def testAddN(self): l1 = list_ops.tensor_list_from_tensor([1.0, 2.0], element_shape=[]) l2 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[]) l3 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[]) result = math_ops.add_n((l1, l2, l3)) result_t = list_ops.tensor_list_stack(result, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(result_t), [9., 12.])
def testConcat(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
l1 = list_ops.tensor_list_from_tensor([-1.0], element_shape=scalar_shape())
l_batch_0 = array_ops.stack([l0, l1])
l_batch_1 = array_ops.stack([l1, l0])
l_concat_01 = list_ops.tensor_list_concat_lists(
l_batch_0, l_batch_1, element_dtype=dtypes.float32)
l_concat_10 = list_ops.tensor_list_concat_lists(
l_batch_1, l_batch_0, element_dtype=dtypes.float32)
l_concat_00 = list_ops.tensor_list_concat_lists(
l_batch_0, l_batch_0, element_dtype=dtypes.float32)
l_concat_11 = list_ops.tensor_list_concat_lists(
l_batch_1, l_batch_1, element_dtype=dtypes.float32)
expected_00 = [[1.0, 2.0, 1.0, 2.0], [-1.0, -1.0]]
expected_01 = [[1.0, 2.0, -1.0], [-1.0, 1.0, 2.0]]
expected_10 = [[-1.0, 1.0, 2.0], [1.0, 2.0, -1.0]]
expected_11 = [[-1.0, -1.0], [1.0, 2.0, 1.0, 2.0]]
for i, (concat, expected) in enumerate(zip(
[l_concat_00, l_concat_01, l_concat_10, l_concat_11],
[expected_00, expected_01, expected_10, expected_11])):
splitted = array_ops.unstack(concat)
splitted_stacked_ret = self.evaluate(
(list_ops.tensor_list_stack(splitted[0], dtypes.float32),
list_ops.tensor_list_stack(splitted[1], dtypes.float32)))
print("Test concat %d: %s, %s, %s, %s"
% (i, expected[0], splitted_stacked_ret[0],
expected[1], splitted_stacked_ret[1]))
self.assertAllClose(expected[0], splitted_stacked_ret[0])
self.assertAllClose(expected[1], splitted_stacked_ret[1])
# Concatenating mismatched shapes fails.
with self.assertRaises((errors.InvalidArgumentError, ValueError)):
self.evaluate(
list_ops.tensor_list_concat_lists(
l_batch_0,
list_ops.empty_tensor_list(scalar_shape(), dtypes.float32),
element_dtype=dtypes.float32))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"element shapes are not identical at index 0"):
l_batch_of_vec_tls = array_ops.stack(
[list_ops.tensor_list_from_tensor([[1.0]], element_shape=[1])] * 2)
self.evaluate(
list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_vec_tls,
element_dtype=dtypes.float32))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r"input_b\[0\].dtype != element_dtype."):
l_batch_of_int_tls = array_ops.stack(
[list_ops.tensor_list_from_tensor([1], element_shape=scalar_shape())]
* 2)
self.evaluate(
list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_int_tls,
element_dtype=dtypes.float32))
def testSerialize(self):
# pylint: disable=g-import-not-at-top
try:
import portpicker
except ImportError:
return
with context.graph_mode():
worker_port = portpicker.pick_unused_port()
ps_port = portpicker.pick_unused_port()
cluster_dict = {
"worker": ["localhost:%s" % worker_port],
"ps": ["localhost:%s" % ps_port]
}
cs = server_lib.ClusterSpec(cluster_dict)
worker = server_lib.Server(
cs, job_name="worker", protocol="grpc", task_index=0, start=True)
unused_ps = server_lib.Server(
cs, job_name="ps", protocol="grpc", task_index=0, start=True)
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=[1])
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps, e = list_ops.tensor_list_pop_back(
l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_e = array_ops.identity(e)
self.assertAllEqual(worker_e.eval(), [2.0])
def testTensorListFromTensor(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=[]) l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 2.0) l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0)
def testGetSetItem(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=[]) e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e0), 1.0) l = list_ops.tensor_list_set_item(l, 0, 3.0) t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [3.0, 2.0])
def test_get_item_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
t = slices.get_item(
l, 1, slices.GetItemOpts(element_dtype=initial_list.dtype))
with self.cached_session() as sess:
self.assertAllEqual(self.evaluate(t), [3, 4])
def tf_tensor_list_new(elements, element_dtype=None, element_shape=None):
"""Overload of new_list that stages a Tensor list creation."""
