Don't create implicit input for outer scope value if there is a subgraph input with the same name.

include/onnxruntime/core/graph/graph.h

@@ -853,7 +853,7 @@ class Graph {
 
   // Build and verify node connection (edges).
   // Verify NodeArg name/type/shape matching correctly.
-  common::Status BuildConnections(std::vector<std::string>& outer_scope_node_args_consumed);
+  common::Status BuildConnections(std::unordered_set<std::string>& outer_scope_node_args_consumed);
 
   common::Status VerifyNoDuplicateName();
 

onnxruntime/core/graph/graph.cc

@@ -898,7 +898,7 @@ void Graph::RemoveEdge(NodeIndex src_node_index, NodeIndex dst_node_index, int s
 }
 
 GSL_SUPPRESS(es .84)  // ignoring return value from unordered_map::insert causes noisy complaint
-Status Graph::BuildConnections(std::vector<std::string>& outer_scope_node_args_consumed) {
+Status Graph::BuildConnections(std::unordered_set<std::string>& outer_scope_node_args_consumed) {
   const std::unordered_set<std::string>& outer_scope_node_args = resolve_context_.outer_scope_node_args;
   std::unordered_set<Node*> inner_nodes;
 
@@ -908,7 +908,7 @@ Status Graph::BuildConnections(std::vector<std::string>& outer_scope_node_args_c
 
     for (auto* node : resolve_context_.nodes_with_subgraphs) {
       for (auto& subgraph : node->MutableSubgraphs()) {
-        std::vector<std::string> node_args_consumed;
+        std::unordered_set<std::string> node_args_consumed;
         ORT_RETURN_IF_ERROR(subgraph->BuildConnections(node_args_consumed));
 
         for (auto& node_arg_name : node_args_consumed) {
@@ -918,7 +918,7 @@ Status Graph::BuildConnections(std::vector<std::string>& outer_scope_node_args_c
             // it's a node arg from outside this graph's scope, so add that to the list we return
             // so that we can add the dependency at the next level up. this happens if you have multiple
             // levels of subgraphs between the graph with the original NodeArg and the subgraph with implicit usage.
-            outer_scope_node_args_consumed.push_back(node_arg_name);
+            ORT_IGNORE_RETURN_VALUE(outer_scope_node_args_consumed.insert(node_arg_name));
 
             if (!parent_graph_) {
               return ORT_MAKE_STATUS(
@@ -987,25 +987,31 @@ Status Graph::BuildConnections(std::vector<std::string>& outer_scope_node_args_c
           continue;
         }
 
-        auto output_arg_iter = resolve_context_.output_args.find(input_arg->Name());
-        if (resolve_context_.output_args.end() == output_arg_iter) {
-          // No such output_arg matching this input_arg.
-          // This input arg should be fed when running evaluation.
-          // See if it's present in the outer scope. If so it will be 'fed' by the execution frame
-          // providing access to the OrtValue from the outer scope. Pass the name back up so nodes can
-          // be linked correctly at that level.
-          if (outer_scope_node_args.find(input_arg->Name()) != outer_scope_node_args.cend()) {
-            outer_scope_node_args_consumed.push_back(input_arg->Name());
-          }
+        const auto& input_arg_name = input_arg->Name();
+        auto output_arg_iter = resolve_context_.output_args.find(input_arg_name);
+        if (resolve_context_.output_args.end() != output_arg_iter) {
+          // The input to this node is an output from a previous node in this graph.
+          // Create relationship between this node (node), and the node providing the output (output_node).
+          Node& output_node = *output_arg_iter->second.first;
+          AddEdge(output_node.Index(), node.Index(), output_arg_iter->second.second, input_slot_index);
 
-          continue;
+          inner_nodes.insert(&output_node);
+        } else {
+          // the value is either an input, an initializer, or coming from outer scope. we only need to take action
+          // if coming from outer scope, so first check if this is a subgraph (otherwise there is no outer scope).
+          if (parent_graph_ != nullptr) {
+            // make sure it's not an input or initializer first as those override any outer scope values
+            if (resolve_context_.inputs_and_initializers.find(input_arg_name) ==
+                resolve_context_.inputs_and_initializers.cend()) {
+              // If it is present in the outer scope it will be 'fed' by the execution frame
+              // providing access to the OrtValue from the outer scope. Pass the name back up so nodes can
+              // be linked correctly at that level.
+              if (outer_scope_node_args.find(input_arg_name) != outer_scope_node_args.cend()) {
+                ORT_IGNORE_RETURN_VALUE(outer_scope_node_args_consumed.insert(input_arg_name));
+              }
+            }
+          }
         }
-
-        // Create relationship between this node (node), and the node providing the output (output_node).
-        Node& output_node = *output_arg_iter->second.first;
-        AddEdge(output_node.Index(), node.Index(), output_arg_iter->second.second, input_slot_index);
-
-        inner_nodes.insert(&output_node);
       }
     } else if (node.OutputDefs().empty()) {
       // This is a useless node.
@@ -1015,7 +1021,7 @@ Status Graph::BuildConnections(std::vector<std::string>& outer_scope_node_args_c
   }
 
   return Status::OK();
-}
+}  // namespace onnxruntime
 
 void Graph::ReverseDFSFrom(const std::vector<NodeIndex>& from,
                            const std::function<void(const Node*)>& enter,
@@ -1869,7 +1875,7 @@ Status Graph::Resolve(bool no_proto_sync_required) {
   // recursively set the outer scope node args.
   ORT_RETURN_IF_ERROR(SetOuterScopeNodeArgs(resolve_context_.outer_scope_node_args));
 
-  std::vector<std::string> outer_scope_node_args_consumed;
+  std::unordered_set<std::string> outer_scope_node_args_consumed;
 
   // recursively build connections between nodes in this graph and all subgraphs
   ORT_RETURN_IF_ERROR(BuildConnections(outer_scope_node_args_consumed));

onnxruntime/test/framework/test_utils.h

@@ -10,6 +10,8 @@
 #include "core/providers/cpu/cpu_execution_provider.h"
 #include "core/framework/ml_value.h"
 
+#include "gsl/gsl_algorithm"
+
 #ifdef USE_CUDA
 #include "core/providers/cuda/cuda_execution_provider.h"
 #endif
@@ -48,6 +50,20 @@ IExecutionProvider* TestOpenVINOExecutionProvider();
 IExecutionProvider* TestNnapiExecutionProvider();
 #endif
 
+template <typename T>
+inline void CopyVectorToTensor(const std::vector<T>& value, Tensor& tensor) {
+  gsl::copy(gsl::make_span(value), tensor.MutableDataAsSpan<T>());
+}
+
+// vector<bool> is specialized so we need to handle it separately
+template <>
+inline void CopyVectorToTensor<bool>(const std::vector<bool>& value, Tensor& tensor) {
+  auto output_span = tensor.MutableDataAsSpan<bool>();
+  for (size_t i = 0, end = value.size(); i < end; ++i) {
+    output_span[i] = value[i];
+  }
+}
+
 template <typename T>
 void CreateMLValue(AllocatorPtr alloc, const std::vector<int64_t>& dims, const std::vector<T>& value,
                    OrtValue* p_mlvalue) {
@@ -57,8 +73,9 @@ void CreateMLValue(AllocatorPtr alloc, const std::vector<int64_t>& dims, const s
                                                               shape,
                                                               alloc);
   if (value.size() > 0) {
-    memcpy(p_tensor->MutableData<T>(), &value[0], element_type->Size() * shape.Size());
+    CopyVectorToTensor(value, *p_tensor);
   }
+
   p_mlvalue->Init(p_tensor.release(),
                   DataTypeImpl::GetType<Tensor>(),
                   DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());

onnxruntime/test/providers/cpu/controlflow/loop_test.cc

@@ -11,6 +11,7 @@
 
 #include "test/providers/provider_test_utils.h"
 #include "test/util/include/default_providers.h"
+#include "test/framework/test_utils.h"
 
 using namespace ONNX_NAMESPACE;
 
@@ -573,6 +574,62 @@ TEST(Loop, InfiniteLoopTermination) {
   terminator_thread.join();
 }
 
+// Regression test that a subgraph input overrides an outer scope value of the same name.
+// Replicate issue from https://github.com/onnx/onnx/issues/2082
+TEST(Loop, SubgraphInputShadowsOuterScopeValue) {
+  SessionOptions so;
+  so.session_logid = "SubgraphInputShadowsOuterScopeValue";
+
+  InferenceSession session_object{so, &DefaultLoggingManager()};
+  Status st;
+  ASSERT_TRUE((st = session_object.Load("testdata/subgraph_input_shadows_outer_scope_value.onnx")).IsOK()) << st;
+  ASSERT_TRUE((st = session_object.Initialize()).IsOK()) << st;
+
+  // prepare inputs
+  std::vector<int64_t> scalar = {1};
+  std::vector<float> a = {3.f}, b = {6.f};
+  std::vector<int64_t> trip_count = {10};
+  std::vector<bool> keep_going = {true};
+
+  NameMLValMap feeds;
+  OrtValue ml_value;
+
+  CreateMLValue<float>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), scalar, a, &ml_value);
+  feeds.insert(std::make_pair("a", ml_value));
+  CreateMLValue<float>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), scalar, b, &ml_value);
+  feeds.insert(std::make_pair("b", ml_value));
+  CreateMLValue<int64_t>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), scalar, trip_count, &ml_value);
+  feeds.insert(std::make_pair("max_trip_count", ml_value));
+  CreateMLValue<bool>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), scalar, keep_going, &ml_value);
+  feeds.insert(std::make_pair("keep_going_inp", ml_value));
+
+  // prepare outputs
+  std::vector<std::string> output_names{"b", "user_defined_vals"};
+  std::vector<OrtValue> fetches;
+
+  // Now run
+  onnxruntime::RunOptions run_options;
+  st = session_object.Run(run_options, feeds, output_names, &fetches);
+  ASSERT_TRUE(st.IsOK()) << st;
+  ASSERT_EQ(2, fetches.size());
+
+  // prepare expected outputs
+  float expected_value_b = 6.f;
+  std::vector<int64_t> expected_dims_user_defined_vals = {2, 1};
+  std::vector<float> expected_user_defined_vals = {-6.f, 12.f};
+
+  auto& b_out = fetches[0].Get<Tensor>();
+  TensorShape expected_shape(scalar);
+  ASSERT_EQ(expected_shape, b_out.Shape());
+  ASSERT_EQ(b_out.DataAsSpan<float>()[0], expected_value_b);
+
+  auto user_defined_vals_out = fetches[1].Get<Tensor>().DataAsSpan<float>();
+  ASSERT_EQ(expected_user_defined_vals.size(), static_cast<size_t>(user_defined_vals_out.size()));
+  for (size_t i = 0, end = expected_user_defined_vals.size(); i < end; ++i) {
+    ASSERT_THAT(user_defined_vals_out[i], testing::FloatEq(expected_user_defined_vals[i]));
+  }
+}
+
 #ifdef USE_CUDA
 // test that when part of the subgraph run on CUDA it executes successfully
 TEST(Loop, MixedExecutionProviders) {

onnxruntime/test/testdata/subgraph_input_shadows_outer_scope_value.onnx

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