def _create_tpu_mirrored_variable( # pylint: disable=missing-docstring
strategy, device_map, logical_device, real_mirrored_creator, *args,
**kwargs):
# Figure out what collections this variable should be added to.
# We'll add the TPUMirroredVariable to those collections instead.
var_collections = kwargs.pop("collections", None)
if var_collections is None:
var_collections = [ops.GraphKeys.GLOBAL_VARIABLES]
kwargs["collections"] = []
# TODO(jhseu): Should we have different behavior for different
# synchronization settings?
# Get aggregation value
# TODO(jhseu): Support aggregation in a replica context.
aggregation = kwargs.pop("aggregation", vs.VariableAggregation.NONE)
if aggregation not in [
vs.VariableAggregation.NONE,
vs.VariableAggregation.SUM,
vs.VariableAggregation.MEAN,
vs.VariableAggregation.ONLY_FIRST_REPLICA,
]:
raise ValueError(
"Invalid variable aggregation mode: {} for variable: {}".format(
aggregation, 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():
devices = device_map.logical_to_actual_devices(logical_device)
value_list = real_mirrored_creator(devices, *args, **kwargs)
result = values.TPUMirroredVariable(strategy,
device_map,
value_list,
aggregation,
logical_device=logical_device)
if not (context.executing_eagerly() or ops.inside_function()):
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):
var_collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
l = g.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)
for v in value_list:
l.remove(v)
g.add_to_collections(var_collections, result)
return result
def testFetchOnFrozenGraph(self):
with context.graph_mode():
v = values.TPUMirroredVariable(
strategy=None,
device_map=values.SingleDeviceMap("/cpu:0"),
values=[variables_lib.Variable(42.)],
aggregation=None)
self.evaluate(variables_lib.global_variables_initializer())
ops.get_default_graph().finalize()
self.assertEqual(42., self.evaluate(v))
def test_supports_distributed_variables(self):
mirrored = distributed_values.MirroredVariable(
None, [variables.Variable(1.)], variables.VariableAggregation.SUM)
tpu = distributed_values.TPUMirroredVariable(
strategy=None,
values=[variables.Variable(42.)],
aggregation=None)
aggregating = distributed_values.AggregatingVariable(
strategy=None, v=variables.Variable(1.), aggregation=None)
m = module.Module()
m.a = mirrored
m.b = tpu
m.c = aggregating
self.assertEqual(m.variables, (mirrored, tpu, aggregating))