e853835634
This option enables saving and restoring models to or from filesystems only accessible from the localhost when using multiple devices. The option is available to - Save models: tf.saved_model.save() - Checkpoints: tf.Checkpoint() PiperOrigin-RevId: 307858098 Change-Id: I4cd0a81424e306f0eac40bfb30d5067dfc02d1be
1711 lines
72 KiB
Python
1711 lines
72 KiB
Python
# Copyright 2017 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import os
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import weakref
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from absl.testing import parameterized
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import six
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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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.framework import test_util
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from tensorflow.python.keras.engine import input_layer
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from tensorflow.python.keras.engine import sequential
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.optimizer_v2 import adam
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.ops import template
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.ops import variables as variables_lib
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.training import checkpoint_management
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from tensorflow.python.training import saver as saver_lib
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from tensorflow.python.training import training_util
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from tensorflow.python.training.saving import checkpoint_options
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from tensorflow.python.training.tracking import base
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from tensorflow.python.training.tracking import graph_view
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from tensorflow.python.training.tracking import tracking
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from tensorflow.python.training.tracking import util as trackable_utils
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class NonLayerTrackable(tracking.AutoTrackable):
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def __init__(self):
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super(NonLayerTrackable, self).__init__()
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self.a_variable = trackable_utils.add_variable(
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self, name="a_variable", shape=[])
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# pylint: disable=not-callable
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class MyModel(training.Model):
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"""A concrete Model for testing."""
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def __init__(self):
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super(MyModel, self).__init__()
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self._named_dense = core.Dense(1, use_bias=True)
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self._second = core.Dense(1, use_bias=False)
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# We can still track Trackables which aren't Layers.
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self._non_layer = NonLayerTrackable()
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def call(self, values):
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ret = self._second(self._named_dense(values))
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return ret
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class InterfaceTests(test.TestCase):
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def testLayerDeduplication(self):
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model = training.Model()
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layer_one = core.Dense(1)
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layer_two = core.Dense(1)
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model.other_path = [layer_one, layer_two]
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model.l2 = layer_two
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model.l1 = layer_one
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self.assertEqual([layer_one, layer_two], model.layers)
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def testSaveWithOnlyKerasSession(self):
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with ops.Graph().as_default():
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inp = input_layer.Input([1])
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dense = core.Dense(1)(inp)
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model = training.Model(inp, dense)
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model.compile(optimizer="sgd", loss="mse")
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model.fit([1.], [2.])
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checkpoint = trackable_utils.Checkpoint(model=model)
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checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
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@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
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def testAddVariable(self):
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obj = NonLayerTrackable()
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with self.assertRaisesRegexp(ValueError, "do not specify shape"):
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trackable_utils.add_variable(
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obj, name="shape_specified_twice", shape=[], initializer=1)
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constant_initializer = trackable_utils.add_variable(
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obj, name="constant_initializer", initializer=1)
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with variable_scope.variable_scope("some_variable_scope"):
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ones_initializer = trackable_utils.add_variable(
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obj,
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name="ones_initializer",
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shape=[2],
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initializer=init_ops.ones_initializer(dtype=dtypes.float32))
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bare_initializer = trackable_utils.add_variable(
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obj,
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name="bare_initializer",
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shape=[2, 2],
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dtype=dtypes.float64,
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initializer=init_ops.zeros_initializer)
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# Even in graph mode, there are no naming conflicts between objects, only
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# naming conflicts within an object.
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other_duplicate = resource_variable_ops.ResourceVariable(
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name="duplicate", initial_value=1.)
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duplicate = trackable_utils.add_variable(
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obj, name="duplicate", shape=[])
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with self.assertRaisesRegexp(ValueError, "'duplicate'.*already declared"):
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trackable_utils.add_variable(obj, name="duplicate", shape=[])
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self.evaluate(trackable_utils.gather_initializers(obj))
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self.assertEqual("constant_initializer:0", constant_initializer.name)
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self.assertEqual(1, self.evaluate(constant_initializer))
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self.assertEqual("some_variable_scope/ones_initializer:0",
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ones_initializer.name)
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self.assertAllEqual([1, 1], self.evaluate(ones_initializer))
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self.assertAllEqual([[0., 0.],
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[0., 0.]], self.evaluate(bare_initializer))
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self.assertEqual("a_variable:0", obj.a_variable.name)
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self.assertEqual("duplicate:0", other_duplicate.name)
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if context.executing_eagerly():
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# When executing eagerly, there's no uniquification of variable names. The
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# checkpoint name will be the same.
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self.assertEqual("duplicate:0", duplicate.name)
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else:
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# The .name attribute may be globally influenced, but the checkpoint name
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# won't be (tested below).
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self.assertEqual("duplicate_1:0", duplicate.name)
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named_variables, _, _ = (
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graph_view.ObjectGraphView(obj).serialize_object_graph())
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expected_checkpoint_names = (
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"a_variable/.ATTRIBUTES/VARIABLE_VALUE",
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"bare_initializer/.ATTRIBUTES/VARIABLE_VALUE",
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"constant_initializer/.ATTRIBUTES/VARIABLE_VALUE",
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"duplicate/.ATTRIBUTES/VARIABLE_VALUE",
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"ones_initializer/.ATTRIBUTES/VARIABLE_VALUE",
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)
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six.assertCountEqual(
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self, expected_checkpoint_names, [v.name for v in named_variables])
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def testInitNotCalled(self):
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class NoInit(tracking.AutoTrackable):
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def __init__(self):
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pass
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# __init__ for Trackable will be called implicitly.
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trackable_utils.add_variable(NoInit(), "var", shape=[])
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def testShapeDtype(self):
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root = tracking.AutoTrackable()
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v1 = trackable_utils.add_variable(
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root, name="v1", initializer=3., dtype=dtypes.float64)
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self.assertEqual(dtypes.float64, v1.dtype)
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v2 = trackable_utils.add_variable(
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root,
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name="v2",
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shape=[3],
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initializer=init_ops.ones_initializer,
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dtype=dtypes.float64)
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self.assertEqual(dtypes.float64, v2.dtype)
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self.assertAllEqual([1., 1., 1.], self.evaluate(v2))
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def testObjectMetadata(self):
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with context.eager_mode():
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checkpoint_directory = self.get_temp_dir()
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checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
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dense = core.Dense(1)
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checkpoint = trackable_utils.Checkpoint(dense=dense)
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dense(constant_op.constant([[1.]]))
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save_path = checkpoint.save(checkpoint_prefix)
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objects = trackable_utils.object_metadata(save_path)
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all_variable_names = []
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for obj in objects.nodes:
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for attribute in obj.attributes:
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all_variable_names.append(attribute.full_name)
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self.assertIn("dense/kernel", all_variable_names)
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def testNotTrackable(self):
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class CallsFunctionalStuff(
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tracking.NotTrackable, tracking.AutoTrackable):
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pass
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test_dir = self.get_temp_dir()
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prefix = os.path.join(test_dir, "ckpt")
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checkpoint = trackable_utils.Checkpoint(x=CallsFunctionalStuff())
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with self.assertRaises(NotImplementedError):
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checkpoint.save(prefix)
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class CallsFunctionalStuffOtherMRO(
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tracking.AutoTrackable, tracking.NotTrackable):
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pass
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checkpoint_reversed = trackable_utils.Checkpoint(
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x=CallsFunctionalStuffOtherMRO())
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with self.assertRaises(NotImplementedError):
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checkpoint_reversed.save(prefix)
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class _MirroringSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
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def __init__(self, primary_variable, mirrored_variable, name):
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self._primary_variable = primary_variable
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self._mirrored_variable = mirrored_variable
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tensor = self._primary_variable.read_value()
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spec = saver_lib.BaseSaverBuilder.SaveSpec(
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tensor=tensor,
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slice_spec="",
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name=name)
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super(_MirroringSaveable, self).__init__(
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tensor, [spec], name)
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def restore(self, restored_tensors, restored_shapes):
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"""Restore the same value into both variables."""
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tensor, = restored_tensors
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return control_flow_ops.group(
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self._primary_variable.assign(tensor),
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self._mirrored_variable.assign(tensor))
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class _OwnsMirroredVariables(base.Trackable):
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"""A Trackable object which returns a more complex SaveableObject."""
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def __init__(self):
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self.non_dep_variable = variable_scope.get_variable(
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name="non_dep_variable", initializer=6., use_resource=True)
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self.mirrored = variable_scope.get_variable(
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name="mirrored", initializer=15., use_resource=True)
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def _gather_saveables_for_checkpoint(self):
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def _saveable_factory(name=self.non_dep_variable.name):
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return _MirroringSaveable(
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primary_variable=self.non_dep_variable,
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mirrored_variable=self.mirrored,
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name=name)
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return {base.VARIABLE_VALUE_KEY: _saveable_factory}
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# The Saver sorts by name before parsing, so we need a name property.
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@property
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def name(self):
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return self.non_dep_variable.name
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class CheckpointingTests(parameterized.TestCase, test.TestCase):
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@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
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def testNamingWithOptimizer(self):
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input_value = constant_op.constant([[3.]])