if tensor_util.is_tensor(elements):
if element_shape is not None:
raise ValueError(
'element shape may not be specified when creating list from tensor')
element_shape = array_ops.shape(elements)[1:]
l = list_ops.tensor_list_from_tensor(elements, element_shape=element_shape)
return l
elements = tuple(ops.convert_to_tensor(el) for el in elements)
all_dtypes = set(el.dtype for el in elements)
if len(all_dtypes) == 1:
inferred_dtype = tuple(all_dtypes)[0]
if element_dtype is not None and element_dtype != inferred_dtype:
raise ValueError(
'incompatible dtype; specified: {}, inferred from {}: {}'.format(
element_dtype, elements, inferred_dtype))
elif all_dtypes:
# Heterogeneous lists are ok.
if element_dtype is not None:
raise ValueError(
'specified dtype {} is inconsistent with that of elements {}'.format(
element_dtype, elements))
inferred_dtype = dtypes.variant
else:
inferred_dtype = dtypes.variant
all_shapes = set(tuple(el.shape.as_list()) for el in elements)
if len(all_shapes) == 1:
inferred_shape = array_ops.shape(elements[0])
if element_shape is not None and element_shape != inferred_shape:
raise ValueError(
'incompatible shape; specified: {}, inferred from {}: {}'.format(
element_shape, elements, inferred_shape))
elif all_shapes:
# Heterogeneous lists are ok.
if element_shape is not None:
raise ValueError(
'specified shape {} is inconsistent with that of elements {}'.format(
element_shape, elements))
inferred_shape = constant_op.constant(-1) # unknown shape, by convention
else:
inferred_shape = constant_op.constant(-1) # unknown shape, by convention
if element_dtype is None:
element_dtype = inferred_dtype
if element_shape is None:
element_shape = inferred_shape
element_shape = ops.convert_to_tensor(element_shape, dtype=dtypes.int32)
l = list_ops.empty_tensor_list(
element_shape=element_shape, element_dtype=element_dtype)
for el in elements:
l = list_ops.tensor_list_push_back(l, el)
return l
def test_set_item_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
l = slices.set_item(l, 0, [5, 6])
with self.cached_session() as sess:
t = list_ops.tensor_list_stack(l, element_dtype=initial_list.dtype)
self.assertAllEqual(self.evaluate(t), [[5, 6], [3, 4]])
def unstack(self, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayUnstack", [self._flow, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_from_tensor(
tensor=value, element_shape=value.shape[1:])
return build_ta_with_new_flow(self, flow_out)
def testGetSet(self):
with self.cached_session(), self.test_scope():
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 1.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [3.0, 2.0])
def testStackFromTensorGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
c2 = list_ops.tensor_list_stack(
l, element_dtype=dtypes.float32)
result = c2 * 2.0
self.assertAllEqual(tape.gradient(result, [c])[0], [2.0, 2.0])
def testStackFromTensorGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
c2 = list_ops.tensor_list_stack(
l, element_dtype=dtypes.float32, num_elements=2)
result = c2 * 2.0
grad = tape.gradient(result, [c])[0]
self.assertAllEqual(self.evaluate(grad), [2.0, 2.0])
def testResourceVariableScatterGather(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
v = vs.get_variable("var", initializer=[l] * 10, use_resource=True)
v_r_0_stacked = list_ops.tensor_list_stack(v[0], dtypes.float32)
self.evaluate(v.initializer)
self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_0_stacked))
v_r_sparse_stacked = list_ops.tensor_list_stack(
v.sparse_read(0), dtypes.float32)
self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_sparse_stacked))
l_new_0 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
l_new_1 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
updated_v = state_ops.scatter_update(v, [3, 5], [l_new_0, l_new_1])
updated_v_elems = array_ops.unstack(updated_v)
updated_v_stacked = [
list_ops.tensor_list_stack(el, dtypes.float32) for el in updated_v_elems
]
expected = ([[1.0, 2.0]] * 3 + [[3.0, 4.0], [1.0, 2.0], [5.0, 6.0]] +
[[1.0, 2.0]] * 4)
self.assertAllEqual(self.evaluate(updated_v_stacked), expected)
def testListFromTensor(self):
with self.cached_session(), self.test_scope():
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e, 1.0)
l, e0 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 2.0)
l, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e1, 1.0)
self.assertAllEqual(list_ops.tensor_list_length(l), 0)
def test_stack_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
opts = data_structures.ListStackOpts(
element_dtype=initial_list.dtype, original_call=None)
with self.test_session() as sess:
t = data_structures.list_stack(l, opts)
self.assertAllEqual(sess.run(t), sess.run(initial_list))
def testScatterUpdateVariant(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable([
list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[])
])
v.scatter_update(
ops.IndexedSlices(
list_ops.tensor_list_from_tensor([1., 2.], element_shape=[]), 0))
self.assertAllEqual(
list_ops.tensor_list_get_item(v[0], 0, element_dtype=dtypes.float32),
1.)
