Refactor to allow creation of an object graph proto with no variable values

Will be useful for creating a SavedModel object proto

PiperOrigin-RevId: 222430965

tensorflow/python/training/checkpointable/BUILD

@@ -152,7 +152,7 @@ py_test(
         "//tensorflow/python:variable_scope",
         "//tensorflow/python/eager:backprop",
         "//tensorflow/python/eager:context",
-        "//tensorflow/python/eager:function",
+        "//tensorflow/python/eager:def_function",
         "//tensorflow/python/eager:test",
         "//tensorflow/python/keras:engine",
         "//tensorflow/python/keras:layers",

tensorflow/python/training/checkpointable/util.py

@@ -549,13 +549,11 @@ def _serialize_slot_variables(checkpointable_objects, node_ids, object_names):
   return slot_variables
 
 
-def _serialize_checkpointables(
+def _add_attributes_to_object_graph(
-    checkpointable_objects, node_ids, object_names, slot_variables,
+    checkpointable_objects, object_graph_proto, node_ids, object_names,
     saveables_cache, object_map):
-  """Name non-slot `Checkpointable`s and add them to `object_graph_proto`."""
+  """Create SaveableObjects and corresponding SerializedTensor protos."""
-  object_graph_proto = (
+  named_saveable_objects = []
-      checkpointable_object_graph_pb2.CheckpointableObjectGraph())
-  named_saveables = []
   if saveables_cache is None:
     # No SaveableObject caching. Either we're executing eagerly, or building a
     # static save which is specialized to the current Python state.
@@ -564,10 +562,9 @@ def _serialize_checkpointables(
     # If we are caching SaveableObjects, we need to build up a feed_dict with
     # functions computing volatile Python state to be saved with the checkpoint.
     feed_additions = {}
-  for checkpoint_id, checkpointable in enumerate(checkpointable_objects):
+  for checkpoint_id, (checkpointable, object_proto) in enumerate(
+      zip(checkpointable_objects, object_graph_proto.nodes)):
     assert node_ids[checkpointable] == checkpoint_id
-    object_proto = object_graph_proto.nodes.add()
-    object_proto.slot_variables.extend(slot_variables.get(checkpointable, ()))
     object_name = object_names[checkpointable]
     if object_map:
       object_to_save = object_map.get(checkpointable, checkpointable)
@@ -645,14 +642,24 @@ def _serialize_checkpointables(
                      "value.")
                     % (checkpointable, new_feed_key))
             feed_additions.update(saveable_feed_dict)
-        named_saveables.append(saveable)
+        named_saveable_objects.append(saveable)
 
+  return named_saveable_objects, feed_additions
+
+
+def _make_object_graph_proto(checkpointable_objects, node_ids, slot_variables):
+  """Name non-slot `Checkpointable`s and add them to `object_graph_proto`."""
+  object_graph_proto = (
+      checkpointable_object_graph_pb2.CheckpointableObjectGraph())
+  for checkpoint_id, checkpointable in enumerate(checkpointable_objects):
+    assert node_ids[checkpointable] == checkpoint_id
+    object_proto = object_graph_proto.nodes.add()
+    object_proto.slot_variables.extend(slot_variables.get(checkpointable, ()))
     for child in checkpointable._checkpoint_dependencies:  # pylint: disable=protected-access
       child_proto = object_proto.children.add()
       child_proto.node_id = node_ids[child.ref]
       child_proto.local_name = child.name
-
+  return object_graph_proto
-  return named_saveables, object_graph_proto, feed_additions
 
 
 def _serialize_gathered_objects(
@@ -668,13 +675,18 @@ def _serialize_gathered_objects(
       checkpointable_objects=checkpointable_objects,
       node_ids=node_ids,
       object_names=object_names)
-  return _serialize_checkpointables(
+  object_graph_proto = _make_object_graph_proto(
       checkpointable_objects=checkpointable_objects,
       node_ids=node_ids,
+      slot_variables=slot_variables)
+  named_saveable_objects, feed_additions = _add_attributes_to_object_graph(
+      checkpointable_objects=checkpointable_objects,
+      object_graph_proto=object_graph_proto,
+      node_ids=node_ids,
       object_names=object_names,
-      slot_variables=slot_variables,
       saveables_cache=saveables_cache,
       object_map=object_map)
+  return named_saveable_objects, object_graph_proto, feed_additions
 
 
 def _serialize_object_graph(root_checkpointable, saveables_cache):
@@ -716,6 +728,23 @@ def named_saveables(root_checkpointable):
   return _serialize_object_graph(root_checkpointable, None)[0]
 
