def test_checkpointable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v", shape=[1], initializer=init_ops.zeros_initializer())
v2 = variable_scope.get_variable(
"v2", shape=[1], initializer=init_ops.zeros_initializer())
return v, v + 1., v2
save_template = template.make_template("s1", _templated)
save_root = checkpointable_utils.Checkpoint(my_template=save_template)
v1_save, _, v2_save = save_template()
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_root = checkpointable_utils.Checkpoint(my_template=load_template)
status = load_root.restore(save_path)
var, var_plus_one, var2 = load_template()
self.assertEqual(2, len(load_template._checkpoint_dependencies))
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
self.assertEqual("v2", load_template._checkpoint_dependencies[1].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))
def testSaveRestore(self):
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network)
input_value = constant_op.constant([[3.]])
if context.in_eager_mode():
optimizer.minimize(
lambda: network(input_value))
else:
train_op = optimizer.minimize(network(input_value))
# TODO(allenl): Make initialization more pleasant when graph building.
root_checkpointable.save_counter # pylint: disable=pointless-statement
self.evaluate(checkpointable_utils.gather_initializers(
root_checkpointable))
self.evaluate(train_op)
prefix = os.path.join(self.get_temp_dir(), "ckpt")
self.evaluate(state_ops.assign(network._named_dense.variables[1], [42.]))
m_bias_slot = optimizer.get_slot(network._named_dense.variables[1], "m")
self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
save_path = root_checkpointable.save(file_prefix=prefix)
self.evaluate(state_ops.assign(network._named_dense.variables[1], [43.]))
self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3))
optimizer_variables = self.evaluate(optimizer.variables())
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
# Immediate restoration
status = root_checkpointable.restore(save_path=save_path).assert_consumed()
status.run_restore_ops()
self.assertAllEqual([42.], self.evaluate(network._named_dense.variables[1]))
self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter))
self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
if context.in_graph_mode():
return # Restore-on-create is only supported when executing eagerly
on_create_network = MyNetwork()
on_create_optimizer = adam.AdamOptimizer(0.001)
on_create_root = checkpointable_utils.Checkpoint(
optimizer=on_create_optimizer, network=on_create_network)
# Deferred restoration
status = on_create_root.restore(save_path=save_path)
on_create_network(constant_op.constant([[3.]])) # create variables
self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
self.assertAllEqual([42.],
self.evaluate(
on_create_network._named_dense.variables[1]))
on_create_m_bias_slot = on_create_optimizer.get_slot(
on_create_network._named_dense.variables[1], "m")
# Optimizer slot variables are created when the original variable is
# restored.
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
self.assertAllEqual(optimizer_variables[2:],
self.evaluate(on_create_optimizer.variables()))
on_create_optimizer._create_slots(
[resource_variable_ops.ResourceVariable([1.])])
status.assert_consumed()
beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators()
self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
def testMultipleGraphsNonSlotVariables(self):
with context.graph_mode():
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
optimizer = adam.AdamOptimizer(0.001)
# Construct a model in one graph
first_graph = ops.Graph()
first_session = session_lib.Session(graph=first_graph)
with first_graph.as_default(), first_session.as_default():
first_variable = resource_variable_ops.ResourceVariable([1.])
first_root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, variable=first_variable)
train_op = optimizer.minimize(first_variable.read_value)
self.evaluate(checkpointable_utils.gather_initializers(
first_root_checkpointable))
self.evaluate(train_op)
self.evaluate(first_variable.assign([1.]))
self.evaluate(optimizer.get_slot(
var=first_variable, name="m").assign([2.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(3.))
# Save and load in a second graph
second_graph = ops.Graph()
with second_graph.as_default(), session_lib.Session(graph=second_graph):
second_variable = resource_variable_ops.ResourceVariable([1.])
second_root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, variable=second_variable)
train_op = optimizer.minimize(second_variable.read_value)
second_root_checkpointable.restore(None).initialize_or_restore()
self.evaluate(train_op)
self.evaluate(second_variable.assign([4.]))
self.evaluate(optimizer.get_slot(
var=second_variable, name="m").assign([5.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(6.))
save_path = second_root_checkpointable.save(checkpoint_prefix)
self.evaluate(second_variable.assign([7.]))