def testSkipEagerSetItemWithMismatchedShapeFails(self):
with self.cached_session() as sess:
ph = array_ops.placeholder(dtypes.float32)
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
# Set a placeholder with unknown shape to satisfy the shape inference
# at graph building time.
l = list_ops.tensor_list_set_item(l, 0, ph)
l_0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape"):
sess.run(l_0, {ph: [3.0]})
def testScatterUpdateVariant(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable([
list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[])
])
v.scatter_update(
ops.IndexedSlices(
list_ops.tensor_list_from_tensor([1., 2.], element_shape=[]), 0))
self.assertAllEqual(
list_ops.tensor_list_get_item(v[0], 0, element_dtype=dtypes.float32),
1.)
def build_graph(parameters):
"""Build the TensorListResize op testing graph."""
data = tf.placeholder(
dtype=parameters["element_dtype"],
shape=[parameters["num_elements"]] + parameters["element_shape"])
tensor_list = list_ops.tensor_list_from_tensor(data,
parameters["element_shape"])
tensor_list = list_ops.tensor_list_resize(tensor_list,
parameters["new_size"])
out = list_ops.tensor_list_stack(
tensor_list, element_dtype=parameters["element_dtype"])
return [data], [out]
def testSkipEagerSetItemWithMismatchedShapeFails(self):
with self.cached_session() as sess:
ph = array_ops.placeholder(dtypes.float32)
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
# Set a placeholder with unknown shape to satisfy the shape inference
# at graph building time.
l = list_ops.tensor_list_set_item(l, 0, ph)
l_0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape"):
sess.run(l_0, {ph: [3.0]})
def test_stack_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list,
element_shape=elem_shape)
opts = data_structures.ListStackOpts(element_dtype=initial_list.dtype,
original_call=None)
with self.cached_session() as sess:
t = data_structures.list_stack(l, opts)
self.assertAllEqual(self.evaluate(t), self.evaluate(initial_list))
def testStackFromTensorGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c,
element_shape=scalar_shape())
c2 = list_ops.tensor_list_stack(l,
element_dtype=dtypes.float32,
num_elements=2)
result = c2 * 2.0
grad = tape.gradient(result, [c])[0]
self.assertAllEqual(self.evaluate(grad), [2.0, 2.0])
def unstack(self, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayUnstack", [self._flow, value]):
# TODO(b/129870929): Fix after all callers provide proper init dtype.
value = ops.convert_to_tensor(
value, preferred_dtype=self._dtype, name="value")
_check_dtypes(value, self._dtype)
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_from_tensor(
tensor=value, element_shape=value.shape[1:])
return build_ta_with_new_flow(self, flow_out)
def testPushBackBatch(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l0 = list_ops.tensor_list_from_tensor(c, element_shape=[])
l1 = list_ops.tensor_list_from_tensor([-1.0], element_shape=[])
l_batch = array_ops.stack([l0, l1])
l_push = list_ops.tensor_list_push_back_batch(l_batch, [3.0, 4.0])
l_unstack = array_ops.unstack(l_push)
l0_ret = list_ops.tensor_list_stack(l_unstack[0], dtypes.float32)
l1_ret = list_ops.tensor_list_stack(l_unstack[1], dtypes.float32)
self.assertAllClose([1.0, 2.0, 3.0], self.evaluate(l0_ret))
self.assertAllClose([-1.0, 4.0], self.evaluate(l1_ret))
with ops.control_dependencies([l_push]):
l_unstack_orig = array_ops.unstack(l_batch)
l0_orig_ret = list_ops.tensor_list_stack(l_unstack_orig[0],
dtypes.float32)
l1_orig_ret = list_ops.tensor_list_stack(l_unstack_orig[1],
dtypes.float32)
# Check that without aliasing, push_back_batch still works; and
# that it doesn't modify the input.
l0_r_v, l1_r_v, l0_orig_v, l1_orig_v = self.evaluate(
(l0_ret, l1_ret, l0_orig_ret, l1_orig_ret))
self.assertAllClose([1.0, 2.0, 3.0], l0_r_v)
self.assertAllClose([-1.0, 4.0], l1_r_v)
self.assertAllClose([1.0, 2.0], l0_orig_v)
self.assertAllClose([-1.0], l1_orig_v)
