def _create_variable(self, next_creator, *args, **kwargs):
"""Create a mirrored variable. See `DistributionStrategy.scope`."""
# Figure out what collections this variable should be added to.
# We'll add the MirroredVariable to those collections instead.
collections = kwargs.pop("collections", None)
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
kwargs["collections"] = []
colocate_with = kwargs.pop("colocate_with", None)
devices = self._get_devices_from(colocate_with)
tower_local = kwargs.pop("tower_local_reduce_method", None)
if tower_local is not None:
kwargs["trainable"] = False
# TODO(josh11b,apassos): It would be better if variable initialization
# was never recorded on the tape instead of having to do this manually
# here.
with tape.stop_recording():
index = {}
for i, d in enumerate(devices):
with ops.device(d):
if i > 0:
# Give replicas meaningful distinct names:
var0name = index[devices[0]].name.split(":")[0]
kwargs["name"] = "%s/replica_%d" % (var0name, i)
# Initialize replicas with the same value:
if context.executing_eagerly():
initial_value = index[devices[0]].value()
else:
initial_value = index[devices[0]].initial_value
kwargs["initial_value"] = array_ops.identity(
initial_value)
with context.context().device_policy(
context.DEVICE_PLACEMENT_SILENT):
v = next_creator(*args, **kwargs)
assert not isinstance(v, values.DistributedVariable)
index[d] = v
if tower_local is None:
result = values.MirroredVariable(index, index[devices[0]])
else:
result = values.TowerLocalVariable(index, index[devices[0]],
tower_local)
if not context.executing_eagerly():
g = ops.get_default_graph()
# If "trainable" is True, next_creator() will add the member variables
# to the TRAINABLE_VARIABLES collection, so we manually remove
# them and replace with the MirroredVariable. We can't set
# "trainable" to False for next_creator() since that causes functions
# like implicit_gradients to skip those variables.
if kwargs.get("trainable", True):
collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
l = g.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)
for v in index.values():
l.remove(v)
g.add_to_collections(collections, result)
return result
def testVariableOnAnotherDevice(self):
v = variable_scope.get_variable(
name="v", initializer=[1.], use_resource=True)
index = {"/job:foo/device:CPU:0": v}
mirrored = values.MirroredVariable(index, v)
self.assertEquals(v.name, mirrored.name)
self.assertEquals(v.dtype, mirrored.dtype)
self.assertEquals(v.shape, mirrored.shape)
def _make_mirrored():
v = []
index = {}
devices = ["/device:GPU:0", "/device:CPU:0"]
for d, n, init in zip(devices, ["v", "v/replica"], [1., 2.]):
with ops.device(d):
v.append(variable_scope.get_variable(
name=n, initializer=init, use_resource=True))
index[d] = v[-1]
mirrored = values.MirroredVariable(index, v[0])
return v, devices, mirrored
def testFetchAMirroredVariable(self):
if context.num_gpus() < 1 or context.executing_eagerly():
self.skipTest("A GPU is not available for this test or it's eager mode.")
with self.test_session(
graph=ops.Graph()) as sess, mirrored_strategy.MirroredStrategy(
["/device:GPU:0"]).scope():
with ops.device("/device:GPU:0"):
v = variable_scope.get_variable(
name="v", initializer=1., use_resource=True)
mirrored = values.MirroredVariable({"/device:GPU:0": v}, v)
sess.run(variables_lib.global_variables_initializer())
sess.run({"complicated": mirrored})
def testOneDevice(self):
result = values.regroup({_device_str(0): _nested_value("1")})
# On one device regroup() and select_device() are basically identity.
self.assertEqual(_nested_value("1"), result)
self.assertEqual(_nested_value("1"),
values.select_device(_device_str(0), result))
# The one exception has to do with MirroredVariables.
d = "/device:CPU:0"
with ops.device(d):
v = variable_scope.get_variable(
name="v", initializer=1., use_resource=True)
index = {d: v}
mirrored = values.MirroredVariable(index, v)
result = values.regroup(index)
self.assertIs(mirrored, result)
def _create_mirrored_variable(devices, real_mirrored_creator, *args, **kwargs): # pylint: disable=g-missing-docstring
# Figure out what collections this variable should be added to.
# We'll add the MirroredVariable to those collections instead.
collections = kwargs.pop("collections", None)
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
kwargs["collections"] = []
# Get synchronization value
synchronization = kwargs.get(
"synchronization", variable_scope.VariableSynchronization.ON_WRITE)
if synchronization == variable_scope.VariableSynchronization.NONE:
raise ValueError(
"`NONE` variable synchronization mode is not "
"supported with `Mirrored` distribution strategy. Please"
" change the `synchronization` for variable: " + kwargs["name"])
elif synchronization == variable_scope.VariableSynchronization.ON_READ:
# Variables that are to be synced on read are replica local.
is_replica_local = True
kwargs["trainable"] = False
elif (synchronization == variable_scope.VariableSynchronization.ON_WRITE
or synchronization == variable_scope.VariableSynchronization.AUTO):
