def test_mnist_training_round_trip(self):
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_mnist_training_example())
# TODO(b/208887729): We disable grappler to work around attempting to hoist
# transformed functions of the same name into the eager context. When this
# execution is C++-backed, this can go away.
grappler_config = tf.compat.v1.ConfigProto()
grappler_config.graph_options.rewrite_options.disable_meta_optimizer = True
mrf = form_utils.get_map_reduce_form_for_iterative_process(
it, grappler_config)
new_it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertAllClose(state1, state2)
whimsy_x = np.array([[0.5] * 784], dtype=np.float32)
whimsy_y = np.array([1], dtype=np.int32)
client_data = [collections.OrderedDict(x=whimsy_x, y=whimsy_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]
self.assertAllClose(state, alt_state)
alt_metrics = alt_round_1[1]
self.assertAllClose(metrics, alt_metrics)
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
it.next.to_building_block()),
tree_analysis.count_tensorflow_variables_under(
new_it.next.to_building_block()))
def test_passes_function_and_compiled_computation_of_same_type(self):
init = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_temperature_sensor_example()).initialize
compiled_computation = self.compiled_computation_for_initialize(init)
function = building_blocks.Reference('f',
compiled_computation.type_signature)
transformations.check_extraction_result(function, compiled_computation)
def test_raises_non_function_and_compiled_computation(self):
init = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_temperature_sensor_example()).initialize
compiled_computation = self.compiled_computation_for_initialize(init)
integer_ref = building_blocks.Reference('x', tf.int32)
with self.assertRaisesRegex(transformations.MapReduceFormCompilationError,
'we have the non-functional type'):
transformations.check_extraction_result(integer_ref, compiled_computation)
def test_raises_function_and_compiled_computation_of_different_type(self):
init = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_temperature_sensor_example()).initialize
compiled_computation = self.compiled_computation_for_initialize(init)
function = building_blocks.Reference(
'f', computation_types.FunctionType(tf.int32, tf.int32))
with self.assertRaisesRegex(transformations.MapReduceFormCompilationError,
'incorrect TFF type'):
transformations.check_extraction_result(function, compiled_computation)
def test_next_computation_returning_tensor_fails_well(self):
mrf = mapreduce_test_utils.get_temperature_sensor_example()
it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
init_result = it.initialize.type_signature.result
lam = building_blocks.Lambda(
'x', init_result, building_blocks.Reference('x', init_result))
bad_it = iterative_process.IterativeProcess(
it.initialize,
computation_impl.ConcreteComputation.from_building_block(lam))
with self.assertRaises(TypeError):
form_utils.get_map_reduce_form_for_iterative_process(bad_it)
def test_already_reduced_case(self):
init = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_temperature_sensor_example()).initialize
comp = init.to_building_block()
result = transformations.consolidate_and_extract_local_processing(
comp, DEFAULT_GRAPPLER_CONFIG)
self.assertIsInstance(result, building_blocks.CompiledComputation)
self.assertIsInstance(result.proto, computation_pb2.Computation)
self.assertEqual(result.proto.WhichOneof('computation'), 'tensorflow')
def test_temperature_example_round_trip(self):
# NOTE: the roundtrip through MapReduceForm->IterProc->MapReduceForm seems
# to lose the python container annotations on the StructType.
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_temperature_sensor_example())
mrf = form_utils.get_map_reduce_form_for_iterative_process(it)
new_it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
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(
it.next.to_building_block()),
tree_analysis.count_tensorflow_variables_under(
new_it.next.to_building_block()))
def test_with_temperature_sensor_example(self):
mrf = mapreduce_test_utils.get_temperature_sensor_example()
it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
state = it.initialize()
self.assertAllEqual(state, collections.OrderedDict(num_rounds=0))
state, metrics = it.next(state, [[28.0], [30.0, 33.0, 29.0]])
self.assertAllEqual(state, collections.OrderedDict(num_rounds=1))
self.assertAllClose(metrics,
collections.OrderedDict(ratio_over_threshold=0.5))
state, metrics = it.next(state, [[33.0], [34.0], [35.0], [36.0]])
self.assertAllClose(metrics,
collections.OrderedDict(ratio_over_threshold=0.75))
def test_mnist_training_round_trip(self):
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_mnist_training_example())
mrf = form_utils.get_map_reduce_form_for_iterative_process(it)
new_it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertAllClose(state1, state2)
whimsy_x = np.array([[0.5] * 784], dtype=np.float32)
whimsy_y = np.array([1], dtype=np.int32)
client_data = [collections.OrderedDict(x=whimsy_x, y=whimsy_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]
self.assertAllClose(state, alt_state)
alt_metrics = alt_round_1[1]
self.assertAllClose(metrics, alt_metrics)
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
it.next.to_building_block()),
tree_analysis.count_tensorflow_variables_under(
new_it.next.to_building_block()))
def test_broadcast_dependent_on_aggregate_fails_well(self):
mrf = mapreduce_test_utils.get_temperature_sensor_example()
it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
next_comp = it.next.to_building_block()
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.Struct([
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'):
form_utils.get_map_reduce_form_for_iterative_process(not_reducible_it)
def test_constructs_map_reduce_form_from_mnist_training_example(self):
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_mnist_training_example())
mrf = form_utils.get_map_reduce_form_for_iterative_process(it)
self.assertIsInstance(mrf, forms.MapReduceForm)
