Allow ops to handle ignoring an empty tensor as input

Author: Honry

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

@@ -69,7 +69,8 @@ bool IsNodeSupported(const Node& node, const GraphViewer& graph_viewer, const We
   }
 }
 
-bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_name, const logging::Logger& logger) {
+bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_name,
+                            const logging::Logger& logger, bool allow_empty_input) {
   const auto& node_arg_name = node_arg.Name();
   const auto* shape_proto = node_arg.Shape();
   // Optional tensors can be indicated by an empty name, just ignore it.
@@ -89,7 +90,7 @@ bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_n
                             << "use sessionOptions.FreeDimensionOverrides to set a fixed shape: " << node_arg_name;
       return false;
     }
-    if (dim.dim_value() == 0) {
+    if (dim.dim_value() == 0 && !allow_empty_input) {
       LOGS(logger, VERBOSE) << "The shape of [" << node_arg_name << "] has 0 dimension which is not supported by WebNN";
       return false;
     }

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

@@ -181,7 +181,8 @@ inline bool IsEmptyTensor(const InitializedTensorSet& initializers, const std::s
   return std::any_of(dims.begin(), dims.end(), [](auto d) { return d == 0; });
 }
 
-bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_name, const logging::Logger& logger);
+bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_name,
+                            const logging::Logger& logger, bool allow_empty_input = false);
 
 // Get a list of groups of supported nodes, each group represents a subgraph supported by WebNN EP.
 std::vector<std::vector<NodeIndex>> GetSupportedNodes(const GraphViewer& graph_viewer,

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

@@ -29,7 +29,7 @@ Status BaseOpBuilder::AddToModelBuilder(ModelBuilder& model_builder, const Node&
 bool BaseOpBuilder::IsOpSupported(const InitializedTensorSet& initializers, const Node& node,
                                   const WebnnDeviceType device_type, const emscripten::val& wnn_limits,
                                   const logging::Logger& logger) const {
-  if (!HasSupportedInputs(initializers, node, wnn_limits, logger))
+  if (!HasSupportedInputs(node, wnn_limits, logger))
     return false;
 
   if (!HasSupportedOutputs(node, wnn_limits, logger))
@@ -41,12 +41,11 @@ bool BaseOpBuilder::IsOpSupported(const InitializedTensorSet& initializers, cons
   return IsOpSupportedImpl(initializers, node, device_type, logger);
 }
 
-bool BaseOpBuilder::HasSupportedInputs(const InitializedTensorSet& initializers, const Node& node,
-                                       const emscripten::val& wnn_limits, const logging::Logger& logger) const {
+bool BaseOpBuilder::HasSupportedInputs(const Node& node, const emscripten::val& wnn_limits,
+                                       const logging::Logger& logger) const {
   const auto node_name = MakeString("Node [", node.Name(), "] type [", node.OpType(), "]");
   for (const auto* input : node.InputDefs()) {
-    // ONNX initializers should have shape information, skip the shape check if the input is an initializer.
-    if (!Contains(initializers, input->Name()) && !IsTensorShapeSupported(*input, node_name, logger)) {
+    if (!IsTensorShapeSupported(*input, node_name, logger, allow_empty_tensor_as_input_)) {
       return false;
     }
   }

onnxruntime/core/providers/webnn/builders/impl/base_op_builder.h

@@ -22,6 +22,9 @@ class BaseOpBuilder : public IOpBuilder {
                            const logging::Logger& logger) const override final ORT_MUST_USE_RESULT;
 
  protected:
+  explicit BaseOpBuilder(bool allow_empty_tensor_as_input = false)
+      : allow_empty_tensor_as_input_(allow_empty_tensor_as_input) {
+  }
   virtual Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
                                        const logging::Logger& logger) const ORT_MUST_USE_RESULT = 0;
 
@@ -53,9 +56,10 @@ class BaseOpBuilder : public IOpBuilder {
 
  private:
   bool HasSupportedOpSet(const Node& node, const logging::Logger& logger) const;
-  bool HasSupportedInputs(const InitializedTensorSet& initializers, const Node& node, const emscripten::val& wnn_limits,
-                          const logging::Logger& logger) const;
+  bool HasSupportedInputs(const Node& node, const emscripten::val& wnn_limits, const logging::Logger& logger) const;
   bool HasSupportedOutputs(const Node& node, const emscripten::val& wnn_limits, const logging::Logger& logger) const;
+
+  const bool allow_empty_tensor_as_input_;  // Some operators can handle ignoring an empty tensor as input.
 };
 
 }  // namespace webnn

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

@@ -21,6 +21,8 @@ namespace webnn {
 class ResizeOpBuilder : public BaseOpBuilder {
   // Add operator related.
  public:
+  // Allow roi and scales potentially being empty inputs that are ignored during processing.
+  ResizeOpBuilder() : BaseOpBuilder(/*allow empty inputs*/ true) {}
   void AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const override;
 
  private: