Convert scalars to 1D to satisfy ML Program requirements. (#21159)

### Description
<!-- Describe your changes. -->
Convert scalars to 1D to satisfy ML Program requirements.


https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1418617&view=logs&j=f7cc61a9-cc70-56e7-b06c-4668ca17e426&t=16d281b5-1bfd-5309-f274-36d0dffd9cb1&l=27167

### 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. -->
Fixes test failure in #17361

onnxruntime/core/providers/coreml/builders/impl/builder_utils.cc

@@ -140,30 +140,44 @@ void CreateCoreMLWeight(CoreML::Specification::WeightParams& weight, gsl::span<c
 
 namespace {
 void SetTensorTypeInfo(MILSpec::TensorType& tensor_type, MILSpec::DataType data_type,
-                       std::optional<gsl::span<const int64_t>> shape) {
+                       std::optional<gsl::span<const int64_t>> shape, bool convert_scalar = false) {
   tensor_type.set_datatype(data_type);
   if (shape) {
-    tensor_type.set_rank(shape->size());
-    for (const auto& dim : *shape) {
-      if (dim >= 0) {
-        tensor_type.add_dimensions()->mutable_constant()->set_size(narrow<int32_t>(dim));
-      } else {
-        tensor_type.add_dimensions()->mutable_unknown()->set_variadic(false);
+    auto rank = shape->size();
+    if (convert_scalar && rank == 0) {
+      // CoreML scalar has shape {1}
+      tensor_type.set_rank(1);
+      tensor_type.add_dimensions()->mutable_constant()->set_size(1);
+    } else {
+      tensor_type.set_rank(rank);
+      for (const auto& dim : *shape) {
+        if (dim >= 0) {
+          tensor_type.add_dimensions()->mutable_constant()->set_size(narrow<int32_t>(dim));
+        } else {
+          tensor_type.add_dimensions()->mutable_unknown()->set_variadic(false);
+        }
       }
     }
   }
 }
 
 void SetTensorTypeInfo(MILSpec::TensorType& tensor_type, MILSpec::DataType data_type,
-                       const ONNX_NAMESPACE::TensorShapeProto* shape) {
+                       const ONNX_NAMESPACE::TensorShapeProto* shape, bool convert_scalar = false) {
   tensor_type.set_datatype(data_type);
   if (shape) {
-    tensor_type.set_rank(shape->dim_size());
-    for (const auto& dim : shape->dim()) {
-      if (dim.has_dim_value()) {
-        tensor_type.add_dimensions()->mutable_constant()->set_size(narrow<int32_t>(dim.dim_value()));
-      } else {
-        tensor_type.add_dimensions()->mutable_unknown()->set_variadic(false);
+    auto rank = shape->dim_size();
+    if (convert_scalar && rank == 0) {
+      // CoreML scalar has shape {1}
+      tensor_type.set_rank(1);
+      tensor_type.add_dimensions()->mutable_constant()->set_size(1);
+    } else {
+      tensor_type.set_rank(rank);
+      for (const auto& dim : shape->dim()) {
+        if (dim.has_dim_value()) {
+          tensor_type.add_dimensions()->mutable_constant()->set_size(narrow<int32_t>(dim.dim_value()));
+        } else {
+          tensor_type.add_dimensions()->mutable_unknown()->set_variadic(false);
+        }
       }
     }
   }
@@ -281,13 +295,13 @@ template MILSpec::Value CreateScalarTensorValue(const int32_t& data);
 template MILSpec::Value CreateScalarTensorValue(const std::string& data);
 template MILSpec::Value CreateScalarTensorValue(const bool& data);
 
-COREML_SPEC::MILSpec::NamedValueType CreateNamedTensorValueType(const NodeArg& node_arg) {
+COREML_SPEC::MILSpec::NamedValueType CreateNamedTensorValueType(const NodeArg& node_arg, bool convert_scalar) {
   MILSpec::NamedValueType nvt;
   nvt.set_name(node_arg.Name());
   MILSpec::TensorType& tensor_type = *nvt.mutable_type()->mutable_tensortype();
 
   SetTensorTypeInfo(tensor_type, OnnxDataTypeToMILSpec(node_arg.TypeAsProto()->tensor_type().elem_type()),
-                    node_arg.Shape());
+                    node_arg.Shape(), convert_scalar);
 
   return nvt;
 }
@@ -308,7 +322,7 @@ void AddOperationOutput(COREML_SPEC::MILSpec::Operation& op, const NodeArg& outp
   MILSpec::TensorType& tensor_type = *value.mutable_tensortype();
 
   SetTensorTypeInfo(tensor_type, OnnxDataTypeToMILSpec(output.TypeAsProto()->tensor_type().elem_type()),
-                    output.Shape());
+                    output.Shape(), /*convert_scalar*/ true);
 }
 
 void AddPadTypeAndPads(COREML_SPEC::MILSpec::Operation& op, ModelBuilder& model_builder, std::string_view op_type,

onnxruntime/core/providers/coreml/builders/impl/builder_utils.h

@@ -114,8 +114,10 @@ template <typename T>
 COREML_SPEC::MILSpec::Value CreateScalarTensorValue(const T& data);
 
 /// <summary>Create a NamedValueType from an ONNX tensor NodeArg.</summary>
+/// <param name="node_arg">NodeArg to create NamedValueType from.</param>
+/// <param name="convert_scalar">If true, scalar shapes are converted to 1D.</param>
 /// <remarks>Used to create inputs for the 'main' function in an ML Program.</remarks>
-COREML_SPEC::MILSpec::NamedValueType CreateNamedTensorValueType(const NodeArg& node_arg);
+COREML_SPEC::MILSpec::NamedValueType CreateNamedTensorValueType(const NodeArg& node_arg, bool convert_scalar = false);
 
 /// <summary>
 /// Add an input argument to a MILSpec::Operation

onnxruntime/core/providers/coreml/builders/model_builder.cc

@@ -838,13 +838,8 @@ Status ModelBuilder::RegisterModelInputOutput(const NodeArg& node_arg, bool is_i
   if (create_ml_program_) {
     if (is_input) {
       // the model inputs need to be wired up as args to the 'main' function.
-      auto tensor_value_type = CreateNamedTensorValueType(node_arg);
+      auto tensor_value_type = CreateNamedTensorValueType(node_arg, /*convert_scalar*/ true);
       tensor_value_type.set_name(name);
-      if (node_arg.Shape()->dim_size() == 0) {
-        // update shape from {} to {1} (same change we made at the model input level above).
-        tensor_value_type.mutable_type()->mutable_tensortype()->set_rank(1);
-        tensor_value_type.mutable_type()->mutable_tensortype()->add_dimensions()->mutable_constant()->set_size(1);
-      }
 
       mlprogram_main_fn_->mutable_inputs()->Add(std::move(tensor_value_type));
     } else {