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