# Pushing back mismatched shapes fails.
with self.assertRaises((errors.InvalidArgumentError, ValueError)):
self.evaluate(list_ops.tensor_list_push_back_batch(l_batch, []))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape to a list at index 0"):
self.evaluate(
list_ops.tensor_list_push_back_batch(l_batch, [[3.0], [4.0]]))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Invalid data type at index 0"):
self.evaluate(list_ops.tensor_list_push_back_batch(l_batch, [3, 4]))
def testPushBackBatch(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
l1 = list_ops.tensor_list_from_tensor([-1.0], element_shape=scalar_shape())
l_batch = array_ops.stack([l0, l1])
l_push = list_ops.tensor_list_push_back_batch(l_batch, [3.0, 4.0])
l_unstack = array_ops.unstack(l_push)
l0_ret = list_ops.tensor_list_stack(l_unstack[0], dtypes.float32)
l1_ret = list_ops.tensor_list_stack(l_unstack[1], dtypes.float32)
self.assertAllClose([1.0, 2.0, 3.0], self.evaluate(l0_ret))
self.assertAllClose([-1.0, 4.0], self.evaluate(l1_ret))
with ops.control_dependencies([l_push]):
l_unstack_orig = array_ops.unstack(l_batch)
l0_orig_ret = list_ops.tensor_list_stack(l_unstack_orig[0],
dtypes.float32)
l1_orig_ret = list_ops.tensor_list_stack(l_unstack_orig[1],
dtypes.float32)
# Check that without aliasing, push_back_batch still works; and
# that it doesn't modify the input.
l0_r_v, l1_r_v, l0_orig_v, l1_orig_v = self.evaluate(
(l0_ret, l1_ret, l0_orig_ret, l1_orig_ret))
self.assertAllClose([1.0, 2.0, 3.0], l0_r_v)
self.assertAllClose([-1.0, 4.0], l1_r_v)
self.assertAllClose([1.0, 2.0], l0_orig_v)
self.assertAllClose([-1.0], l1_orig_v)
# Pushing back mismatched shapes fails.
with self.assertRaises((errors.InvalidArgumentError, ValueError)):
self.evaluate(list_ops.tensor_list_push_back_batch(l_batch, []))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape to a list at index 0"):
self.evaluate(
list_ops.tensor_list_push_back_batch(l_batch, [[3.0], [4.0]]))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Invalid data type at index 0"):
self.evaluate(list_ops.tensor_list_push_back_batch(l_batch, [3, 4]))
def testSerializeListWithUnknownRank(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=-1)
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
element_shape = list_ops.tensor_list_element_shape(
l_ps, shape_type=dtypes.int32)
with ops.device("/job:worker"):
element_shape = array_ops.identity(element_shape)
self.assertEqual(self.evaluate(element_shape), -1)
def testAddNNestedList(self):
l1 = list_ops.tensor_list_from_tensor([1.0, 2.0], element_shape=[])
l2 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
l3 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
l4 = list_ops.tensor_list_from_tensor([7.0, 8.0], element_shape=[])
a = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
a = list_ops.tensor_list_push_back(a, l1)
a = list_ops.tensor_list_push_back(a, l2)
b = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
b = list_ops.tensor_list_push_back(b, l3)
b = list_ops.tensor_list_push_back(b, l4)
result = math_ops.add_n((a, b))
result_0 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 0, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
result_1 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 1, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(result_0), [6., 8.])
self.assertAllEqual(self.evaluate(result_1), [10., 12.])
def testSerializeListWithUnknownRank(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=None)
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
element_shape = list_ops.tensor_list_element_shape(
l_ps, shape_type=dtypes.int32)
with ops.device("/job:worker"):
element_shape = array_ops.identity(element_shape)
self.assertEqual(self.evaluate(element_shape), -1)
def testSerialize(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=[1])
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps, e = list_ops.tensor_list_pop_back(
l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_e = array_ops.identity(e)
self.assertAllEqual(self.evaluate(worker_e), [2.0])
def testAddNNestedList(self):
l1 = list_ops.tensor_list_from_tensor([1.0, 2.0], element_shape=[])
l2 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
l3 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
l4 = list_ops.tensor_list_from_tensor([7.0, 8.0], element_shape=[])
a = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
a = list_ops.tensor_list_push_back(a, l1)
a = list_ops.tensor_list_push_back(a, l2)
b = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
b = list_ops.tensor_list_push_back(b, l3)
b = list_ops.tensor_list_push_back(b, l4)
result = math_ops.add_n((a, b))
result_0 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 0, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
result_1 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 1, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(result_0), [6., 8.])
