def create_dummy_computation_tuple():
"""Returns a tuple computation and type."""
element_type = computation_types.NamedTupleType([])
element_value = pb.Computation(
type=type_serialization.serialize_type(element_type),
tuple=pb.Tuple(element=[]))
element = pb.Tuple.Element(value=element_value)
type_signature = computation_types.NamedTupleType([element_type])
value = pb.Computation(
type=type_serialization.serialize_type(type_signature),
tuple=pb.Tuple(element=[element]))
return value, type_signature
def proto(self):
elements = []
for k, v in anonymous_tuple.to_elements(self):
if k is not None:
element = pb.Tuple.Element(name=k, value=v.proto)
else:
element = pb.Tuple.Element(value=v.proto)
elements.append(element)
return pb.Computation(type=type_serialization.serialize_type(
self.type_signature),
tuple=pb.Tuple(element=elements))
def create_dummy_computation_tuple():
"""Returns a tuple computation and type."""
names = ['a', 'b', 'c']
element_value, element_type = create_dummy_computation_tensorflow_constant(
)
elements = [pb.Tuple.Element(name=n, value=element_value) for n in names]
type_signature = computation_types.NamedTupleType(
(n, element_type) for n in names)
value = pb.Computation(
type=type_serialization.serialize_type(type_signature),
tuple=pb.Tuple(element=elements))
return value, type_signature
def create_dummy_computation_lambda_empty():
"""Returns a lambda computation and type `( -> <>)`."""
result_type = computation_types.NamedTupleType([])
type_signature = computation_types.FunctionType(None, result_type)
result = pb.Computation(
type=type_serialization.serialize_type(result_type),
tuple=pb.Tuple(element=[]))
fn = pb.Lambda(parameter_name=None, result=result)
# We are unpacking the lambda argument here because `lambda` is a reserved
# keyword in Python, but it is also the name of the parameter for a
# `pb.Computation`.
# https://developers.google.com/protocol-buffers/docs/reference/python-generated#keyword-conflicts
value = pb.Computation(
type=type_serialization.serialize_type(type_signature), **{'lambda': fn}) # pytype: disable=wrong-keyword-args
return value, type_signature
def create_dummy_computation_selection():
element_value = executor_test_utils.create_dummy_empty_tensorflow_computation(
)
element_type = computation_types.FunctionType(
None, computation_types.NamedTupleType([]))
element = pb.Tuple.Element(value=element_value)
source = pb.Computation(type=type_serialization.serialize_type(
[element_type]),
tuple=pb.Tuple(element=[element]))
value = pb.Computation(
type=type_serialization.serialize_type(element_type),
selection=pb.Selection(source=source, index=0))
type_signature = computation_types.FunctionType(
None, computation_types.NamedTupleType([]))
return value, type_signature
def test_raises_value_error_with_unrecognized_computation_selection(self):
executor = create_test_executor(num_clients=3)
element_value = executor_test_utils.create_dummy_empty_tensorflow_computation(
)
element_type = computation_types.FunctionType(
None, computation_types.NamedTupleType([]))
element = pb.Tuple.Element(value=element_value)
source = pb.Computation(type=type_serialization.serialize_type(
[element_type]),
tuple=pb.Tuple(element=[element]))
# A `ValueError` will be raised because `create_value` can not handle the
# following `pb.Selection`, because does not set either a name or an index
# field.
value = pb.Computation(
type=type_serialization.serialize_type(element_type),
selection=pb.Selection(source=source))
type_signature = computation_types.FunctionType(
None, computation_types.NamedTupleType([]))
with self.assertRaises(ValueError):
self.run_sync(executor.create_value(value, type_signature))
def create_dummy_computation_tuple():
value = pb.Computation(type=type_serialization.serialize_type(
computation_types.NamedTupleType([])),
tuple=pb.Tuple(element=[]))
type_signature = computation_types.NamedTupleType([])
return value, type_signature
