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"""An object-local variable management scheme."""
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import collections
import weakref
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_control_flow_ops
from tensorflow.python.trackable import constants
from tensorflow.python.training.saving import saveable_object
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_decorator
from tensorflow.python.util.tf_export import tf_export
OBJECT_GRAPH_PROTO_KEY = constants.OBJECT_GRAPH_PROTO_KEY
VARIABLE_VALUE_KEY = constants.VARIABLE_VALUE_KEY
OBJECT_CONFIG_JSON_KEY = constants.OBJECT_CONFIG_JSON_KEY
SaveType = constants.SaveType
@tf_export("__internal__.tracking.TrackableReference", v1=[])
class TrackableReference(object):
"""A named reference to a trackable object for use with the `Trackable` class.
These references mark named `Trackable` dependencies of a `Trackable` object
and should be created when overriding `Trackable._checkpoint_dependencies`.
Attributes:
name: The local name for this dependency.
ref: The `Trackable` object being referenced.
"""
__slots__ = ("_name", "_ref")
def __init__(self, name, ref):
self._name = name
self._ref = ref
@property
def name(self):
return self._name
@property
def ref(self):
return self._ref
def __iter__(self):
yield self.name
yield self.ref
def __repr__(self):
return f"{self.__class__.__name__}(name={self.name}, ref={self.ref})"
def __eq__(self, o):
if isinstance(o, tuple):
return (self.name, self.ref) == o
elif isinstance(o, TrackableReference):
return self.name == o.name and self.ref == o.ref
else:
return False
class WeakTrackableReference(TrackableReference):
"""TrackableReference that stores weak references."""
__slots__ = ()
def __init__(self, name, ref):
if not isinstance(self, weakref.ref):
ref = weakref.ref(ref)
super(WeakTrackableReference, self).__init__(name=name, ref=ref)
@property
def ref(self):
return self._ref()
# TODO(bfontain): Update once sharded initialization interface is finalized.
ShardInfo = collections.namedtuple("CheckpointInitialValueShardInfo",
["shape", "offset"])
@tf_export("__internal__.tracking.CheckpointInitialValueCallable", v1=[])
class CheckpointInitialValueCallable(object):
"""A callable object that returns a CheckpointInitialValue.
See CheckpointInitialValue for more information.
"""
def __init__(self, checkpoint_position):
self._checkpoint_position = checkpoint_position
@property
def checkpoint_position(self):
return self._checkpoint_position
def __call__(self, shape=None, dtype=None, shard_info=None):
# Note that the signature here is for compatibility with normal callable
# initializers which take shape and dtype. Although dtype isn't used, it
# will get passed in by a functool.partial_wrapper in places like
# base_layer_utils.py's make_variable.
return CheckpointInitialValue(
self._checkpoint_position, shape, shard_info=shard_info)
@property
def restore_uid(self):
return self._checkpoint_position.restore_uid
@tf_export("__internal__.tracking.CheckpointInitialValue", v1=[])
class CheckpointInitialValue(object):
"""Tensor wrapper for managing update UIDs in `Variables`.
When supplied as an initial value, objects of this type let a `Variable`
(`Variable`, `ResourceVariable`, etc.) know the UID of the restore the initial
value came from. This allows deferred restorations to be sequenced in the
order the user specified them, and lets us fall back on assignment if an
initial value is not set (e.g. due to a custom getter interfering).
See comments in _add_variable_with_custom_getter for more information about
how `CheckpointInitialValue` is used.
