Add basic TensorList op support in bridge.

* Add kernels for TensorListReserve. EmptyTensorList, TensorListElementShape, TensorListPushBack, TensorlistPopBack;
* Treat list type pretty much identical to Stack in the bridge for now;
* Support variant output by treating variant like a uint8 and leaving the interpretation up to the XlaExpression (variant type does not support tensor_data());

PiperOrigin-RevId: 215809335

2 files changed, 121 insertions, 0 deletions

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index ee36729fd1..ba2401ed26 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -895,6 +895,22 @@ tf_xla_py_test(
 )
 
 tf_xla_py_test(
+    name = "tensor_list_ops_test",
+    size = "small",
+    srcs = ["tensor_list_ops_test.py"],
+    # TensorList ops are not implemented in the on-demand compilation model yet.
+    disabled_backends = "cpu_ondemand",
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:list_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python/eager:function",
+    ],
+)
+
+tf_xla_py_test(
     name = "ternary_ops_test",
     size = "small",
     srcs = ["ternary_ops_test.py"],
diff --git a/tensorflow/compiler/tests/tensor_list_ops_test.py b/tensorflow/compiler/tests/tensor_list_ops_test.py
new file mode 100644
index 0000000000..b556723eec
--- /dev/null
+++ b/tensorflow/compiler/tests/tensor_list_ops_test.py
@@ -0,0 +1,105 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ops which manipulate lists of tensors via bridge."""
+
+# pylint: disable=g-bad-name
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+from tensorflow.compiler.tests import xla_test
+from tensorflow.python.client import session
+from tensorflow.python.eager import backprop
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import list_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.platform import test
+from tensorflow.python.training import server_lib
+
+
+def scalar_shape():
+  return ops.convert_to_tensor([], dtype=dtypes.int32)
+
+
+class ListOpsTest(xla_test.XLATestCase):
+
+  def testElementShape(self):
+    with self.cached_session() as sess, self.test_scope():
+      dim = array_ops.placeholder(dtypes.int32)
+      l = list_ops.tensor_list_reserve(
+          element_shape=(dim, 15), num_elements=20,
+          element_dtype=dtypes.float32)
+      e32 = list_ops.tensor_list_element_shape(l, shape_type=dtypes.int32)
+      e64 = list_ops.tensor_list_element_shape(l, shape_type=dtypes.int64)
+      self.assertAllEqual(sess.run(e32, {dim: 10}), (10, 15))
+      self.assertAllEqual(sess.run(e64, {dim: 7}), (7, 15))
+
+  def testPushPop(self):
+    with self.cached_session() as sess, self.test_scope():
+      num = array_ops.placeholder(dtypes.int32)
+      l = list_ops.tensor_list_reserve(
+          element_shape=(7, 15), num_elements=num, element_dtype=dtypes.float32)
+      l = list_ops.tensor_list_push_back(
+          l, constant_op.constant(1.0, shape=(7, 15)))
+      l = list_ops.tensor_list_push_back(
+          l, constant_op.constant(2.0, shape=(7, 15)))
+      l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
+      _, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
+      self.assertAllEqual(sess.run(e2, {num: 10}), 2.0 * np.ones((7, 15)))
+      self.assertAllEqual(sess.run(e1, {num: 10}), 1.0 * np.ones((7, 15)))
+
+  def testPushPopSeparateLists(self):
+    with self.cached_session() as sess, self.test_scope():
+      num = array_ops.placeholder(dtypes.int32)
+      l = list_ops.tensor_list_reserve(
+          element_shape=scalar_shape(),
+          num_elements=num,
+          element_dtype=dtypes.float32)
+      l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
+      l2 = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
+      l3 = list_ops.tensor_list_push_back(l, constant_op.constant(3.0))
+      _, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
+      l2, e21 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
+      l2, e22 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
+      l3, e31 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
+      l3, e32 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
+      result = sess.run([e11, [e21, e22], [e31, e32]], {num: 20})
+      self.assertEqual(result, [1.0, [2.0, 1.0], [3.0, 1.0]])
+
+  def testEmptyTensorList(self):
+    dim = 7
+    with self.cached_session() as sess, self.test_scope():
+      p = array_ops.placeholder(dtypes.int32)
+      l = list_ops.empty_tensor_list(
+          element_shape=(p, 15), element_dtype=dtypes.float32)
+      l = list_ops.tensor_list_push_back(
+          l, constant_op.constant(1.0, shape=(dim, 15)))
+      _, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
+      with self.assertRaisesRegexp(errors.InvalidArgumentError,
+                                   "Use TensorListReserve instead"):
+        self.assertEqual(sess.run(e, {p: dim}), 1.0 * np.ones((dim, 15)))
+
+
+if __name__ == "__main__":
+  test.main()