diff --git a/tensorflow/python/compiler/tensorrt/trt_convert.py b/tensorflow/python/compiler/tensorrt/trt_convert.py
index b47012469e0..8ea5c96f4cc 100644
--- a/tensorflow/python/compiler/tensorrt/trt_convert.py
+++ b/tensorflow/python/compiler/tensorrt/trt_convert.py
@@ -1058,6 +1058,10 @@ class TrtGraphConverterV2(object):
         [tensor.name for tensor in func.outputs])
     rebuilt_func.graph.structured_outputs = nest.pack_sequence_as(
         func.graph.structured_outputs, rebuilt_func.graph.structured_outputs)
+    # Copy structured input signature from original function (used during
+    # serialization)
+    rebuilt_func.graph.structured_input_signature = (
+        func.structured_input_signature)
     return rebuilt_func
 
   # TODO(laigd): provide a utility function to optimize a ConcreteFunction and
@@ -1110,6 +1114,10 @@ class TrtGraphConverterV2(object):
     self._converted_func.graph.structured_outputs = nest.pack_sequence_as(
         func.graph.structured_outputs,
         self._converted_func.graph.structured_outputs)
+    # Copy structured input signature from original function (used during
+    # serialization)
+    self._converted_func.graph.structured_input_signature = (
+        func.structured_input_signature)
 
     if self._need_calibration:
       for inp in calibration_input_fn():
@@ -1265,6 +1273,8 @@ class TrtGraphConverterV2(object):
       reset_converted_func.graph.structured_outputs = nest.pack_sequence_as(
           self._converted_func.graph.structured_outputs,
           reset_converted_func.graph.structured_outputs)
+      reset_converted_func.graph.strucutred_input_signature = (
+          self._converted_func.structured_input_signature)
       self._converted_func = reset_converted_func
 
     signatures[self._input_saved_model_signature_key] = self._converted_func
diff --git a/tensorflow/python/keras/engine/functional.py b/tensorflow/python/keras/engine/functional.py
index 892773fa656..57ae9ee92a7 100644
--- a/tensorflow/python/keras/engine/functional.py
+++ b/tensorflow/python/keras/engine/functional.py
@@ -869,6 +869,13 @@ class Functional(training_lib.Model):
   def _trackable_saved_model_saver(self):
     return network_serialization.NetworkSavedModelSaver(self)
 
+  def _get_save_spec(self, dynamic_batch=True):
+    if getattr(self, '_has_explicit_input_shape', True):
+      # Functional models and Sequential models that have an explicit input
+      # shape should use the batch size set by the input layer.
+      dynamic_batch = False
+    return super(Functional, self)._get_save_spec(dynamic_batch)
+
 
 def _make_node_key(layer_name, node_index):
   return layer_name + '_ib-' + str(node_index)
diff --git a/tensorflow/python/saved_model/save_test.py b/tensorflow/python/saved_model/save_test.py
index cf25bed9d9d..3de9d70e9dc 100644
--- a/tensorflow/python/saved_model/save_test.py
+++ b/tensorflow/python/saved_model/save_test.py
@@ -668,6 +668,23 @@ class SaveTest(test.TestCase, parameterized.TestCase):
       with self.assertRaises(ValueError):
         loader.load(session, [tag_constants.SERVING], export_dir)
 
+  def test_concrete_function_with_set_shape(self,):
+    # Serialized concrete function should retain the shape from the TensorSpec,
+    # instead of using the shape of the inputs (which are changed by set_shape).
+    @def_function.function
+    def f(x):
+      x.set_shape((5, 1))
+      return x
+
+    root = tracking.AutoTrackable()
+    path = os.path.join(self.get_temp_dir(), "saved_model")
+    concrete = f.get_concrete_function(
+        tensor_spec.TensorSpec((None, 1), name="name"))
+    save.save(root, path, signatures={"key": concrete})
+    imported = load.load(path)
+    self.assertEqual(imported.signatures["key"].structured_input_signature[1],
+                     {"name": tensor_spec.TensorSpec((None, 1), name="name")})
+
 
 class VariablePolicyEnumTest(test.TestCase):
 
diff --git a/tensorflow/python/saved_model/signature_serialization.py b/tensorflow/python/saved_model/signature_serialization.py
index 74f76c690f2..14f4df81380 100644
--- a/tensorflow/python/saved_model/signature_serialization.py
+++ b/tensorflow/python/saved_model/signature_serialization.py
@@ -124,15 +124,26 @@ def canonicalize_signatures(signatures):
       structured_outputs = signature_function(**kwargs)
       return _normalize_outputs(
           structured_outputs, signature_function.name, signature_key)
-    # TODO(b/123902469): Use ConcreteFunction.structured_inputs once their names
-    # always match keyword arguments.
     tensor_spec_signature = {}
-    for keyword, tensor in zip(
+    if signature_function.structured_input_signature is not None:
+      # The structured input signature may contain other non-tensor arguments.
+      inputs = filter(
+          lambda x: isinstance(x, tensor_spec.TensorSpec),
+          nest.flatten(signature_function.structured_input_signature,
+                       expand_composites=True))
+    else:
+      # Structured input signature isn't always defined for some functions.
+      inputs = signature_function.inputs
+
+    for keyword, inp in zip(
         signature_function._arg_keywords,  # pylint: disable=protected-access
-        signature_function.inputs):
+        inputs):
       keyword = compat.as_str(keyword)
-      tensor_spec_signature[keyword] = tensor_spec.TensorSpec.from_tensor(
-          tensor, name=keyword)
+      if isinstance(inp, tensor_spec.TensorSpec):
+        spec = tensor_spec.TensorSpec(inp.shape, inp.dtype, name=keyword)
+      else:
+        spec = tensor_spec.TensorSpec.from_tensor(inp, name=keyword)
+      tensor_spec_signature[keyword] = spec
     final_concrete = signature_wrapper._get_concrete_function_garbage_collected(  # pylint: disable=protected-access
         **tensor_spec_signature)
     # pylint: disable=protected-access