"""
def __init__(self, checkpoint_position, shape=None, shard_info=None):
if shard_info:
full_shape_str = " ".join("%d" % d for d in shape) + " "
slice_spec = ":".join(
"%d,%d" % (o, s) for o, s in zip(shard_info.offset, shard_info.shape))
shape_and_slice = full_shape_str + slice_spec
else:
shape_and_slice = ""
self.wrapped_value = checkpoint_position.value_tensors(
{VARIABLE_VALUE_KEY: shape_and_slice})[VARIABLE_VALUE_KEY]
self._checkpoint_position = checkpoint_position
def __tf_tensor__(self, dtype=None, name=None):
del dtype
del name
return self.wrapped_value
@property
def checkpoint_position(self):
return self._checkpoint_position
class NoRestoreSaveable(saveable_object.SaveableObject):
"""Embeds a tensor in a checkpoint with no restore ops."""
def __init__(self, tensor, name, dtype=None, device=None):
spec = saveable_object.SaveSpec(
tensor, "", name, dtype=dtype, device=device)
super(NoRestoreSaveable, self).__init__(tensor, [spec], name)
def restore(self, restored_tensors, restored_shapes):
return gen_control_flow_ops.no_op()
_SlotVariableRestoration = collections.namedtuple(
"_SlotVariableRestoration",
[
# The checkpoint proto id of the optimizer object.
"optimizer_id",
# The checkpoint proto id of the slot variable.
"slot_variable_id",
"slot_name",
])
@tf_export("__internal__.tracking.no_automatic_dependency_tracking", v1=[])
def no_automatic_dependency_tracking(method):
"""Disables automatic dependency tracking on attribute assignment.
Use to decorate any method of a Trackable object. Attribute assignment in
that method will not add dependencies (also respected in Model). Harmless if
used in a class which does not do automatic dependency tracking (which means
it's safe to use in base classes which may have subclasses which also inherit
from Trackable).
Args:
method: The method to decorate.
Returns:
A decorated method which sets and un-sets automatic dependency tracking for
the object the method is called on (not thread safe).
"""
def _method_wrapper(self, *args, **kwargs):
previous_value = getattr(self, "_self_setattr_tracking", True)
self._self_setattr_tracking = False # pylint: disable=protected-access
try:
result = method(self, *args, **kwargs)
finally:
self._self_setattr_tracking = previous_value # pylint: disable=protected-access
return result
return tf_decorator.make_decorator(
target=method, decorator_func=_method_wrapper)
@tf_contextlib.contextmanager
def no_manual_dependency_tracking_scope(obj):
"""A context that disables manual dependency tracking for the given `obj`.
Sometimes library methods might track objects on their own and we might want
to disable that and do the tracking on our own. One can then use this context
manager to disable the tracking the library method does and do your own
tracking.
For example:
class TestLayer(tf.keras.Layer):
def build():
with no_manual_dependency_tracking_scope(self):
var = self.add_variable("name1") # Creates a var and doesn't track it
self._track_trackable("name2", var) # We track variable with name `name2`
Args:
obj: A trackable object.
Yields:
a scope in which the object doesn't track dependencies manually.
"""
# pylint: disable=protected-access
previous_value = getattr(obj, "_manual_tracking", True)
obj._manual_tracking = False
try:
yield
finally:
obj._manual_tracking = previous_value
@tf_contextlib.contextmanager
def no_automatic_dependency_tracking_scope(obj):
"""A context that disables automatic dependency tracking when assigning attrs.
Objects that inherit from Autotrackable automatically creates dependencies
to trackable objects through attribute assignments, and wraps data structures
(lists or dicts) with trackable classes. This scope may be used to temporarily
disable this behavior. This works similar to the decorator
`no_automatic_dependency_tracking`.
Example usage:
```
model = tf.keras.Model()
model.arr1 = [] # Creates a ListWrapper object
with no_automatic_dependency_tracking_scope(model):
model.arr2 = [] # Creates a regular, untracked python list
```
Args:
obj: A trackable object.
Yields:
a scope in which the object doesn't track dependencies.
"""
previous_value = getattr(obj, "_setattr_tracking", True)
obj._setattr_tracking = False # pylint: disable=protected-access
try:
yield
finally:
obj._setattr_tracking = previous_value # pylint: disable=protected-access
@tf_export("__internal__.tracking.Trackable", v1=[])
class Trackable(object):
"""Base class for `Trackable` objects without automatic dependencies.
