Remove object metadata when saving SavedModel.

This change also fixes a few bugs when loading the metadata file, and fixes Keras tests so that they use model.save instead of tf.saved_model.save

PiperOrigin-RevId: 378963258
Change-Id: I261d9ee80b3982a253a9479bc312e4fce6165981

RELEASE.md

@@ -58,7 +58,8 @@
         enable this feature. The documentation in [Advanced autodiff]
         (https://www.tensorflow.org/guide/advanced_autodiff#custom_gradients)
         has been updated.
-
+    *   Object metadata has now been deprecated and no longer saved to the
+        SavedModel.
 *   TF Core:
     *   Added `tf.config.experimental.reset_memory_stats` to reset the tracked
         peak memory returned by `tf.config.experimental.get_memory_info`.

tensorflow/core/protobuf/saved_object_graph.proto

@@ -77,11 +77,12 @@ message SavedUserObject {
   string identifier = 1;
   // Version information from the producer of this SavedUserObject.
   VersionDef version = 2;
+  // Metadata for deserializing this object.
+  //
   // Deprecated! At the time of deprecation, Keras was the only user of this
   // field, and its saving and loading code will be updated shortly.
-  // Please save your application-specific metadata to separate file
-  // Initialization-related metadata.
-  string metadata = 3;
+  // Please save your application-specific metadata to a separate file.
+  string metadata = 3 [deprecated = true];
 }
 
 // A SavedAsset points to an asset in the MetaGraph.

tensorflow/python/keras/engine/base_layer.py

@@ -3048,6 +3048,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
 
   @property
   def _tracking_metadata(self):
+    """Info about this layer to be saved into the SavedModel."""
     return self._trackable_saved_model_saver.tracking_metadata
 
   def _list_extra_dependencies_for_serialization(self, serialization_cache):

tensorflow/python/keras/integration_test/tpu_strategy_test.py

@@ -215,8 +215,8 @@ class TpuStrategyTest(tf.test.TestCase):
       serving_fn = create_serving_signature(model)
 
       saved_model_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
-      tf.saved_model.save(
-          model, saved_model_dir, signatures={"serving_default": serving_fn})
+      model.save(saved_model_dir, save_format="tf",
+                 signatures={"serving_default": serving_fn})
 
     # Test the saved_model.
     loaded_serving_fn = tf.keras.models.load_model(

tensorflow/python/keras/saving/saved_model/load.py

@@ -195,6 +195,12 @@ def _read_legacy_metadata(object_graph_def, metadata):
   for node_id, proto in enumerate(object_graph_def.nodes):
     if (proto.WhichOneof('kind') == 'user_object' and
         proto.user_object.identifier in constants.KERAS_OBJECT_IDENTIFIERS):
+      if not proto.user_object.metadata:
+        raise ValueError('Unable to create a Keras model from this SavedModel. '
+                         'This SavedModel was created with '
+                         '`tf.saved_model.save`, and lacks the Keras metadata.'
+                         'Please save your Keras model by calling `model.save`'
+                         'or `tf.keras.models.save_model`.')
       metadata.nodes.add(
           node_id=node_id,
           node_path=node_paths[node_id],
@@ -253,7 +259,7 @@ class KerasObjectLoader(object):
   """
 
   def __init__(self, metadata, object_graph_def):
-    self._metadata = metadata
+    self._metadata = {x.node_id: x for x in metadata.nodes}
     self._proto = object_graph_def
 
     self._node_paths = {node_data.node_id: node_data.node_path
@@ -309,7 +315,7 @@ class KerasObjectLoader(object):
     self._traversed_nodes_from_config.add(node_id)
     obj._maybe_initialize_trackable()
     if isinstance(obj, base_layer.Layer) and not obj.built:
-      metadata = json_utils.decode(proto.user_object.metadata)
+      metadata = json_utils.decode(self._metadata[node_id].metadata)
       self._try_build_layer(obj, node_id, metadata.get('build_input_shape'))
 
