Organize the lookup table ops into it's own lookup_ops.cc file instead of data_flow_ops.cc

Change: 155119120

26 files changed, 950 insertions, 811 deletions

diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
index b2dad0162e..a09cc53571 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
@@ -33,9 +33,9 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
@@ -224,7 +224,7 @@ class TransformerTest(test.TestCase):
     self.assertEqual(len(output), 1)
     self.assertIn(keys_sparse, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertEqual(output[keys_sparse].values.dtype, dtypes.int64)
       self.assertAllEqual(output[keys_sparse].values.eval(), [1, 2, 0])
       self.assertAllEqual(output[keys_sparse].indices.eval(),
@@ -242,7 +242,7 @@ class TransformerTest(test.TestCase):
     output = feature_column_ops._Transformer(features).transform(keys_sparse)
 
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       # While the input is a dense Tensor, the output should be a SparseTensor.
       self.assertIsInstance(output, sparse_tensor.SparseTensor)
       self.assertEqual(output.dtype, dtypes.int64)
@@ -311,7 +311,7 @@ class TransformerTest(test.TestCase):
     self.assertIn(weighted_ids, output)
 
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(output[weighted_ids][0].dense_shape.eval(),
                           ids_tensor.dense_shape.eval())
       self.assertAllEqual(output[weighted_ids][0].indices.eval(),
@@ -341,7 +341,7 @@ class TransformerTest(test.TestCase):
     self.assertEqual(len(output), 1)
     self.assertIn(vocab_sparse, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64)
       self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0])
       self.assertAllEqual(output[vocab_sparse].indices.eval(),
@@ -363,7 +363,7 @@ class TransformerTest(test.TestCase):
     self.assertEqual(len(output), 1)
     self.assertIn(vocab_sparse, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64)
       self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0, 1])
       self.assertAllEqual(output[vocab_sparse].indices.eval(),
@@ -387,7 +387,7 @@ class TransformerTest(test.TestCase):
     self.assertEqual(len(output), 1)
     self.assertIn(vocab_sparse, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64)
       self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0])
       self.assertAllEqual(output[vocab_sparse].indices.eval(),
@@ -409,7 +409,7 @@ class TransformerTest(test.TestCase):
     self.assertEqual(len(output), 1)
     self.assertIn(vocab_sparse, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64)
       self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0, 1])
       self.assertAllEqual(output[vocab_sparse].indices.eval(),
@@ -601,7 +601,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
         one_hot_column, embedding_column, real_valued_column])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(output.eval().shape, [3, 2 + 4 + 10])
 
   def testRealValuedColumn(self):
@@ -714,7 +714,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [one_hot_column])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual([[0, 0, 10., 0], [0, 20., 0, 0], [30., 0, 40., 0]],
                           output.eval())
 
@@ -732,7 +732,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
 
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual([[0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]],
                           output.eval())
 
@@ -750,7 +750,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
 
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual([[0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 1, 0]],
                           output.eval())
 
@@ -784,7 +784,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [one_hot_sparse])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual([3, 10], output.eval().shape)
 
   def testEmbeddingColumnSucceedsForDNN(self):
@@ -891,7 +891,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [embeded_sparse])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(output.eval().shape, [2, 10])
 
   def testEmbeddingColumnWithIntegerWeightedSparseColumnSucceedsForDNN(self):
@@ -914,7 +914,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [embeded_sparse])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(output.eval().shape, [2, 10])
 
   def testEmbeddingColumnWithCrossedColumnSucceedsForDNN(self):
@@ -965,7 +965,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
       with self.assertRaisesRegexp(
           ValueError,
           "Error creating input layer for column: ids_weighted_by_weights"):
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         feature_column_ops.input_from_feature_columns(features, [weighted_ids])
 
   def testCrossedColumnFailsForDNN(self):
@@ -1072,7 +1072,7 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [embeded_sparse])
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       # score: (sum of weights)
       self.assertAllEqual(output.eval(), [[10.], [50.], [0.]])
 