This class has no __setattr__ override for performance reasons. Dependencies
must be added explicitly. Unless attribute assignment is performance-critical,
use `AutoTrackable` instead. Use `Trackable` for `isinstance`
checks.
"""
# For compatibility with wrapt.ObjectProxy, attributes are all prefixed with
# _self_. We have some properties to forward semi-public attributes to their
# _self_ equivalents.
@property
def _setattr_tracking(self):
if not hasattr(self, "_self_setattr_tracking"):
self._self_setattr_tracking = True
return self._self_setattr_tracking
@_setattr_tracking.setter
def _setattr_tracking(self, value):
self._self_setattr_tracking = value
@property
def _update_uid(self):
return self._self_update_uid
@_update_uid.setter
def _update_uid(self, value):
self._self_update_uid = value
@property
def _unconditional_checkpoint_dependencies(self):
return self._self_unconditional_checkpoint_dependencies
@property
def _unconditional_dependency_names(self):
return self._self_unconditional_dependency_names
@property
def _name_based_restores(self):
return self._self_name_based_restores
# Trackable does not do automatic dependency tracking, but uses the
# no_automatic_dependency_tracking decorator so it can avoid adding
# dependencies if a subclass is Trackable / inherits from Model (both of
# which have __setattr__ overrides).
@no_automatic_dependency_tracking
def _maybe_initialize_trackable(self):
"""Initialize dependency management.
Not __init__, since most objects will forget to call it.
"""
if hasattr(self, "_self_unconditional_checkpoint_dependencies"):
# __init__ already called. This check means that we don't need
# Trackable.__init__() in the constructor of every TensorFlow object.
return
# A list of TrackableReference objects. Some classes implementing
# `Trackable`, notably `Optimizer`s, may override the
# _checkpoint_dependencies property with conditional dependencies
# (e.g. based on the current graph when saving).
self._self_unconditional_checkpoint_dependencies = []
# Maps names -> Trackable objects
self._self_unconditional_dependency_names = {}
# Restorations for other Trackable objects on which this object may
# eventually depend. Maps local name -> CheckpointPosition list. Optimizers
# tack on conditional dependencies, and so need separate management of
# deferred dependencies too.
self._self_unconditional_deferred_dependencies = {}
# The UID of the highest assignment to this object. Used to ensure that the
# last requested assignment determines the final value of an object.
if hasattr(self, "_self_update_uid"):
raise AssertionError(
"Internal error: the object had an update UID set before its "
"initialization code was run.")
self._self_update_uid = -1
# When executing eagerly, holds a collection of _NameBasedRestoreCoordinator
# instances, which should be checked when creating variables or other
# saveables. These are passed on recursively to all dependencies, since
# unlike object-based checkpoint restores we don't know which subgraph is
# being restored in advance. This mechanism is only necessary for
# restore-on-create when executing eagerly, and so is unused when graph
# building.
self._self_name_based_restores = set()
# Dictionary of SaveableObjects factories. This dictionary is defined when
# the object is loaded from the SavedModel. When writing a custom class,
# prefer overriding "_gather_saveables_from_checkpoint" to using this
# attribute.
self._self_saveable_object_factories = {}
@property
def _object_identifier(self):
"""String used to identify this object in a SavedModel.
THIS FIELD HAS BEEN DEPRECATED IN FAVOR OF THE NAME REGISTERED WITH
`register_serializable`.
Generally, the object identifier is constant across objects of the same
class, while the metadata field is used for instance-specific data.
Returns:
String object identifier.
"""
return "_generic_user_object"
def _no_dependency(self, value):
"""If automatic dependency tracking is enabled, ignores `value`."""
return value
def _name_based_attribute_restore(self, checkpoint):
"""Restore the object's attributes from a name-based checkpoint."""
self._self_name_based_restores.add(checkpoint)
if self._self_update_uid < checkpoint.restore_uid:
checkpoint.eager_restore(self)
self._self_update_uid = checkpoint.restore_uid
@property
def _checkpoint_dependencies(self):
"""All dependencies of this object.
