diff --git a/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.cpp b/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.cpp
index 0ba67cf33fd4c..3cbc435cac989 100644
--- a/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.cpp
+++ b/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.cpp
@@ -638,8 +638,7 @@ Status NodeImporter::ImportAll() {
 
   for (const auto &node : nodes) {
     if (torch_mlir_onnx::failed(ImportNode(node))) {
-      return SetError("Failed to import node '" + node.name() +
-                      "': " + "(node:\n" + node.DebugString() + "\n)");
+      return failure();
     }
   }
 
@@ -728,7 +727,8 @@ Status NodeImporter::ImportGeneralNode(const onnx::NodeProto &node) {
     if (found_it == nv_map_.end()) {
       std::string msg = "Non topologically produced ONNX node input '";
       msg.append(input_name);
-      msg.append("'");
+      msg.append("': ");
+      msg.append(node.DebugString());
       return SetError(std::move(msg));
     }
     input_values.push_back(found_it->second);
@@ -739,8 +739,9 @@ Status NodeImporter::ImportGeneralNode(const onnx::NodeProto &node) {
   for (auto &output_name : node.output()) {
     const onnx::TypeProto *type_proto =
         graph_info_.graph_viewer().GetNodeArg(output_name)->TypeAsProto();
-    if (!type_proto)
-      return failure();
+    if (!type_proto) {
+      return SetError("Failed to obtain TypeProto for tensor");
+    }
 
     MlirType t = cc_.ConvertTypeProto(*type_proto);
     if (mlirTypeIsNull(t))
@@ -906,38 +907,83 @@ Status NodeImporter::ImportConstantOfShapeNode(const onnx::NodeProto &node) {
     return mlirRankedTensorTypeGet(shape.size(), shape.data(), element_type,
                                    /*encoding*/ {nullptr});
   };
+  const bool has_raw_data = tensor_proto.has_raw_data();
   MlirAttribute splat_attr = {nullptr};
+  size_t out_size;
   switch (tensor_proto.data_type()) {
-  case onnx::TensorProto::DataType::TensorProto_DataType_FLOAT:
+  case onnx::TensorProto::DataType::TensorProto_DataType_FLOAT: {
+    const float *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<float>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
     splat_attr = mlirDenseElementsAttrFloatSplatGet(
-        tensorTypeFor(mlirF32TypeGet(context_)), tensor_proto.float_data(0));
+        tensorTypeFor(mlirF32TypeGet(context_)),
+        has_raw_data ? data[0] : tensor_proto.float_data(0));
     break;
-  case onnx::TensorProto::DataType::TensorProto_DataType_INT32:
-    splat_attr = mlirDenseElementsAttrFloatSplatGet(
+  }
+  case onnx::TensorProto::DataType::TensorProto_DataType_INT32: {
+    const int32_t *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<int32_t>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
+    splat_attr = mlirDenseElementsAttrInt32SplatGet(
         tensorTypeFor(mlirIntegerTypeSignedGet(context_, 32)),
-        tensor_proto.int32_data(0));
+        has_raw_data ? data[0] : tensor_proto.int32_data(0));
     break;
-  case onnx::TensorProto::DataType::TensorProto_DataType_INT64:
-    splat_attr = mlirDenseElementsAttrFloatSplatGet(
+  }
+  case onnx::TensorProto::DataType::TensorProto_DataType_INT64: {
+    const int64_t *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<int64_t>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
+    splat_attr = mlirDenseElementsAttrInt64SplatGet(
         tensorTypeFor(mlirIntegerTypeSignedGet(context_, 64)),
-        tensor_proto.int64_data(0));
+        has_raw_data ? data[0] : tensor_proto.int64_data(0));
     break;
-  case onnx::TensorProto::DataType::TensorProto_DataType_DOUBLE:
-    splat_attr = mlirDenseElementsAttrFloatSplatGet(
-        tensorTypeFor(mlirF64TypeGet(context_)), tensor_proto.double_data(0));
+  }
+  case onnx::TensorProto::DataType::TensorProto_DataType_DOUBLE: {
+    const double *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<double>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
+    splat_attr = mlirDenseElementsAttrDoubleSplatGet(
+        tensorTypeFor(mlirF64TypeGet(context_)),
+        has_raw_data ? data[0] : tensor_proto.double_data(0));
     break;
-  case onnx::TensorProto::DataType::TensorProto_DataType_UINT64:
-    splat_attr = mlirDenseElementsAttrFloatSplatGet(
+  }
+  case onnx::TensorProto::DataType::TensorProto_DataType_UINT64: {
+    const uint64_t *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<uint64_t>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
+    splat_attr = mlirDenseElementsAttrUInt64SplatGet(
         tensorTypeFor(mlirIntegerTypeUnsignedGet(context_, 64)),
-        tensor_proto.uint64_data(0));
+        has_raw_data ? data[0] : tensor_proto.uint64_data(0));
     break;
-  case onnx::TensorProto::DataType::TensorProto_DataType_UINT32:
-    // Special case: inline data is stored in uint64.
-    splat_attr = mlirDenseElementsAttrFloatSplatGet(
+  }
+  case onnx::TensorProto::DataType::TensorProto_DataType_UINT32: {
+    const uint32_t *data = nullptr;
+    if (has_raw_data) {
+      data = graph_info_.GetOptionalRawData<uint32_t>(tensor_proto, out_size);
+      ORT_ENFORCE(data, "GetOptionalRawData() returned null for tensor proto: ",
+                  tensor_proto.DebugString());
+    }
+    splat_attr = mlirDenseElementsAttrUInt32SplatGet(
         tensorTypeFor(mlirIntegerTypeUnsignedGet(context_, 32)),
-        tensor_proto.uint64_data(0));
+        has_raw_data ? data[0] : tensor_proto.float_data(0));
     break;
   }
+  }
 
