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diff --git a/tensorflow/python/kernel_tests/broadcast_to_ops_test.py b/tensorflow/python/kernel_tests/broadcast_to_ops_test.py
new file mode 100644
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+++ b/tensorflow/python/kernel_tests/broadcast_to_ops_test.py
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+# Copyright 2015 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 broadcast_to ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test as test_lib
+
+
+class BroadcastToTest(test_util.TensorFlowTestCase):
+
+  def testBroadcastToBasic(self):
+    for dtype in [np.uint8, np.uint16, np.int8, np.int16, np.int32, np.int64]:
+      with self.test_session(use_gpu=True):
+        x = np.array([1, 2, 3], dtype=dtype)
+        v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
+        v_np = np.broadcast_to(x, [3, 3])
+        self.assertAllEqual(v_tf.eval(), v_np)
+
+  def testBroadcastToString(self):
+    with self.test_session(use_gpu=True):
+      x = np.array([b"1", b"2", b"3"])
+      v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
+      v_np = np.broadcast_to(x, [3, 3])
+      self.assertAllEqual(v_tf.eval(), v_np)
+
+  def testBroadcastToBool(self):
+    with self.test_session(use_gpu=True):
+      x = np.array([True, False, True], dtype=np.bool)
+      v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
+      v_np = np.broadcast_to(x, [3, 3])
+      self.assertAllEqual(v_tf.eval(), v_np)
+
+  def testBroadcastToShape(self):
+    for input_dim in range(1, 6):
+      for output_dim in range(input_dim, 6):
+        with self.test_session(use_gpu=True):
+          input_shape = [2] * input_dim
+          output_shape = [2] * output_dim
+          x = np.array(np.random.randint(5, size=input_shape), dtype=np.int32)
+          v_tf = array_ops.broadcast_to(constant_op.constant(x), output_shape)
+          v_np = np.broadcast_to(x, output_shape)
+          self.assertAllEqual(v_tf.eval(), v_np)
+
+  def testBroadcastToScalar(self):
+    with self.test_session(use_gpu=True):
+      x = np.array(1, dtype=np.int32)
+      v_tf = array_ops.broadcast_to(constant_op.constant(x), [3, 3])
+      v_np = np.broadcast_to(x, [3, 3])
+      self.assertAllEqual(v_tf.eval(), v_np)
+
+  def testBroadcastToShapeTypeAndInference(self):
+    for dtype in [dtypes.int32, dtypes.int64]:
+      with self.test_session(use_gpu=True):
+        x = np.array([1, 2, 3])
+        v_tf = array_ops.broadcast_to(
+            constant_op.constant(x),
+            constant_op.constant([3, 3], dtype=dtype))
+        shape = v_tf.get_shape().as_list()
+        v_np = np.broadcast_to(x, [3, 3])
+        self.assertAllEqual(v_tf.eval(), v_np)
+        # check shape inference when shape input is constant
+        self.assertAllEqual(shape, v_np.shape)
+
+if __name__ == "__main__":
+  test_lib.main()