diff --git a/tensorflow/compiler/mlir/tensorflow/tests/promote_var_handles_to_args.mlir b/tensorflow/compiler/mlir/tensorflow/tests/promote_var_handles_to_args.mlir
index 8b8a070cfab..925062ea4ff 100644
--- a/tensorflow/compiler/mlir/tensorflow/tests/promote_var_handles_to_args.mlir
+++ b/tensorflow/compiler/mlir/tensorflow/tests/promote_var_handles_to_args.mlir
@@ -1,4 +1,5 @@
-// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-promote-var-handles-to-args | FileCheck %s -dump-input-on-failure
+// Run a pass for promoting tf.VarHandleOps to function arguments in a format of TensorFlowSavedModelDialect.
+// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-saved-model-promote-var-handles-to-args | FileCheck %s -dump-input-on-failure
 
 // Tests main function with multiple blocks.
 
@@ -11,24 +12,27 @@ func @main() {
 
 // -----
 
+"tf_saved_model.global_tensor"() {sym_name = "x", type = tensor<f32>, value = dense<1.67482901> : tensor<f32>} : () -> ()
+"tf_saved_model.global_tensor"() {sym_name = "y", type = tensor<i32>, value = dense<0> : tensor<i32>} : () -> ()
+
 // CHECK-LABEL: func @no_args
-// CHECK-SAME: (%arg0: tensor<!tf.resource> {tf.resource_name = "x"})
+// CHECK-SAME: (%arg0: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @x})
 // CHECK-NOT: "tf.VarHandleOp"
 func @no_args() {
-  %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource>
+  %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource<tensor<f32>>>
   return
 }
 
 // CHECK-LABEL: func @some_args
-// CHECK-SAME: (%arg0: tensor<i1>, %arg1: tensor<!tf.resource> {tf.resource_name = "x"})
+// CHECK-SAME: (%arg0: tensor<i1>, %arg1: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @x})
 // CHECK-NOT: "tf.VarHandleOp"
 func @some_args(%arg0: tensor<i1>) {
-  %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource>
+  %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource<tensor<f32>>>
   return
 }
 
 // CHECK-LABEL: func @unique_vars
-// CHECK-SAME: (%arg0: tensor<!tf.resource<tensor<f32>>> {tf.resource_name = "x"}, %arg1: tensor<!tf.resource<tensor<i32>>> {tf.resource_name = "y"})
+// CHECK-SAME: (%arg0: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @x}, %arg1: tensor<!tf.resource<tensor<i32>>> {tf_saved_model.bound_input = @y})
 // CHECK-NOT: "tf.VarHandleOp"
 func @unique_vars() {
   %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource<tensor<f32>>>
@@ -37,7 +41,7 @@ func @unique_vars() {
 }
 
 // CHECK-LABEL: func @duplicate_vars
-// CHECK-SAME: (%arg0: tensor<!tf.resource<tensor<f32>>> {tf.resource_name = "x"})
+// CHECK-SAME: (%arg0: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @x})
 // CHECK-NOT: "tf.VarHandleOp"
 func @duplicate_vars() {
   %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf.resource<tensor<f32>>>
@@ -46,7 +50,7 @@ func @duplicate_vars() {
 }
 
 // CHECK-LABEL: func @duplicate_vars_with_users
-// CHECK-SAME: (%arg0: tensor<f32>, %arg1: tensor<!tf.resource<tensor<f32>>> {tf.resource_name = "x"})
+// CHECK-SAME: (%arg0: tensor<f32>, %arg1: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @x})
 // CHECK: "tf.ReadVariableOp"(%arg1)
 // CHECK: "tf.AssignAddVariableOp"(%arg1, %arg0)
 // CHECK-NOT: "tf.VarHandleOp"
diff --git a/tensorflow/compiler/mlir/tensorflow/transforms/passes.h b/tensorflow/compiler/mlir/tensorflow/transforms/passes.h
index 5c140ddd6aa..93d7af96c1e 100644
--- a/tensorflow/compiler/mlir/tensorflow/transforms/passes.h
+++ b/tensorflow/compiler/mlir/tensorflow/transforms/passes.h
@@ -91,9 +91,11 @@ std::unique_ptr<OperationPass<ModuleOp>> CreateResourceDeviceInferencePass();
 // of their aliasing output arguments.
 std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteResourcesToArgsPass();
 
