116 lines
3.4 KiB
Python
116 lines
3.4 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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"""Checkpoint compatibility functions with SaveableObject.
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Compatibility methods to ensure that checkpoints are saved with the same
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metadata attributes before/after the SaveableObject deprecation.
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"""
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_LEGACY_SAVEABLE_NAME = "_LEGACY_SAVEABLE_NAME"
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def legacy_saveable_name(name):
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"""Decorator to set the local name to use in the Checkpoint.
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Needed for migrating certain Trackables (see next paragraph) from the legacy
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`_gather_saveables_for_checkpoint` to the new `_serialize_to_tensors`
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function.
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This decorator should be used if the SaveableObject generates tensors with
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different names from the name that is passed to the factory.
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Example migration:
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*Before*
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```
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class MyTrackable(Trackable):
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def _gather_saveables_for_checkpoint(self):
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return {"key": _MySaveable}
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class _MySaveable(SaveableObject):
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def __init__(self, name):
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specs = [
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SaveSpec(tensor1, "", name + "-1")
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SaveSpec(tensor2, "", name + "-2")
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]
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super().__init__(None, specs, name)
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```
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*After*
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```
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@legacy_saveable_name("key")
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class MyTrackable(Trackable):
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def _serialize_to_tensors(self):
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return {"key-1": tensor1, "key-2": tensor2}
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```
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Args:
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name: String name of the SaveableObject factory (the key returned in the
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`_gather_saveables_for_checkpoint` function)
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Returns:
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A decorator.
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"""
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def decorator(cls_or_obj):
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setattr(cls_or_obj, _LEGACY_SAVEABLE_NAME, name)
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return cls_or_obj
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return decorator
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def get_saveable_name(cls_or_obj):
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return getattr(cls_or_obj, _LEGACY_SAVEABLE_NAME, None)
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_FORCE_CHECKPOINT_CONVERSION = False
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def force_checkpoint_conversion(value=True):
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"""Forces checkpoint to use the new implementation.
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The new checkpoint implementation is changing the saved metadata slightly,
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and therefore may break forward compatibility in newly saved checkpoints. This
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means:
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- Previous versions of TensorFlow may not be able to load new checkpoints.
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- Backwards compatibility is unchanged: Old checkpoints can still be loaded.
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TensorFlow guarantees 3 weeks of forward compatibility, so this flag will be
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removed in the future weeks, after which checkpoint conversion will happen by
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default.
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**What happens when this flag is enabled?**
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The checkpoint will be saved with different metadata, meaning that previous
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versions of TensorFlow (<=2.10) will not be able to load this checkpoint.
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Args:
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value: Boolean value, whether or not to force checkpoint conversion to the
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new implementation.
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"""
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# TODO(kathywu): Add definite date for flag removal.
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global _FORCE_CHECKPOINT_CONVERSION
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_FORCE_CHECKPOINT_CONVERSION = value
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def force_checkpoint_conversion_enabled():
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return _FORCE_CHECKPOINT_CONVERSION
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class CheckpointConversionError(Exception):
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pass
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