May be overridden to include conditional dependencies.
Returns:
A list of `TrackableReference` objects indicating named
`Trackable` dependencies which should be saved along with this
object.
"""
return self._self_unconditional_checkpoint_dependencies
@property
def _deferred_dependencies(self):
"""A dictionary with deferred dependencies.
Stores restorations for other Trackable objects on which this object
may eventually depend. May be overridden by sub-classes (e.g. Optimizers use
conditional dependencies based the current graph, and so need separate
management of deferred dependencies too).
Returns:
A dictionary mapping from local name to a list of CheckpointPosition
objects.
"""
return self._self_unconditional_deferred_dependencies
def _lookup_dependency(self, name):
"""Look up a dependency by name.
May be overridden to include conditional dependencies.
Args:
name: The local name of the dependency.
Returns:
A `Trackable` object, or `None` if no dependency by this name was
found.
"""
return self._self_unconditional_dependency_names.get(name, None)
def _add_variable_with_custom_getter(self,
name,
shape=None,
dtype=dtypes.float32,
initializer=None,
getter=None,
overwrite=False,
**kwargs_for_getter):
"""Restore-on-create for a variable be saved with this `Trackable`.
If the user has requested that this object or another `Trackable` which
depends on this object be restored from a checkpoint (deferred loading
before variable object creation), `initializer` may be ignored and the value
from the checkpoint used instead.
Args:
name: A name for the variable. Must be unique within this object.
shape: The shape of the variable.
dtype: The data type of the variable.
initializer: The initializer to use. Ignored if there is a deferred
restoration stored in the Trackable.
getter: The getter to wrap which actually fetches the variable.
overwrite: If True, disables unique name and type checks.
**kwargs_for_getter: Passed to the getter.
Returns:
The new variable object.
Raises:
ValueError: If the variable name is not unique.
"""
self._maybe_initialize_trackable()
with ops.init_scope():
if context.executing_eagerly():
# If this is a variable with a single Tensor stored in the checkpoint,
# we can set that value as an initializer rather than initializing and
# then assigning (when executing eagerly). This call returns None if
# there is nothing to restore.
checkpoint_initializer = self._preload_simple_restoration(name=name)
else:
checkpoint_initializer = None
if (checkpoint_initializer is not None and
not (isinstance(initializer, CheckpointInitialValueCallable) and
(initializer.restore_uid > checkpoint_initializer.restore_uid))):
# If multiple Trackable objects are "creating" the same variable
# via the magic of custom getters, the one with the highest restore UID
# (the one called last) has to make the final initializer. If another
# custom getter interrupts this process by overwriting the initializer,
# then we'll catch that when we call _track_trackable. So this is
# "best effort" to set the initializer with the highest restore UID.
initializer = checkpoint_initializer
new_variable = getter(
name=name,
shape=shape,
dtype=dtype,
initializer=initializer,
**kwargs_for_getter)
# If we set an initializer and the variable processed it, tracking will not
# assign again. It will add this variable to our dependencies, and if there
# is a non-trivial restoration queued, it will handle that. This also
# handles slot variables.
if not overwrite or isinstance(new_variable, Trackable):
return self._track_trackable(new_variable, name=name, overwrite=overwrite)
else:
# TODO(allenl): Some variable types are not yet supported. Remove this
# fallback once all get_variable() return types are Trackable.
return new_variable
def _preload_simple_restoration(self, name):
"""Return a dependency's value for restore-on-create.
Note the restoration is not deleted; if for some reason preload is called
and then not assigned to the variable (for example because a custom getter
overrides the initializer), the assignment will still happen once the
variable is tracked (determined based on checkpoint.restore_uid).
Args:
name: The object-local name of the dependency holding the variable's
value.