     # Create list of all possible children
@@ -378,7 +384,7 @@ class KerasObjectLoader(object):
     # and layers will create the metric when initialized (this avoids wasting
     # time by creating objects multiple times).
     metric_list = []
-    for node_metadata in self._metadata.nodes:
+    for node_metadata in self._metadata.values():
       if node_metadata.identifier == constants.METRIC_IDENTIFIER:
         metric_list.append(node_metadata)
         continue
@@ -666,8 +672,7 @@ class KerasObjectLoader(object):
 
   def _reconstruct_model(self, model_id, model, layers):
     """Reconstructs the network structure."""
-    config = json_utils.decode(
-        self._proto.nodes[model_id].user_object.metadata)['config']
+    config = json_utils.decode(self._metadata[model_id].metadata)['config']
 
     # Set up model inputs
     if model.inputs:

tensorflow/python/keras/saving/saved_model/saved_model_test.py

@@ -23,6 +23,7 @@ Tests that focus on the model structure should go in revive_test.py
 
 import os
 import shutil
+import sys
 
 from absl.testing import parameterized
 import numpy as np
@@ -151,7 +152,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
 
   def _save_and_load(self, model):
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
     loaded = keras_load.load(saved_model_dir)
     return loaded
 
@@ -194,7 +195,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
   def test_trainable_weights(self):
     """Tests that trainable status of individual weights is preserved."""
     layer = keras.layers.Dense(4, name='custom_layer')
-    layer.build([3,])
+    layer.build([None, 3])
     layer.add_weight(
         'extra_weight', shape=[],
         initializer=init_ops.constant_initializer(11),
@@ -203,12 +204,15 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
         'extra_weight_2', shape=[],
         initializer=init_ops.constant_initializer(12),
         trainable=False)
+    model = keras.Sequential([keras.Input([3,]), layer])
 
     saved_model_dir = self._save_model_dir()
     self.evaluate(variables.variables_initializer(layer.variables))
-    tf_save.save(layer, saved_model_dir)
-    loaded = keras_load.load(saved_model_dir)
-    self.evaluate(variables.variables_initializer(loaded.variables))
+    model.save(saved_model_dir, save_format='tf')
+    loaded_model = keras_load.load(saved_model_dir)
+    self.evaluate(variables.variables_initializer(loaded_model.variables))
+
+    loaded = loaded_model.layers[-1]
 
     equal_attrs = ['name', '_expects_training_arg', 'trainable']
     for attr in equal_attrs:
@@ -259,7 +263,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     previous_losses = model.losses[:]
 
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     with previous_losses[0].graph.as_default():
       # If we try to compare symbolic Tensors in eager mode assertAllEqual will
@@ -270,15 +274,18 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
       # Test that eager losses are maintained.
       model(input_arr)  # Calls model eagerly, creating eager losses.
       previous_losses = model.losses[:]
-      tf_save.save(model, saved_model_dir)
+      model.save(saved_model_dir, save_format='tf')
       self.assertAllEqual(previous_losses, model.losses)
 
   def test_layer_with_learning_phase(self):
     layer = LayerWithLearningPhase()
     layer.build([None, None])
     saved_model_dir = self._save_model_dir()
-    tf_save.save(layer, saved_model_dir)
-    loaded = keras_load.load(saved_model_dir)
+    model = testing_utils.get_model_from_layers(
+        [layer], input_shape=[None], model_type='functional')
+    model.save(saved_model_dir, save_format='tf')
+    loaded_model = keras_load.load(saved_model_dir)
+    loaded = loaded_model.layers[-1]
     input_arr = array_ops.ones((4, 3))
 
     # Run the layer, and use the keras backend learning phase
@@ -306,7 +313,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     model.predict(np.random.random((1, 3)).astype(np.float32))
     saved_model_dir = self._save_model_dir()
 
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     loaded = tf_load.load(saved_model_dir)
     self.evaluate(variables.variables_initializer(loaded.variables))
@@ -338,7 +345,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     # Compile and save model.
     model.compile('rmsprop', 'mse')
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     loaded = keras_load.load(saved_model_dir)
     actual_predict = loaded.predict(input_arr)
@@ -364,7 +371,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
         super(LayerWithNestedSpec, self).__init__()
         self.input_spec = {
             'a': keras.layers.InputSpec(max_ndim=3, axes={-1: 2}),
-            'b': keras.layers.InputSpec(shape=(None, 2, 3), dtype='float16')}
+            'b': keras.layers.InputSpec(shape=(None, 2, 3), dtype='int32')}
 