@@ -1310,7 +1310,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
 
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input = sess.run(model_input_tensor)
 
     expected_input_shape = np.array([4, 3, 4])
@@ -1344,7 +1344,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
 
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input = sess.run(model_input_tensor)
 
     expected_input_shape = np.array([4, 3, hash_buckets])
@@ -1374,7 +1374,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
 
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input = sess.run(model_input_tensor)
 
     self.assertAllEqual(expected_input_shape, model_input.shape)
@@ -1403,7 +1403,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
 
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input = sess.run(model_input_tensor)
 
     self.assertAllEqual(expected_input_shape, model_input.shape)
@@ -1433,7 +1433,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
                                                embedding_weights)
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input, gradients = sess.run([model_input_tensor, gradient_tensor])
 
     expected_input_shape = [4, 3, embedding_dimension]
@@ -1500,7 +1500,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase):
 
     with self.test_session() as sess:
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       model_input = sess.run(model_input_tensor)
 
     expected_input_shape = [
@@ -1581,7 +1581,7 @@ class WeightedSumTest(test.TestCase):
         features, [weighted_ids], num_outputs=5)
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(logits.eval().shape, [2, 5])
 
   def testWeightedSparseColumnWithDenseInputTensor(self):
@@ -1597,7 +1597,7 @@ class WeightedSumTest(test.TestCase):
 
     with self.test_session():
       variables_lib.global_variables_initializer().run()
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(logits.eval().shape, [2, 5])
 
   def testCrossedColumn(self):
@@ -1651,7 +1651,7 @@ class WeightedSumTest(test.TestCase):
               features, [movies], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.initialize_all_variables().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[movies][0]
         self.assertEqual(weights.get_shape(), (3, 1))
@@ -1726,7 +1726,7 @@ class WeightedSumTest(test.TestCase):
               features, [age, language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         self.assertAllClose(output.eval(), [[0.], [0.]])
 
@@ -1766,7 +1766,7 @@ class WeightedSumTest(test.TestCase):
       self.assertEqual(len(variables), 1)
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         self.assertAllClose(output.eval(), [[0.], [0.]])
 
@@ -1830,7 +1830,7 @@ class WeightedSumTest(test.TestCase):
               features, [weighted_language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         self.assertAllClose(output.eval(), [[0.], [0.]])
 
