def _count_ops_parameterized_by_layers(k):
inlined_tuple_with_k_layers = _construct_inlined_tuple(k)
tf_representing_block_with_k_layers, _ = compiler_transformations.remove_duplicate_called_graphs(
inlined_tuple_with_k_layers)
block_ops_with_k_layers = tree_analysis.count_tensorflow_ops_under(
tf_representing_block_with_k_layers)
parser_callable = transformations.TFParser()
naively_generated_tf_with_k_layers, _ = transformation_utils.transform_postorder(
inlined_tuple_with_k_layers, parser_callable)
naive_ops_with_k_layers = tree_analysis.count_tensorflow_ops_under(
naively_generated_tf_with_k_layers)
return block_ops_with_k_layers, naive_ops_with_k_layers
def test_ops_not_duplicated_in_resulting_tensorflow(self):
def _construct_block_and_inlined_tuple(k):
concrete_int_type = computation_types.TensorType(tf.int32)
concrete_int = building_block_factory.create_tensorflow_constant(
concrete_int_type, 1)
first_tf_id_type = computation_types.TensorType(tf.int32)
first_tf_id = building_block_factory.create_compiled_identity(
first_tf_id_type)
called_tf_id = building_blocks.Call(first_tf_id, concrete_int)
for _ in range(k):
# Simulating large TF computation
called_tf_id = building_blocks.Call(first_tf_id, called_tf_id)
ref_to_call = building_blocks.Reference(
'call', called_tf_id.type_signature)
block_locals = [('call', called_tf_id)]
block = building_blocks.Block(
block_locals, building_blocks.Tuple([ref_to_call,
ref_to_call]))
inlined_tuple = building_blocks.Tuple([called_tf_id, called_tf_id])
return block, inlined_tuple
block_with_5_ids, inlined_tuple_with_5_ids = _construct_block_and_inlined_tuple(
5)
block_with_10_ids, inlined_tuple_with_10_ids = _construct_block_and_inlined_tuple(
10)
tf_representing_block_with_5_ids, _ = transformations.create_tensorflow_representing_block(
block_with_5_ids)
tf_representing_block_with_10_ids, _ = transformations.create_tensorflow_representing_block(
block_with_10_ids)
block_ops_with_5_ids = tree_analysis.count_tensorflow_ops_under(
tf_representing_block_with_5_ids)
block_ops_with_10_ids = tree_analysis.count_tensorflow_ops_under(
tf_representing_block_with_10_ids)
parser_callable = tree_to_cc_transformations.TFParser()
naively_generated_tf_with_5_ids, _ = transformation_utils.transform_postorder(
inlined_tuple_with_5_ids, parser_callable)
naively_generated_tf_with_10_ids, _ = transformation_utils.transform_postorder(
inlined_tuple_with_10_ids, parser_callable)
tuple_ops_with_5_ids = tree_analysis.count_tensorflow_ops_under(
naively_generated_tf_with_5_ids)
tuple_ops_with_10_ids = tree_analysis.count_tensorflow_ops_under(
naively_generated_tf_with_10_ids)
# asserting that block ops are linear in k with slope 1.
self.assertEqual((block_ops_with_10_ids - block_ops_with_5_ids) / 5, 1)
# asserting that tuple ops are linear in k with slope 2.
self.assertEqual((tuple_ops_with_10_ids - tuple_ops_with_5_ids) / 5, 2)
def test_tensorflow_op_count_doubles_number_of_ops_in_two_tuple(self):
integer_identity = building_block_factory.create_compiled_identity(tf.int32)
node_tf_op_count = building_block_analysis.count_tensorflow_ops_in(
integer_identity)
tf_tuple = building_blocks.Tuple([integer_identity, integer_identity])
tree_tf_op_count = tree_analysis.count_tensorflow_ops_under(tf_tuple)
self.assertEqual(tree_tf_op_count, 2 * node_tf_op_count)
def test_single_tensorflow_node_count_agrees_with_node_count(self):
integer_identity = building_block_factory.create_compiled_identity(tf.int32)
node_tf_op_count = building_block_analysis.count_tensorflow_ops_in(
integer_identity)
tree_tf_op_count = tree_analysis.count_tensorflow_ops_under(
integer_identity)
self.assertEqual(node_tf_op_count, tree_tf_op_count)
def test_returns_zero_no_tensorflow(self):
no_tensorflow_comp = building_block_test_utils.create_nested_syntax_tree(
)
tf_count = tree_analysis.count_tensorflow_ops_under(no_tensorflow_comp)
self.assertEqual(tf_count, 0)
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.count_tensorflow_ops_under(None)
