def test_invoke_federated_computation_fails(self):
@computations.federated_computation(
computation_types.FederatedType(tf.int32, placements.SERVER, True))
def foo(x):
return intrinsics.federated_broadcast(x)
context = tf_computation_context.TensorFlowComputationContext(
tf.get_default_graph())
with self.assertRaisesRegexp(ValueError,
'Expected a TensorFlow computation.'):
context.invoke(foo, None)
def serialize_py_fn_as_tf_computation(target, parameter_type, context_stack):
"""Serializes the 'target' as a TF computation with a given parameter type.
See also `serialize_tf2_as_tf_computation` for TensorFlow 2
serialization.
Args:
target: The entity to convert into and serialize as a TF computation. This
can currently only be a Python function. In the future, we will add here
support for serializing the various kinds of non-eager and eager
functions, and eventually aim at full support for and compliance with TF
2.0. This function is currently required to declare either zero parameters
if `parameter_type` is `None`, or exactly one parameter if it's not
`None`. The nested structure of this parameter must correspond to the
structure of the 'parameter_type'. In the future, we may support targets
with multiple args/keyword args (to be documented in the API and
referenced from here).
parameter_type: The parameter type specification if the target accepts a
parameter, or `None` if the target doesn't declare any parameters. Either
an instance of `types.Type`, or something that's convertible to it by
`types.to_type()`.
context_stack: The context stack to use.
Returns:
A tuple of (`pb.Computation`, `tff.Type`), where the computation contains
the instance with the `pb.TensorFlow` variant set, and the type is an
instance of `tff.Type`, potentially including Python container annotations,
for use by TensorFlow computation wrappers.
Raises:
TypeError: If the arguments are of the wrong types.
ValueError: If the signature of the target is not compatible with the given
parameter type.
"""
# TODO(b/113112108): Support a greater variety of target type signatures,
# with keyword args or multiple args corresponding to elements of a tuple.
# Document all accepted forms with examples in the API, and point to there
# from here.
py_typecheck.check_type(target, types.FunctionType)
py_typecheck.check_type(context_stack, context_stack_base.ContextStack)
parameter_type = computation_types.to_type(parameter_type)
argspec = inspect.getargspec(target) # pylint: disable=deprecated-method
with tf.Graph().as_default() as graph:
args = []
if parameter_type is not None:
if len(argspec.args) != 1:
raise ValueError(
'Expected the target to declare exactly one parameter, '
'found {}.'.format(repr(argspec.args)))
parameter_name = argspec.args[0]
parameter_value, parameter_binding = graph_utils.stamp_parameter_in_graph(
parameter_name, parameter_type, graph)
args.append(parameter_value)
else:
if argspec.args:
raise ValueError(
'Expected the target to declare no parameters, found {}.'.
format(repr(argspec.args)))
parameter_binding = None
context = tf_computation_context.TensorFlowComputationContext(graph)
with context_stack.install(context):
result = target(*args)
# TODO(b/122081673): This needs to change for TF 2.0. We may also
# want to allow the person creating a tff.tf_computation to specify
# a different initializer; e.g., if it is known that certain
# variables will be assigned immediately to arguments of the function,
# then it is wasteful to initialize them before this.
#
# The following is a bit of a work around: the collections below may
# contain variables more than once, hence we throw into a set. TFF needs
# to ensure all variables are initialized, but not all variables are
# always in the collections we expect. tff.learning._KerasModel tries to
# pull Keras variables (that may or may not be in GLOBAL_VARIABLES) into
# TFF_MODEL_VARIABLES for now.
all_variables = set(
tf.compat.v1.global_variables() +
tf.compat.v1.local_variables() + tf.compat.v1.get_collection(
graph_keys.GraphKeys.VARS_FOR_TFF_TO_INITIALIZE))
if all_variables:
# Use a readable but not-too-long name for the init_op.
name = 'init_op_for_' + '_'.join(
[v.name.replace(':0', '') for v in all_variables])
if len(name) > 50:
name = 'init_op_for_{}_variables'.format(
len(all_variables))
with tf.control_dependencies(context.init_ops):
# Before running the main new init op, run any initializers for sub-
# computations from context.init_ops. Variables from import_graph_def
# will not make it into the global collections, and so will not be
# initialized without this code path.
init_op_name = tf.compat.v1.initializers.variables(
all_variables, name=name).name
elif context.init_ops:
init_op_name = tf.group(*context.init_ops,
name='subcomputation_init_ops').name
else:
init_op_name = None
result_type, result_binding = graph_utils.capture_result_from_graph(
result, graph)
annotated_type = computation_types.FunctionType(parameter_type,
result_type)
return pb.Computation(type=pb.Type(function=pb.FunctionType(
parameter=type_serialization.serialize_type(parameter_type),
result=type_serialization.serialize_type(result_type))),
tensorflow=pb.TensorFlow(
graph_def=serialization_utils.pack_graph_def(
graph.as_graph_def()),
parameter=parameter_binding,
result=result_binding,
initialize_op=init_op_name)), annotated_type
def serialize_py_fn_as_tf_computation(target, parameter_type, context_stack):
"""Serializes the 'target' as a TF computation with a given parameter type.