@@ -1858,7 +1858,7 @@ class WeightedSumTest(test.TestCase):
               features, [language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         # score: 0.1 + language_weight['hindi'] + language_weight['english']
         sess.run(bias.assign([0.1]))
@@ -1881,7 +1881,7 @@ class WeightedSumTest(test.TestCase):
               features, [movies], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[movies][0]
         self.assertEqual(weights.get_shape(), (15, 1))
@@ -1915,7 +1915,7 @@ class WeightedSumTest(test.TestCase):
               features, [country_language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[country_language][0]
         sess.run(weights.assign(weights + 0.4))
@@ -1939,7 +1939,7 @@ class WeightedSumTest(test.TestCase):
               features, [language_language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[language_language][0]
         sess.run(weights.assign(weights + 0.4))
@@ -1972,7 +1972,7 @@ class WeightedSumTest(test.TestCase):
               features, [country_language], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[country_language][0]
         sess.run(weights.assign(weights + 0.4))
@@ -2013,7 +2013,7 @@ class WeightedSumTest(test.TestCase):
                 scope=scope))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         self.assertEqual(2, len(column_to_variable[country]))
         self.assertEqual(3, len(column_to_variable[language]))
@@ -2050,7 +2050,7 @@ class WeightedSumTest(test.TestCase):
               features, [country, age, incomes], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         incomes_weights = column_to_variable[incomes][0]
         sess.run(incomes_weights.assign([[0.1], [0.2], [0.3]]))
@@ -2086,7 +2086,7 @@ class WeightedSumTest(test.TestCase):
               features, [country, age, height, incomes], num_outputs=5))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         height_weights = column_to_variable[height][0]
         sess.run(
@@ -2116,7 +2116,7 @@ class WeightedSumTest(test.TestCase):
               features, [bucket], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         sess.run(column_to_variable[bucket][0].assign([[0.1], [0.2], [0.3],
                                                        [0.4]]))
@@ -2144,7 +2144,7 @@ class WeightedSumTest(test.TestCase):
               features, [bucket, country], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         # dimension = 2, bucket_size = 4, num_classes = 1
         sess.run(column_to_variable[bucket][0].assign(
@@ -2173,7 +2173,7 @@ class WeightedSumTest(test.TestCase):
               features, [bucket, country], num_outputs=5))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         # dimension = 2, bucket_size = 4, num_classes = 5
         sess.run(column_to_variable[bucket][0].assign(
@@ -2209,7 +2209,7 @@ class WeightedSumTest(test.TestCase):
               features, [country_price], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[country_price][0]
         sess.run(weights.assign(weights + 0.4))
@@ -2248,7 +2248,7 @@ class WeightedSumTest(test.TestCase):
               features, [country_language_price], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[country_language_price][0]
         sess.run(weights.assign(weights + 0.4))
@@ -2272,7 +2272,7 @@ class WeightedSumTest(test.TestCase):
               features, [product], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         product_weights = column_to_variable[product][0]
         sess.run(product_weights.assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
         self.assertAllClose(output.eval(), [[0.1], [0.5], [0.3]])
@@ -2287,7 +2287,7 @@ class WeightedSumTest(test.TestCase):
               features, [product], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         product_weights = column_to_variable[product][0]
         sess.run(product_weights.assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
         self.assertAllClose(output.eval(), [[0.1], [0.5], [0.3]])
@@ -2302,7 +2302,7 @@ class WeightedSumTest(test.TestCase):
               features, [product], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         product_weights = column_to_variable[product][0]
         sess.run(product_weights.assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
         self.assertAllClose(output.eval(), [[0.6], [0.7]])
@@ -2323,7 +2323,7 @@ class WeightedSumTest(test.TestCase):
               features, [product], num_outputs=1))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         product_weights = column_to_variable[product][0]
         sess.run(product_weights.assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
         self.assertAllClose(output.eval(), [[0.1], [0.5], [0.3]])
@@ -2335,7 +2335,7 @@ class WeightedSumTest(test.TestCase):
           features, [feature_column.real_valued_column("age")], num_outputs=3)
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         sess.run(bias.assign([0.1, 0.2, 0.3]))
         self.assertAllClose(output.eval(), [[0.1, 0.2, 0.3], [0.1, 0.2, 0.3],
                                             [0.1, 0.2, 0.3], [0.1, 0.2, 0.3]])
@@ -2349,7 +2349,7 @@ class WeightedSumTest(test.TestCase):
               features, [column], num_outputs=3))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         weights = column_to_variable[column][0]
         self.assertEqual(weights.get_shape(), (1, 3))
         sess.run(weights.assign([[0.01, 0.03, 0.05]]))
@@ -2373,7 +2373,7 @@ class WeightedSumTest(test.TestCase):
               features, [column], num_outputs=3))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         weights = column_to_variable[column][0]
         self.assertEqual(weights.get_shape(), (5, 3))
         sess.run(
@@ -2399,7 +2399,7 @@ class WeightedSumTest(test.TestCase):
               features, [column], num_outputs=3))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[column][0]
         self.assertEqual(weights.get_shape(), (5, 3))
@@ -2439,7 +2439,7 @@ class WeightedSumTest(test.TestCase):
               features, [column], num_outputs=3))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[column][0]
         self.assertEqual(weights.get_shape(), (5, 3))
@@ -2468,7 +2468,7 @@ class WeightedSumTest(test.TestCase):
               features, [column], num_outputs=3))
       with self.test_session() as sess:
         variables_lib.global_variables_initializer().run()
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
         weights = column_to_variable[column][0]
         self.assertEqual(weights.get_shape(), (5, 3))
@@ -2533,7 +2533,7 @@ class ParseExampleTest(test.TestCase):
     self.assertIn(bucket, output)
     self.assertIn(wire_cast, output)
     with self.test_session():
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(output[bucket].eval(), [[2, 3, 0]])
       self.assertAllEqual(output[wire_cast].indices.eval(), [[0, 0], [0, 1]])
       self.assertAllEqual(output[wire_cast].values.eval(), [2, 0])
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
index 43b3d2a78f..58072500d1 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
@@ -38,8 +38,8 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import functional_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
@@ -157,7 +157,7 @@ class DynamicRnnEstimatorTest(test.TestCase):
         self.context_feature_columns)
     with self.test_session() as sess:
       sess.run(variables.global_variables_initializer())
-      sess.run(data_flow_ops.tables_initializer())
+      sess.run(lookup_ops.tables_initializer())
       sequence_input_val = sess.run(sequence_input)
     expected_shape = np.array([
         3,  # expected batch size
@@ -178,7 +178,7 @@ class DynamicRnnEstimatorTest(test.TestCase):
     # Obtain values of activations and final state.
     with session.Session() as sess:
       sess.run(variables.global_variables_initializer())
-      sess.run(data_flow_ops.tables_initializer())
+      sess.run(lookup_ops.tables_initializer())
       activations, final_state = sess.run([activations_t, final_state_t])
 