 
+def _find_objects(root_checkpointable):
+  """Find and number objects which are dependencies of `root_checkpointable`."""
+  checkpointable_objects, path_to_root = (
+      _breadth_first_checkpointable_traversal(root_checkpointable))
+  object_names = _ObjectIdentityDictionary()
+  for obj, path in path_to_root.items():
+    object_names[obj] = _object_prefix_from_path(path)
+  node_ids = _ObjectIdentityDictionary()
+  for node_id, node in enumerate(checkpointable_objects):
+    node_ids[node] = node_id
+  slot_variables = _serialize_slot_variables(
+      checkpointable_objects=checkpointable_objects,
+      node_ids=node_ids,
+      object_names=object_names)
+  return checkpointable_objects, node_ids, slot_variables
+
+
 def list_objects(root_checkpointable):
   """Traverse the object graph and list all accessible objects.
 
@@ -730,23 +759,18 @@ def list_objects(root_checkpointable):
   Returns:
     A flat list of objects.
   """
-  # TODO(allenl): Extract out gathering logic so the naming logic doesn't have
+  checkpointable_objects, _, _ = _find_objects(root_checkpointable)
-  # to run.
-  checkpointable_objects, path_to_root = (
-      _breadth_first_checkpointable_traversal(root_checkpointable))
-  object_names = _ObjectIdentityDictionary()
-  for obj, path in path_to_root.items():
-    object_names[obj] = _object_prefix_from_path(path)
-  node_ids = _ObjectIdentityDictionary()
-  for node_id, node in enumerate(checkpointable_objects):
-    node_ids[node] = node_id
-  _serialize_slot_variables(
-      checkpointable_objects=checkpointable_objects,
-      node_ids=node_ids,
-      object_names=object_names)
   return checkpointable_objects
 
 
+def make_object_graph_without_attributes(root_checkpointable):
+  """Construct a CheckpointableObjectGraph proto with no variable values."""
+  checkpointable_objects, node_ids, slot_variables = _find_objects(
+      root_checkpointable)
+  return _make_object_graph_proto(
+      checkpointable_objects, node_ids, slot_variables)
+
+
 def gather_initializers(root_checkpointable):
   """Traverse the object graph and find initialization ops.
 

tensorflow/python/training/checkpointable/util_test.py

@@ -26,7 +26,7 @@ from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
-from tensorflow.python.eager import function
+from tensorflow.python.eager import def_function
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -44,6 +44,7 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.training import adam
 from tensorflow.python.training import checkpoint_management
+from tensorflow.python.training import momentum
 from tensorflow.python.training import saver as saver_lib
 from tensorflow.python.training import training_util
 from tensorflow.python.training.checkpointable import base
@@ -198,6 +199,17 @@ class InterfaceTests(test.TestCase):
     with self.assertRaises(NotImplementedError):
       checkpoint_reversed.save(prefix)
 
+  @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
+  def test_object_graph_no_attributes(self):
+    root = tracking.Checkpointable()
+    root.v = resource_variable_ops.ResourceVariable(1.)
+    root.opt = momentum.MomentumOptimizer(0.01, 0.5)
+    root.opt.minimize(root.v.read_value)
+    object_graph = checkpointable_utils.make_object_graph_without_attributes(
+        root)
+    # Four objects: Root, v, opt, and a slot variable for v
+    self.assertEqual(4, len(object_graph.nodes))
+
 
 class _MirroringSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
 
@@ -632,7 +644,7 @@ class CheckpointingTests(test.TestCase):
             checkpoint_directory)
         status = root.restore(save_path=checkpoint_path)
         def train_fn():
-          @function.defun
+          @def_function.function
           def _call_model(x):
             return model(x)
           with backprop.GradientTape() as tape: