experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Split out SaveableObjects into their own file

Browse Source 
Pulls a couple build rules out of tensorflow/python:training. I'd like to use a SaveableObject in :checkpointable (for saving some Python state by default), which means the file with SaveableObject has to be essientially dependency-free.

PiperOrigin-RevId: 194473987
This commit is contained in:
Allen Lavoie 2018-04-26 16:40:16 -07:00 committed by TensorFlower Gardener
parent 7d3e3fd76a
commit 236120d32d
3 changed files with 115 additions and 79 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
tensorflow/python/BUILD
View File

@ -2967,7 +2967,11 @@ py_library(
["training/**/*.py"],
exclude = [
"**/*test*",
"training/training_util.py", # See :training_util
# The following targets have their own build rules (same name as the
# file):
"training/checkpointable.py",
"training/saveable_object.py",
"training/training_util.py",
],
),
srcs_version = "PY2AND3",
@ -2975,6 +2979,7 @@ py_library(
":array_ops",
":array_ops_gen",
":checkpoint_ops_gen",
":checkpointable",
":client",
":control_flow_ops",
":data_flow_ops",
@ -2998,6 +3003,7 @@ py_library(
":random_ops",
":resource_variable_ops",
":resources",
":saveable_object",
":sdca_ops",
":sparse_ops",
":state_ops",
@ -3043,6 +3049,12 @@ py_test(
],
)
py_library(
name = "saveable_object",
srcs = ["training/saveable_object.py"],
srcs_version = "PY2AND3",
)
py_library(
name = "device_util",
srcs = ["training/device_util.py"],

99
tensorflow/python/training/saveable_object.py Normal file
View File

@ -0,0 +1,99 @@
# Copyright 2015 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.
# ==============================================================================
"""Types for specifying saving and loading behavior."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
class SaveSpec(object):
"""Class used to describe tensor slices that need to be saved."""
def __init__(self, tensor, slice_spec, name, dtype=None):
"""Creates a `SaveSpec` object.
Args:
tensor: the tensor to save or callable that produces a tensor to save.
slice_spec: the slice to be saved. See `Variable.SaveSliceInfo`.
name: the name to save the tensor under.
dtype: The data type of the Tensor. Required if `tensor` is callable.
Used for error checking in the restore op.
"""
self._tensor = tensor
self.slice_spec = slice_spec
self.name = name
if callable(self._tensor):
if dtype is None:
raise AssertionError(
"When passing a callable `tensor` to a SaveSpec, an explicit "
"dtype must be provided.")
self.dtype = dtype
else:
self.dtype = tensor.dtype
@property
def tensor(self):
return self._tensor() if callable(self._tensor) else self._tensor
class SaveableObject(object):
"""Base class for saving and restoring saveable objects."""
def __init__(self, op, specs, name):
"""Creates a `SaveableObject` object.
Args:
op: the "producer" object that this class wraps; it produces a list of
tensors to save. E.g., a "Variable" object saving its backing tensor.
specs: a list of SaveSpec, each element of which describes one tensor to
save under this object. All Tensors must be on the same device.
name: the name to save the object under.
"""
self.op = op
self.specs = specs
self.name = name
self._device = None
@property
def device(self):
"""The device for SaveSpec Tensors."""
# Note that SaveSpec.tensor runs Tensor-gathering ops when executing
# eagerly, making this call potentially very expensive.
#
# TODO(allenl): Consider another way to gather device information. Lower
# priority since this property isn't part of the normal save()/restore()
# workflow, but does come up when some alternative builders are passed to
# the Saver.
if self._device is None:
self._device = self.specs[0].tensor.device
return self._device
def restore(self, restored_tensors, restored_shapes):
"""Restores this object from 'restored_tensors'.
Args:
restored_tensors: the tensors that were loaded from a checkpoint
restored_shapes: the shapes this object should conform to after
restore, or None.
Returns:
An operation that restores the state of the object.
Raises:
ValueError: If the object cannot be restored using the provided
parameters.
"""
# pylint: disable=unused-argument
raise ValueError("Calling an abstract method.")

81
tensorflow/python/training/saver.py
View File

@ -54,6 +54,7 @@ from tensorflow.python.ops import variables
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import checkpointable
from tensorflow.python.training import saveable_object
from tensorflow.python.training import training_util
from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState
from tensorflow.python.util import compat
@ -91,84 +92,8 @@ class BaseSaverBuilder(object):
Can be extended to create different Ops.
"""
class SaveSpec(object):
"""Class used to describe tensor slices that need to be saved."""
def __init__(self, tensor, slice_spec, name, dtype=None):
"""Creates a `SaveSpec` object.
Args:
tensor: the tensor to save or callable that produces a tensor to save.
slice_spec: the slice to be saved. See `Variable.SaveSliceInfo`.
name: the name to save the tensor under.
dtype: The data type of the Tensor. Required if `tensor` is callable.
Used for error checking in the restore op.
"""
self._tensor = tensor
self.slice_spec = slice_spec
self.name = name
if callable(self._tensor):
if dtype is None:
raise AssertionError(
"When passing a callable `tensor` to a SaveSpec, an explicit "
"dtype must be provided.")
self.dtype = dtype
else:
self.dtype = tensor.dtype
@property
def tensor(self):
return self._tensor() if callable(self._tensor) else self._tensor
class SaveableObject(object):
"""Base class for saving and restoring saveable objects."""
def __init__(self, op, specs, name):
"""Creates a `SaveableObject` object.
Args:
op: the "producer" object that this class wraps; it produces a list of
tensors to save. E.g., a "Variable" object saving its backing tensor.
specs: a list of SaveSpec, each element of which describes one tensor to
save under this object. All Tensors must be on the same device.
name: the name to save the object under.
"""
self.op = op
self.specs = specs
self.name = name
self._device = None
@property
def device(self):
"""The device for SaveSpec Tensors."""
# Note that SaveSpec.tensor runs Tensor-gathering ops when executing
# eagerly, making this call potentially very expensive.
#
# TODO(allenl): Consider another way to gather device information. Lower
# priority since this property isn't part of the normal save()/restore()
# workflow, but does come up when some alternative builders are passed to
# the Saver.
if self._device is None:
self._device = self.specs[0].tensor.device
return self._device
def restore(self, restored_tensors, restored_shapes):
"""Restores this object from 'restored_tensors'.
Args:
restored_tensors: the tensors that were loaded from a checkpoint
restored_shapes: the shapes this object should conform to after
restore, or None.
Returns:
An operation that restores the state of the object.
Raises:
ValueError: If the object cannot be restored using the provided
parameters.
"""
# pylint: disable=unused-argument
raise ValueError("Calling an abstract method.")
SaveSpec = saveable_object.SaveSpec
SaveableObject = saveable_object.SaveableObject
class VariableSaveable(SaveableObject):
"""SaveableObject implementation that handles Variables."""