def test_returns_tree(self):
ip = get_iterative_process_for_sum_example_with_no_federated_secure_sum(
)
next_tree = building_blocks.ComputationBuildingBlock.from_proto(
ip.next._computation_proto)
next_tree = canonical_form_utils._replace_intrinsics_with_bodies(
next_tree)
before_aggregate, after_aggregate = canonical_form_utils._create_before_and_after_aggregate_for_no_federated_secure_sum(
next_tree)
before_federated_aggregate, after_federated_aggregate = (
transformations.force_align_and_split_by_intrinsics(
next_tree, [intrinsic_defs.FEDERATED_AGGREGATE.uri]))
self.assertIsInstance(before_aggregate, building_blocks.Lambda)
self.assertIsInstance(before_aggregate.result, building_blocks.Struct)
self.assertLen(before_aggregate.result, 2)
# trees_equal will fail if computations refer to unbound references, so we
# create a new dummy computation to bind them.
unbound_refs_in_before_agg_result = transformation_utils.get_map_of_unbound_references(
before_aggregate.result[0])[before_aggregate.result[0]]
unbound_refs_in_before_fed_agg_result = transformation_utils.get_map_of_unbound_references(
before_federated_aggregate.result)[
before_federated_aggregate.result]
dummy_data = building_blocks.Data('data',
computation_types.AbstractType('T'))
blk_binding_refs_in_before_agg = building_blocks.Block(
[(name, dummy_data) for name in unbound_refs_in_before_agg_result],
before_aggregate.result[0])
blk_binding_refs_in_before_fed_agg = building_blocks.Block(
[(name, dummy_data)
for name in unbound_refs_in_before_fed_agg_result],
before_federated_aggregate.result)
self.assertTrue(
tree_analysis.trees_equal(blk_binding_refs_in_before_agg,
blk_binding_refs_in_before_fed_agg))
# pyformat: disable
self.assertEqual(
before_aggregate.result[1].formatted_representation(), '<\n'
' federated_value_at_clients(<>),\n'
' <>\n'
'>')
# pyformat: enable
self.assertIsInstance(after_aggregate, building_blocks.Lambda)
self.assertIsInstance(after_aggregate.result, building_blocks.Call)
self.assertTrue(
tree_analysis.trees_equal(after_aggregate.result.function,
after_federated_aggregate))
# pyformat: disable
self.assertEqual(
after_aggregate.result.argument.formatted_representation(), '<\n'
' _var1[0],\n'
' _var1[1][0]\n'
'>')
def test_returns_false_for_references_with_different_names(self):
reference_1 = building_blocks.Reference('a', tf.int32)
reference_2 = building_blocks.Reference('b', tf.int32)
self.assertFalse(tree_analysis.trees_equal(reference_1, reference_2))
def test_returns_false_for_selections_with_different_indexes(self):
ref_1 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_1 = building_blocks.Selection(ref_1, index=0)
ref_2 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_2 = building_blocks.Selection(ref_2, index=1)
self.assertFalse(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_true_for_lambdas_referring_to_same_unbound_variables(self):
ref_to_x = building_blocks.Reference('x', tf.int32)
fn_1 = building_blocks.Lambda('a', tf.int32, ref_to_x)
fn_2 = building_blocks.Lambda('a', tf.int32, ref_to_x)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_placements_with_literals(self): placement_1 = building_blocks.Placement(placements.CLIENTS) placement_2 = building_blocks.Placement(placements.SERVER) self.assertFalse(tree_analysis.trees_equal(placement_1, placement_2))
def test_returns_false_for_intrinsics_with_different_names(self):
type_signature_1 = computation_types.TensorType(tf.int32)
intrinsic_1 = building_blocks.Intrinsic('a', type_signature_1)
type_signature_2 = computation_types.TensorType(tf.int32)
intrinsic_2 = building_blocks.Intrinsic('b', type_signature_2)
self.assertFalse(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_true_for_lambdas_representing_identical_functions(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda('a', ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('b', tf.int32)
fn_2 = building_blocks.Lambda('b', ref_2.type_signature, ref_2)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_true_for_tuples(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Tuple([data_1, data_1])
data_2 = building_blocks.Data('data', tf.int32)
tuple_2 = building_blocks.Tuple([data_2, data_2])
self.assertTrue(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_raises_type_error(self):
data = building_blocks.Data('data', tf.int32)
with self.assertRaises(TypeError):
tree_analysis.trees_equal(data, 0)
with self.assertRaises(TypeError):
tree_analysis.trees_equal(0, data)
def test_returns_false_for_tuples_with_different_names(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Tuple([('a', data_1), ('b', data_1)])
data_2 = building_blocks.Data('data', tf.float32)
tuple_2 = building_blocks.Tuple([('c', data_2), ('d', data_2)])