       @property
       def _use_input_spec_as_call_signature(self):
@@ -372,12 +379,17 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
 
     layer = LayerWithNestedSpec()
     saved_model_dir = self._save_model_dir()
-    tf_save.save(layer, saved_model_dir)
-    loaded = keras_load.load(saved_model_dir)
+    model = testing_utils.get_model_from_layers(
+        [layer], model_type='subclass')
+    model({'a': constant_op.constant([[2, 4]]),
+           'b': array_ops.ones([1, 2, 3], dtype=dtypes.int32)})
+    model.save(saved_model_dir, save_format='tf')
+    loaded_model = keras_load.load(saved_model_dir)
+    loaded = loaded_model.layers[-1]
     self.assertEqual(3, loaded.input_spec['a'].max_ndim)
     self.assertEqual({-1: 2}, loaded.input_spec['a'].axes)
     self.assertAllEqual([None, 2, 3], loaded.input_spec['b'].shape)
-    self.assertEqual('float16', loaded.input_spec['b'].dtype)
+    self.assertEqual('int32', loaded.input_spec['b'].dtype)
 
   def test_must_restore_from_config_fails_if_layer_is_not_in_scope(self):
 
@@ -386,7 +398,9 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
 
     layer = LayerThatShouldFailIfNotAdded()
     saved_model_dir = self._save_model_dir()
-    tf_save.save(layer, saved_model_dir)
+    model = testing_utils.get_model_from_layers(
+        [layer], input_shape=[3], model_type='functional')
+    model.save(saved_model_dir, save_format='tf')
     with self.assertRaisesRegex(RuntimeError, 'Unable to restore a layer of'):
       _ = keras_load.load(saved_model_dir)
 
@@ -396,8 +410,10 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
       _must_restore_from_config = True
 
     layer = LayerThatShouldFailIfNotAdded()
+    model = testing_utils.get_model_from_layers(
+        [layer], input_shape=[3], model_type='functional')
     saved_model_dir = self._save_model_dir()
-    tf_save.save(layer, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
     with generic_utils.CustomObjectScope(
         {'LayerThatShouldFailIfNotAdded': LayerThatShouldFailIfNotAdded}):
       _ = keras_load.load(saved_model_dir)
@@ -405,7 +421,9 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
   def test_must_restore_from_config_registration(self):
     layer = GlobalLayerThatShouldFailIfNotAdded()
     saved_model_dir = self._save_model_dir()
-    tf_save.save(layer, saved_model_dir)
+    model = testing_utils.get_model_from_layers(
+        [layer], input_shape=[3], model_type='functional')
+    model.save(saved_model_dir, save_format='tf')
     _ = keras_load.load(saved_model_dir)
 
   def test_multi_input_model(self):
@@ -454,16 +472,14 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     self.evaluate(variables.variables_initializer(model.variables))
     saved_model_dir = self._save_model_dir()
 
-    # TODO(kathywu): Re-enable this check after removing the tf.saved_model.save
-    # metadata warning.
-    # with self.captureWritesToStream(sys.stderr) as captured_logs:
-    model.save(saved_model_dir, save_format='tf')
-    loaded = keras_load.load(saved_model_dir)
+    with self.captureWritesToStream(sys.stderr) as captured_logs:
+      model.save(saved_model_dir, save_format='tf')
+      loaded = keras_load.load(saved_model_dir)
 
     # Assert that saving does not log deprecation warnings
     # (even if it needs to set learning phase for compat reasons)
-    # if context.executing_eagerly():
-    #   self.assertNotIn('deprecated', captured_logs.contents())
+    if context.executing_eagerly():
+      self.assertNotIn('deprecated', captured_logs.contents())
 
     input_arr = array_ops.constant([[11], [12], [13]], dtype=dtypes.float32)
     input_arr2 = array_ops.constant([[14], [15], [16]], dtype=dtypes.float32)
@@ -806,7 +822,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
         np.zeros((batch, 64)).astype('float32'))
 
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     loaded = keras_load.load(saved_model_dir)
     loaded_layer = loaded.layers[1]
@@ -834,7 +850,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     self.evaluate([v.initializer for v in model.variables])
     saved_model_dir = self._save_model_dir()
 