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model = MyModel()
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# A nuisance Model using the same optimizer. Its slot variables should not
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# go in the checkpoint, since it is never depended on.
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other_model = MyModel()
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optimizer = adam.Adam(0.001)
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step = training_util.get_or_create_global_step()
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root_trackable = trackable_utils.Checkpoint(
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optimizer=optimizer, model=model, step=step)
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with backprop.GradientTape() as tape:
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loss = model(input_value)
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variables = model.trainable_variables
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gradients = tape.gradient(loss, variables)
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train_op = control_flow_ops.group(
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optimizer.apply_gradients(zip(gradients, variables)),
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step.assign_add(1))
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with backprop.GradientTape() as tape:
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loss = other_model(input_value)
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variables = other_model.trainable_variables
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gradients = tape.gradient(loss, variables)
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optimizer.apply_gradients(zip(gradients, variables))
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self.evaluate(trackable_utils.gather_initializers(
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root_trackable))
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self.evaluate(train_op)
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named_variables, serialized_graph, _ = graph_view.ObjectGraphView(
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root_trackable).serialize_object_graph()
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expected_slot_keys = (
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"model/_second/kernel/.OPTIMIZER_SLOT/optimizer/m",
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"model/_second/kernel/.OPTIMIZER_SLOT/optimizer/v",
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"model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m",
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"model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v",
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"model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m",
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"model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v",
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)
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expected_checkpoint_names = (
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# Created in the root node, so no prefix.
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"step",
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"model/_second/kernel",
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"model/_named_dense/kernel",
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"model/_named_dense/bias",
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# non-Layer dependency of the model
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"model/_non_layer/a_variable",
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"optimizer/learning_rate",
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"optimizer/beta_1",
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"optimizer/beta_2",
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"optimizer/iter",
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"optimizer/decay",
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) + expected_slot_keys
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suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
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expected_checkpoint_names = [
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name + suffix for name in expected_checkpoint_names]
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named_variables = {v.name: v for v in named_variables}
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six.assertCountEqual(self, expected_checkpoint_names,
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named_variables.keys())
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# Check that we've mapped to the right variable objects (not exhaustive)
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self.assertEqual(
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"global_step",
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named_variables["step" + suffix].full_name)
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self.assertEqual(
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"my_model/dense_1/kernel",
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named_variables["model/_second/kernel" + suffix].full_name)
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self.assertEqual(
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"my_model/dense/kernel",
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named_variables["model/_named_dense/kernel" + suffix].full_name)
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self.assertEqual("Adam/beta_1",
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named_variables["optimizer/beta_1" + suffix].full_name)
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self.assertEqual("Adam/beta_2",
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named_variables["optimizer/beta_2" + suffix].full_name)
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# Spot check the generated protocol buffers.
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self.assertEqual("optimizer",
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serialized_graph.nodes[0].children[1].local_name)
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optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[
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1].node_id]
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children = [node.local_name for node in optimizer_node.children]
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six.assertCountEqual(
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self,
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# hyper variable dependencies
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["beta_1", "beta_2", "iter", "decay", "learning_rate"],
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children)
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serialized_slot_keys = []
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for slot in optimizer_node.slot_variables:
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for attribute in (
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serialized_graph.nodes[slot.slot_variable_node_id].attributes):
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serialized_slot_keys.append(attribute.checkpoint_key)
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six.assertCountEqual(
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self,
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[key + suffix for key in expected_slot_keys],
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serialized_slot_keys)
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@test_util.run_in_graph_and_eager_modes
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def testMoreComplexSaveableReturned(self):
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v = _OwnsMirroredVariables()
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checkpoint = trackable_utils.Checkpoint(v=v)
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test_dir = self.get_temp_dir()
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prefix = os.path.join(test_dir, "ckpt")
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self.evaluate(v.non_dep_variable.assign(42.))
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save_path = checkpoint.save(prefix)
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self.evaluate(v.non_dep_variable.assign(43.))
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self.evaluate(v.mirrored.assign(44.))
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checkpoint.restore(save_path).assert_consumed().initialize_or_restore()
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self.assertEqual(42., self.evaluate(v.non_dep_variable))
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self.assertEqual(42., self.evaluate(v.mirrored))
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self.evaluate(v.non_dep_variable.assign(44.))
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save_path = checkpoint.save(prefix)
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self.evaluate(v.non_dep_variable.assign(45.))
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checkpoint.restore(save_path).assert_consumed().initialize_or_restore()
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self.assertEqual(44., self.evaluate(v.non_dep_variable))
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self.assertEqual(44., self.evaluate(v.mirrored))
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@test_util.run_in_graph_and_eager_modes
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def testMoreComplexSaveableReturnedWithGlobalName(self):
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# The same object can also be saved using the name-based saver.
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v = _OwnsMirroredVariables()
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saver = saver_lib.Saver(var_list=[v])
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test_dir = self.get_temp_dir()
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prefix = os.path.join(test_dir, "ckpt")
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with self.cached_session() as sess:
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self.evaluate(v.non_dep_variable.assign(42.))
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save_path = saver.save(sess, prefix)
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self.evaluate(v.non_dep_variable.assign(43.))
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self.evaluate(v.mirrored.assign(44.))
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saver.restore(sess, save_path)
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self.assertEqual(42., self.evaluate(v.non_dep_variable))
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self.assertEqual(42., self.evaluate(v.mirrored))
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@test_util.run_in_graph_and_eager_modes
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def testAssertConsumedNoCheckpoint(self):
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prefix = os.path.join(self.get_temp_dir(), "ckpt")
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v = variable_scope.get_variable(name="v", initializer=0.)
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self.evaluate(v.initializer)
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ckpt = trackable_utils.Checkpoint(v=v)
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self.evaluate(trackable_utils.gather_initializers(ckpt))
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save_path = ckpt.save(file_prefix=prefix)
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status = ckpt.restore(save_path=save_path)
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del ckpt
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status.assert_consumed()
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@test_util.run_in_graph_and_eager_modes
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def testPassingCheckpointOptions(self):
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localhost = "/job:localhost/device:CPU:0"
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options = checkpoint_options.CheckpointOptions(
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experimental_io_device=localhost)
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prefix = os.path.join(self.get_temp_dir(), "ckpt")
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v = variable_scope.get_variable(name="v", initializer=0.)
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self.evaluate(v.initializer)
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ckpt = trackable_utils.Checkpoint(v=v)
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self.evaluate(trackable_utils.gather_initializers(ckpt))
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save_path = ckpt.save(file_prefix=prefix, options=options)
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status = ckpt.restore(save_path=save_path, options=options)
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del ckpt
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status.assert_consumed()
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# In graph mode, verify that the save and restore ops were set to run on
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# localhost.
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if not context.executing_eagerly():
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for op in ops.get_default_graph().get_operations():
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if op.type in ("SaveV2", "RestoreV2"):
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self.assertEqual(localhost, op.device)
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@test_util.run_in_graph_and_eager_modes
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def testSaveRestore(self):
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model = MyModel()
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optimizer = adam.Adam(0.001)
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root_trackable = trackable_utils.Checkpoint(
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optimizer=optimizer, model=model)
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input_value = constant_op.constant([[3.]])
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with backprop.GradientTape() as tape:
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loss = model(input_value)
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variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
train_op = optimizer.apply_gradients(zip(gradients, variables))
|
|
self.assertFalse(root_trackable.save_counter.trainable)
|
|
self.evaluate(trackable_utils.gather_initializers(
|
|
root_trackable))
|
|
self.evaluate(train_op)
|
|
prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
self.evaluate(state_ops.assign(model._named_dense.variables[1], [42.]))
|
|
m_bias_slot = optimizer.get_slot(model._named_dense.variables[1], "m")
|
|
self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
|
|
save_path = root_trackable.save(file_prefix=prefix)
|
|
self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.]))
|
|
self.evaluate(state_ops.assign(root_trackable.save_counter, 3))
|
|
optimizer_variables = self.evaluate(
|
|
sorted(optimizer.variables(), key=lambda v: v.name))
|
|
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
|
|
# Immediate restoration
|
|
status = root_trackable.restore(save_path=save_path).assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertAllEqual([42.], self.evaluate(model._named_dense.variables[1]))
|
|
self.assertAllEqual(1, self.evaluate(root_trackable.save_counter))
|
|
self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
|
|
if not context.executing_eagerly():
|
|
return # Restore-on-create is only supported when executing eagerly
|
|
on_create_model = MyModel()
|
|
on_create_optimizer = adam.Adam(0.001)
|
|
on_create_root = trackable_utils.Checkpoint(
|
|
optimizer=on_create_optimizer, model=on_create_model)
|
|
# Deferred restoration
|
|
status = on_create_root.restore(save_path=save_path)
|
|
status.assert_nontrivial_match()
|
|
status.assert_existing_objects_matched()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
on_create_model(constant_op.constant([[3.]])) # create variables
|
|
self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
|
|
self.assertAllEqual([42.],
|
|
self.evaluate(
|
|
on_create_model._named_dense.variables[1]))
|
|
on_create_m_bias_slot = on_create_optimizer.get_slot(
|
|
on_create_model._named_dense.variables[1], "m")
|
|
status.assert_existing_objects_matched()
|
|
if not context.executing_eagerly():
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
# Optimizer slot variables are created when the original variable is
|
|
# restored.
|
|
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
|
|
dummy_var = resource_variable_ops.ResourceVariable([1.])