     expected_activations_shape = np.array([3, 2, self.NUM_LABEL_COLUMNS])
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 74a6da20d4..36f843ba8e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -57,7 +57,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
@@ -1292,7 +1292,7 @@ class Estimator(BaseEstimator):
         init_op = control_flow_ops.group(
             variables.local_variables_initializer(),
             resources.initialize_resources(resources.shared_resources()),
-            data_flow_ops.tables_initializer())
+            lookup_ops.tables_initializer())
 
         # Perform the export
         builder = saved_model_builder.SavedModelBuilder(export_dir)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head_test.py b/tensorflow/contrib/learn/python/learn/estimators/head_test.py
index 207a189a94..d5777088de 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head_test.py
@@ -32,7 +32,7 @@ from tensorflow.core.framework import summary_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.ops.losses import losses as losses_lib
 from tensorflow.python.platform import test
@@ -1214,7 +1214,7 @@ class MultiClassHeadTest(test.TestCase):
           train_op_fn=head_lib.no_op_train_fn,
           logits=((1., 0., 0.), (0., 0., 1.),))
       with session.Session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         self.assertAllEqual(
             [0, 2],
             model_fn_ops.predictions["classes"].eval())
@@ -1266,7 +1266,7 @@ class MultiClassHeadTest(test.TestCase):
           train_op_fn=head_lib.no_op_train_fn,
           logits=((1., 0., 0.), (0., 0., 1.),))
       with session.Session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         self.assertAllEqual(
             [b"key0", b"key2"],
             model_fn_ops.predictions["classes"].eval())
@@ -1301,7 +1301,7 @@ class MultiClassHeadTest(test.TestCase):
           train_op_fn=head_lib.no_op_train_fn,
           logits=((1., 0., 0.),))
       with session.Session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         self.assertIsNone(model_fn_ops.train_op)
         _assert_no_variables(self)
         _assert_summary_tags(self, ["loss"])
@@ -1327,7 +1327,7 @@ class MultiClassHeadTest(test.TestCase):
           train_op_fn=head_lib.no_op_train_fn,
           logits=((0., 0., 1.),))
       with session.Session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
         self.assertIsNone(model_fn_ops.train_op)
         _assert_no_variables(self)
         _assert_summary_tags(self, ["loss"])
diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
index f5bd03429c..feea6c5fed 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
@@ -35,8 +35,8 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
@@ -55,7 +55,7 @@ class PrepareInputsForRnnTest(test.TestCase):
 
     with self.test_session() as sess:
       sess.run(variables.global_variables_initializer())
-      sess.run(data_flow_ops.initialize_all_tables())
+      sess.run(lookup_ops.tables_initializer())
       features_val = sess.run(features_by_time)
       self.assertAllEqual(expected, features_val)
 
@@ -316,7 +316,7 @@ class StateSavingRnnEstimatorTest(test.TestCase):
 
     with self.test_session() as sess:
       sess.run(variables.global_variables_initializer())
-      sess.run(data_flow_ops.initialize_all_tables())
+      sess.run(lookup_ops.tables_initializer())
       actual_sequence, actual_context = sess.run(
           [sequence, context])
       assert_equal(expected_sequence, actual_sequence)
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py
index 4b7867f2d0..98365c05f6 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions.py
@@ -37,8 +37,8 @@ from tensorflow.python.client import session as tf_session
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import logging_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
@@ -429,11 +429,14 @@ def _get_ready_op():
 
