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chore: import upstream snapshot with attribution
2026-07-13 12:14:16 +08:00

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Python

# Copyright 2022 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.
# ==============================================================================
"""Tracing utilities used by SavedModel."""
from tensorflow.python.checkpoint import saveable_compat
from tensorflow.python.checkpoint import tensor_callable
from tensorflow.python.eager import def_function
from tensorflow.python.eager import function as defun
def trace_save_and_restore(obj):
"""Traces `Trackable` serialize- and restore-from-tensors functions.
Args:
obj: A `Trackable` object.
Returns:
A concrete Function.
"""
legacy_name = saveable_compat.get_saveable_name(obj)
obj_save_fn = obj._serialize_to_tensors # pylint: disable=protected-access
obj_restore_fn = obj._restore_from_tensors # pylint: disable=protected-access
if isinstance(obj_save_fn, defun.ConcreteFunction):
concrete_save = obj_save_fn
else:
@def_function.function
def save_fn():
tensor_dict = obj_save_fn()
if any(isinstance(v, tensor_callable.Callable)
for v in tensor_dict.values()):
raise NotImplementedError(
f"Unable to export SavedModel with object of type {type(obj)} "
"because it returns a Callable in `_serialize_to_tensors`. "
"If you need this functionality please file a feature request.")
if legacy_name:
# If there is a legacy decorator, append the name to the keys.
return {f"{legacy_name}{key}": value
for key, value in tensor_dict.items()}
return tensor_dict
concrete_save = save_fn.get_concrete_function()
if isinstance(obj_restore_fn, defun.ConcreteFunction):
concrete_restore = obj_restore_fn
else:
@def_function.function
def restore_fn(restored_tensors):
if legacy_name:
# Do the opposite operation of save_fn()
restored_tensors = {key[len(legacy_name):]: value
for key, value in restored_tensors.items()}
obj_restore_fn(restored_tensors)
concrete_restore = restore_fn.get_concrete_function(
concrete_save.structured_outputs)
return concrete_save, concrete_restore