-// Creates a pass that promotes tf.VarHandleOp to resource arguments for all
-// functions.
-std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteVarHandlesToArgsPass();
+// Creates a pass that promotes tf.VarHandleOp to to resource arguments of where
+// resource names are `tf_saved_model.bound_input` symbol argument attributes
+// for all functions.
+std::unique_ptr<OperationPass<ModuleOp>>
+CreatePromoteVarHandlesToSavedModelArgsPass();
 
 // Creates a pass that converts readonly reference variables to the
 // corresponding resource variables.
diff --git a/tensorflow/compiler/mlir/tensorflow/transforms/promote_resources_to_args.cc b/tensorflow/compiler/mlir/tensorflow/transforms/promote_resources_to_args.cc
index cece23b4750..0d331686c46 100644
--- a/tensorflow/compiler/mlir/tensorflow/transforms/promote_resources_to_args.cc
+++ b/tensorflow/compiler/mlir/tensorflow/transforms/promote_resources_to_args.cc
@@ -389,15 +389,18 @@ void PromoteResourcesToArgsPass::runOnOperation() {
     return signalPassFailure();
 }
 
-class PromoteVarHandlesToArgsPass
-    : public PassWrapper<PromoteVarHandlesToArgsPass, OperationPass<ModuleOp>> {
+// This pass is for promoting Varhandle ops to tf_saved_model.bound_input
+// attributes, which are required for TensorFlowSavedModelDialect.
+class PromoteVarHandlesToSavedModelArgsPass
+    : public PassWrapper<PromoteVarHandlesToSavedModelArgsPass,
+                         OperationPass<ModuleOp>> {
  public:
   void runOnOperation() override;
 };
 
-void PromoteVarHandlesToArgsPass::runOnOperation() {
+void PromoteVarHandlesToSavedModelArgsPass::runOnOperation() {
   ModuleOp module = getOperation();
-  MLIRContext* context = module.getContext();
+
   for (auto function : module.getOps<FuncOp>()) {
     if (failed(CheckSingleBlockFunction(function))) return signalPassFailure();
 
@@ -406,13 +409,15 @@ void PromoteVarHandlesToArgsPass::runOnOperation() {
                                  &var_handle_shared_names);
 
     // Add resource names for each `tf.VarHandleOp` that were promoted to
-    // resource arguments.
+    // saved model arguments.
     const int var_handle_args_offset =
         function.getNumArguments() - var_handle_shared_names.size();
-    for (auto var_name_and_index : llvm::enumerate(var_handle_shared_names))
+    for (auto var_name_and_index : llvm::enumerate(var_handle_shared_names)) {
+      auto symbol_ref =
+          SymbolRefAttr::get(var_name_and_index.value(), &getContext());
       function.setArgAttr(var_name_and_index.index() + var_handle_args_offset,
-                          kResourceNameArgAttr,
-                          StringAttr::get(var_name_and_index.value(), context));
+                          "tf_saved_model.bound_input", symbol_ref);
+    }
   }
 }
 
@@ -422,17 +427,19 @@ std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteResourcesToArgsPass() {
   return std::make_unique<PromoteResourcesToArgsPass>();
 }
 
-std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteVarHandlesToArgsPass() {
-  return std::make_unique<PromoteVarHandlesToArgsPass>();
+std::unique_ptr<OperationPass<ModuleOp>>
+CreatePromoteVarHandlesToSavedModelArgsPass() {
+  return std::make_unique<PromoteVarHandlesToSavedModelArgsPass>();
 }
 
 static PassRegistration<PromoteResourcesToArgsPass> pass(
     "tf-promote-resources-to-args",
     "Promote resources reads/writes to function inputs/outputs.");
 
-static PassRegistration<PromoteVarHandlesToArgsPass> var_handle_pass(
-    "tf-promote-var-handles-to-args",
-    "Promote tf.VarHandleOps to function arguments.");
+static PassRegistration<PromoteVarHandlesToSavedModelArgsPass> saved_model_pass(
+    "tf-saved-model-promote-var-handles-to-args",
+    "Promote tf.VarHandleOps to function arguments in a format of "
+    "TensorFlowSavedModelDialect.");
 
 }  // namespace TF
 }  // namespace mlir