Returns:
An callable for use as a variable's initializer/initial_value, or None if
one should not be set (either because there was no variable with this name
in the checkpoint or because it needs more complex deserialization). Any
non-trivial deserialization will happen when the variable object is
tracked.
"""
deferred_dependencies_list = self._deferred_dependencies.get(name, ())
if not deferred_dependencies_list:
# Nothing to do; we don't have a restore for this dependency queued up.
return
for checkpoint_position in deferred_dependencies_list:
if not checkpoint_position.is_simple_variable():
# If _any_ pending restoration is too complicated to fit in an
# initializer (because it has dependencies, or because there are
# multiple Tensors to restore), bail and let the general tracking code
# handle it.
return None
checkpoint_position = max(
deferred_dependencies_list,
key=lambda restore: restore.checkpoint.restore_uid)
return CheckpointInitialValueCallable(
checkpoint_position=checkpoint_position)
def _track_trackable(self, trackable, name, overwrite=False):
"""Declare a dependency on another `Trackable` object.
Indicates that checkpoints for this object should include variables from
`trackable`.
Variables in a checkpoint are mapped to `Trackable`s based on the names
provided when the checkpoint was written. To avoid breaking existing
checkpoints when modifying a class, neither variable names nor dependency
names (the names passed to `_track_trackable`) may change.
Args:
trackable: A `Trackable` which this object depends on.
name: A local name for `trackable`, used for loading checkpoints into the
correct objects.
overwrite: Boolean, whether silently replacing dependencies is OK. Used
for __setattr__, where throwing an error on attribute reassignment would
be inappropriate.
Returns:
`trackable`, for convenience when declaring a dependency and
assigning to a member variable in one statement.
Raises:
TypeError: If `trackable` does not inherit from `Trackable`.
ValueError: If another object is already tracked by this name.
"""
self._maybe_initialize_trackable()
if not isinstance(trackable, Trackable):
raise TypeError(
"Trackable._track_trackable() can only be used to track objects of "
f"type Trackable. Got type {type(trackable)}.")
if not getattr(self, "_manual_tracking", True):
return trackable
new_reference = TrackableReference(name=name, ref=trackable)
current_object = self._lookup_dependency(name)
if (current_object is not None and current_object is not trackable):
if not overwrite:
raise ValueError(
f"Called Trackable._track_trackable() with name='{name}', "
"but a Trackable with this name is already declared as a "
"dependency. Names must be unique (or overwrite=True).")
# This is a weird thing to do, but we're not going to stop people from
# using __setattr__.
for index, (old_name, _) in enumerate(
self._self_unconditional_checkpoint_dependencies):
if name == old_name:
self._self_unconditional_checkpoint_dependencies[
index] = new_reference
elif current_object is None:
self._self_unconditional_checkpoint_dependencies.append(new_reference)
self._handle_deferred_dependencies(name=name, trackable=trackable)
self._self_unconditional_dependency_names[name] = trackable
return trackable
def _handle_deferred_dependencies(self, name, trackable):
"""Pop and load any deferred checkpoint restores into `trackable`.
This method does not add a new dependency on `trackable`, but it does
check if any outstanding/deferred dependencies have been queued waiting for
this dependency to be added (matched based on `name`). If so,
`trackable` and its dependencies are restored. The restorations are
considered fulfilled and so are deleted.
`_track_trackable` is more appropriate for adding a
normal/unconditional dependency, and includes handling for deferred
restorations. This method allows objects such as `Optimizer` to use the same
restoration logic while managing conditional dependencies themselves, by
overriding `_checkpoint_dependencies` and `_lookup_dependency` to change the
object's dependencies based on the context it is saved/restored in (a single
optimizer instance can have state associated with multiple graphs).
Args:
name: The name of the dependency within this object (`self`), used to
match `trackable` with values saved in a checkpoint.
trackable: The Trackable object to restore (inheriting from `Trackable`).