|
|
on_create_optimizer.minimize(loss=dummy_var.read_value,
|
|
var_list=[dummy_var])
|
|
status.assert_existing_objects_matched()
|
|
status.assert_consumed()
|
|
self.assertAllEqual(
|
|
optimizer_variables,
|
|
# Creation order is different, so .variables() needs to be re-sorted.
|
|
self.evaluate(sorted(optimizer.variables(), key=lambda v: v.name)))
|
|
|
|
# TODO(allenl): Debug garbage created by this test in python3.
|
|
def testDeferredRestorationUsageEager(self):
|
|
"""An idiomatic eager execution example."""
|
|
num_training_steps = 10
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
for training_continuation in range(3):
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
root.restore(checkpoint_management.latest_checkpoint(
|
|
checkpoint_directory))
|
|
for _ in range(num_training_steps):
|
|
# TODO(allenl): Use a Dataset and serialize/checkpoint it.
|
|
input_value = constant_op.constant([[3.]])
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
optimizer.apply_gradients(zip(gradients, variables))
|
|
root.save(file_prefix=checkpoint_prefix)
|
|
self.assertEqual((training_continuation + 1) * num_training_steps,
|
|
root.optimizer.iterations.numpy())
|
|
|
|
def testUsageGraph(self):
|
|
"""Expected usage when graph building."""
|
|
with context.graph_mode():
|
|
num_training_steps = 10
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
for training_continuation in range(3):
|
|
with ops.Graph().as_default():
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root = trackable_utils.CheckpointV1(
|
|
optimizer=optimizer, model=model)
|
|
input_value = constant_op.constant([[3.]])
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
train_op = optimizer.apply_gradients(zip(gradients, variables))
|
|
|
|
checkpoint_path = checkpoint_management.latest_checkpoint(
|
|
checkpoint_directory)
|
|
with self.session(graph=ops.get_default_graph()) as session:
|
|
status = root.restore(save_path=checkpoint_path)
|
|
status.initialize_or_restore(session=session)
|
|
if checkpoint_path is None:
|
|
self.assertEqual(0, training_continuation)
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_existing_objects_matched()
|
|
else:
|
|
status.assert_consumed()
|
|
status.assert_existing_objects_matched()
|
|
for _ in range(num_training_steps):
|
|
session.run(train_op)
|
|
root.save(file_prefix=checkpoint_prefix, session=session)
|
|
self.assertEqual((training_continuation + 1) * num_training_steps,
|
|
session.run(root.optimizer.iterations))
|
|
self.assertEqual(training_continuation + 1,
|
|
session.run(root.save_counter))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testAgnosticUsage(self):
|
|
"""Graph/eager agnostic usage."""
|
|
# Does create garbage when executing eagerly due to ops.Graph() creation.
|
|
num_training_steps = 10
|
|
checkpoint_directory = self.get_temp_dir()
|
|
def _train_fn(model, input_value):
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
return optimizer.apply_gradients(zip(gradients, variables))
|
|
for training_continuation in range(3):
|
|
with test_util.device(use_gpu=True):
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
manager = checkpoint_management.CheckpointManager(
|
|
root, checkpoint_directory, max_to_keep=1)
|
|
status = root.restore(save_path=manager.latest_checkpoint)
|
|
input_value = constant_op.constant([[3.]])
|
|
train_fn = functools.partial(_train_fn, model, input_value)
|
|
if not context.executing_eagerly():
|
|
train_fn = functools.partial(self.evaluate, train_fn())
|
|
status.initialize_or_restore()
|
|
for _ in range(num_training_steps):
|
|
train_fn()
|
|
manager.save()
|
|
self.assertEqual((training_continuation + 1) * num_training_steps,
|
|
self.evaluate(root.optimizer.iterations))
|
|
self.assertEqual(training_continuation + 1,
|
|
self.evaluate(root.save_counter))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testFreezing(self):
|
|
with test_util.use_gpu():
|
|
# Save an object-based checkpoint using a frozen saver
|
|
directory = self.get_temp_dir()
|
|
prefix = os.path.join(directory, "ckpt")
|
|
v = resource_variable_ops.ResourceVariable(0, dtype=dtypes.int64)
|
|
checkpoint = trackable_utils.Checkpoint(v=v)
|
|
self.evaluate(v.assign(3))
|
|
# Create the save counter so assert_consumed doesn't complain about it not
|
|
# existing in the checkpoint on restore.
|
|
self.evaluate(checkpoint.save_counter.assign(12))
|
|
saver = trackable_utils.frozen_saver(checkpoint)
|
|
with ops.device("cpu:0"):
|
|
prefix_tensor = constant_op.constant(prefix)
|
|
self.evaluate(saver.save(prefix_tensor))
|
|
self.evaluate(v.assign(10))
|
|
# Use the frozen saver to restore the same object graph
|
|
self.evaluate(saver.restore(prefix_tensor))
|
|
self.assertEqual(3, self.evaluate(v))
|
|
|
|
# Restore using another frozen saver on an identical object graph
|
|
del v, checkpoint, saver
|
|
v = resource_variable_ops.ResourceVariable(0, dtype=dtypes.int64)
|
|
checkpoint = trackable_utils.Checkpoint(v=v)
|
|
saver = trackable_utils.frozen_saver(checkpoint)
|
|
self.evaluate(saver.restore(prefix_tensor))
|
|
self.assertEqual(3, self.evaluate(v))
|
|
|
|
# Restore as an object-based checkpoint
|
|
del v, checkpoint, saver
|
|
checkpoint = trackable_utils.Checkpoint()
|
|
status = checkpoint.restore(prefix)
|
|
v = resource_variable_ops.ResourceVariable(0, dtype=dtypes.int64)
|
|
if context.executing_eagerly():
|
|
self.assertEqual(12, self.evaluate(checkpoint.save_counter))
|
|
self.assertEqual(0, self.evaluate(v))
|
|
checkpoint.v = v
|
|
status.assert_consumed().run_restore_ops()
|
|
self.assertEqual(3, self.evaluate(v))
|
|
self.assertEqual(12, self.evaluate(checkpoint.save_counter))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testCustomNumbering(self):
|
|
directory = self.get_temp_dir()
|
|
prefix = os.path.join(directory, "ckpt")
|
|
step = resource_variable_ops.ResourceVariable(0, dtype=dtypes.int64)
|
|
checkpoint = trackable_utils.Checkpoint(step=step)
|
|
self.evaluate(step.initializer)
|
|
for i in range(5):
|
|
path = checkpoint.write("%s-%d" % (prefix, self.evaluate(step)))
|
|
expected_suffix = "-%d" % (2 * i,)
|
|
if not path.endswith(expected_suffix):
|
|
self.fail("%s should have suffix %s" % (path, expected_suffix))
|
|
self.evaluate(step.assign_add(2))
|
|
|
|
def testPartialRestoreWarningObject(self):
|
|
with context.eager_mode():
|
|
optimizer = adam.Adam(0.0)
|
|
original_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(2.),
|
|
v2=variables_lib.Variable(3.),
|
|
optimizer=optimizer)
|
|
# Create a slot variable to save
|
|
optimizer.minimize(original_root.v1.read_value, [original_root.v1])
|
|
prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
save_path = original_root.save(prefix)
|
|
partial_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(0.))
|
|
weak_partial_root = weakref.ref(partial_root)
|
|
weak_v1 = weakref.ref(partial_root.v1)
|
|
partial_root.restore(save_path)
|
|
self.assertEqual(2., partial_root.v1.numpy())
|
|
with test.mock.patch.object(logging, "warning") as mock_log:
|
|
del partial_root
|
|
self.assertIsNone(weak_partial_root())
|
|
self.assertIsNone(weak_v1())
|
|
messages = str(mock_log.call_args_list)
|
|
self.assertIn("(root).v2'", messages)
|
|
self.assertIn("(root).optimizer's state 'm' for (root).v1", messages)
|
|
self.assertNotIn("(root).v1'", messages)
|
|
self.assertIn("expect_partial()", messages)
|
|
|
|
def testPartialRestoreWarningAttribute(self):
|
|
with context.eager_mode():
|
|
original_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(2.),
|
|
v2=variables_lib.Variable(3.))
|
|
prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
save_path = original_root.save(prefix)
|
|
partial_root = trackable_utils.Checkpoint(v1=base.Trackable(),
|
|
v2=variables_lib.Variable(0.))
