def test_temperature_example_round_trip(self):
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_temperature_sensor_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
new_it = canonical_form_utils.get_iterative_process_for_canonical_form(cf)
state = new_it.initialize()
self.assertLen(state, 1)
self.assertAllEqual(anonymous_tuple.name_list(state), ['num_rounds'])
self.assertEqual(state[0], 0)
state, metrics, stats = new_it.next(state, [[28.0], [30.0, 33.0, 29.0]])
self.assertLen(state, 1)
self.assertAllEqual(anonymous_tuple.name_list(state), ['num_rounds'])
self.assertEqual(state[0], 1)
self.assertLen(metrics, 1)
self.assertAllEqual(
anonymous_tuple.name_list(metrics), ['ratio_over_threshold'])
self.assertEqual(metrics[0], 0.5)
self.assertCountEqual([self.evaluate(x.num_readings) for x in stats],
[1, 3])
state, metrics, stats = new_it.next(state, [[33.0], [34.0], [35.0], [36.0]])
self.assertAllEqual(state, (2,))
self.assertAllClose(metrics, {'ratio_over_threshold': 0.75})
self.assertCountEqual([x.num_readings for x in stats], [1, 1, 1, 1])
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(it.next)),
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(new_it.next)))
def test_mnist_training_round_trip(self):
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_mnist_training_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
new_it = canonical_form_utils.get_iterative_process_for_canonical_form(cf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertEqual(str(state1), str(state2))
dummy_x = np.array([[0.5] * 784], dtype=np.float32)
dummy_y = np.array([1], dtype=np.int32)
client_data = [collections.OrderedDict(x=dummy_x, y=dummy_y)]
round_1 = it.next(state1, [client_data])
state = round_1[0]
metrics = round_1[1]
alt_round_1 = new_it.next(state2, [client_data])
alt_state = alt_round_1[0]
alt_metrics = alt_round_1[1]
self.assertAllEqual(
anonymous_tuple.name_list(state), anonymous_tuple.name_list(alt_state))
self.assertAllEqual(
anonymous_tuple.name_list(metrics),
anonymous_tuple.name_list(alt_metrics))
self.assertAllClose(state, alt_state)
self.assertAllClose(metrics, alt_metrics)
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(it.next)),
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(new_it.next)))
def test_passes_function_and_compiled_computation_of_same_type(self):
init = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_temperature_sensor_example()).initialize
compiled_computation = (
test_utils.computation_to_building_block(init).argument.function)
function = building_blocks.Reference(
'f', compiled_computation.type_signature)
mapreduce_transformations.check_extraction_result(
function, compiled_computation)
def test_raises_non_function_and_compiled_computation(self):
init = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_temperature_sensor_example()).initialize
compiled_computation = (
test_utils.computation_to_building_block(init).argument.function)
integer_ref = building_blocks.Reference('x', tf.int32)
with self.assertRaisesRegex(
mapreduce_transformations.CanonicalFormCompilationError,
'we have the non-functional type'):
mapreduce_transformations.check_extraction_result(
integer_ref, compiled_computation)
def test_already_reduced_case(self):
init = canonical_form_utils.get_iterative_process_for_canonical_form(
mapreduce_test_utils.get_temperature_sensor_example()).initialize
comp = mapreduce_test_utils.computation_to_building_block(init)
result = transformations.consolidate_and_extract_local_processing(comp)
self.assertIsInstance(result, building_blocks.CompiledComputation)
self.assertIsInstance(result.proto, computation_pb2.Computation)
self.assertEqual(result.proto.WhichOneof('computation'), 'tensorflow')
def test_raises_function_and_compiled_computation_of_different_type(self):
init = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_temperature_sensor_example()).initialize
compiled_computation = (
test_utils.computation_to_building_block(init).argument.function)
function = building_blocks.Reference(
'f', computation_types.FunctionType(tf.int32, tf.int32))
with self.assertRaisesRegex(
mapreduce_transformations.CanonicalFormCompilationError,
'incorrect TFF type'):
mapreduce_transformations.check_extraction_result(
function, compiled_computation)
def test_temperature_example_round_trip(self):
# NOTE: the roundtrip through CanonicalForm->IterProc->CanonicalForm seems
# to lose the python container annotations on the StructType.
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_temperature_sensor_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
new_it = canonical_form_utils.get_iterative_process_for_canonical_form(cf)
state = new_it.initialize()
self.assertEqual(state.num_rounds, 0)
state, metrics = new_it.next(state, [[28.0], [30.0, 33.0, 29.0]])
self.assertEqual(state.num_rounds, 1)
self.assertAllClose(metrics,
collections.OrderedDict(ratio_over_threshold=0.5))
state, metrics = new_it.next(state, [[33.0], [34.0], [35.0], [36.0]])
self.assertAllClose(metrics,
collections.OrderedDict(ratio_over_threshold=0.75))
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(it.next)),
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(new_it.next)))
def test_returns_comps_with_federated_aggregate(self):
iterative_process = test_utils.construct_example_training_comp()
comp = test_utils.computation_to_building_block(iterative_process.next)
uri = intrinsic_defs.FEDERATED_AGGREGATE.uri
before, after = mapreduce_transformations.force_align_and_split_by_intrinsic(
comp, uri)
def _predicate(comp):
return building_block_analysis.is_called_intrinsic(comp, uri)
self.assertIsInstance(comp, building_blocks.Lambda)
self.assertGreater(tree_analysis.count(comp, _predicate), 0)
self.assertIsInstance(before, building_blocks.Lambda)
self.assertEqual(tree_analysis.count(before, _predicate), 0)
self.assertEqual(before.parameter_type, comp.parameter_type)
self.assertIsInstance(after, building_blocks.Lambda)
self.assertEqual(tree_analysis.count(after, _predicate), 0)
self.assertEqual(after.result.type_signature, comp.result.type_signature)
def test_broadcast_dependent_on_aggregate_fails_well(self):
cf = test_utils.get_temperature_sensor_example()
it = canonical_form_utils.get_iterative_process_for_canonical_form(cf)
next_comp = test_utils.computation_to_building_block(it.next)
top_level_param = building_blocks.Reference(next_comp.parameter_name,
next_comp.parameter_type)
first_result = building_blocks.Call(next_comp, top_level_param)
middle_param = building_blocks.Tuple([
building_blocks.Selection(first_result, index=0),
building_blocks.Selection(top_level_param, index=1)
])
second_result = building_blocks.Call(next_comp, middle_param)
not_reducible = building_blocks.Lambda(next_comp.parameter_name,
next_comp.parameter_type,
second_result)
not_reducible_it = iterative_process.IterativeProcess(
it.initialize,
computation_wrapper_instances.building_block_to_computation(
not_reducible))
with self.assertRaisesRegex(ValueError, 'broadcast dependent on aggregate'):
canonical_form_utils.get_canonical_form_for_iterative_process(
not_reducible_it)
def test_example_training_comp_reduces(self):
training_comp = mapreduce_test_utils.construct_example_training_comp()
self.assertIsInstance(
mapreduce_test_utils.computation_to_building_block(
training_comp.next), building_blocks.Lambda)
def compiled_computation_for_initialize(self, initialize): block = mapreduce_test_utils.computation_to_building_block(initialize) return self.get_function_from_first_symbol_binding_in_lambda_result(block)