"""
self._maybe_initialize_trackable()
trackable._maybe_initialize_trackable() # pylint: disable=protected-access
deferred_dependencies_list = self._deferred_dependencies.pop(name, ())
for checkpoint_position in sorted(
deferred_dependencies_list,
key=lambda restore: restore.checkpoint.restore_uid,
reverse=True):
checkpoint_position.restore(trackable)
# Pass on any name-based restores queued in this object.
for name_based_restore in sorted(
self._self_name_based_restores,
key=lambda checkpoint: checkpoint.restore_uid,
reverse=True):
trackable._name_based_attribute_restore(name_based_restore) # pylint: disable=protected-access
def _gather_saveables_for_checkpoint(self):
"""Returns a dictionary of values to checkpoint with this object.
NOTE: This method is deprecated, prefer implementing `_serialize_to_tensors`
and `_restore_from_tensors` instead. This method is only used in the
deprecated `tf.compat.v1.train.Saver`.
Keys in the returned dictionary are local to this object and in a separate
namespace from dependencies. Values may either be `SaveableObject` factories
or variables easily converted to `SaveableObject`s (as in
`tf.compat.v1.train.Saver`'s
`var_list` constructor argument).
`SaveableObjects` have a name set, which Trackable needs to generate
itself. So rather than returning `SaveableObjects` directly, this method
should return a dictionary of callables which take `name` arguments and
return `SaveableObjects` with that name.
If this object may also be passed to the global-name-based
`tf.compat.v1.train.Saver`,
the returned callables should have a default value for their name argument
(i.e. be callable with no arguments).
Returned values must be saved only by this object; if any value may be
shared, it should instead be a dependency. For example, variable objects
save their own values with the key `VARIABLE_VALUE_KEY`, but objects which
reference variables simply add a dependency.
Returns:
The dictionary mapping attribute names to `SaveableObject` factories
described above. For example:
{VARIABLE_VALUE_KEY:
lambda name="global_name_for_this_object":
SaveableObject(name=name, ...)}
"""
return getattr(self, "_self_saveable_object_factories", {})
def _serialize_to_tensors(self):
"""Gathers tensors to save to the checkpoint.
You should only override `_serialize_to_tensors` and `_restore_from_tensors`
if you are defining a custom resource or variable with custom ops.
Otherwise, please store the state of your trackable in `tf.Variable` objects
and add them to Trackable object hierarchy using `setattr` (for subclasses
of `AutoTrackable`) or overriding the `_trackable_children` method.
For an example of a valid implementation of these two methods, please see
`DenseHashTable`.
**Invalid implementation**
````
class NamedTrackable(Trackable):
def __init__(self, name: str):
self.name = name
def _serialize_to_tensors(self):
return {"name": self.name}
def _restore_from_tensors(self, restored_tensors):
self.name = restored_tensors["name"]
```
In this example, `NamedTrackable` can be saved and restored from
checkpoints, but is incompatible with SavedModel, which tries to convert
the serialize/restore functions into tf.functions. This fails because
attribute assignment (`self.attr = new_value`) is not graph-friendly.
**Suggested fix**
```
class NamedTrackable(Trackable):
def __init__(self, name: str):
self.name = tf.Variable(name)
def _trackable_children(self):
return {"name": self.name}
```
If the `name` attribute should be saved to the checkpoint, then convert it
a `tf.Variable`.
**TF1 Saver Compatibility**
If your Trackable needs to be comatible with `tf.compat.v1.train.Saver`,
implement `_gather_saveables_from_checkpoint`.
Returns:
A dictionary mapping names to tensors.
"""
raise NotImplementedError
def _restore_from_tensors(self, restored_tensors):
"""Restores checkpointed values to this `Trackable`.
Please see the documentation for `Trackable._serialize_to_tensors`.
Args:
restored_tensors: A dictionary mapping names to tensors. The keys to this
dictionary matches the names passed to _serialize_to_tensors.
Returns:
An op that runs the restoration.
"""
raise NotImplementedError
def _serialize_to_proto(self, object_proto=None, **kwargs):
"""Returns a proto of any type to be saved into the SavedModel.