|
|
weak_partial_root = weakref.ref(partial_root)
|
|
with test.mock.patch.object(logging, "warning") as mock_log:
|
|
# Note: Unlike in testPartialRestoreWarningObject, the warning actually
|
|
# prints immediately here, since all of the objects have been created
|
|
# and there's no deferred restoration sitting around.
|
|
partial_root.restore(save_path)
|
|
self.assertEqual(3., partial_root.v2.numpy())
|
|
del partial_root
|
|
self.assertIsNone(weak_partial_root())
|
|
messages = str(mock_log.call_args_list)
|
|
self.assertIn("(root).v1", messages)
|
|
self.assertNotIn("(root).v2", messages)
|
|
self.assertIn("expect_partial()", messages)
|
|
|
|
def testAttributeException(self):
|
|
with context.eager_mode():
|
|
original_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(2.),
|
|
v2=variables_lib.Variable(3.))
|
|
prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
save_path = original_root.save(prefix)
|
|
partial_root = trackable_utils.Checkpoint(v1=base.Trackable(),
|
|
v2=variables_lib.Variable(0.))
|
|
status = partial_root.restore(save_path)
|
|
with self.assertRaisesRegexp(
|
|
AssertionError,
|
|
r"Unused attributes(.|\n)*\(root\).v1"):
|
|
status.assert_consumed()
|
|
|
|
def testSilencePartialWarning(self):
|
|
with context.eager_mode():
|
|
original_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(2.),
|
|
v2=variables_lib.Variable(3.))
|
|
prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
save_path = original_root.save(prefix)
|
|
partial_root = trackable_utils.Checkpoint(v1=variables_lib.Variable(0.))
|
|
weak_partial_root = weakref.ref(partial_root)
|
|
weak_v1 = weakref.ref(partial_root.v1)
|
|
partial_root.restore(save_path).expect_partial()
|
|
self.assertEqual(2., partial_root.v1.numpy())
|
|
with test.mock.patch.object(logging, "warning") as mock_log:
|
|
del partial_root
|
|
self.assertIsNone(weak_partial_root())
|
|
self.assertIsNone(weak_v1())
|
|
self.assertEmpty(mock_log.call_args_list)
|
|
|
|
# pylint: disable=cell-var-from-loop
|
|
@test_util.run_in_graph_and_eager_modes
|
|
@test_util.run_v1_only("b/120545219")
|
|
def testWithDefun(self):
|
|
num_training_steps = 2
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
for training_continuation in range(3):
|
|
with test_util.device(use_gpu=True):
|
|
model = MyModel()
|
|
# Don't actually train so we can test variable values
|
|
optimizer = adam.Adam(0.)
|
|
root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
checkpoint_path = checkpoint_management.latest_checkpoint(
|
|
checkpoint_directory)
|
|
status = root.restore(save_path=checkpoint_path)
|
|
def train_fn():
|
|
@def_function.function
|
|
def _call_model(x):
|
|
return model(x)
|
|
with backprop.GradientTape() as tape:
|
|
loss = _call_model(constant_op.constant([[3.]]))
|
|
gradients = tape.gradient(loss, model.variables)
|
|
return optimizer.apply_gradients(zip(gradients, model.variables))
|
|
if not context.executing_eagerly():
|
|
train_fn = functools.partial(
|
|
self.evaluate, train_fn())
|
|
status.initialize_or_restore()
|
|
for _ in range(num_training_steps):
|
|
train_fn()
|
|
if training_continuation > 0:
|
|
status.assert_consumed()
|
|
self.assertAllClose([[42.]], self.evaluate(model.variables[0]))
|
|
else:
|
|
self.evaluate(model.variables[0].assign([[42.]]))
|
|
root.save(file_prefix=checkpoint_prefix)
|
|
self.assertEqual((training_continuation + 1) * num_training_steps,
|
|
self.evaluate(optimizer.iterations))
|
|
self.assertEqual(training_continuation + 1,
|
|
self.evaluate(root.save_counter))
|
|
# pylint: enable=cell-var-from-loop
|
|
|
|
def _get_checkpoint_name(self, name):
|
|
root = tracking.AutoTrackable()
|
|
trackable_utils.add_variable(
|
|
root, name=name, shape=[1, 2], dtype=dtypes.float64)
|
|
(named_variable,), _, _ = graph_view.ObjectGraphView(
|
|
root).serialize_object_graph()
|
|
with ops.name_scope("root/" + named_variable.name):
|
|
pass # Make sure we can use this as an op name if we prefix it.
|
|
return named_variable.name
|
|
|
|
@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
|
|
def testVariableNameEscaping(self):
|
|
suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
|
|
self.assertEqual(r"a.Sb.Sc" + suffix, self._get_checkpoint_name(r"a/b/c"))
|
|
self.assertEqual(r"b" + suffix, self._get_checkpoint_name(r"b"))
|
|
self.assertEqual(r"c.S" + suffix, self._get_checkpoint_name(r"c/"))
|
|
self.assertEqual(r"d.S..S" + suffix, self._get_checkpoint_name(r"d/.S"))
|
|
self.assertEqual(r"d.S..ATTRIBUTES.Sf" + suffix,
|
|
self._get_checkpoint_name(r"d/.ATTRIBUTES/f"))
|
|
|
|
@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
|
|
def testNumberedPath(self):
|
|
root = tracking.AutoTrackable()
|
|
leaf = tracking.AutoTrackable()
|
|
root.leaf = leaf
|
|
trackable_utils.add_variable(leaf, name="v", shape=[])
|
|
(named_variable,), _, _ = graph_view.ObjectGraphView(
|
|
root).serialize_object_graph()
|
|
self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", named_variable.name)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testLocalNameValidation(self):
|
|
root = tracking.AutoTrackable()
|
|
leaf = tracking.AutoTrackable()
|
|
# Dots are escaped, which avoids conflicts with reserved names.
|
|
root._track_trackable(leaf, name=".ATTRIBUTES")
|
|
trackable_utils.add_variable(trackable=leaf, name="a", shape=[])
|
|
(named_variable,), _, _ = graph_view.ObjectGraphView(
|
|
root).serialize_object_graph()
|
|
self.assertEqual("..ATTRIBUTES/a/.ATTRIBUTES/VARIABLE_VALUE",
|
|
named_variable.name)
|
|
|
|
def testAnonymousVarsInInit(self):
|
|
|
|
class Model(training.Model):
|
|
|
|
def __init__(self):
|
|
super(Model, self).__init__()
|
|
self.w = resource_variable_ops.ResourceVariable(0.0)
|
|
self.b = resource_variable_ops.ResourceVariable(0.0)
|
|
self.vars = [self.w, self.b]
|
|
|
|
def call(self, x):
|
|
return x * self.w + self.b
|
|
|
|
with context.eager_mode():
|
|
model = Model()
|
|
optimizer = adam.Adam(learning_rate=0.05)
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
checkpoint = trackable_utils.Checkpoint(
|
|
model=model, optimizer=optimizer)
|
|
for _ in range(2):
|
|
checkpoint.save(checkpoint_prefix)
|
|
with backprop.GradientTape() as tape:
|
|
loss = (constant_op.constant(1.)
|
|
- model(constant_op.constant(1.))) ** 2
|
|
grad = tape.gradient(loss, model.vars)
|
|
optimizer.apply_gradients(
|
|
[(g, v) for g, v in zip(grad, model.vars)])
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testLateDependencyTracking(self):
|
|
|
|
class Dependency(tracking.AutoTrackable):
|
|
|
|
def build(self):
|
|
self.var = trackable_utils.add_variable(
|
|
self, "var", initializer=0.)
|
|
|
|
class LateDependencies(trackable_utils.Checkpoint):
|
|
|
|
def add_dep(self):
|
|
self.dep = Dependency()
|
|
self.dep.build()
|
|
|
|
original = LateDependencies()
|
|
original.add_dep()
|
|
self.evaluate(state_ops.assign(original.dep.var, 123.))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = original.save(checkpoint_prefix)
|
|
load_into = LateDependencies()
|
|
status = load_into.restore(save_path)
|
|
status.assert_existing_objects_matched()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
load_into.add_dep()
|
|
status.assert_consumed()
|
|
status.assert_existing_objects_matched().run_restore_ops()
|
|
self.assertEqual(123., self.evaluate(load_into.dep.var))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDepAfterVar(self):
|
|
|
|
class Dependency(tracking.AutoTrackable):
|
|
|
|
def build(self):
|
|
self.var = trackable_utils.add_variable(
|
|
self, "var", initializer=0.)
|
|
|
|
class DepAfterVar(trackable_utils.Checkpoint):
|
|
|
|
def add_dep(self):
|
|
dep = Dependency()
|
|
dep.build()
|
|
self.dep = dep
|
|
|
|
dep_after_var = DepAfterVar()
|
|
dep_after_var.add_dep()
|
|
self.evaluate(state_ops.assign(dep_after_var.dep.var, -14.))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = dep_after_var.save(checkpoint_prefix)
|
|
|
|
loaded_dep_after_var = DepAfterVar()
|
|
status = loaded_dep_after_var.restore(save_path)
|
|
loaded_dep_after_var.add_dep()
|
|
status.assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDeferredSlotRestoration(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
|
|
root = trackable_utils.Checkpoint()
|
|
root.var = trackable_utils.add_variable(
|
|
root, name="var", initializer=0.)