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
     del model
 
     loaded = keras_load.load(saved_model_dir)
@@ -874,7 +890,7 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     model = keras.Model(f_inputs, out)
     self.evaluate(variables.variables_initializer(model.variables))
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     loaded = keras_load.load(saved_model_dir)
     self.evaluate(variables.variables_initializer(loaded.variables))
@@ -973,7 +989,7 @@ class TestSavedModelFormat(test.TestCase):
     inp = constant_op.constant([[1.0]])
     model(inp)
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     loaded = keras_load.load(saved_model_dir)
     self.assertAllEqual([[1.0]], self.evaluate(loaded(inp)))
@@ -1026,6 +1042,13 @@ class TestSavedModelFormat(test.TestCase):
     with self.assertRaisesRegex(ValueError, 'I said do not trace'):
       loaded.attached_layer(constant_op.constant([1.]))
 
+  def test_load_non_keras_saved_model(self):
+    model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
+    saved_model_dir = self._save_model_dir()
+    tf_save.save(model, saved_model_dir)
+    with self.assertRaisesRegex(ValueError, 'Unable to create a Keras model'):
+      keras_load.load(saved_model_dir)
+
 
 class TestLayerCallTracing(test.TestCase, parameterized.TestCase):
 
@@ -1124,6 +1147,16 @@ class TestLayerCallTracing(test.TestCase, parameterized.TestCase):
     self.assertAllEqual(previous_losses, layer.losses)
 
 
+@generic_utils.register_keras_serializable('Testing')
+class CustomMeanMetric(keras.metrics.Mean):
+
+  def update_state(self, *args):  # pylint: disable=useless-super-delegation
+    # Sometimes built-in metrics return an op in update_state. Custom
+    # metrics don't support returning ops, so wrap the update_state method
+    # while returning nothing.
+    super(CustomMeanMetric, self).update_state(*args)
+
+
 @combinations.generate(combinations.combine(mode=['graph', 'eager']))
 class MetricTest(test.TestCase, parameterized.TestCase):
 
@@ -1145,8 +1178,12 @@ class MetricTest(test.TestCase, parameterized.TestCase):
                                  shape=(1, 5),
                                  test_sample_weight=True):
     with self.cached_session():
-      tf_save.save(metric, save_dir)
-      loaded = keras_load.load(save_dir)
+      model = testing_utils.get_model_from_layers(
+          [keras.layers.Layer()], input_shape=[3], model_type='functional')
+      model.saved_metric = metric
+      model.save(save_dir, save_format='tf')
+      loaded_model = keras_load.load(save_dir)
+      loaded = loaded_model.saved_metric
       self.evaluate([v.initializer for v in loaded.variables])
       self.assertEqual(metric.name, loaded.name)
       self.assertEqual(metric.dtype, loaded.dtype)
@@ -1236,15 +1273,6 @@ class MetricTest(test.TestCase, parameterized.TestCase):
 
   def test_registered_custom_metric(self):
 
-    @generic_utils.register_keras_serializable('Testing')
-    class CustomMeanMetric(keras.metrics.Mean):
-
-      def update_state(self, *args):  # pylint: disable=useless-super-delegation
-        # Sometimes built-in metrics return an op in update_state. Custom
-        # metrics don't support returning ops, so wrap the update_state method
-        # while returning nothing.
-        super(CustomMeanMetric, self).update_state(*args)
-
     with self.cached_session():
       metric = CustomMeanMetric()
       save_dir = self._save_model_dir('first_save')
@@ -1298,7 +1326,7 @@ class MetricTest(test.TestCase, parameterized.TestCase):
                   metrics=[CustomMetric(), zero_metric])
     model.fit(x, y)
     saved_model_dir = self._save_model_dir()
-    tf_save.save(model, saved_model_dir)
+    model.save(saved_model_dir, save_format='tf')
 
     with self.assertRaisesRegex(ValueError, 'custom_objects'):
       keras_load.load(saved_model_dir)

tensorflow/python/keras/testing_utils.py

@@ -572,7 +572,8 @@ def get_model_from_layers(model_layers,
                           input_dtype=None,
                           name=None,
                           input_ragged=None,
-                          input_sparse=None):
+                          input_sparse=None,
+                          model_type=None):
   """Builds a model from a sequence of layers.
 