Trackable classes decorated with `register_serializable` should overwrite
this method to save metadata for this object to the SavedModel. The proto
returned by this function will be passed to `_deserialize_from_proto` in the
form of a `google.protobuf.Any` proto.
This data is only saved and used by the Python API. Existing C++ loading
APIs such as `tensorflow::LoadSavedModel` will not read this field at all.
Args:
object_proto: A `SavedObject` proto that may be filled by this function.
Only the core serializable types (Variable, Function, Constant, Asset)
should modify this argument.
**kwargs: Future keyword arguments passed to the object during saving.
Returns:
A proto that serializes this class's type.
"""
del object_proto, kwargs # Unused.
return None
@classmethod
def _deserialize_from_proto(cls,
proto=None,
dependencies=None,
object_proto=None,
export_dir=None,
asset_file_def=None,
operation_attributes=None,
**kwargs):
"""Returns a new object restored by the SavedModel.
Trackable classes decorated with `register_serializable` should overwrite
this method to change how the object is loaded from SavedModel. By default,
the object is initialized with no arguments.
Example:
```
def _serialize_to_proto(self, **unused_kwargs):
return Message(name="a")
@classmethod
def _deserialize_from_proto(cls, proto, **unused_kwargs):
if proto.Is(Message.DESCRIPTOR):
unpacked = Message()
proto.Unpack(unpacked)
return cls(unpacked.name)
else:
return cls()
```
This function is only used by the Python API. C++ and TensorFlow Serving do
not have access to your registered class and cannot execute any of the
non-tf.functions attached to the Python class. However, all signatures and
tf.functions are still accessible.
**Avoid creating duplicate trackables**
SavedModel is saved by recursively gathering all of the trackables and their
children. SavedModel loading reverses those steps by creating all
trackables, then reconnecting the children trackables to their parents using
`Trackable._add_trackable_child`.
That means that if `_deserialize_from_proto` calls the `__init__` function,
which creates all of the children trackables, then those children end up
being created *twice*.
To avoid this, structure your code so that Trackables are not created
when deserialized from SavedModel:
```
@register_serializable()
class Serializable(trackable):
def __init __(self, from_proto=False):
create_non_trackable_objects()
if not from_proto:
create_variables_and_other_trackables()
def _deserialize_from_proto(cls, **kwargs):
return cls(from_proto=True)
def _add_trackable_child(self, name, value):
self.__setattr__(name, value)
```
Args:
proto: A `google.protobuf.Any` proto read from the `SavedModel`.
dependencies: A dictionary mapping names to dependencies (see
`_deserialization_dependencies`)
object_proto: The `SavedObject` proto for this object.
export_dir: The `SavedModel` directory
asset_file_def: The `MetaGraphDef`'s `asset_file_def` field.
operation_attributes: Dictionary mapping nodes to attribute from the
imported `GraphDef`.
**kwargs: Future keyword arguments passed to the object when loading.
Returns:
A new object.
"""
del (proto, dependencies, object_proto, export_dir, asset_file_def,
operation_attributes, kwargs)
return cls()
def _add_trackable_child(self, name, value):
"""Restores a connection between trackables when loading from SavedModel.
SavedModel stores both the object metadata and its list of children. When
loading, this function is used along with `_deserialize_from_proto` to load
objects from the SavedModel: First, all saved objects are created with
`_deserialize_from_proto`. After that is complete, the children are
connected using `_add_trackable_child`.
**Example**
`tf.Module`, `tf.keras.Model` and Keras layers use `__setattr__` to track
children. This is why users can call `model.v = tf.Variable(...)`, and the
variable will be automatically saved to the checkpoint. The implementation
of this method for the listed objects is:
```
def _add_trackable_child(self, name, value):
self.__setattr__(name, value)
```
Args:
name: The name of the connection between the parent and child `Trackable`.
value: The child `Trackable` object.