self.assertFalse(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_false_for_tuples_with_different_elements(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Tuple([data_1, data_1])
data_2 = building_blocks.Data('data', tf.float32)
tuple_2 = building_blocks.Tuple([data_2, data_2])
self.assertFalse(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_true_for_intrinsics(self):
intrinsic_1 = building_blocks.Intrinsic('intrinsic', tf.int32)
intrinsic_2 = building_blocks.Intrinsic('intrinsic', tf.int32)
self.assertTrue(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_false_for_intrinsics_with_different_names(self):
intrinsic_1 = building_blocks.Intrinsic('a', tf.int32)
intrinsic_2 = building_blocks.Intrinsic('b', tf.int32)
self.assertFalse(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_true_for_compiled_computations_with_different_names(self):
compiled_1 = building_block_factory.create_compiled_identity(
tf.int32, 'a')
compiled_2 = building_block_factory.create_compiled_identity(
tf.int32, 'b')
self.assertTrue(tree_analysis.trees_equal(compiled_1, compiled_2))
def test_returns_false_for_data_with_different_names(self):
data_1 = building_blocks.Data('a', tf.int32)
data_2 = building_blocks.Data('b', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data_1, data_2))
def test_returns_false_for_block_and_none(self):
data = building_blocks.Data('data', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data, None))
self.assertFalse(tree_analysis.trees_equal(None, data))
def test_returns_true_for_data(self):
data_1 = building_blocks.Data('data', tf.int32)
data_2 = building_blocks.Data('data', tf.int32)
self.assertTrue(tree_analysis.trees_equal(data_1, data_2))
def test_returns_true_for_none_and_none(self): self.assertTrue(tree_analysis.trees_equal(None, None))
def test_returns_true_for_intrinsics(self):
type_signature_1 = computation_types.TensorType(tf.int32)
intrinsic_1 = building_blocks.Intrinsic('intrinsic', type_signature_1)
type_signature_2 = computation_types.TensorType(tf.int32)
intrinsic_2 = building_blocks.Intrinsic('intrinsic', type_signature_2)
self.assertTrue(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_true_for_the_same_comp(self):
data = building_blocks.Data('data', tf.int32)
self.assertTrue(tree_analysis.trees_equal(data, data))
def test_returns_false_for_lambdas_with_different_parameter_types(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('a', tf.float32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, ref_2)
self.assertFalse(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_comps_with_different_types(self):
data = building_blocks.Data('data', tf.int32)
ref = building_blocks.Reference('a', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data, ref))
self.assertFalse(tree_analysis.trees_equal(ref, data))
def test_returns_true_for_lambdas(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('a', tf.int32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, ref_2)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_blocks_with_different_results(self):
data_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([], data_1)
data_2 = building_blocks.Data('data', tf.float32)
comp_2 = building_blocks.Block([], data_2)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_placements(self): placement_1 = building_blocks.Placement(placements.CLIENTS) placement_2 = building_blocks.Placement(placements.CLIENTS) self.assertTrue(tree_analysis.trees_equal(placement_1, placement_2))
def test_returns_true_for_blocks_with_different_variable_names(self):
data = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([('a', data)], data)
comp_2 = building_blocks.Block([('b', data)], data)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_references(self):
reference_1 = building_blocks.Reference('a', tf.int32)
reference_2 = building_blocks.Reference('a', tf.int32)
self.assertTrue(tree_analysis.trees_equal(reference_1, reference_2))
def test_returns_true_for_blocks(self):
data_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([('a', data_1)], data_1)
data_2 = building_blocks.Data('data', tf.int32)
comp_2 = building_blocks.Block([('a', data_2)], data_2)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_selections_with_differet_names(self):
ref_1 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_1 = building_blocks.Selection(ref_1, name='a')
ref_2 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_2 = building_blocks.Selection(ref_2, name='b')
self.assertFalse(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_true_for_identical_graphs_with_nans(self):
tf_comp1 = _create_tensorflow_graph_with_nan()
tf_comp2 = _create_tensorflow_graph_with_nan()
self.assertTrue(tree_analysis.trees_equal(tf_comp1, tf_comp2))