self.assertAllEqual(self.evaluate(result_1), [10., 12.])
def testSerialize(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=[1])
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps, e = list_ops.tensor_list_pop_back(
l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_e = array_ops.identity(e)
self.assertAllEqual(self.evaluate(worker_e), [2.0])
def testResourceVariableScatterGather(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
v = vs.get_variable("var", initializer=[l] * 10, use_resource=True)
v_r_0_stacked = list_ops.tensor_list_stack(v[0], dtypes.float32)
self.evaluate(v.initializer)
self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_0_stacked))
v_r_sparse_stacked = list_ops.tensor_list_stack(
v.sparse_read(0), dtypes.float32)
self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_sparse_stacked))
l_new_0 = list_ops.tensor_list_from_tensor(
[3.0, 4.0], element_shape=scalar_shape())
l_new_1 = list_ops.tensor_list_from_tensor(
[5.0, 6.0], element_shape=scalar_shape())
updated_v = state_ops.scatter_update(v, [3, 5], [l_new_0, l_new_1])
updated_v_elems = array_ops.unstack(updated_v)
updated_v_stacked = [
list_ops.tensor_list_stack(el, dtypes.float32) for el in updated_v_elems
]
expected = ([[1.0, 2.0]] * 3 + [[3.0, 4.0], [1.0, 2.0], [5.0, 6.0]] +
[[1.0, 2.0]] * 4)
self.assertAllEqual(self.evaluate(updated_v_stacked), expected)
def _to_tensor_list(self, value):
if not isinstance(value, tensor_array_ops.TensorArray):
raise TypeError("value must be a TensorArray, but saw: {}".format(
type(value)))
if value.flow is not None and value.flow.dtype == dtypes.variant:
return [value.flow]
else:
# Convert to a TF2-style TensorArray.
# TODO(ebrevdo): Add an "_as_variant" method to TensorArray class, or
# "implementation / as_variant" arg to TensorArray constructor.
with ops.name_scope("convert_tensor_array"):
flow = list_ops.tensor_list_from_tensor(
tensor=value.stack(), element_shape=value.element_shape)
return [flow]
def testGetSetGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
c2 = constant_op.constant(3.0)
tape.watch(c2)
l = list_ops.tensor_list_set_item(l, 0, c2)
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
ee = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
y = e * e + ee * ee
grad_c, grad_c2 = tape.gradient(y, [c, c2])
self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0])
self.assertAllEqual(self.evaluate(grad_c2), 6.0)
def _to_tensor_list(self, value):
if not isinstance(value, tensor_array_ops.TensorArray):
raise TypeError("value must be a TensorArray, but saw: {}"
.format(type(value)))
if value.flow is not None and value.flow.dtype == dtypes.variant:
return [value.flow]
else:
# Convert to a TF2-style TensorArray.
# TODO(ebrevdo): Add an "_as_variant" method to TensorArray class, or
# "implementation / as_variant" arg to TensorArray constructor.
with ops.name_scope("convert_tensor_array"):
flow = list_ops.tensor_list_from_tensor(
tensor=value.stack(), element_shape=value.element_shape)
return [flow]
def testGetSetGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
c2 = constant_op.constant(3.0)
tape.watch(c2)
l = list_ops.tensor_list_set_item(l, 0, c2)
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
ee = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
y = e * e + ee * ee
grad_c, grad_c2 = tape.gradient(y, [c, c2])
self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0])
self.assertAllEqual(self.evaluate(grad_c2), 6.0)
def testListFromTensor(self):
with self.cached_session(), self.test_scope():
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
e = list_ops.tensor_list_get_item(l,
0,
element_dtype=dtypes.float32)
self.assertAllEqual(e, 1.0)
l, e0 = list_ops.tensor_list_pop_back(l,
element_dtype=dtypes.float32)
self.assertAllEqual(e0, 2.0)
l, e1 = list_ops.tensor_list_pop_back(l,
element_dtype=dtypes.float32)
self.assertAllEqual(e1, 1.0)
self.assertAllEqual(list_ops.tensor_list_length(l), 0)
def test_basic(self):
self.assertEqual(special_functions.stack(1), 1)
self.assertListEqual(special_functions.stack([1, 2, 3]), [1, 2, 3])
