def test_orchestration_type_signature(self):
iterative_process = optimizer_utils.build_model_delta_optimizer_process(
model_fn=model_examples.TrainableLinearRegression,
model_to_client_delta_fn=DummyClientDeltaFn,
server_optimizer_fn=lambda: gradient_descent.SGD(learning_rate=1.0
))
expected_model_weights_type = model_utils.ModelWeights(
collections.OrderedDict([('a', tff.TensorType(tf.float32, [2, 1])),
('b', tf.float32)]),
collections.OrderedDict([('c', tf.float32)]))
# ServerState consists of a model and optimizer_state. The optimizer_state
# is provided by TensorFlow, TFF doesn't care what the actual value is.
expected_federated_server_state_type = tff.FederatedType(
optimizer_utils.ServerState(expected_model_weights_type,
test.AnyType(), test.AnyType(),
test.AnyType()),
placement=tff.SERVER,
all_equal=True)
expected_federated_dataset_type = tff.FederatedType(tff.SequenceType(
model_examples.TrainableLinearRegression().input_spec),
tff.CLIENTS,
all_equal=False)
expected_model_output_types = tff.FederatedType(
collections.OrderedDict([
('loss', tff.TensorType(tf.float32, [])),
('num_examples', tff.TensorType(tf.int32, [])),
]),
tff.SERVER,
all_equal=True)
# `initialize` is expected to be a funcion of no arguments to a ServerState.
self.assertEqual(
tff.FunctionType(parameter=None,
result=expected_federated_server_state_type),
iterative_process.initialize.type_signature)
# `next` is expected be a function of (ServerState, Datasets) to
# ServerState.
self.assertEqual(
tff.FunctionType(parameter=[
expected_federated_server_state_type,
expected_federated_dataset_type
],
result=(expected_federated_server_state_type,
expected_model_output_types)),
iterative_process.next.type_signature)
def test_orchestration_typecheck(self):
iterative_process = federated_sgd.build_federated_sgd_process(
model_fn=model_examples.LinearRegression)
expected_model_weights_type = model_utils.ModelWeights(
collections.OrderedDict([('a', tff.TensorType(tf.float32, [2, 1])),
('b', tf.float32)]),
collections.OrderedDict([('c', tf.float32)]))
# ServerState consists of a model and optimizer_state. The optimizer_state
# is provided by TensorFlow, TFF doesn't care what the actual value is.
expected_federated_server_state_type = tff.FederatedType(
optimizer_utils.ServerState(expected_model_weights_type,
test.AnyType()),
placement=tff.SERVER,
all_equal=True)
expected_federated_dataset_type = tff.FederatedType(
tff.SequenceType(
model_examples.LinearRegression.make_batch(
tff.TensorType(tf.float32, [None, 2]),
tff.TensorType(tf.float32, [None, 1]))),
tff.CLIENTS,
all_equal=False)
expected_model_output_types = tff.FederatedType(
collections.OrderedDict([
('loss', tff.TensorType(tf.float32, [])),
('num_examples', tff.TensorType(tf.int32, [])),
]),
tff.SERVER,
all_equal=True)
# `initialize` is expected to be a funcion of no arguments to a ServerState.
self.assertEqual(
tff.FunctionType(
parameter=None, result=expected_federated_server_state_type),
iterative_process.initialize.type_signature)
# `next` is expected be a function of (ServerState, Datasets) to
# ServerState.
self.assertEqual(
tff.FunctionType(
parameter=[
expected_federated_server_state_type,
expected_federated_dataset_type
],
result=(expected_federated_server_state_type,
expected_model_output_types)),
iterative_process.next.type_signature)