   Args:
@@ -582,12 +583,14 @@ def get_model_from_layers(model_layers,
     name: Name for the model.
     input_ragged: Boolean, whether the input data is a ragged tensor.
     input_sparse: Boolean, whether the input data is a sparse tensor.
+    model_type: One of "subclass", "subclass_custom_build", "sequential", or
+      "functional". When None, defaults to `get_model_type`.
 
   Returns:
     A Keras model.
   """
-
-  model_type = get_model_type()
+  if model_type is None:
+    model_type = get_model_type()
   if model_type == 'subclass':
     inputs = None
     if input_ragged or input_sparse:

tensorflow/python/keras/utils/kpl_test_utils.py

@@ -27,7 +27,6 @@ from tensorflow.python.keras.layers.preprocessing import string_lookup
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
-from tensorflow.python.saved_model import save as tf_save
 
 
 class DistributeKplTestUtils(test.TestCase):
@@ -170,8 +169,8 @@ class DistributeKplTestUtils(test.TestCase):
                                                label_inverse_lookup_layer)
 
     saved_model_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
-    tf_save.save(
-        model, saved_model_dir, signatures={"serving_default": serving_fn})
+    model.save(saved_model_dir, save_format="tf",
+               signatures={"serving_default": serving_fn})
 
     # Test the saved_model.
     loaded_serving_fn = keras.saving.save.load_model(

tensorflow/python/saved_model/save.py

@@ -927,17 +927,14 @@ def _serialize_object_graph(saveable_view, asset_file_def_index):
     if serialized is not None:
       proto.concrete_functions[name].CopyFrom(serialized)
 
-  saved_object_metadata = False
   for obj, obj_proto in zip(saveable_view.nodes, proto.nodes):
-    has_saved_object_metadata = _write_object_proto(
-        obj, obj_proto, asset_file_def_index, saveable_view.function_name_map)
-    saved_object_metadata = saved_object_metadata or has_saved_object_metadata
-  return proto, saved_object_metadata
+    _write_object_proto(obj, obj_proto, asset_file_def_index,
+                        saveable_view.function_name_map)
+  return proto
 
 
 def _write_object_proto(obj, proto, asset_file_def_index, function_name_map):
   """Saves an object into SavedObject proto."""
-  has_saved_object_metadata = False  # The metadata field will be deprecated.
   if isinstance(obj, tracking.Asset):
     proto.asset.SetInParent()
     proto.asset.asset_file_def_index = asset_file_def_index[obj]
@@ -963,19 +960,13 @@ def _write_object_proto(obj, proto, asset_file_def_index, function_name_map):
     if registered_type_proto is None:
       # Fallback for types with no matching registration
       # pylint:disable=protected-access
-      metadata = obj._tracking_metadata
-      if metadata:
-        has_saved_object_metadata = True
       registered_type_proto = saved_object_graph_pb2.SavedUserObject(
           identifier=obj._object_identifier,
           version=versions_pb2.VersionDef(
-              producer=1, min_consumer=1, bad_consumers=[]),
-          metadata=metadata)
+              producer=1, min_consumer=1, bad_consumers=[]))
       # pylint:enable=protected-access
     proto.user_object.CopyFrom(registered_type_proto)
 
-  return has_saved_object_metadata
-
 
 def _export_debug_info(exported_graph, export_dir):
   """Exports debug information from graph to file.
@@ -1188,8 +1179,7 @@ def save(obj, export_dir, signatures=None, options=None):
   """
   # pylint: enable=line-too-long
   metrics.IncrementWriteApi(_SAVE_V2_LABEL)
-  result = save_and_return_nodes(
-      obj, export_dir, signatures, options, raise_metadata_warning=True)
+  result = save_and_return_nodes(obj, export_dir, signatures, options)
   metrics.IncrementWrite()
   return result
 
@@ -1198,7 +1188,6 @@ def save_and_return_nodes(obj,
                           export_dir,
                           signatures=None,
                           options=None,
-                          raise_metadata_warning=False,
                           experimental_skip_checkpoint=False):
   """Saves a SavedModel while returning all saved nodes and their paths.
 
