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)