self.evaluate(optimizer.get_slot(
var=second_variable, name="m").assign([8.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.assertAllEqual(6., self.evaluate(beta1_power))
status = second_root_checkpointable.restore(save_path)
status.assert_consumed().run_restore_ops()
self.assertAllEqual([4.], self.evaluate(second_variable))
self.assertAllEqual([5.], self.evaluate(optimizer.get_slot(
var=second_variable, name="m")))
beta1_power, _ = optimizer._get_beta_accumulators()
self.assertAllEqual(6., self.evaluate(beta1_power))
# Check that the first graph is unmolested
with first_graph.as_default(), first_session.as_default():
self.assertAllEqual([1.], self.evaluate(first_variable))
self.assertAllEqual([2.], self.evaluate(optimizer.get_slot(
var=first_variable, name="m")))
beta1_power, _ = optimizer._get_beta_accumulators()
self.assertAllEqual(3., self.evaluate(beta1_power))
def testSaveRestoreMultipleIterator(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
dataset = Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
dataset = dataset.map(math_ops.square).batch(2)
iterator_1 = datasets.Iterator(dataset)
iterator_2 = datasets.Iterator(dataset)
dataset_2 = Dataset.range(10)
iterator_3 = datasets.Iterator(dataset_2)
checkpoint = checkpointable_utils.Checkpoint(
iterator_1=iterator_1, iterator_2=iterator_2, iterator_3=iterator_3)
self.assertAllEqual([1, 4], iterator_1.get_next().numpy())
self.assertEqual(0, iterator_3.get_next().numpy())
self.assertEqual(1, iterator_3.get_next().numpy())
self.assertEqual(2, iterator_3.get_next().numpy())
save_path = checkpoint.save(checkpoint_prefix)
self.assertAllEqual([1, 4], iterator_2.get_next().numpy())
self.assertAllEqual([9, 16], iterator_2.get_next().numpy())
self.assertEqual(3, iterator_3.get_next().numpy())
checkpoint.restore(save_path)
self.assertAllEqual([9, 16], iterator_1.get_next().numpy())
self.assertAllEqual([1, 4], iterator_2.get_next().numpy())
self.assertEqual(3, iterator_3.get_next().numpy())
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.AdamOptimizer(learning_rate=0.05)
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
checkpoint = checkpointable_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)])
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()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
for training_continuation in range(3):
with ops.Graph().as_default(), self.test_session(
graph=ops.get_default_graph()):
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
root = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network,
global_step=training_util.get_or_create_global_step())
checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
status = root.restore(save_path=checkpoint_path)
input_value = constant_op.constant([[3.]])
train_fn = functools.partial(
optimizer.minimize,
functools.partial(network, input_value),
global_step=root.global_step)
if context.in_graph_mode():
train_fn = functools.partial(self.evaluate, train_fn())
status.initialize_or_restore()
for _ in range(num_training_steps):
train_fn()
root.save(file_prefix=checkpoint_prefix)
self.assertEqual((training_continuation + 1) * num_training_steps,
self.evaluate(root.global_step))
self.assertEqual(training_continuation + 1,
self.evaluate(root.save_counter))
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():
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
root = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network,
global_step=training_util.get_or_create_global_step())
input_value = constant_op.constant([[3.]])
train_op = optimizer.minimize(
network(input_value),
global_step=root.global_step)
checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
with self.test_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()
else:
status.assert_consumed()
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.global_step))
self.assertEqual(training_continuation + 1,
session.run(root.save_counter))
def test_checkpointable_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 = checkpointable_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 = checkpointable_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.assertEqual(2, len(outer_template_dependencies))
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.assertEqual(1, len(inner_template_one._checkpoint_dependencies))
self.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name)
self.assertEqual(1, len(inner_template_two._checkpoint_dependencies))
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))
def testRestoreExhaustedIterator(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
dataset = Dataset.range(3)
iterator = datasets.Iterator(dataset)
checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
self.assertEqual(0, iterator.get_next().numpy())
self.assertEqual(1, iterator.get_next().numpy())
save_path = checkpoint.save(checkpoint_prefix)
self.assertEqual(2, iterator.get_next().numpy())
checkpoint.restore(save_path)
self.assertEqual(2, iterator.get_next().numpy())
def testSaveRestore(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt') dataset = Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) dataset = dataset.map(math_ops.square).batch(2) iterator = datasets.Iterator(dataset) checkpoint = checkpointable_utils.Checkpoint(iterator=iterator) self.assertAllEqual([1, 4], iterator.get_next().numpy()) save_path = checkpoint.save(checkpoint_prefix) self.assertAllEqual([9, 16], iterator.get_next().numpy()) self.assertAllEqual([25, 36], iterator.get_next().numpy()) checkpoint.restore(save_path) self.assertAllEqual([9, 16], iterator.get_next().numpy()) self.assertAllEqual([25, 36], iterator.get_next().numpy())
def testSaveRestore(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
mean = metrics.Mean()
checkpoint = checkpointable_utils.Checkpoint(mean=mean)
mean.build()
mean._built = True
self.evaluate(mean.init_variables())
self.evaluate(mean(100.))
self.evaluate(mean(200.))