|
|
optimizer = adam.Adam(0.1)
|
|
variables = [root.var]
|
|
gradients = [1.]
|
|
train_op = optimizer.apply_gradients(zip(gradients, variables))
|
|
# Note that `optimizer` has not been added as a dependency of
|
|
# `root`. Create a one-off grouping so that slot variables for `root.var`
|
|
# get initialized too.
|
|
self.evaluate(trackable_utils.gather_initializers(
|
|
trackable_utils.Checkpoint(root=root, optimizer=optimizer)))
|
|
self.evaluate(train_op)
|
|
self.evaluate(state_ops.assign(root.var, 12.))
|
|
no_slots_path = root.save(os.path.join(checkpoint_directory, "no_slots"))
|
|
root.optimizer = optimizer
|
|
self.evaluate(state_ops.assign(root.var, 13.))
|
|
self.evaluate(state_ops.assign(
|
|
optimizer.get_slot(slot_name="m", var=root.var),
|
|
14.))
|
|
slots_path = root.save(os.path.join(checkpoint_directory, "with_slots"))
|
|
new_root = trackable_utils.Checkpoint()
|
|
# Load the slot-containing checkpoint (deferred), then immediately overwrite
|
|
# the non-slot variable (also deferred).
|
|
slot_status = new_root.restore(slots_path)
|
|
no_slot_status = new_root.restore(no_slots_path)
|
|
with self.assertRaises(AssertionError):
|
|
no_slot_status.assert_consumed()
|
|
new_root.var = trackable_utils.add_variable(
|
|
new_root, name="var", shape=[])
|
|
no_slot_status.assert_consumed()
|
|
no_slot_status.run_restore_ops()
|
|
self.assertEqual(12., self.evaluate(new_root.var))
|
|
new_root.optimizer = adam.Adam(0.1)
|
|
slot_status.assert_existing_objects_matched()
|
|
if not context.executing_eagerly():
|
|
with self.assertRaisesRegexp(AssertionError, "Unresolved object"):
|
|
slot_status.assert_consumed()
|
|
self.assertEqual(12., self.evaluate(new_root.var))
|
|
if context.executing_eagerly():
|
|
# Slot variables are only created with restoring initializers when
|
|
# executing eagerly.
|
|
self.assertEqual(14., self.evaluate(
|
|
new_root.optimizer.get_slot(slot_name="m", var=new_root.var)))
|
|
else:
|
|
# Slot variables are not created eagerly when graph building.
|
|
with self.assertRaises(KeyError):
|
|
new_root.optimizer.get_slot(slot_name="m", var=new_root.var)
|
|
variables = [new_root.var]
|
|
gradients = [1.]
|
|
train_op = new_root.optimizer.apply_gradients(zip(gradients, variables))
|
|
# The slot variable now exists; restore() didn't create it, but we should
|
|
# now have a restore op for it.
|
|
slot_status.run_restore_ops()
|
|
if not context.executing_eagerly():
|
|
# The train op hasn't run when graph building, so the slot variable has
|
|
# its restored value. It has run in eager, so the value will be different.
|
|
self.assertEqual(14., self.evaluate(
|
|
new_root.optimizer.get_slot(slot_name="m", var=new_root.var)))
|
|
self.evaluate(train_op)
|
|
slot_status.assert_consumed()
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testOverlappingRestores(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
save_root = trackable_utils.Checkpoint()
|
|
save_root.dep = tracking.AutoTrackable()
|
|
save_root.dep.var = trackable_utils.add_variable(
|
|
save_root.dep, name="var", initializer=0.)
|
|
self.evaluate(state_ops.assign(save_root.dep.var, 12.))
|
|
first_path = save_root.save(os.path.join(checkpoint_directory, "first"))
|
|
self.evaluate(state_ops.assign(save_root.dep.var, 13.))
|
|
second_path = save_root.save(os.path.join(checkpoint_directory, "second"))
|
|
|
|
first_root = trackable_utils.Checkpoint()
|
|
second_root = trackable_utils.Checkpoint()
|
|
first_status = first_root.restore(first_path)
|
|
second_status = second_root.restore(second_path)
|
|
load_dep = tracking.AutoTrackable()
|
|
load_dep.var = trackable_utils.add_variable(
|
|
load_dep, name="var", shape=[])
|
|
first_root.dep = load_dep
|
|
first_status.assert_consumed()
|
|
first_status.run_restore_ops()
|
|
self.assertEqual(12., self.evaluate(load_dep.var))
|
|
second_root.dep = load_dep
|
|
second_status.assert_consumed()
|
|
second_status.run_restore_ops()
|
|
self.assertEqual(13., self.evaluate(load_dep.var))
|
|
|
|
# Try again with the order of the restore() reversed. The last restore
|
|
# determines the final value.
|
|
first_root = trackable_utils.Checkpoint()
|
|
second_root = trackable_utils.Checkpoint()
|
|
second_status = second_root.restore(second_path)
|
|
first_status = first_root.restore(first_path)
|
|
load_dep = tracking.AutoTrackable()
|
|
load_dep.var = trackable_utils.add_variable(
|
|
load_dep, name="var", shape=[])
|
|
first_root.dep = load_dep
|
|
first_status.assert_consumed()
|
|
first_status.run_restore_ops()
|
|
self.assertEqual(12., self.evaluate(load_dep.var))
|
|
second_root.dep = load_dep
|
|
second_status.assert_consumed()
|
|
second_status.run_restore_ops()
|
|
self.assertEqual(12., self.evaluate(load_dep.var))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testAmbiguousLoad(self):
|
|
# Not OK to split one checkpoint object into two
|
|
checkpoint_directory = self.get_temp_dir()
|
|
save_root = trackable_utils.Checkpoint()
|
|
save_root.dep_one = tracking.AutoTrackable()
|
|
save_root.dep_two = tracking.AutoTrackable()
|
|
dep_three = tracking.AutoTrackable()
|
|
save_root.dep_one.dep_three = dep_three
|
|
save_root.dep_two.dep_three = dep_three
|
|
trackable_utils.add_variable(dep_three, name="var", initializer=0.)
|
|
self.evaluate(trackable_utils.gather_initializers(save_root))
|
|
save_path = save_root.save(os.path.join(checkpoint_directory, "ckpt"))
|
|
load_root = trackable_utils.Checkpoint()
|
|
status = load_root.restore(save_path)
|
|
load_root.dep_one = tracking.AutoTrackable()
|
|
load_root.dep_two = tracking.AutoTrackable()
|
|
load_root.dep_one.dep_three = tracking.AutoTrackable()
|
|
load_root.dep_two.dep_three = tracking.AutoTrackable()
|
|
trackable_utils.add_variable(
|
|
load_root.dep_one.dep_three, name="var", initializer=0.)
|
|
trackable_utils.add_variable(
|
|
load_root.dep_two.dep_three, name="var", initializer=0.)
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_existing_objects_matched()
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testObjectsCombined(self):
|
|
# Currently fine to load two checkpoint objects into one Python object
|
|
checkpoint_directory = self.get_temp_dir()
|
|
save_root = trackable_utils.Checkpoint()
|
|
save_root.dep_one = tracking.AutoTrackable()
|
|
save_root.dep_two = tracking.AutoTrackable()
|
|
trackable_utils.add_variable(
|
|
save_root.dep_one, name="var1", initializer=32., dtype=dtypes.float64)
|
|
trackable_utils.add_variable(
|
|
save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64)
|
|
self.evaluate(trackable_utils.gather_initializers(save_root))
|
|
save_path = save_root.save(os.path.join(checkpoint_directory, "ckpt"))
|
|
load_root = trackable_utils.Checkpoint()
|
|
load_root.dep_one = tracking.AutoTrackable()
|
|
load_root.dep_two = load_root.dep_one
|
|
v1 = trackable_utils.add_variable(
|
|
load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64)
|
|
v2 = trackable_utils.add_variable(
|
|
load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64)
|
|
status = load_root.restore(
|
|
save_path).assert_consumed().assert_existing_objects_matched()
|
|
status.run_restore_ops()
|
|
self.assertEqual(32., self.evaluate(v1))
|
|
self.assertEqual(64., self.evaluate(v2))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testEmptyContainersIgnored(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
save_root = trackable_utils.Checkpoint(a=[])
|
|
path = save_root.save(checkpoint_directory)
|
|
load_root = trackable_utils.Checkpoint(b=[])
|
|
load_root.dep = []
|
|
load_root.dep.append([])
|
|
status = load_root.restore(path)
|
|
status.assert_consumed()
|
|
status.assert_existing_objects_matched()
|
|
status.assert_nontrivial_match()
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDependencyLoop(self):
|
|
# Note: this test creates garbage during eager execution because it
|
|
# purposefully creates a reference cycle.
|
|
first = trackable_utils.Checkpoint()
|
|
second = trackable_utils.Checkpoint()
|
|
first.second = second
|
|
second.first = first
|
|
first.v = trackable_utils.add_variable(
|
|
first, "v1", initializer=[3., 1., 4.])
|
|
second.v = trackable_utils.add_variable(
|
|
second, "v2", initializer=[1., 1., 2., 3.])
|
|
self.evaluate(trackable_utils.gather_initializers(first))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
save_path = first.save(os.path.join(checkpoint_directory, "ckpt"))
|
|
|
|
# Test deferred loading
|
|
first_load = trackable_utils.Checkpoint()
|
|
status = first_load.restore(save_path)
|
|
second_load = tracking.AutoTrackable()
|
|
first_load.second = second_load
|
|
second_load.first = first_load
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
first_load.v = trackable_utils.add_variable(
|
|
first_load, "v1", shape=[3])
|
|
second_load.v = trackable_utils.add_variable(
|
|
second_load, "v2", shape=[4])
|
|
status.assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
|
|
self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
|
|
|
|
# Test loading when variables have already been created
|
|
self.evaluate(first_load.v.assign([2., 7., 1.]))
|
|
self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v))
|
|
self.evaluate(second_load.v.assign([2., 7., 1., 8.]))
|
|
self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v))
|
|
status = first_load.restore(save_path).assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
|
|
self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testRestoreOnAssign(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
first = trackable_utils.Checkpoint()
|
|
first.var1 = variables_lib.Variable(0., name="outside_var")
|
|
first.var2 = variables_lib.Variable(0., name="blah")
|
|
self.evaluate(first.var1.assign(4.))
|
|
self.evaluate(first.var2.assign(8.))
|
|
save_path = first.save(checkpoint_prefix)
|
|
|
|
second = trackable_utils.Checkpoint()
|
|
second.var2 = variables_lib.Variable(0., name="blah")
|
|
status = second.restore(save_path)
|
|
recreated_var1 = variables_lib.Variable(0., name="outside_var")
|
|
status.run_restore_ops()
|
|
self.assertEqual(8., self.evaluate(second.var2))
|
|
self.evaluate(recreated_var1.assign(-2.))
|
|
self.assertEqual(-2., self.evaluate(recreated_var1))
|
|
second.var1 = recreated_var1
|
|
status.run_restore_ops()
|
|
self.assertEqual(4., self.evaluate(recreated_var1))
|
|
|
|
def testManySavesGraph(self):
|
|
"""Saves after the first should not modify the graph."""
|
|
with context.graph_mode():
|
|
graph = ops.Graph()
|
|
with graph.as_default(), self.session(graph):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
obj = trackable_utils.Checkpoint()
|
|
obj.var = variables_lib.Variable(0., name="v")
|
|
obj.opt = adam.Adam(0.1)
|
|
variables = [obj.var]
|
|
gradients = [1.]
|
|
obj.opt.apply_gradients(zip(gradients, variables))
|
|
self.evaluate(trackable_utils.gather_initializers(obj))
|
|
obj.save(checkpoint_prefix)
|
|
graph.finalize()
|
|
obj.save(checkpoint_prefix)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testCheckpointState(self):
|
|
# No checkpoints are deleted by default
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
obj = tracking.AutoTrackable()
|
|
obj.var = variable_scope.get_variable(name="v", initializer=0.)
|
|
self.evaluate(trackable_utils.gather_initializers(obj))
|
|
saver = trackable_utils.Checkpoint(obj=obj)
|
|
for _ in range(10):
|
|
saver.save(checkpoint_prefix)
|
|
expected_filenames = ["checkpoint"]
|
|
for checkpoint_number in range(1, 11):
|
|
expected_filenames.append("ckpt-%d.index" % (checkpoint_number,))
|
|
self.assertEmpty(
|
|
set(expected_filenames)
|
|
- set(os.listdir(checkpoint_directory)))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testCheckpointStateChangingVarList(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
obj = tracking.AutoTrackable()
|
|
obj.var = variable_scope.get_variable(name="v", initializer=0.)
|
|
self.evaluate(trackable_utils.gather_initializers(obj))
|
|
checkpoint = trackable_utils.Checkpoint(obj=obj)
|
|
looped_variables = []
|
|
for iteration in range(10):
|
|
new_variable = resource_variable_ops.ResourceVariable(iteration)
|
|
self.evaluate(new_variable.initializer)
|
|
setattr(checkpoint, "var_%d" % iteration, new_variable)
|
|
checkpoint.save(checkpoint_prefix)
|
|
looped_variables.append(new_variable)
|
|
expected_filenames = ["checkpoint"]
|
|
# We've copied the saver each time, but checkpoint management should still
|
|
# be consistent. Nothing gets deleted.
|
|
for checkpoint_number in range(1, 11):
|
|
expected_filenames.append("ckpt-%d.index" % (checkpoint_number,))
|
|
self.assertEmpty(
|
|
set(expected_filenames)
|
|
- set(os.listdir(checkpoint_directory)))
|
|
self.assertEqual(
|
|
checkpoint_prefix + "-10",
|
|
checkpoint_management.latest_checkpoint(checkpoint_directory))
|
|
# The checkpoint list only contains the most recent checkpoint, but they're
|
|
# all on disk. This means we won't eventually run into proto size limits.
|
|
self.assertEqual(
|
|
[checkpoint_prefix + "-10"],
|
|
(checkpoint_management.get_checkpoint_state(checkpoint_directory)
|
|
.all_model_checkpoint_paths))
|
|
for v in looped_variables:
|
|
self.evaluate(v.assign(314))
|
|
checkpoint.restore(checkpoint_prefix + "-6").run_restore_ops()
|
|
self.assertEqual(314, self.evaluate(checkpoint.var_9))
|
|
self.assertEqual(314, self.evaluate(checkpoint.var_8))
|
|
self.assertEqual(314, self.evaluate(checkpoint.var_6))
|
|
self.assertEqual(5, self.evaluate(checkpoint.var_5))
|
|
self.assertEqual(1, self.evaluate(checkpoint.var_1))
|
|
self.assertEqual(0, self.evaluate(checkpoint.var_0))
|
|
checkpoint.restore(checkpoint_prefix + "-10").run_restore_ops()
|
|
self.assertEqual(9, self.evaluate(checkpoint.var_9))
|
|
self.assertEqual(8, self.evaluate(checkpoint.var_8))
|
|
self.assertEqual(1, self.evaluate(checkpoint.var_1))
|
|
self.assertEqual(0, self.evaluate(checkpoint.var_0))
|
|
|
|
def testManyRestoresGraph(self):
|
|
"""Restores after the first should not modify the graph."""
|
|
with context.graph_mode():
|
|
graph = ops.Graph()
|
|
with graph.as_default(), self.session(graph):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
obj = trackable_utils.Checkpoint()
|
|
obj.var = variables_lib.Variable(0., name="v")
|
|
obj.opt = adam.Adam(0.1)
|
|
variables = [obj.var]
|
|
gradients = [1.]
|
|
obj.opt.apply_gradients(zip(gradients, variables))
|
|
self.evaluate(trackable_utils.gather_initializers(obj))
|
|
save_path = obj.save(checkpoint_prefix)
|
|
obj.restore(save_path)
|
|
graph.finalize()
|
|
obj.restore(save_path)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_sequential(self):
|
|
model = sequential.Sequential()
|
|
checkpoint = trackable_utils.Checkpoint(model=model)
|
|
model.add(core.Dense(4))
|
|
second_dense = core.Dense(5)
|
|
model.add(second_dense)
|
|
model(constant_op.constant([[1.]]))
|
|
checkpoint.restore(None).initialize_or_restore()
|
|
self.evaluate(second_dense.bias.assign(
|
|
constant_op.constant([1., 2., 3., 4., 5.])))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = checkpoint.save(checkpoint_prefix)
|
|
self.evaluate(second_dense.bias.assign(
|
|
constant_op.constant([5., 6., 7., 8., 9.])))
|
|
checkpoint.restore(save_path).assert_consumed().run_restore_ops()
|
|
self.assertAllEqual([1., 2., 3., 4., 5.], self.evaluate(second_dense.bias))
|
|
|
|
deferred_sequential = sequential.Sequential()
|
|
deferred_sequential_checkpoint = trackable_utils.Checkpoint(
|
|
model=deferred_sequential)
|
|
status = deferred_sequential_checkpoint.restore(save_path)
|
|
deferred_sequential.add(core.Dense(4))
|
|
deferred_second_dense = core.Dense(5)
|
|
deferred_sequential.add(deferred_second_dense)
|
|
deferred_sequential(constant_op.constant([[1.]]))
|
|
status.run_restore_ops()
|
|
self.assertAllEqual([1., 2., 3., 4., 5.],
|
|
self.evaluate(deferred_second_dense.bias))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_initialize_if_not_restoring(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
optimizer_only_prefix = os.path.join(checkpoint_directory, "opt")
|
|
with test_util.device(use_gpu=True):
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root = trackable_utils.Checkpoint(
|
|
model=model) # Do not save the optimizer with the checkpoint.