See also `serialize_tf2_as_tf_computation` for TensorFlow 2
serialization.
Args:
target: The entity to convert into and serialize as a TF computation. This
can currently only be a Python function. In the future, we will add here
support for serializing the various kinds of non-eager and eager
functions, and eventually aim at full support for and compliance with TF
2.0. This function is currently required to declare either zero parameters
if `parameter_type` is `None`, or exactly one parameter if it's not
`None`. The nested structure of this parameter must correspond to the
structure of the 'parameter_type'. In the future, we may support targets
with multiple args/keyword args (to be documented in the API and
referenced from here).
parameter_type: The parameter type specification if the target accepts a
parameter, or `None` if the target doesn't declare any parameters. Either
an instance of `types.Type`, or something that's convertible to it by
`types.to_type()`.
context_stack: The context stack to use.
Returns:
A tuple of (`pb.Computation`, `tff.Type`), where the computation contains
the instance with the `pb.TensorFlow` variant set, and the type is an
instance of `tff.Type`, potentially including Python container annotations,
for use by TensorFlow computation wrappers.
Raises:
TypeError: If the arguments are of the wrong types.
ValueError: If the signature of the target is not compatible with the given
parameter type.
"""
# TODO(b/113112108): Support a greater variety of target type signatures,
# with keyword args or multiple args corresponding to elements of a tuple.
# Document all accepted forms with examples in the API, and point to there
# from here.
py_typecheck.check_type(target, types.FunctionType)
py_typecheck.check_type(context_stack, context_stack_base.ContextStack)
parameter_type = computation_types.to_type(parameter_type)
signature = function_utils.get_signature(target)
with tf.Graph().as_default() as graph:
if parameter_type is not None:
if len(signature.parameters) != 1:
raise ValueError(
'Expected the target to declare exactly one parameter, found {!r}.'
.format(signature.parameters))
parameter_name = next(iter(signature.parameters))
parameter_value, parameter_binding = tensorflow_utils.stamp_parameter_in_graph(
parameter_name, parameter_type, graph)
else:
if signature.parameters:
raise ValueError(
'Expected the target to declare no parameters, found {!r}.'.format(
signature.parameters))
parameter_value = None
parameter_binding = None
context = tf_computation_context.TensorFlowComputationContext(graph)
with context_stack.install(context):
with variable_utils.record_variable_creation_scope() as all_variables:
if parameter_value is not None:
result = target(parameter_value)
else:
result = target()
initializer_ops = []
if all_variables:
# Use a readable but not-too-long name for the init_op.
name = 'init_op_for_' + '_'.join(
[v.name.replace(':0', '') for v in all_variables])
if len(name) > 50:
name = 'init_op_for_{}_variables'.format(len(all_variables))
initializer_ops.append(
tf.compat.v1.initializers.variables(all_variables, name=name))
initializer_ops.extend(
tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.TABLE_INITIALIZERS))
if initializer_ops:
# Before running the main new init op, run any initializers for sub-
# computations from context.init_ops. Variables from import_graph_def
# will not make it into the global collections, and so will not be
# initialized without this code path.
with tf.compat.v1.control_dependencies(context.init_ops):
init_op_name = tf.group(
*initializer_ops, name='grouped_initializers').name
elif context.init_ops:
init_op_name = tf.group(
*context.init_ops, name='subcomputation_init_ops').name
else:
init_op_name = None
result_type, result_binding = tensorflow_utils.capture_result_from_graph(
result, graph)
type_signature = computation_types.FunctionType(parameter_type, result_type)
# WARNING: we do not really want to be modifying the graph here if we can
# avoid it. This is purely to work around performance issues uncovered with
# the non-standard usage of Tensorflow and have been discussed with the
# Tensorflow core team before being added.
clean_graph_def = _clean_graph_def(graph.as_graph_def())
tensorflow = pb.TensorFlow(
graph_def=serialization_utils.pack_graph_def(clean_graph_def),
parameter=parameter_binding,
result=result_binding,
initialize_op=init_op_name)
return pb.Computation(
type=type_serialization.serialize_type(type_signature),
tensorflow=tensorflow), type_signature