save_path = checkpoint.save(checkpoint_prefix)
self.evaluate(mean(1000.))
checkpoint.restore(save_path).assert_consumed().run_restore_ops()
self.evaluate(mean(300.))
self.assertAllEqual(200., self.evaluate(mean.value()))
restore_mean = metrics.Mean()
restore_checkpoint = checkpointable_utils.Checkpoint(mean=restore_mean)
status = restore_checkpoint.restore(save_path)
restore_update = restore_mean(300.)
status.assert_consumed().run_restore_ops()
self.evaluate(restore_update)
self.assertAllEqual(200., self.evaluate(restore_mean.value()))
self.assertEqual(3, self.evaluate(restore_mean.denom))
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 ops.Graph().as_default(), self.test_session(
graph=ops.get_default_graph()), test_util.device(
use_gpu=True):
model = MyModel()
# Don't actually train so we can test variable values
optimizer = adam.AdamOptimizer(0.)
root = checkpointable_utils.Checkpoint(
optimizer=optimizer,
model=model,
global_step=training_util.get_or_create_global_step())
checkpoint_path = core_saver.latest_checkpoint(
checkpoint_directory)
status = root.restore(save_path=checkpoint_path)
def train_fn():
@function.defun
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),
global_step=root.global_step)
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(root.global_step))
self.assertEqual(training_continuation + 1,
self.evaluate(root.save_counter))
def _initialized_model(self):
input_value = constant_op.constant([[3.]])
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
optimizer_step = training_util.get_or_create_global_step()
root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network, optimizer_step=optimizer_step)
train_op = optimizer.minimize(
functools.partial(network, input_value),
global_step=optimizer_step)
self.evaluate(checkpointable_utils.gather_initializers(
root_checkpointable))
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(network._named_dense.bias.assign([1.]))
self.evaluate(optimizer.get_slot(
var=network._named_dense.bias, name="m").assign([2.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(3.))
return root_checkpointable
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):
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
root = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network,
optimizer_step=training_util.get_or_create_global_step())
root.restore(core_saver.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.]])
optimizer.minimize(
lambda: network(input_value), # pylint: disable=cell-var-from-loop
global_step=root.optimizer_step)
root.save(file_prefix=checkpoint_prefix)
self.assertEqual((training_continuation + 1) * num_training_steps,
root.optimizer_step.numpy())
def testNamingWithOptimizer(self):
input_value = constant_op.constant([[3.]])
network = MyNetwork()
# A nuisance Network using the same optimizer. Its slot variables should not
# go in the checkpoint, since it is never depended on.
other_network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
optimizer_step = training_util.get_or_create_global_step()
root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network, optimizer_step=optimizer_step)
if context.in_eager_mode():
optimizer.minimize(
lambda: network(input_value),
global_step=optimizer_step)
optimizer.minimize(
lambda: other_network(input_value),
global_step=optimizer_step)
else:
train_op = optimizer.minimize(
network(input_value), global_step=optimizer_step)
optimizer.minimize(
other_network(input_value),
global_step=optimizer_step)
self.evaluate(checkpointable_utils.gather_initializers(
root_checkpointable))
self.evaluate(train_op)
named_variables, serialized_graph = (
checkpointable_utils._serialize_object_graph(root_checkpointable))
expected_checkpoint_names = (
# Created in the root node, so no prefix.
"optimizer_step",
# No name provided to track_checkpointable(), so the position is used
# instead (one-based).
"network/via_track_layer/kernel",
# track_checkpointable() with a name provided, so that's used
"network/_named_dense/kernel",
"network/_named_dense/bias",
# non-Layer dependency of the network
"network/_non_layer/a_variable",
# The optimizer creates two non-slot variables
"optimizer/beta1_power",
"optimizer/beta2_power",
# Slot variables
"network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/m",
"network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/v",
"network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m",
"network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v",
"network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m",
"network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v",
)
suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
expected_checkpoint_names = [
name + suffix for name in expected_checkpoint_names]
six.assertCountEqual(self, expected_checkpoint_names,
named_variables.keys())
# Check that we've mapped to the right variable objects (not exhaustive)
self.assertEqual(
"global_step:0",
named_variables["optimizer_step" + suffix].name)
self.assertEqual(
"my_network/dense_1/kernel:0",
named_variables["network/via_track_layer/kernel" + suffix].name)
self.assertEqual(
"my_network/dense/kernel:0",
named_variables["network/_named_dense/kernel" + suffix].name)
self.assertEqual(
"beta1_power:0",
named_variables["optimizer/beta1_power" + suffix].name)
self.assertEqual(
"beta2_power:0",
named_variables["optimizer/beta2_power" + suffix].name)
