def test_get_key_for_identical_computations(self): foo_proto = computation_impl.ComputationImpl.get_proto(foo) foo_type = foo.type_signature first_key = caching_executor._get_hashable_key(foo_proto, foo_type) second_foo_proto = computation_impl.ComputationImpl.get_proto(foo) second_key = caching_executor._get_hashable_key(second_foo_proto, foo_type) self.assertEqual(hash(first_key), hash(second_key))
def test_get_key_for_different_list_tuple_type(self): tuple_type = computation_types.StructType([tf.int32, tf.float32]) first_list = [0, 1.] first_key = caching_executor._get_hashable_key(first_list, tuple_type) second_list = [0, 2.] second_key = caching_executor._get_hashable_key(second_list, tuple_type) self.assertNotEqual(hash(first_key), hash(second_key))
def test_get_key_for_different_computations(self):
foo_proto = computation_impl.ComputationImpl.get_proto(foo)
foo_type = foo.type_signature
first_key = caching_executor._get_hashable_key(foo_proto, foo_type)
bar_proto = computation_impl.ComputationImpl.get_proto(bar)
bar_type = bar.type_signature
second_key = caching_executor._get_hashable_key(bar_proto, bar_type)
self.assertNotEqual(hash(first_key), hash(second_key))
def test_get_key_for_identical_list_tuple_type(self):
tuple_type = computation_types.NamedTupleType([tf.int32, tf.float32])
first_list = [0, 1.]
first_key = caching_executor._get_hashable_key(first_list, tuple_type)
second_list = [0, 1.]
second_key = caching_executor._get_hashable_key(
second_list, tuple_type)
self.assertEqual(hash(first_key), hash(second_key))
def test_get_key_for_same_tensors(self):
array_1 = np.ones(shape=[100, 100])
tensor_1 = tf.convert_to_tensor(array_1)
first_key = caching_executor._get_hashable_key(
tensor_1, computation_types.TensorType(tensor_1.dtype, tensor_1.shape))
second_key = caching_executor._get_hashable_key(
tensor_1, computation_types.TensorType(tensor_1.dtype, tensor_1.shape))
self.assertNotEqual(first_key, second_key)
def test_get_key_for_different_list_federated_type(self):
federated_type = computation_types.FederatedType(
computation_types.TensorType(tf.float32), placement_literals.CLIENTS)
first_list = [0., 1.]
first_key = caching_executor._get_hashable_key(first_list, federated_type)
second_list = [0., 2.]
second_key = caching_executor._get_hashable_key(second_list, federated_type)
self.assertNotEqual(hash(first_key), hash(second_key))
def test_get_key_for_identical_ndarray(self):
array_1 = np.ones(shape=[100, 100])
tensor_1 = tf.convert_to_tensor(array_1)
first_key = caching_executor._get_hashable_key(
array_1, computation_types.TensorType(tensor_1.dtype, tensor_1.shape))
array_2 = np.ones(shape=[100, 100])
tensor_2 = tf.convert_to_tensor(array_2)
second_key = caching_executor._get_hashable_key(
array_2, computation_types.TensorType(tensor_2.dtype, tensor_2.shape))
self.assertEqual(first_key, second_key)
def test_get_key_for_different_but_identical_tensors(self):
array_1 = np.ones(shape=[100, 100])
tensor_1 = tf.convert_to_tensor(array_1)
first_key = caching_executor._get_hashable_key(
tensor_1, computation_types.TensorType(tensor_1.dtype, tensor_1.shape))
# Tensors compare on ids, so constructing a new object should cause them to
# hash differently
tensor_2 = tf.convert_to_tensor(array_1)
second_key = caching_executor._get_hashable_key(
tensor_2, computation_types.TensorType(tensor_2.dtype, tensor_2.shape))
self.assertNotEqual(first_key, second_key)
def test_get_key_for_identical_structures_tuple_type(self):
tuple_type = computation_types.StructType([tf.int32, tf.float32])
anon_tuple = structure.Struct([(
None,
0,
), (None, 1.)])
first_key = caching_executor._get_hashable_key(anon_tuple, tuple_type)
second_anon_tuple = structure.Struct([(
None,
0,
), (None, 1.)])
second_key = caching_executor._get_hashable_key(
second_anon_tuple, tuple_type)
self.assertEqual(hash(first_key), hash(second_key))
def test_get_key_for_identical_anonymous_tuples_tuple_type(self):
tuple_type = computation_types.NamedTupleType([tf.int32, tf.float32])
anon_tuple = anonymous_tuple.AnonymousTuple([(
None,
0,
), (None, 1.)])
first_key = caching_executor._get_hashable_key(anon_tuple, tuple_type)
second_anon_tuple = anonymous_tuple.AnonymousTuple([(
None,
0,
), (None, 1.)])
second_key = caching_executor._get_hashable_key(
second_anon_tuple, tuple_type)
self.assertEqual(hash(first_key), hash(second_key))
