320 lines
14 KiB
Python
320 lines
14 KiB
Python
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Extracts tensors for checkpointing while updating a TrackableObjectGraph.
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This is labelled "v1" because the methods here use SaveableObject, which will
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soon be deprecated.
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"""
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import collections
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from tensorflow.core.protobuf import trackable_object_graph_pb2
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from tensorflow.python.checkpoint import saveable_compat
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from tensorflow.python.checkpoint import util
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.saved_model import registration
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from tensorflow.python.trackable import base
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from tensorflow.python.trackable import python_state
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from tensorflow.python.trackable import trackable_utils
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from tensorflow.python.training.saving import saveable_object as saveable_object_lib
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from tensorflow.python.training.saving import saveable_object_util
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from tensorflow.python.util import object_identity
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# Factory and related info used to build a SaveableObject that saves a Trackable
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# to checkpoint.
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_CheckpointFactoryData = collections.namedtuple(
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"_CheckpointFactoryData", ["factory", "name", "checkpoint_key"])
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def get_checkpoint_factories_and_keys(object_names, object_map=None):
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"""Gets a map of saveable factories and corresponding checkpoint keys.
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Args:
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object_names: a dictionary that maps `Trackable` objects to auto-generated
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string names.
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object_map: a dictionary mapping `Trackable` to copied `Trackable` objects.
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The copied objects are generated from `Trackable.
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_export_to_saved_model_graph()` which copies the object into another
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graph. Generally only resource objects (e.g. Variables, Tables) will be
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in this map.
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Returns:
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A tuple of (
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Dictionary mapping trackable -> list of _CheckpointFactoryData,
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Dictionary mapping registered saver name -> {object name -> trackable})
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"""
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checkpoint_factory_map = object_identity.ObjectIdentityDictionary()
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unmapped_registered_savers = collections.defaultdict(dict)
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for trackable, object_name in object_names.items():
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# object_to_save is only used to retrieve the saving functionality. For keys
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# and other data, use the original `trackable`.
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object_to_save = util.get_mapped_trackable(trackable, object_map)
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saver_name = registration.get_registered_saver_name(object_to_save)
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if saver_name:
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# Add the original trackable instead of `object_to_save` to the returned
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# dict because the original is needed for writing the object proto.
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unmapped_registered_savers[saver_name][object_name] = trackable
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else:
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checkpoint_factory_map[trackable] = []
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for name, saveable_factory in (
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saveable_object_util.saveable_objects_from_trackable(
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object_to_save).items()): # pylint: disable=protected-access
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# Retrieve the legacy saveable name (for compatibility purposes during
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# SaveableObject deprecation)
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key_suffix = saveable_compat.get_saveable_name(object_to_save) or name
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checkpoint_key = trackable_utils.checkpoint_key(object_name, key_suffix)
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if not saveable_compat.force_checkpoint_conversion_enabled():
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# Make sure the set the name as the legacy saveable name if there
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# is one (only when checkpoint conversion is diabled)
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name = key_suffix
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checkpoint_factory_map[trackable].append(
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_CheckpointFactoryData(
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factory=saveable_factory,
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name=name,
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checkpoint_key=checkpoint_key))
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return checkpoint_factory_map, unmapped_registered_savers
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def _add_attributes_to_object_graph(trackable_objects, object_graph_proto,
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node_ids, object_names, object_map,
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call_with_mapped_captures, saveables_cache):
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"""Create saveables/savers and corresponding protos in the object graph."""
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# The loop below creates TrackableObject protos in the TrackableObjectGraph,
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# which are filled in the `_add_attributes_to_object_graph_for_*` methods.
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for checkpoint_id, (trackable, unused_object_proto) in enumerate(
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zip(trackable_objects, object_graph_proto.nodes)):
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assert node_ids[trackable] == checkpoint_id
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checkpoint_factory_map, unmapped_registered_savers = (
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get_checkpoint_factories_and_keys(object_names, object_map))
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# Add attributes, which describe what values are saved in checkpoint for
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# this trackable.
