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_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_tensorflow_op_count_doubles_number_of_ops_in_two_tuple(self):
two_variable_comp = _create_two_variable_tensorflow()
node_tf_variable_count = building_block_analysis.count_tensorflow_variables_in(
two_variable_comp)
tf_tuple = building_blocks.Struct([two_variable_comp, two_variable_comp])
tree_tf_variable_count = tree_analysis.count_tensorflow_variables_under(
tf_tuple)
self.assertEqual(tree_tf_variable_count, 2 * node_tf_variable_count)
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_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_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_returns_zero_tensorflow_with_no_variables(self):
no_variable_comp = _create_no_variable_tensorflow()
variable_count = tree_analysis.count_tensorflow_variables_under(
no_variable_comp)
self.assertEqual(variable_count, 0)
def test_returns_zero_no_tensorflow(self):
no_tensorflow_comp = building_block_test_utils.create_nested_syntax_tree(
)
variable_count = tree_analysis.count_tensorflow_variables_under(
no_tensorflow_comp)
self.assertEqual(variable_count, 0)
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.count_tensorflow_variables_under(None)