   if (mlirAttributeIsNull(splat_attr)) {
     std::string message =
@@ -958,8 +1004,7 @@ Status NodeImporter::ImportConstantOfShapeNode(const onnx::NodeProto &node) {
       toMlirNamedAttribute("value", splat_attr));
   MlirValue result = mlirOperationGetResult(op, 0);
 
-  // Export to the nv_map.
-  auto inserted = nv_map_.insert(std::make_pair(name, result));
+  auto inserted = nv_map_.emplace(node.output(0), result);
   if (!inserted.second) {
     std::string msg = "Multiple nodes produced a value for '";
     msg.append(name);
@@ -973,8 +1018,17 @@ Status NodeImporter::ImportConstantOfShapeNode(const onnx::NodeProto &node) {
 
 Status NodeImporter::GetImmediateShapeTensor(const std::string &name,
                                              std::vector<int64_t> &shape) {
-  const onnx::TensorProto &tp =
-      *graph_info_.graph_viewer().GetConstantInitializer(name, false);
+  const onnx::TensorProto *tensor =
+      graph_info_.graph_viewer().GetConstantInitializer(name, false);
+  if (!tensor) {
+    std::string msg = "Could not find the immediate shape tensor ";
+    msg.append(name);
+    msg.append(" in constant graph initializers. It was possibly produced "
+               "dynamically.");
+    return SetError(msg);
+  }
+  const onnx::TensorProto &tp = *tensor;
+
   shape.clear();
 
   // Since this is being interpreted as a shape, we only support some limited
diff --git a/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.h b/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.h
index 733aefbda2583..e916da6b8a7d3 100644
--- a/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.h
+++ b/onnxruntime/core/providers/iree/compiler/torch-mlir-import-onnx/OnnxImporter.h
@@ -93,6 +93,10 @@ class GraphInfo {
     return nullptr;
   }
 
+  std::unordered_map<std::string_view, const onnx::ValueInfoProto &> &
+  value_info_map() {
+    return value_info_map_;
+  }
   std::vector<const onnx::ValueInfoProto *> &inputs() { return inputs_; }
   std::unordered_map<std::string_view, const onnx::ValueInfoProto &> &
   input_map() {