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registered_savers = _add_attributes_to_object_graph_for_registered_savers(
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unmapped_registered_savers, object_graph_proto, node_ids, object_map)
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named_saveable_objects, feed_additions = (
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generate_saveable_objects(checkpoint_factory_map, object_graph_proto,
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node_ids, object_map, call_with_mapped_captures,
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saveables_cache))
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return named_saveable_objects, feed_additions, registered_savers
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def _add_attributes_to_object_graph_for_registered_savers(
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unmapped_registered_savers, object_graph_proto, node_ids, object_map):
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"""Fills the object graph proto with data about the registered savers."""
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registered_savers = collections.defaultdict(dict)
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for saver_name, trackables in unmapped_registered_savers.items():
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for object_name, trackable in trackables.items():
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object_proto = object_graph_proto.nodes[node_ids[trackable]]
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object_proto.registered_saver.name = saver_name
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object_proto.registered_saver.object_name = object_name
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object_to_save = util.get_mapped_trackable(trackable, object_map)
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registered_savers[saver_name][object_name] = object_to_save
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return registered_savers
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def generate_saveable_objects(checkpoint_factory_map,
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object_graph_proto=None,
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node_ids=None,
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object_map=None,
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call_with_mapped_captures=None,
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saveables_cache=None):
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"""Create SaveableObjects and corresponding SerializedTensor protos."""
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named_saveable_objects = []
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if saveables_cache is None:
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# No SaveableObject caching. Either we're executing eagerly, or building a
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# static save which is specialized to the current Python state.
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feed_additions = None
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else:
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# If we are caching SaveableObjects, we need to build up a feed_dict with
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# functions computing volatile Python state to be saved with the
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# checkpoint.
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feed_additions = {}
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for trackable, factory_data_list in checkpoint_factory_map.items():
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fill_object_proto = object_graph_proto is not None and node_ids is not None
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if fill_object_proto:
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object_proto = object_graph_proto.nodes[node_ids[trackable]]
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object_to_save = util.get_mapped_trackable(trackable, object_map)
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if saveables_cache is not None:
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cached_attributes = saveables_cache.setdefault(object_to_save, {})
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else:
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cached_attributes = None
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for factory_data in factory_data_list:
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name = factory_data.name
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key = factory_data.checkpoint_key
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saveable_factory = factory_data.factory
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# See if we can skip saving this checkpoint key.
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saveables = cached_attributes.get(name) if cached_attributes else None
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if saveables is not None:
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for saveable in saveables:
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if key not in saveable.name:
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# The checkpoint key for this SaveableObject is different. We
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# need to re-create it.
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saveables = None
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del cached_attributes[name]
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break
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if saveables is None:
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if callable(saveable_factory):
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maybe_saveable = saveable_object_util.create_saveable_object(
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name, key, saveable_factory, call_with_mapped_captures)
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else:
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maybe_saveable = saveable_factory
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if isinstance(maybe_saveable, saveable_object_lib.SaveableObject):
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saveables = (maybe_saveable,)
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else:
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saveables = tuple(
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saveable_object_util.saveable_objects_for_op(
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op=maybe_saveable, name=key))
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for saveable in saveables:
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if key not in saveable.name:
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raise AssertionError(
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f"The object {trackable} produced a SaveableObject with name "
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f"'{saveable.name}' for attribute '{name}'. Expected a name"
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f" containing '{key}'.")
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if cached_attributes is not None:
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cached_attributes[name] = saveables
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if isinstance(object_to_save, python_state.PythonState):
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assert len(saveables) == 1
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saveable = saveables[0]
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if feed_additions is None:
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assert saveables_cache is None
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# If we're not caching saveables, then we're either executing
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# eagerly or building a static save/restore (e.g. for a
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# SavedModel). In either case, we should embed the current Python
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# state in the graph rather than relying on a feed dict.
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saveables = (saveable.freeze(),)
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else:
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feed_additions.update(saveable.feed_dict_additions())
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named_saveable_objects.extend(saveables)
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# Update the object proto.
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# For updated Trackables that override serialize_to_tensors, add an
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# attribute for each tensor that is serialized.
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# For Trackables that have SaveableObjects or a legacy saveable name,
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# add a single attribute to the proto.
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if not fill_object_proto:
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continue
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if (isinstance(saveables[0], saveable_object_util.TrackableSaveable) and
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(saveable_compat.force_checkpoint_conversion_enabled() or
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saveable_compat.get_saveable_name(object_to_save) is None)):
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for local_name, local_key in (
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saveables[0].get_proto_names_and_checkpoint_keys()):
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object_proto.attributes.add(
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name=local_name,
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checkpoint_key=local_key,
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full_name=util.get_full_name(object_to_save))
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else:
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object_proto.attributes.add(
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name=name,
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checkpoint_key=key,
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full_name=util.get_full_name(object_to_save))
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return named_saveable_objects, feed_additions
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def _fill_object_graph_proto(graph_view,
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trackable_objects,
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node_ids,
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slot_variables):
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"""Name non-slot `Trackable`s and add them to `object_graph_proto`."""
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object_graph_proto = trackable_object_graph_pb2.TrackableObjectGraph()
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for checkpoint_id, trackable in enumerate(trackable_objects):
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assert node_ids[trackable] == checkpoint_id
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object_proto = object_graph_proto.nodes.add(
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slot_variables=slot_variables.get(trackable, ())
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)
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for child in graph_view.list_children(trackable):
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object_proto.children.add(
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node_id=node_ids[child.ref],
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local_name=child.name)
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return object_graph_proto
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def serialize_gathered_objects(graph_view,
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object_map=None,
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call_with_mapped_captures=None,
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saveables_cache=None):
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"""Create SaveableObjects and protos for gathered objects."""
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trackable_objects, node_paths = graph_view.breadth_first_traversal()
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object_names = object_identity.ObjectIdentityDictionary()
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for obj, path in node_paths.items():
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object_names[obj] = trackable_utils.object_path_to_string(path)
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node_ids = object_identity.ObjectIdentityDictionary()
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for node_id, node in enumerate(trackable_objects):
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node_ids[node] = node_id
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slot_variables = util.serialize_slot_variables(
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trackable_objects=trackable_objects,
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node_ids=node_ids,
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object_names=object_names)
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object_graph_proto = _fill_object_graph_proto(
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graph_view=graph_view,
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trackable_objects=trackable_objects,
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node_ids=node_ids,
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slot_variables=slot_variables)
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named_saveable_objects, feed_additions, registered_savers = (
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_add_attributes_to_object_graph(
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trackable_objects=trackable_objects,
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object_graph_proto=object_graph_proto,
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node_ids=node_ids,
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object_names=object_names,
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object_map=object_map,
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call_with_mapped_captures=call_with_mapped_captures,
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saveables_cache=saveables_cache))
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# Gather all trackables that have checkpoint values or descendants with
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# checkpoint values, and add that info to the proto.
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util.add_checkpoint_values_check(object_graph_proto)
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return (named_saveable_objects, object_graph_proto, feed_additions,
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registered_savers)
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def serialize_object_graph_with_registered_savers(graph_view, saveables_cache):
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"""Determine checkpoint keys for variables and build a serialized graph."""
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return serialize_gathered_objects(graph_view, saveables_cache=saveables_cache)
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def frozen_saveables_and_savers(graph_view,
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object_map=None,
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to_graph=None,
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call_with_mapped_captures=None,
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saveables_cache=None):
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"""Generates SaveableObjects and registered savers in the frozen graph."""
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if to_graph:
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target_context = to_graph.as_default
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else:
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target_context = ops.NullContextmanager
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with target_context():
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named_saveable_objects, graph_proto, _, registered_savers = (
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serialize_gathered_objects(graph_view, object_map,
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call_with_mapped_captures, saveables_cache))
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with ops.device("/cpu:0"):
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object_graph_tensor = constant_op.constant(
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graph_proto.SerializeToString(), dtype=dtypes.string)
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named_saveable_objects.append(
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base.NoRestoreSaveable(
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tensor=object_graph_tensor, name=base.OBJECT_GRAPH_PROTO_KEY))
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return named_saveable_objects, registered_savers
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