"""
self._track_trackable(value, name, overwrite=True)
def _deserialization_dependencies(self, children):
"""Returns a dictionary containing `Trackables` that this object depends on.
Dependencies define the order to serialize and deserialize objects in the
SavedModel. For example:
class A(Trackable):
b = B()
def _deserialization_dependencies(self, children):
return {'b': self.b}
class B(Trackable):
pass
We say that object `a=A()` depends on `a.b`.
Dependencies are guaranteed to be serialized and deserialized before the
object depending on them. The following methods use dependencies:
- `_deserialize_from_proto` [loading]
SavedModel loads with the bottom-up approach, by first creating all objects
in the order defined by the dependencies, then connecting the children.
Unlike `_trackable_children`, this function does not define the
`SavedObjectGraph`. It only changes the order in which things are
saved/loaded. Therefore, if there are dependencies that are not in the
`SavedObjectGraph`, saving will fail.
Args:
children: Dict returned from `_trackable_children`.
Returns:
A dictionary mapping names to `Trackable`.
"""
del children # Unused.
return {}
def _trackable_children(self,
save_type=SaveType.CHECKPOINT,
cache=None,
**kwargs):
"""Returns this object's `Trackable` attributes.
This method is used to build the object graph (or the object hierarchy,
in pickling terms) for checkpoint save/restore, and `SavedModel` export.
Override this method to define the children of this instance. Please read
the implementation restrictions:
**Rule 1: All children must be convertable to `Trackable`.**
Must pass `isinstance` check or `converter.convert_to_trackable`.
**Rule 2: [Checkpoint-only] Do not create new objects.**
When saving to a `SavedModel`, this method is called *exactly once* for each
`Trackable` in the object graph. When saving or restoring from a checkpoint,
this method may be called *multiple times*. Thus, this method may create
new Trackables when `save_type == SaveType.SAVEDMODEL` but not when
`save_type == SaveType.CHECKPOINT`.
When saving to `SavedModel`, new `Trackable` children can be created to save
non-Trackable attributes to the `SavedModel`. In the example below, `hyper`
is a regular python float hyperparameter. To save this value, a new Variable
is created to store the value of `hyper`:
```
def __init__(self):
self.hyper = 1e-5
def _trackable_children(self, save_type, **unused_kwargs):
# Correct implementation
children = {}
if format == 'saved_model':
children['hyper'] = tf.Variable(self.hyper)
return children
```
An incorrect implementation of `_trackable_children` is shown below. This
function would cause failures when loading the checkpoint, and calling
`load_status.assert_consumed()` or
`load_status.assert_existing_objects_matched`. If you want a value to be
saved in the checkpoint, hyper must be defined as a `tf.Variable` from the
start.
```
def _trackable_children(self, save_type, **unused_kwargs):
# Incorrect implementation
return {'hyper': tf.Variable(self.hyper)}
```
**Rule 3: [`SavedModel`-only] Watch out for un-traced tf.functions.**
At the begining of `_trackable_children`, always call
`get_concrete_function()` for any `tf.function` that has an input signature.
When `tf.functions` are saved to `SavedModel`, any `tf.functions` that have
an input signature and has never been called is traced at export time in
order to copy the op graph into the `SavedModel`. `tf.functions` that are
traced for the first time are allowed to create new state:
```
@tf.function(input_signature=[]):
def fn(self);
if self.v is None:
self.v = tf.Variable(1.)
return self.v
```
A problem occurs when there is a `Trackable` that returns `fn` as one of its
children and `self.v` has not been created yet. When `fn` is traced,
`self.v` is added to the `Trackable`, but `SavedModel` does not see this
modification since the `Trackable`'s children have already been gathered.
Therefore, as a precaution, call `get_concrete_function()` at the very
start of `_trackable_children` to ensure that the function is traced:
```
def _trackable_children(self):
self.fn.get_concrete_function()
return {"v": self.v, "fn": self.fn}
```
Args:
save_type: A string, can be 'savedmodel' or 'checkpoint'. Defaults to