helper.Get("axes", std::vector<int64_t>{});
+      std::transform(
+          axes.begin(), axes.end(), std::back_inserter(axes_data),
+          [input_rank](int64_t axis) -> int32_t { return HandleNegativeAxis(axis, input_rank); });
+    }
+  }
+  if (axes_data.size() > 0) {
+    options.set("axes", emscripten::val::array(axes_data));
+  }
+
+  const auto& op_type = node.OpType();
+  if (op_type == "ReduceMax") {
+    output = model_builder.GetBuilder().call<emscripten::val>("reduceMax", input, options);
+  } else if (op_type == "ReduceMean") {
+    output = model_builder.GetBuilder().call<emscripten::val>("reduceMean", input, options);
+  } else {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "ReductionOpBuilder, unknown op: ", op_type);
+  }
+
+  model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
+  return Status::OK();
+}
+
+// Operator support related.
+bool ReductionOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& /* initializers */, const Node& node,
+                                           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 CreateReductionOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
+  if (op_registrations.op_builder_map.find(op_type) != op_registrations.op_builder_map.cend())
+    return;
+
+  static std::vector<std::string> op_types =
+      {
+          "ReduceMax",
+          "ReduceMean",
+      };
+
+  op_registrations.builders.push_back(std::make_unique<ReductionOpBuilder>());
+  for (const auto& type : op_types) {
+    op_registrations.op_builder_map.emplace(type, op_registrations.builders.back().get());
+  }
+}
+
+}  // namespace webnn
+}  // namespace onnxruntime

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

@@ -60,6 +60,11 @@ static OpBuilderRegistrations CreateOpBuilderRegistrations() {
     CreatePoolOpBuilder("MaxPool", op_registrations);
   }
 
+  {  // Reduction
+    CreateReductionOpBuilder("ReduceMax", op_registrations);
+    CreateReductionOpBuilder("ReduceMean", op_registrations);
+  }
+
   {  // Reshape
     CreateReshapeOpBuilder("Reshape", op_registrations);
   }

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

@@ -26,6 +26,7 @@ void CreateConvOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_
 void CreateConcatOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateGemmOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreatePoolOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
+void CreateReductionOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateReshapeOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateResizeOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 void CreateSoftmaxOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);