|
|
optimizer_checkpoint = trackable_utils.Checkpoint(
|
|
optimizer=optimizer)
|
|
|
|
checkpoint_path = checkpoint_management.latest_checkpoint(
|
|
checkpoint_directory)
|
|
status = root.restore(save_path=checkpoint_path)
|
|
input_value = constant_op.constant([[3.]])
|
|
def train_fn():
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
return optimizer.apply_gradients(zip(gradients, variables))
|
|
if not context.executing_eagerly():
|
|
train_fn = functools.partial(self.evaluate, train_fn())
|
|
status.initialize_or_restore()
|
|
# TODO(tanzheny): Add hyper variables to .variables(), and set them with
|
|
# set_weights etc.
|
|
variables_not_in_the_variables_property = [
|
|
obj for obj in optimizer._hyper.values()
|
|
if isinstance(obj, variables_lib.Variable)]
|
|
self.evaluate([v.initializer for v
|
|
in optimizer.variables()
|
|
+ variables_not_in_the_variables_property])
|
|
train_fn()
|
|
model_save_path = root.save(file_prefix=checkpoint_prefix)
|
|
self.evaluate(optimizer.beta_1.assign(42.))
|
|
optimizer_save_path = optimizer_checkpoint.save(optimizer_only_prefix)
|
|
del train_fn
|
|
|
|
# Restore into a graph with the optimizer
|
|
with test_util.device(use_gpu=True):
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
status = root.restore(save_path=model_save_path)
|
|
input_value = constant_op.constant([[3.]])
|
|
def train_fn1():
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
return optimizer.apply_gradients(zip(gradients, variables))
|
|
if not context.executing_eagerly():
|
|
train_fn1 = functools.partial(self.evaluate, train_fn1())
|
|
status.initialize_or_restore()
|
|
train_fn1()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_existing_objects_matched()
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_consumed()
|
|
del train_fn1
|
|
|
|
# Make sure initialization doesn't clobber later restores
|
|
with test_util.device(use_gpu=True):
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001, beta_1=1.0)
|
|
root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
opt_root = trackable_utils.Checkpoint(
|
|
optimizer=optimizer)
|
|
status = root.restore(save_path=model_save_path)
|
|
init_only_optimizer_status = opt_root.restore(save_path=None)
|
|
optimizer_status = opt_root.restore(save_path=optimizer_save_path)
|
|
input_value = constant_op.constant([[3.]])
|
|
def train_fn2():
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
return optimizer.apply_gradients(zip(gradients, variables))
|
|
if not context.executing_eagerly():
|
|
train_fn2 = functools.partial(self.evaluate, train_fn2())
|
|
optimizer_status.run_restore_ops()
|
|
status.initialize_or_restore()
|
|
init_only_optimizer_status.initialize_or_restore()
|
|
train_fn2()
|
|
self.assertEqual(42., self.evaluate(optimizer.beta_1))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_restore_after_adding_empty_trackable_data_structure(self):
|
|
model = NonLayerTrackable()
|
|
checkpoint = trackable_utils.Checkpoint(model=model)
|
|
checkpoint.restore(None).initialize_or_restore()
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = checkpoint.save(checkpoint_prefix)
|
|
|
|
del model, checkpoint
|
|
|
|
model = NonLayerTrackable()
|
|
model.dict = {"a": 1}
|
|
model.list = {"b": 1}
|
|
checkpoint = trackable_utils.Checkpoint(model=model)
|
|
load_status = checkpoint.restore(save_path)
|
|
load_status.assert_existing_objects_matched().run_restore_ops()
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_write_checkpoint_from_function(self):
|
|
checkpoint_prefix = os.path.join(self.get_temp_dir(), "ckpt")
|
|
save_checkpoint = trackable_utils.Checkpoint(v=variables_lib.Variable(1.))
|
|
|
|
@def_function.function
|
|
def _write_checkpoint():
|
|
save_path = save_checkpoint.write(checkpoint_prefix)
|
|
return save_path
|
|
|
|
self.evaluate([save_checkpoint.v.initializer])
|
|
self.evaluate(_write_checkpoint())
|
|
load_checkpoint = trackable_utils.Checkpoint(v=variables_lib.Variable(0.))
|
|
# Use read() instead of restore() which allows us to check that all
|
|
# existing objects were loaded.
|
|
status = load_checkpoint.read(checkpoint_prefix)
|
|
status.assert_existing_objects_matched()
|
|
status.assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertEqual(1., self.evaluate(load_checkpoint.v))
|
|
self.evaluate(save_checkpoint.v.assign(3.))
|
|
self.evaluate(_write_checkpoint())
|
|
self.evaluate(save_checkpoint.v.assign(0.))
|
|
status = load_checkpoint.read(checkpoint_prefix)
|
|
status.assert_existing_objects_matched()
|
|
status.assert_consumed()
|
|
status.run_restore_ops()
|
|
self.assertEqual(3., self.evaluate(load_checkpoint.v))
|
|
|
|
def test_inititialize_with_data_structures(self):
|
|
checkpoint = trackable_utils.Checkpoint(
|
|
a=[variables_lib.Variable(0.), variables_lib.Variable(1.)],
|
|
b={"a": variables_lib.Variable(2.), "b": variables_lib.Variable(3.)})
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = checkpoint.save(checkpoint_prefix)
|
|
load_checkpoint = trackable_utils.Checkpoint(
|
|
a=[variables_lib.Variable(4.), variables_lib.Variable(5.)],
|
|
b={"a": variables_lib.Variable(6.), "b": variables_lib.Variable(7.)})
|
|
load_checkpoint.restore(save_path)
|
|
self.assertAllClose(self.evaluate(load_checkpoint.a), [0, 1])
|
|
self.assertAllClose(self.evaluate(load_checkpoint.b), {"a": 2, "b": 3})
|
|
|
|
|
|
class _ManualScope(tracking.AutoTrackable):
|
|
|
|
def __call__(self):
|
|
with variable_scope.variable_scope("ManualScope") as vs:
|
|
self.variable_scope = vs
|
|
with trackable_utils.capture_dependencies(template=self):
|
|
return self._build()
|
|
|
|
def _build(self):
|
|
return variable_scope.get_variable(name="in_manual_scope", shape=[])
|
|
|
|
|
|
class TemplateTests(parameterized.TestCase, test.TestCase):
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_trackable_save_restore(self):
|
|
|
|
def _templated():
|
|
v = variable_scope.get_variable(
|
|
"v", shape=[1], initializer=init_ops.zeros_initializer(),
|
|
use_resource=True)
|
|
v2 = variable_scope.get_variable(
|
|
"v2", shape=[1], initializer=init_ops.zeros_initializer(),
|
|
use_resource=True)
|
|
manual = _ManualScope()
|
|
return v, v + 1., v2, manual, manual()
|
|
|
|
save_template = template.make_template("s1", _templated)
|
|
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
|
|
six.assertCountEqual(
|
|
self,
|
|
[id(v1_save), id(v2_save), id(manual_scope),
|
|
id(manual_scope_v), id(save_template)],
|
|
map(id, trackable_utils.list_objects(save_template)))
|
|
manual_dep, = manual_scope._checkpoint_dependencies
|
|
self.assertEqual("in_manual_scope", manual_dep.name)
|
|
self.assertIs(manual_scope_v, manual_dep.ref)
|
|
optimizer = adam.Adam(0.0)
|
|
save_root = trackable_utils.Checkpoint(
|
|
my_template=save_template, optimizer=optimizer)
|
|
optimizer.minimize(v1_save.read_value,
|
|
var_list=[v1_save])
|
|
self.evaluate([v.initializer for v in save_template.variables])
|
|
optimizer_variables = optimizer.variables() + list(
|
|
optimizer._hyper.values())
|
|
self.evaluate([v.initializer for v in optimizer_variables])
|
|
self.evaluate(v1_save.assign([12.]))
|
|
self.evaluate(v2_save.assign([14.]))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = save_root.save(checkpoint_prefix)
|
|
|
|
load_template = template.make_template("s2", _templated)
|
|
load_optimizer = adam.Adam(0.0)
|
|
load_root = trackable_utils.Checkpoint(
|
|
my_template=load_template, optimizer=load_optimizer)
|
|
status = load_root.restore(save_path)
|
|
var, var_plus_one, var2, _, _ = load_template()
|
|
load_optimizer.minimize(var.read_value, var_list=[var])
|
|
self.assertLen(load_template._checkpoint_dependencies, 3)
|
|
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
|
|
self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
|
|
self.assertEqual("ManualScope",
|
|
load_template._checkpoint_dependencies[2].name)
|
|
status.assert_consumed().run_restore_ops()
|
|
self.assertAllEqual([12.], self.evaluate(var))
|
|
self.assertAllEqual([13.], self.evaluate(var_plus_one))
|
|
self.assertAllEqual([14.], self.evaluate(var2))
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def test_trackable_save_restore_nested(self):
|
|
|
|
def _inner_template():
|
|
v = variable_scope.get_variable(
|
|
"v", shape=[1], initializer=init_ops.zeros_initializer())
|
|
return v
|
|
|
|
def _outer_template():
|
|
first_inner = template.make_template("i1", _inner_template)
|
|
second_inner = template.make_template("i2", _inner_template)
|
|
v1 = first_inner()
|
|
v2 = second_inner()
|
|
v3 = second_inner()
|
|
return (first_inner, second_inner), (v1, v2, v3)
|
|
|
|
with variable_scope.variable_scope("ignored"):
|
|
save_template = template.make_template("s1", _outer_template)
|
|
save_root = trackable_utils.Checkpoint(my_template=save_template)
|
|
(inner_template_one, inner_template_two), _ = save_template()
|
|
self.evaluate(inner_template_one.variables[0].assign([20.]))
|
|
self.evaluate(inner_template_two.variables[0].assign([25.]))