# TODO(mdan): This should probably forward to tf.stack.
self.assertTrue(
isinstance(
special_functions.stack(
[constant_op.constant(1),
constant_op.constant(2)]), list))
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(
t, element_shape=constant_op.constant([], dtype=dtypes.int32))
self.assertTrue(
tensor_util.is_tensor(
special_functions.stack(l, element_dtype=dtypes.float32)))
def testCPUGPUCopy(self):
if not context.num_gpus():
return
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape())
with context.device("gpu:0"):
l_gpu = array_ops.identity(l)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
l_gpu, element_dtype=dtypes.float32)[1]), 2.0)
l_cpu = array_ops.identity(l_gpu)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
l_cpu, element_dtype=dtypes.float32)[1]), 2.0)
def testCPUGPUCopy(self):
if not context.num_gpus():
return
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
with context.device("gpu:0"):
l_gpu = array_ops.identity(l)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
l_gpu, element_dtype=dtypes.float32)[1]), 2.0)
l_cpu = array_ops.identity(l_gpu)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
l_cpu, element_dtype=dtypes.float32)[1]), 2.0)
def test_basic(self):
self.assertEqual(special_functions.stack(1), 1)
self.assertListEqual(special_functions.stack([1, 2, 3]), [1, 2, 3])
# TODO(mdan): This should probably forward to tf.stack.
self.assertTrue(
isinstance(
special_functions.stack(
[constant_op.constant(1),
constant_op.constant(2)]), list))
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(
t, element_shape=constant_op.constant([], dtype=dtypes.int32))
self.assertTrue(
tensor_util.is_tensor(
special_functions.stack(l, element_dtype=dtypes.float32)))
def build_graph(parameters):
"""Build the TensorListSetItem op testing graph."""
data = tf.placeholder(dtype=parameters["element_dtype"],
shape=[parameters["num_elements"]] +
parameters["element_shape"])
item = tf.placeholder(dtype=parameters["element_dtype"],
shape=parameters["element_shape"])
tensor_list = list_ops.tensor_list_from_tensor(
data, parameters["element_shape"])
tensor_list = list_ops.tensor_list_set_item(tensor_list,
parameters["index"], item)
out = list_ops.tensor_list_stack(
tensor_list,
num_elements=parameters["num_elements"],
element_dtype=parameters["element_dtype"])
return [data, item], [out]
def unstack(self, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayUnstack", [self._flow, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_from_tensor(
tensor=value, element_shape=value.shape[1:])
ta = TensorArray(dtype=self._dtype,
handle=self.handle,
flow=flow_out,
colocate_with_first_write_call=self.
_colocate_with_first_write_call)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
ta._colocate_with = self._colocate_with
return ta
def unstack(self, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayUnstack", [self._flow, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_from_tensor(
tensor=value, element_shape=value.shape[1:])
ta = TensorArray(
dtype=self._dtype,
handle=self.handle,
flow=flow_out,
colocate_with_first_write_call=self._colocate_with_first_write_call)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
ta._colocate_with = self._colocate_with
return ta
def test_index_access(self):
def test_fn(l):
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
node = slices.transform(node, ctx)
with self.compiled(node, {}, dtypes.int32) as result:
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2],
element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def test_pop_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
opts = data_structures.ListPopOpts(
element_dtype=initial_list.dtype,
element_shape=(2,))
with self.assertRaises(NotImplementedError):
data_structures.list_pop(l, 0, opts)
with self.test_session() as sess:
l, x = data_structures.list_pop(l, None, opts)
self.assertAllEqual(sess.run(x), [3, 4])
t = list_ops.tensor_list_stack(l, element_dtype=initial_list.dtype)
self.assertAllEqual(sess.run(t), [[1, 2]])
def test_index_access(self):
def test_fn(l):
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
node = slices.transform(node, ctx)
with self.compiled(node, {}, dtypes.int32) as result:
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def test_pop_tensor_list(self):
initial_list = constant_op.constant([[1, 2], [3, 4]])
elem_shape = constant_op.constant([2])
l = list_ops.tensor_list_from_tensor(initial_list,
element_shape=elem_shape)
opts = data_structures.ListPopOpts(element_dtype=initial_list.dtype,
element_shape=(2, ))
with self.assertRaises(NotImplementedError):
data_structures.list_pop(l, 0, opts)
with self.test_session() as sess:
l, x = data_structures.list_pop(l, None, opts)
self.assertAllEqual(sess.run(x), [3, 4])
t = list_ops.tensor_list_stack(l, element_dtype=initial_list.dtype)
self.assertAllEqual(sess.run(t), [[1, 2]])
def test_stack(self):
self.assertEqual(special_functions.stack(1, strict=False), 1)
self.assertListEqual(
special_functions.stack([1, 2, 3], strict=False), [1, 2, 3])