@@ -1210,8 +1199,6 @@ def save_and_return_nodes(obj,
     signatures: A function or dictionary of functions to save in the SavedModel
       as signatures.
     options: `tf.saved_model.SaveOptions` object for configuring save options.
-    raise_metadata_warning: Whether to raise the metadata warning. This arg will
-      be removed in TF 2.5.
     experimental_skip_checkpoint: If set to `True`, the checkpoint will not
       be written.
 
@@ -1228,8 +1215,7 @@ def save_and_return_nodes(obj,
   meta_graph_def = saved_model.meta_graphs.add()
 
   _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (
-      _build_meta_graph(obj, signatures, options, meta_graph_def,
-                        raise_metadata_warning))
+      _build_meta_graph(obj, signatures, options, meta_graph_def))
   saved_model.saved_model_schema_version = (
       pywrap_libexport.SAVED_MODEL_SCHEMA_VERSION)
 
@@ -1322,8 +1308,7 @@ def export_meta_graph(obj, filename, signatures=None, options=None):
 def _build_meta_graph_impl(obj,
                            signatures,
                            options,
-                           meta_graph_def=None,
-                           raise_metadata_warning=True):
+                           meta_graph_def=None):
   """Creates a MetaGraph containing the resources and functions of an object."""
   if ops.inside_function():
     raise AssertionError(
@@ -1364,26 +1349,10 @@ def _build_meta_graph_impl(obj,
       for fdef in func._stateless_fn._function_cache.all_values():  # pylint: disable=protected-access
         function_aliases[fdef.name] = alias
 
-  object_graph_proto, saved_object_metadata = _serialize_object_graph(
+  object_graph_proto = _serialize_object_graph(
       saveable_view, asset_info.asset_index)
   meta_graph_def.object_graph_def.CopyFrom(object_graph_proto)
 
-  if saved_object_metadata and raise_metadata_warning:
-    tf_logging.warning(
-        'FOR KERAS USERS: The object that you are saving contains one or more '
-        'Keras models or layers. If you are loading the SavedModel with '
-        '`tf.keras.models.load_model`, continue reading (otherwise, you may '
-        'ignore the following instructions). Please change your code to save '
-        'with `tf.keras.models.save_model` or `model.save`, and confirm that '
-        'the file "keras.metadata" exists in the export directory. In the '
-        'future, Keras will only load the SavedModels that have this file. In '
-        'other words, `tf.saved_model.save` will no longer write SavedModels '
-        'that can be recovered as Keras models (this will apply in TF 2.5).'
-        '\n\nFOR DEVS: If you are overwriting _tracking_metadata in your class,'
-        ' this property has been used to save metadata in the SavedModel. The '
-        'metadata field will be deprecated soon, so please move the metadata to'
-        ' a different file.')
-
   return (meta_graph_def, exported_graph, object_saver, asset_info,
           saveable_view.nodes, saveable_view.node_paths)
 
@@ -1391,8 +1360,7 @@ def _build_meta_graph_impl(obj,
 def _build_meta_graph(obj,
                       signatures,
                       options,
-                      meta_graph_def=None,
-                      raise_metadata_warning=True):
+                      meta_graph_def=None):
   """Creates a MetaGraph under a save context.
 
   Args:
@@ -1405,8 +1373,6 @@ def _build_meta_graph(obj,
     options: `tf.saved_model.SaveOptions` object that specifies options for
       saving.
     meta_graph_def: Optional, the MetaGraphDef proto fill.
-    raise_metadata_warning: Whether to raise a warning when user objects contain
-      non-empty metadata.
 
   Raises:
     AssertionError: If `export_meta_graph` is executing inside a `tf.function`.
@@ -1420,5 +1386,4 @@ def _build_meta_graph(obj,
   """
 
   with save_context.save_context(options):
-    return _build_meta_graph_impl(obj, signatures, options, meta_graph_def,
-                                  raise_metadata_warning)
+    return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)

tensorflow/python/training/tracking/base.py

@@ -704,11 +704,6 @@ class Trackable(object):
     """
     return "_generic_user_object"
 
-  @property
-  def _tracking_metadata(self):
-    """String containing object metadata, which is saved to the SavedModel."""
-    return ""
-
   def _no_dependency(self, value):
     """If automatic dependency tracking is enabled, ignores `value`."""
     return value