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
save_path = save_root.save(checkpoint_prefix)
|
|
|
|
load_template = template.make_template("s2", _outer_template)
|
|
load_root = trackable_utils.Checkpoint(my_template=load_template)
|
|
status = load_root.restore(save_path)
|
|
(inner_template_one, inner_template_two), (v1, v2, v3) = load_template()
|
|
outer_template_dependencies = load_root.my_template._checkpoint_dependencies
|
|
self.assertLen(outer_template_dependencies, 2)
|
|
self.assertEqual("i1", outer_template_dependencies[0].name)
|
|
self.assertIs(inner_template_one, outer_template_dependencies[0].ref)
|
|
self.assertEqual("i2", outer_template_dependencies[1].name)
|
|
self.assertIs(inner_template_two, outer_template_dependencies[1].ref)
|
|
self.assertLen(inner_template_one._checkpoint_dependencies, 1)
|
|
self.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name)
|
|
self.assertLen(inner_template_two._checkpoint_dependencies, 1)
|
|
self.assertEqual("v", inner_template_two._checkpoint_dependencies[0].name)
|
|
status.assert_consumed().run_restore_ops()
|
|
self.assertAllEqual([20.], self.evaluate(v1))
|
|
self.assertAllEqual([25.], self.evaluate(v2))
|
|
self.assertAllEqual([25.], self.evaluate(v3))
|
|
|
|
|
|
class CheckpointCompatibilityTests(test.TestCase):
|
|
|
|
def _initialized_model(self):
|
|
input_value = constant_op.constant([[3.]])
|
|
model = MyModel()
|
|
optimizer = adam.Adam(0.001)
|
|
root_trackable = trackable_utils.Checkpoint(
|
|
optimizer=optimizer, model=model)
|
|
with backprop.GradientTape() as tape:
|
|
loss = model(input_value)
|
|
variables = model.trainable_variables
|
|
gradients = tape.gradient(loss, variables)
|
|
train_op = optimizer.apply_gradients(zip(gradients, variables))
|
|
self.evaluate(trackable_utils.gather_initializers(
|
|
root_trackable))
|
|
self.evaluate(train_op)
|
|
# A regular variable, a slot variable, and a non-slot Optimizer variable
|
|
# with known values to check when loading.
|
|
self.evaluate(model._named_dense.bias.assign([1.]))
|
|
self.evaluate(optimizer.get_slot(
|
|
var=model._named_dense.bias, slot_name="m").assign([2.]))
|
|
self.evaluate(optimizer.beta_1.assign(3.))
|
|
return root_trackable
|
|
|
|
def _set_sentinels(self, root_trackable):
|
|
self.evaluate(root_trackable.model._named_dense.bias.assign([101.]))
|
|
self.evaluate(
|
|
root_trackable.optimizer.get_slot(
|
|
var=root_trackable.model._named_dense.bias, slot_name="m")
|
|
.assign([102.]))
|
|
self.evaluate(root_trackable.optimizer.beta_1.assign(103.))
|
|
|
|
def _check_sentinels(self, root_trackable):
|
|
self.assertAllEqual(
|
|
[1.], self.evaluate(root_trackable.model._named_dense.bias))
|
|
self.assertAllEqual([2.], self.evaluate(
|
|
root_trackable.optimizer.get_slot(
|
|
var=root_trackable.model._named_dense.bias, slot_name="m")))
|
|
self.assertAllEqual(3.,
|
|
self.evaluate(root_trackable.optimizer.beta_1))
|
|
|
|
def _write_name_based_checkpoint(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
with context.graph_mode():
|
|
save_graph = ops.Graph()
|
|
with save_graph.as_default(), self.session(
|
|
graph=save_graph) as session:
|
|
root = self._initialized_model()
|
|
name_saver = saver_lib.Saver()
|
|
return name_saver.save(
|
|
sess=session,
|
|
save_path=checkpoint_prefix,
|
|
global_step=root.optimizer.iterations)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testLoadFromNameBasedSaver(self):
|
|
"""Save a name-based checkpoint, load it using the object-based API."""
|
|
with test_util.device(use_gpu=True):
|
|
save_path = self._write_name_based_checkpoint()
|
|
root = self._initialized_model()
|
|
self._set_sentinels(root)
|
|
with self.assertRaises(AssertionError):
|
|
self._check_sentinels(root)
|
|
object_saver = trackable_utils.TrackableSaver(
|
|
graph_view.ObjectGraphView(root))
|
|
self._set_sentinels(root)
|
|
status = object_saver.restore(save_path)
|
|
if context.executing_eagerly():
|
|
self._check_sentinels(root)
|
|
if context.executing_eagerly():
|
|
status.assert_consumed()
|
|
status.assert_existing_objects_matched()
|
|
status.assert_nontrivial_match()
|
|
else:
|
|
# When graph building, we haven't read any keys, so we don't know
|
|
# whether the restore will be complete.
|
|
with self.assertRaisesRegexp(AssertionError, "not restored"):
|
|
status.assert_consumed()
|
|
with self.assertRaisesRegexp(AssertionError, "not restored"):
|
|
status.assert_existing_objects_matched()
|
|
with self.assertRaisesRegexp(AssertionError, "not restored"):
|
|
status.assert_nontrivial_match()
|
|
status.run_restore_ops()
|
|
self._check_sentinels(root)
|
|
self._set_sentinels(root)
|
|
status = object_saver.restore(save_path)
|
|
status.initialize_or_restore()
|
|
status.assert_nontrivial_match()
|
|
self._check_sentinels(root)
|
|
# Check that there is no error when keys are missing from the name-based
|
|
# checkpoint.
|
|
root.not_in_name_checkpoint = resource_variable_ops.ResourceVariable([1.])
|
|
status = object_saver.restore(save_path)
|
|
with self.assertRaises(AssertionError):
|
|
status.assert_existing_objects_matched()
|
|
|
|
def testSaveGraphLoadEager(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
with context.graph_mode():
|
|
save_graph = ops.Graph()
|
|
with save_graph.as_default(), self.session(
|
|
graph=save_graph):
|
|
root = self._initialized_model()
|
|
save_path = root.save(file_prefix=checkpoint_prefix)
|
|
with context.eager_mode():
|
|
root = self._initialized_model()
|
|
self._set_sentinels(root)
|
|
root.restore(save_path).assert_consumed()
|
|
self._check_sentinels(root)
|
|
|
|
def testSaveEagerLoadGraph(self):
|
|
checkpoint_directory = self.get_temp_dir()
|
|
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
|
|
with context.eager_mode():
|
|
root = self._initialized_model()
|
|
save_path = root.save(file_prefix=checkpoint_prefix)
|
|
with context.graph_mode():
|
|
save_graph = ops.Graph()
|
|
with save_graph.as_default(), self.session(
|
|
graph=save_graph):
|
|
root = self._initialized_model()
|
|
self._set_sentinels(root)
|
|
root.restore(save_path).assert_consumed().run_restore_ops()
|
|
self._check_sentinels(root)
|
|
|
|
def testIgnoreSaveCounter(self):
|
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checkpoint_directory = self.get_temp_dir()
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checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
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with self.cached_session() as session:
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# Create and save a model using Saver() before using a Checkpoint. This
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# generates a snapshot without the Checkpoint's `save_counter`.
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model = sequential.Sequential()
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model.add(core.Flatten(input_shape=(1,)))
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model.add(core.Dense(1))
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name_saver = saver_lib.Saver(model.trainable_variables)
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save_path = name_saver.save(
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sess=session, save_path=checkpoint_prefix, global_step=1)
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# Checkpoint.restore must successfully load that checkpoint.
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ckpt = trackable_utils.Checkpoint(model=model)
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status = ckpt.restore(save_path)
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status.assert_existing_objects_matched()
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# It should, however, refuse to load a checkpoint where an unrelated
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# `save_counter` variable is missing.
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model.layers[1].var = variables_lib.Variable(0., name="save_counter")
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status = ckpt.restore(save_path)
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with self.assertRaises(AssertionError):
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status.assert_existing_objects_matched()
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if __name__ == "__main__":
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ops.enable_eager_execution()
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test.main()
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