# `AUTO` synchronization for `MirroredStrategy` is `ON_WRITE`.
is_replica_local = False
else:
raise ValueError("Invalid variable synchronization mode: " +
synchronization + " for variable: " + kwargs["name"])
# Get aggregation value
aggregation = kwargs.pop("aggregation",
variable_scope.VariableAggregation.NONE)
if aggregation not in (
variable_scope.VariableAggregation.NONE,
variable_scope.VariableAggregation.SUM,
variable_scope.VariableAggregation.MEAN,
variable_scope.VariableAggregation.ONLY_FIRST_REPLICA):
raise ValueError("Invalid variable aggregation mode: " + aggregation +
" for variable: " + kwargs["name"])
# Ignore user-specified caching device, not needed for mirrored variables.
kwargs.pop("caching_device", None)
# TODO(josh11b,apassos): It would be better if variable initialization
# was never recorded on the tape instead of having to do this manually
# here.
with tape.stop_recording():
index = real_mirrored_creator(devices, *args, **kwargs)
if is_replica_local:
result = values.ReplicaLocalVariable(index, index[devices[0]],
aggregation)
else:
result = values.MirroredVariable(index, index[devices[0]],
aggregation)
# Add the wrapped variable to the requested collections.
# The handling of eager mode and the global step matches
# ResourceVariable._init_from_args().
if not context.executing_eagerly():
g = ops.get_default_graph()
# If "trainable" is True, next_creator() will add the member variables
# to the TRAINABLE_VARIABLES collection, so we manually remove
# them and replace with the MirroredVariable. We can't set
# "trainable" to False for next_creator() since that causes functions
# like implicit_gradients to skip those variables.
if kwargs.get("trainable", True):
collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
l = g.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)
for v in index.values():
if v in l:
l.remove(v)
g.add_to_collections(collections, result)
elif ops.GraphKeys.GLOBAL_STEP in collections:
ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, result)
return result
def _create_variable(self, next_creator, *args, **kwargs):
"""Create a mirrored variable. See `DistributionStrategy.scope`."""
# Figure out what collections this variable should be added to.
# We'll add the MirroredVariable to those collections instead.
collections = kwargs.pop("collections", None)
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
kwargs["collections"] = []
colocate_with = kwargs.pop("colocate_with", None)
devices = self._get_devices_from(colocate_with)
# Get synchronization value
synchronization = kwargs.get(
"synchronization", variable_scope.VariableSynchronization.ON_WRITE)
if synchronization == variable_scope.VariableSynchronization.NONE:
raise ValueError(
"`NONE` variable synchronization mode is not "
"supported with `Mirrored` distribution strategy. Please"
" change the `synchronization` for variable: " +
kwargs["name"])
elif synchronization == variable_scope.VariableSynchronization.ON_READ:
# Variables that are to be synced on read are tower local.
is_tower_local = True
kwargs["trainable"] = False
elif (synchronization
== variable_scope.VariableSynchronization.ON_WRITE or
synchronization == variable_scope.VariableSynchronization.AUTO):
# `AUTO` synchronization for `MirroredStrategy` is `ON_WRITE`.
is_tower_local = False
else:
raise ValueError("Invalid variable synchronization mode: " +
synchronization + " for variable: " +
kwargs["name"])
# Get aggregation value
aggregation = kwargs.pop("aggregation",
variable_scope.VariableAggregation.NONE)
if aggregation not in [a for a in variable_scope.VariableAggregation]:
raise ValueError("Invalid variable aggregation mode: " +
aggregation + " for variable: " + kwargs["name"])
# Ignore user-specified caching device, not needed for mirrored variables.
kwargs.pop("caching_device", None)
# TODO(josh11b,apassos): It would be better if variable initialization
# was never recorded on the tape instead of having to do this manually
# here.
with tape.stop_recording():
index = {}
for i, d in enumerate(devices):
with ops.device(d):
if i > 0:
# Give replicas meaningful distinct names:
var0name = index[devices[0]].name.split(":")[0]
# We append a / to variable names created on towers with id > 0 to
# ensure that we ignore the name scope and instead use the given
# name as the absolute name of the variable.
kwargs["name"] = "%s/replica_%d/" % (var0name, i)
# Initialize replicas with the same value:
if context.executing_eagerly():
kwargs["initial_value"] = array_ops.identity(
index[devices[0]].value())
else:
def initial_value_fn(device=d):
with ops.device(device):
return array_ops.identity(
index[devices[0]].initial_value)
kwargs["initial_value"] = initial_value_fn
with context.context().device_policy(
context.DEVICE_PLACEMENT_SILENT):
v = next_creator(*args, **kwargs)
assert not isinstance(v, values.DistributedVariable)
index[d] = v
if is_tower_local:
result = values.TowerLocalVariable(index, index[devices[0]],
aggregation)
else:
result = values.MirroredVariable(index, index[devices[0]],
aggregation)
if not context.executing_eagerly():
g = ops.get_default_graph()
# If "trainable" is True, next_creator() will add the member variables
# to the TRAINABLE_VARIABLES collection, so we manually remove
# them and replace with the MirroredVariable. We can't set
# "trainable" to False for next_creator() since that causes functions
# like implicit_gradients to skip those variables.
if kwargs.get("trainable", True):
collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
l = g.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)
for v in index.values():
l.remove(v)
g.add_to_collections(collections, result)
return result
