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diff --git a/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py b/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py
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+# Copyright 2017 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 MultitaskOptimizerWrapper."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.opt.python.training import multitask_optimizer_wrapper
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import momentum
+
+import numpy as np
+import six
+
+class MultitaskOptimizerWrapperTest(test.TestCase):
+  """
+  Tests for the multitask optimizer wrapper.
+  """
+  def testWrapper(self):
+    with self.test_session():
+      var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
+      var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
+      grads0 = constant_op.constant([0.1, 0.1], dtype=dtypes.float32)
+      grads1 = constant_op.constant([0.01, 0.01], dtype=dtypes.float32)
+      grads_allzero = constant_op.constant([0.0, 0.0], dtype=dtypes.float32)
+      mom_opt_impl = momentum.MomentumOptimizer(
+          learning_rate=2.0, momentum=0.9)
+      mom_opt = multitask_optimizer_wrapper.MultitaskOptimizerWrapper(
+          mom_opt_impl)
+      mom_update = mom_opt.apply_gradients(
+          zip([grads0, grads1], [var0, var1]))
+      mom_update_partial = mom_opt.apply_gradients(
+          zip([grads_allzero, grads1], [var0, var1]))
+      mom_update_no_action = mom_opt.apply_gradients(
+          zip([grads_allzero, grads_allzero], [var0, var1]))
+      self.evaluate(variables.global_variables_initializer())
+      # Fetch params to validate initial values
+      self.assertAllClose([1.0, 2.0], self.evaluate(var0))
+      self.assertAllClose([3.0, 4.0], self.evaluate(var1))
+
+      self.assertEqual(["momentum"], mom_opt.get_slot_names())
+      slot0 = mom_opt.get_slot(var0, "momentum")
+      self.assertEquals(slot0.get_shape(), var0.get_shape())
+      slot1 = mom_opt.get_slot(var1, "momentum")
+      self.assertEquals(slot1.get_shape(), var1.get_shape())
+
+      # Step 1: normal momentum update.
+      self.evaluate(mom_update)
+      # Check that the momentum accumulators have been updated.
+      self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
+                                         self.evaluate(slot0))
+      self.assertAllCloseAccordingToType(np.array([0.01, 0.01]),
+                                         self.evaluate(slot1))
+      # Check that the parameters have been updated.
+      self.assertAllCloseAccordingToType(
+          np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]),
+          self.evaluate(var0))
+      self.assertAllCloseAccordingToType(
+          np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]),
+          self.evaluate(var1))
+
+      # Step 2: momentum update that changes only slot1 but not slot0.
+      self.evaluate(mom_update_partial)
+      # Check that only the relevant momentum accumulator has been updated.
+      self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
+                                         self.evaluate(slot0))
+      self.assertAllCloseAccordingToType(
+          np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
+          self.evaluate(slot1))
+
+      # Step 3: momentum update that does not change anything.
+      self.evaluate(mom_update_no_action)
+      # Check that the momentum accumulators have *NOT* been updated.
+      self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
+                                         self.evaluate(slot0))
+      self.assertAllCloseAccordingToType(
+          np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
+          self.evaluate(slot1))
+
+  def testGradientClipping(self):
+    with self.test_session():
+      var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
+      var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
+      var2 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
+      var3 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
+      grads0 = constant_op.constant([10.0, 15.0], dtype=dtypes.float32)
+      grads1 = constant_op.constant([0.0, 5.0], dtype=dtypes.float32)
+      grads2 = constant_op.constant([0.0, 0.0], dtype=dtypes.float32)
+      grads3 = None
+      varlist = [var0, var1, var2, var3]
+      gradients = [grads0, grads1, grads2, grads3]
+      clipped_gradvars, global_norm = multitask_optimizer_wrapper.clip_gradients_by_global_norm(
+          six.moves.zip(gradients, varlist), clip_norm=1.0)
+      clipped_grads = list(six.moves.zip(*clipped_gradvars))[0]
+      reference_global_norm = np.sqrt(np.sum(np.square([10.0, 15.0, 0.0, 5.0])))
+      self.assertAllCloseAccordingToType(
+          self.evaluate(global_norm), reference_global_norm)
+      self.assertAllCloseAccordingToType(
+          self.evaluate(clipped_grads[2]), np.array([0., 0.]))
+      self.assertEqual(clipped_grads[3], None)
+
+if __name__ == "__main__":
+  test.main()