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.assertEqual(str(round_1), str(alt_round_1)) self.assertTrue( np.array_equal(state.model.weights, state.model.weights)) self.assertTrue(np.array_equal(state.model.bias, alt_state.model.bias)) self.assertTrue(np.array_equal(state.num_rounds, alt_state.num_rounds)) self.assertTrue( np.array_equal(metrics.num_rounds, alt_metrics.num_rounds)) self.assertTrue( np.array_equal(metrics.num_examples, alt_metrics.num_examples)) self.assertTrue(np.array_equal(metrics.loss, alt_metrics.loss))
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_with_temperature_sensor_example(self): cf = test_utils.get_temperature_sensor_example() it = canonical_form_utils.get_iterative_process_for_canonical_form(cf) state = it.initialize() self.assertLen(state, 1) self.assertAllEqual(anonymous_tuple.name_list(state), ['num_rounds']) self.assertEqual(state[0], 0) state, metrics, stats = 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 = 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])
def test_passes_function_and_compiled_computation_of_same_type(self): init = canonical_form_utils.get_iterative_process_for_canonical_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 = canonical_form_utils.get_iterative_process_for_canonical_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.CanonicalFormCompilationError, 'we have the non-functional type'): transformations.check_extraction_result(integer_ref, compiled_computation)
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.assertEqual(str(state), '<num_rounds=0>') state, metrics, stats = new_it.next(state, [[28.0], [30.0, 33.0, 29.0]]) self.assertEqual(str(state), '<num_rounds=1>') self.assertEqual(str(metrics), '<ratio_over_threshold=0.5>') self.assertCountEqual([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.assertEqual(str(state), '<num_rounds=2>') self.assertEqual(str(metrics), '<ratio_over_threshold=0.75>') self.assertCountEqual([x.num_readings for x in stats], [1, 1, 1, 1])
def test_raises_function_and_compiled_computation_of_different_type(self): init = canonical_form_utils.get_iterative_process_for_canonical_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.CanonicalFormCompilationError, 'incorrect TFF type'): transformations.check_extraction_result(function, 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_next_computation_returning_tensor_fails_well(self): cf = test_utils.get_temperature_sensor_example() it = canonical_form_utils.get_iterative_process_for_canonical_form(cf) 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_wrapper_instances.building_block_to_computation(lam)) with self.assertRaises(TypeError): canonical_form_utils.get_canonical_form_for_iterative_process(bad_it)
def test_returns_canonical_form_from_tff_learning_structure(self): it = test_utils.construct_example_training_comp() cf = canonical_form_utils.get_canonical_form_for_iterative_process(it) new_it = canonical_form_utils.get_iterative_process_for_canonical_form( cf) self.assertIsInstance(cf, canonical_form.CanonicalForm) self.assertEqual(it.initialize.type_signature, new_it.initialize.type_signature) # Notice next type_signatures need not be equal, since we may have appended # an empty tuple as client side-channel outputs if none existed self.assertEqual(it.next.type_signature.parameter, new_it.next.type_signature.parameter) self.assertEqual(it.next.type_signature.result[0], new_it.next.type_signature.result[0]) self.assertEqual(it.next.type_signature.result[1], new_it.next.type_signature.result[1]) state1 = it.initialize() state2 = new_it.initialize() sample_batch = collections.OrderedDict(x=np.array([[1., 1.]], dtype=np.float32), y=np.array([[0]], dtype=np.int32)) client_data = [sample_batch] round_1 = it.next(state1, [client_data]) state = round_1[0] state_names = anonymous_tuple.name_list(state) state_arrays = anonymous_tuple.flatten(state) metrics = round_1[1] metrics_names = [x[0] for x in anonymous_tuple.iter_elements(metrics)] metrics_arrays = anonymous_tuple.flatten(metrics) alt_round_1 = new_it.next(state2, [client_data]) alt_state = alt_round_1[0] alt_state_names = anonymous_tuple.name_list(alt_state) alt_state_arrays = anonymous_tuple.flatten(alt_state) alt_metrics = alt_round_1[1] alt_metrics_names = [ x[0] for x in anonymous_tuple.iter_elements(alt_metrics) ] alt_metrics_arrays = anonymous_tuple.flatten(alt_metrics) self.assertEmpty(state.delta_aggregate_state) self.assertEmpty(state.model_broadcast_state) self.assertAllEqual(state_names, alt_state_names) self.assertAllEqual(metrics_names, alt_metrics_names) self.assertAllClose(state_arrays, alt_state_arrays) self.assertAllClose(metrics_arrays[:2], alt_metrics_arrays[:2]) # Final metric is execution time self.assertAlmostEqual(metrics_arrays[2], alt_metrics_arrays[2], delta=1e-3)
def test_get_canonical_form_from_fl_api(self): it = test_utils.construct_example_training_comp() cf = canonical_form_utils.get_canonical_form_for_iterative_process(it) new_it = canonical_form_utils.get_iterative_process_for_canonical_form( cf) self.assertIsInstance(cf, canonical_form.CanonicalForm) self.assertEqual(it.initialize.type_signature, new_it.initialize.type_signature) # Notice next type_signatures need not be equal, since we may have appended # an empty tuple as client side-channel outputs if none existed self.assertEqual(it.next.type_signature.parameter, new_it.next.type_signature.parameter) self.assertEqual(it.next.type_signature.result[0], new_it.next.type_signature.result[0]) self.assertEqual(it.next.type_signature.result[1], new_it.next.type_signature.result[1]) state1 = it.initialize() state2 = new_it.initialize() self.assertEqual(str(state1), str(state2)) sample_batch = collections.OrderedDict([ ('x', np.array([[1., 1.]], dtype=np.float32)), ('y', np.array([[0]], dtype=np.int32)) ]) client_data = [sample_batch] 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.assertTrue( np.array_equal(state.model.trainable[0], alt_state.model.trainable[0])) self.assertTrue( np.array_equal(state.model.trainable[1], alt_state.model.trainable[1])) self.assertEqual(str(state.model.non_trainable), str(alt_state.model.non_trainable)) self.assertEqual(state.optimizer_state[0], alt_state.optimizer_state[0]) self.assertEmpty(state.delta_aggregate_state) self.assertEmpty(alt_state.delta_aggregate_state) self.assertEmpty(state.model_broadcast_state) self.assertEmpty(alt_state.model_broadcast_state) self.assertEqual(metrics.sparse_categorical_accuracy, alt_metrics.sparse_categorical_accuracy) self.assertEqual(metrics.loss, alt_metrics.loss)
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_with_temperature_sensor_example(self): cf = test_utils.get_temperature_sensor_example() it = canonical_form_utils.get_iterative_process_for_canonical_form(cf) 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_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_constructs_canonical_form_from_mnist_training_example(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) self.assertIsInstance(cf, canonical_form.CanonicalForm)
def test_get_canonical_form_for_iterative_process(self): cf = test_utils.get_temperature_sensor_example() it = canonical_form_utils.get_iterative_process_for_canonical_form(cf) cf = canonical_form_utils.get_canonical_form_for_iterative_process(it) self.assertIsInstance(cf, canonical_form.CanonicalForm)