# TODO(mdan): This should probably forward to tf.stack.
self.assertTrue(
isinstance(
special_functions.stack(
[constant_op.constant(1),
constant_op.constant(2)], strict=False), list))
with self.assertRaises(ValueError):
special_functions.stack([1, 2, 3])
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(
t, element_shape=constant_op.constant([], dtype=dtypes.int32))
self.assertTrue(
tensor_util.is_tf_type(
special_functions.stack(l, element_dtype=dtypes.float32)))
def testSerialize(self):
# pylint: disable=g-import-not-at-top
try:
import portpicker
except ImportError:
return
with context.graph_mode():
worker_port = portpicker.pick_unused_port()
ps_port = portpicker.pick_unused_port()
cluster_dict = {
"worker": ["localhost:%s" % worker_port],
"ps": ["localhost:%s" % ps_port]
}
cs = server_lib.ClusterSpec(cluster_dict)
worker = server_lib.Server(cs,
job_name="worker",
protocol="grpc",
task_index=0,
start=True)
unused_ps = server_lib.Server(cs,
job_name="ps",
protocol="grpc",
task_index=0,
start=True)
with ops.Graph().as_default(), session.Session(
target=worker.target):
with ops.device("/job:worker"):
t = constant_op.constant([[1.0], [2.0]])
l = list_ops.tensor_list_from_tensor(t, element_shape=[1])
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps, e = list_ops.tensor_list_pop_back(
l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_e = array_ops.identity(e)
self.assertAllEqual(self.evaluate(worker_e), [2.0])
def testCPUGPUCopyNested(self):
if not context.num_gpus():
return
t = constant_op.constant([1.0, 2.0])
child_l = list_ops.tensor_list_from_tensor(t, element_shape=[])
l = list_ops.empty_tensor_list(
element_shape=constant_op.constant([], dtype=dtypes.int32),
element_dtype=dtypes.variant)
l = list_ops.tensor_list_push_back(l, child_l)
with context.device("gpu:0"):
l_gpu = array_ops.identity(l)
_, child_l_gpu = list_ops.tensor_list_pop_back(
l_gpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_gpu, element_dtype=dtypes.float32)[1]), 2.0)
l_cpu = array_ops.identity(l_gpu)
_, child_l_cpu = list_ops.tensor_list_pop_back(
l_cpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_cpu, element_dtype=dtypes.float32)[1]), 2.0)
def testCPUGPUCopyNested(self):
if not context.num_gpus():
return
t = constant_op.constant([1.0, 2.0])
child_l = list_ops.tensor_list_from_tensor(t, element_shape=[])
l = list_ops.empty_tensor_list(
element_shape=constant_op.constant([], dtype=dtypes.int32),
element_dtype=dtypes.variant)
l = list_ops.tensor_list_push_back(l, child_l)
with context.device("gpu:0"):
l_gpu = array_ops.identity(l)
_, child_l_gpu = list_ops.tensor_list_pop_back(
l_gpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_gpu, element_dtype=dtypes.float32)[1]), 2.0)
l_cpu = array_ops.identity(l_gpu)
_, child_l_cpu = list_ops.tensor_list_pop_back(
l_cpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_cpu, element_dtype=dtypes.float32)[1]), 2.0)
def test_index_access(self):
def test_fn(l):
utils.set_element_type(l, dtypes.int32)
return l[1]
node = self.parse_and_analyze(
test_fn,
{
'utils': utils,
'dtypes': dtypes
},
include_type_analysis=True,
)
node = slices.transform(node, self.ctx)
with self.compiled(node, dtypes.int32) as result:
result.utils = utils
result.dtypes = dtypes
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def testConcat(self):
c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
l1 = list_ops.tensor_list_from_tensor([-1.0],
element_shape=scalar_shape())
l_batch_0 = array_ops.stack([l0, l1])
l_batch_1 = array_ops.stack([l1, l0])
l_concat_01 = list_ops.tensor_list_concat_lists(
l_batch_0, l_batch_1, element_dtype=dtypes.float32)
l_concat_10 = list_ops.tensor_list_concat_lists(
l_batch_1, l_batch_0, element_dtype=dtypes.float32)
l_concat_00 = list_ops.tensor_list_concat_lists(
l_batch_0, l_batch_0, element_dtype=dtypes.float32)
l_concat_11 = list_ops.tensor_list_concat_lists(
l_batch_1, l_batch_1, element_dtype=dtypes.float32)
expected_00 = [[1.0, 2.0, 1.0, 2.0], [-1.0, -1.0]]
expected_01 = [[1.0, 2.0, -1.0], [-1.0, 1.0, 2.0]]
expected_10 = [[-1.0, 1.0, 2.0], [1.0, 2.0, -1.0]]
expected_11 = [[-1.0, -1.0], [1.0, 2.0, 1.0, 2.0]]
for i, (concat, expected) in enumerate(
zip([l_concat_00, l_concat_01, l_concat_10, l_concat_11],
[expected_00, expected_01, expected_10, expected_11])):
splitted = array_ops.unstack(concat)
splitted_stacked_ret = self.evaluate(
(list_ops.tensor_list_stack(splitted[0], dtypes.float32),
list_ops.tensor_list_stack(splitted[1], dtypes.float32)))
print("Test concat %d: %s, %s, %s, %s" %
(i, expected[0], splitted_stacked_ret[0], expected[1],
splitted_stacked_ret[1]))
self.assertAllClose(expected[0], splitted_stacked_ret[0])
self.assertAllClose(expected[1], splitted_stacked_ret[1])
