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diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py
new file mode 100644
index 0000000000..46bba7514d
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+++ b/tensorflow/python/kernel_tests/clip_ops_test.py
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+"""Tests for tensorflow.ops.clip_ops."""
+
+import tensorflow.python.platform
+
+import tensorflow as tf
+
+
+class ClipTest(tf.test.TestCase):
+
+  # ClipByValue test
+  def testClipByValue(self):
+    with self.test_session():
+      x = tf.constant([-5.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
+      np_ans = [[-4.4, 2.0, 3.0],
+                [4.0, 4.4, 4.4]]
+      clip_value = 4.4
+      ans = tf.clip_by_value(x, -clip_value, clip_value)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByValueNonFinite(self):
+    with self.test_session():
+      x = tf.constant([float('NaN'), float('Inf'), -float('Inf')])
+      np_ans = [float('NaN'), 4.0, -4.0]
+      clip_value = 4.0
+      ans = tf.clip_by_value(x, -clip_value, clip_value)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  # ClipByNorm tests
+  def testClipByNormClipped(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Norm of x = sqrt(3^2 + 4^2) = 5
+      np_ans = [[-2.4, 0.0, 0.0],
+                [3.2, 0.0, 0.0]]
+      clip_norm = 4.0
+      ans = tf.clip_by_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByNormNotClipped(self):
+    # No norm clipping when clip_norm >= 5
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Norm of x = sqrt(3^2 + 4^2) = 5
+      np_ans = [[-3.0, 0.0, 0.0],
+                [4.0, 0.0, 0.0]]
+      clip_norm = 6.0
+      ans = tf.clip_by_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByNormZero(self):
+    # No norm clipping when norm = 0
+    with self.test_session():
+      x = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
+      # Norm = 0, no changes
+      np_ans = [[0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0]]
+      clip_norm = 6.0
+      ans = tf.clip_by_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByGlobalNormClipped(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([1.0, -2.0])
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      clip_norm = 4.0
+
+      # Answers are the original tensors scaled by 4.0/5.0
+      np_ans_0 = [[-1.6, 0.0, 0.0],
+                  [3.2, 0.0, 0.0]]
+      np_ans_1 = [0.8, -1.6]
+
+      ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
+  def testClipByGlobalNormSupportsNone(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([1.0, -2.0])
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      clip_norm = 4.0
+
+      # Answers are the original tensors scaled by 4.0/5.0
+      np_ans_0 = [[-1.6, 0.0, 0.0],
+                  [3.2, 0.0, 0.0]]
+      np_ans_1 = [0.8, -1.6]
+
+      ans, norm = tf.clip_by_global_norm((x0, None, x1, None), clip_norm)
+      self.assertTrue(ans[1] is None)
+      self.assertTrue(ans[3] is None)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[2].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
+  # ClipByGlobalNorm tests
+  def testClipByGlobalNormWithIndexedSlicesClipped(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.IndexedSlices(tf.constant([1.0, -2.0]),
+                            tf.constant([3, 4]))
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      clip_norm = 4.0
+
+      # Answers are the original tensors scaled by 4.0/5.0
+      np_ans_0 = [[-1.6, 0.0, 0.0],
+                  [3.2, 0.0, 0.0]]
+      np_ans_1 = [0.8, -1.6]
+
+      ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].values.eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
+  def testClipByGlobalNormNotClipped(self):
+    # No norm clipping when clip_norm >= 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([1.0, -2.0])
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      np_ans_0 = [[-2.0, 0.0, 0.0],
+                  [4.0, 0.0, 0.0]]
+      np_ans_1 = [1.0, -2.0]
+      clip_norm = 6.0
+
+      ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
+  def testClipByGlobalNormZero(self):
+    # No norm clipping when norm = 0
+    with self.test_session():
+      x0 = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([0.0, 0.0])
+      # Norm = 0, no changes
+      np_ans_0 = [[0.0, 0.0, 0.0],
+                  [0.0, 0.0, 0.0]]
+      np_ans_1 = [0.0, 0.0]
+      clip_norm = 6.0
+
+      ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 0.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
+  def testClipByAverageNormClipped(self):
+    # Norm clipping when average clip_norm < 0.83333333
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
+      np_ans = [[-2.88, 0.0, 0.0],
+                [3.84, 0.0, 0.0]]
+      clip_norm = 0.8
+      ans = tf.clip_by_average_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByAverageNormNotClipped(self):
+    # No norm clipping when average clip_norm >= 0.83333333
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
+      np_ans = [[-3.0, 0.0, 0.0],
+                [4.0, 0.0, 0.0]]
+      clip_norm = 0.9
+      ans = tf.clip_by_average_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testClipByAverageNormZero(self):
+    # No norm clipping when average clip_norm = 0
+    with self.test_session():
+      x = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
+      # Average norm = 0, no changes
+      np_ans = [[0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0]]
+      clip_norm = 0.9
+      ans = tf.clip_by_average_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
+if __name__ == "__main__":
+  tf.test.main()