# Spot check the generated protocol buffers.
self.assertEqual("optimizer",
serialized_graph.nodes[0].children[1].local_name)
optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[
1].node_id]
self.assertEqual("beta1_power",
optimizer_node.children[0].local_name)
self.assertEqual("beta1_power",
serialized_graph.nodes[optimizer_node.children[0].node_id]
.attributes[0].full_name)
self.assertEqual(
"my_network/dense/kernel",
serialized_graph.nodes[optimizer_node.slot_variables[0]
.original_variable_node_id]
.attributes[0].full_name)
# We strip off the :0 suffix, as variable.name-based saving does.
self.assertEqual(
"my_network/dense/kernel/Adam",
serialized_graph.nodes[optimizer_node.slot_variables[0]
.slot_variable_node_id]
.attributes[0].full_name)
self.assertEqual(
"my_network/dense/kernel/Adam:0",
optimizer.get_slot(
var=named_variables["network/_named_dense/kernel" + suffix],
name="m").name)
self.assertEqual(
"network/_named_dense/kernel" + suffix,
serialized_graph.nodes[
optimizer_node.slot_variables[0]
.original_variable_node_id].attributes[0].checkpoint_key)
self.assertEqual("m", optimizer_node.slot_variables[0].slot_name)
self.assertEqual(
"network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix,
serialized_graph.nodes[
optimizer_node.slot_variables[0]
.slot_variable_node_id].attributes[0].checkpoint_key)
def testSaveRestore(self):
model = MyModel()
optimizer = adam.AdamOptimizer(0.001)
root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, model=model)
input_value = constant_op.constant([[3.]])
if context.executing_eagerly():
optimizer.minimize(lambda: model(input_value))
else:
train_op = optimizer.minimize(model(input_value))
# TODO(allenl): Make initialization more pleasant when graph building.
root_checkpointable.save_counter # pylint: disable=pointless-statement
self.evaluate(
checkpointable_utils.gather_initializers(root_checkpointable))
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_checkpointable.save(file_prefix=prefix)
self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.]))
self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3))
optimizer_variables = self.evaluate(optimizer.variables())
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
# Immediate restoration
status = root_checkpointable.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_checkpointable.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.AdamOptimizer(
0.001,
# Preserve beta1_power and beta2_power when appying gradients so we can
# test that they've been restored correctly.
beta1=1.0,
beta2=1.0)
on_create_root = checkpointable_utils.Checkpoint(
optimizer=on_create_optimizer, model=on_create_model)
# Deferred restoration
status = on_create_root.restore(save_path=save_path)
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")
# Optimizer slot variables are created when the original variable is
# restored.
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
self.assertAllEqual(optimizer_variables[2:],
self.evaluate(on_create_optimizer.variables()))
dummy_var = resource_variable_ops.ResourceVariable([1.])
on_create_optimizer.minimize(loss=dummy_var.read_value)
status.assert_consumed()
beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators()
self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
def testDeferredSlotRestoration(self):
checkpoint_directory = self.get_temp_dir()
root = checkpointable.Checkpointable()
root.var = checkpointable_utils.add_variable(
root, name="var", initializer=0.)
optimizer = adam.AdamOptimizer(0.1)
if context.in_graph_mode():
train_op = optimizer.minimize(root.var)
# 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(checkpointable_utils.gather_initializers(
checkpointable_utils.Checkpoint(root=root, optimizer=optimizer)))
self.evaluate(train_op)
else:
optimizer.minimize(root.var.read_value)
self.evaluate(state_ops.assign(root.var, 12.))
no_slots_path = checkpointable_utils.CheckpointableSaver(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(name="m", var=root.var),
14.))
slots_path = checkpointable_utils.CheckpointableSaver(root).save(
os.path.join(checkpoint_directory, "with_slots"))
new_root = checkpointable.Checkpointable()
# Load the slot-containing checkpoint (deferred), then immediately overwrite
# the non-slot variable (also deferred).
slot_status = checkpointable_utils.CheckpointableSaver(
new_root).restore(slots_path)
no_slot_status = checkpointable_utils.CheckpointableSaver(
new_root).restore(no_slots_path)
with self.assertRaises(AssertionError):
no_slot_status.assert_consumed()
new_root.var = checkpointable_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.AdamOptimizer(0.1)
with self.assertRaisesRegexp(AssertionError, "beta1_power"):
slot_status.assert_consumed()
self.assertEqual(12., self.evaluate(new_root.var))
if context.in_eager_mode():
# Slot variables are only created with restoring initializers when
# executing eagerly.
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
else:
self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var),
None)
if context.in_graph_mode():
train_op = new_root.optimizer.minimize(new_root.var)
# 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()
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
self.evaluate(train_op)
else:
new_root.optimizer.minimize(new_root.var.read_value)
slot_status.assert_consumed()