# Concatenating mismatched shapes fails.
with self.assertRaises((errors.InvalidArgumentError, ValueError)):
self.evaluate(
list_ops.tensor_list_concat_lists(
l_batch_0,
list_ops.empty_tensor_list(scalar_shape(), dtypes.float32),
element_dtype=dtypes.float32))
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"element shapes are not identical at index 0"):
l_batch_of_vec_tls = array_ops.stack(
[list_ops.tensor_list_from_tensor([[1.0]], element_shape=[1])
] * 2)
self.evaluate(
list_ops.tensor_list_concat_lists(
l_batch_0,
l_batch_of_vec_tls,
element_dtype=dtypes.float32))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r"input_b\[0\].dtype != element_dtype."):
l_batch_of_int_tls = array_ops.stack([
list_ops.tensor_list_from_tensor([1],
element_shape=scalar_shape())
] * 2)
self.evaluate(
list_ops.tensor_list_concat_lists(
l_batch_0,
l_batch_of_int_tls,
element_dtype=dtypes.float32))
def testSetOutOfBounds(self):
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(list_ops.tensor_list_set_item(l, 20, 3.0))
def tf_tensor_list_new(elements, element_dtype=None, element_shape=None):
"""Overload of new_list that stages a Tensor list creation."""
if tensor_util.is_tensor(elements):
if element_shape is not None:
raise ValueError(
'element shape may not be specified when creating list from tensor'
)
element_shape = array_ops.shape(elements)[1:]
l = list_ops.tensor_list_from_tensor(elements,
element_shape=element_shape)
return l
elements = tuple(ops.convert_to_tensor(el) for el in elements)
all_dtypes = set(el.dtype for el in elements)
if len(all_dtypes) == 1:
inferred_dtype = tuple(all_dtypes)[0]
if element_dtype is not None and element_dtype != inferred_dtype:
raise ValueError(
'incompatible dtype; specified: {}, inferred from {}: {}'.
format(element_dtype, elements, inferred_dtype))
elif all_dtypes:
# Heterogeneous lists are ok.
if element_dtype is not None:
raise ValueError(
'specified dtype {} is inconsistent with that of elements {}'.
format(element_dtype, elements))
inferred_dtype = dtypes.variant
else:
inferred_dtype = dtypes.variant
all_shapes = set(tuple(el.shape.as_list()) for el in elements)
if len(all_shapes) == 1:
inferred_shape = array_ops.shape(elements[0])
if element_shape is not None and element_shape != inferred_shape:
raise ValueError(
'incompatible shape; specified: {}, inferred from {}: {}'.
format(element_shape, elements, inferred_shape))
elif all_shapes:
# Heterogeneous lists are ok.
if element_shape is not None:
raise ValueError(
'specified shape {} is inconsistent with that of elements {}'.
format(element_shape, elements))
inferred_shape = constant_op.constant(
-1) # unknown shape, by convention
else:
inferred_shape = constant_op.constant(
-1) # unknown shape, by convention
if element_dtype is None:
element_dtype = inferred_dtype
if element_shape is None:
element_shape = inferred_shape
element_shape = ops.convert_to_tensor(element_shape, dtype=dtypes.int32)
l = list_ops.empty_tensor_list(element_shape=element_shape,
element_dtype=element_dtype)
for el in elements:
l = list_ops.tensor_list_push_back(l, el)
return l
