1 files changed, 24 insertions, 1 deletions
diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py
index 051a25080b..6cdc7872f9 100644
--- a/tensorflow/python/kernel_tests/slice_op_test.py
+++ b/tensorflow/python/kernel_tests/slice_op_test.py
@@ -217,6 +217,30 @@ class SliceTest(test.TestCase):
     self.assertEqual(expected_val.shape, slice_t.get_shape())
     self.assertEqual(expected_val.shape, slice2_t.get_shape())
 
+  def testRandomHighRank(self):
+    # Random dims of rank 8
+    input_shape = np.random.randint(0, 20, size=8)
+    inp = np.random.rand(*input_shape).astype("f")
+    with self.test_session(use_gpu=True) as sess:
+      a = constant_op.constant(
+          [float(x) for x in inp.ravel(order="C")],
+          shape=input_shape,
+          dtype=dtypes.float32)
+      indices = [0 if x == 0 else np.random.randint(x) for x in input_shape]
+      sizes = [
+          np.random.randint(0, input_shape[i] - indices[i] + 1)
+          for i in range(8)
+      ]
+      slice_t = array_ops.slice(a, indices, sizes)
+      slice_val = sess.run(slice_t)
+
+    expected_val = inp[indices[0]:indices[0] + sizes[0], indices[1]:indices[1] + sizes[
+      1], indices[2]:indices[2] + sizes[2], indices[3]:indices[3] + sizes[3], indices[
+        4]:indices[4] + sizes[4], indices[5]:indices[5] + sizes[5], indices[6]:indices[
+          6] + sizes[6], indices[7]:indices[7] + sizes[7]]
+    self.assertAllEqual(slice_val, expected_val)
+    self.assertEqual(expected_val.shape, slice_t.get_shape())
+
   def testPartialShapeInference(self):
     z = array_ops.zeros((1, 2, 3))
     self.assertAllEqual(z.get_shape().as_list(), [1, 2, 3])
@@ -227,7 +251,6 @@ class SliceTest(test.TestCase):
     m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1])
     self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None])
 
-
   def _testGradientSlice(self, input_shape, slice_begin, slice_size):
     with self.test_session(use_gpu=True):
       num_inputs = np.prod(input_shape)