 
 def _get_local_init_op():
+  """Returns the local init ops to initialize tables and local variables."""
   local_init_op = _get_first_op_from_collection(
       ops.GraphKeys.LOCAL_INIT_OP)
   if local_init_op is None:
-    op_list = [variables.local_variables_initializer(),
-               data_flow_ops.tables_initializer()]
+    op_list = [
+        variables.local_variables_initializer(),
+        lookup_ops.tables_initializer()
+    ]
     if op_list:
       local_init_op = control_flow_ops.group(*op_list)
       ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
@@ -680,7 +683,7 @@ def run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path=None):
       else:
         session.run(variables.global_variables_initializer())
       session.run(variables.local_variables_initializer())
-      session.run(data_flow_ops.tables_initializer())
+      session.run(lookup_ops.tables_initializer())
       coord = coordinator.Coordinator()
       threads = None
       try:
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index b53be29283..36a1f5f60c 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -28,7 +28,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import saver as tf_saver
@@ -67,17 +67,17 @@ def _export_graph(graph, saver, checkpoint_path, export_dir,
   with graph.as_default():
     with tf_session.Session('') as session:
       variables.local_variables_initializer()
-      data_flow_ops.tables_initializer()
+      lookup_ops.tables_initializer()
       saver.restore(session, checkpoint_path)
 
       export = exporter.Exporter(saver)
-      export.init(init_op=control_flow_ops.group(
-          variables.local_variables_initializer(),
-          data_flow_ops.tables_initializer()),
-                  default_graph_signature=default_graph_signature,
-                  named_graph_signatures=named_graph_signatures,
-                  assets_collection=ops.get_collection(
-                      ops.GraphKeys.ASSET_FILEPATHS))
+      export.init(
+          init_op=control_flow_ops.group(
+              variables.local_variables_initializer(),
+              lookup_ops.tables_initializer()),
+          default_graph_signature=default_graph_signature,
+          named_graph_signatures=named_graph_signatures,
+          assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
       return export.export(export_dir, contrib_variables.get_global_step(),
                            session, exports_to_keep=exports_to_keep)
 
diff --git a/tensorflow/contrib/lookup/BUILD b/tensorflow/contrib/lookup/BUILD
index 5966c86dfb..bbbd340352 100644
--- a/tensorflow/contrib/lookup/BUILD
+++ b/tensorflow/contrib/lookup/BUILD
@@ -30,11 +30,11 @@ py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:data_flow_ops",
         "//tensorflow/python:errors",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:lookup_ops",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py
index 0ec40a63f2..5ec169b6db 100644
--- a/tensorflow/contrib/lookup/lookup_ops_test.py
+++ b/tensorflow/contrib/lookup/lookup_ops_test.py
@@ -31,7 +31,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import saver
@@ -125,7 +125,7 @@ class HashTableOpTest(test.TestCase):
       table3 = lookup.HashTable(
           lookup.KeyValueTensorInitializer(keys, values), default_val)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(3, table1.size().eval())
       self.assertAllEqual(3, table2.size().eval())
       self.assertAllEqual(3, table3.size().eval())
@@ -1184,7 +1184,7 @@ class IndexTableFromFile(test.TestCase):
       ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_int32_index_table_from_file(self):
@@ -1198,7 +1198,7 @@ class IndexTableFromFile(test.TestCase):
           constant_op.constant((1, -1000, 11), dtype=dtypes.int32))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_int64_index_table_from_file(self):
@@ -1212,7 +1212,7 @@ class IndexTableFromFile(test.TestCase):
           constant_op.constant((1, -1000, 11), dtype=dtypes.int64))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_index_table_from_file_with_default_value(self):
@@ -1224,7 +1224,7 @@ class IndexTableFromFile(test.TestCase):
       ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, default_value), ids.eval())
 
   def test_index_table_from_file_with_oov_buckets(self):
@@ -1236,7 +1236,7 @@ class IndexTableFromFile(test.TestCase):
           constant_op.constant(["salad", "surgery", "tarkus", "toccata"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual(
           (
               1,  # From vocabulary file.
@@ -1259,7 +1259,7 @@ class IndexTableFromFile(test.TestCase):
       ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, -1, -1), ids.eval())
       self.assertEqual(2, table.size().eval())
 
@@ -1286,7 +1286,7 @@ class IndexTableFromFile(test.TestCase):
       ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, -1), ids.eval())
       self.assertEqual(3, table.size().eval())
 
@@ -1345,7 +1345,7 @@ class IndexTableFromTensor(test.TestCase):
       ids = table.lookup(constant_op.constant(("salad", "surgery", "tarkus")))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_int32_index_table_from_tensor_with_tensor_init(self):
@@ -1356,7 +1356,7 @@ class IndexTableFromTensor(test.TestCase):
           constant_op.constant((1, -1000, 11), dtype=dtypes.int32))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_int64_index_table_from_tensor_with_tensor_init(self):
@@ -1367,7 +1367,7 @@ class IndexTableFromTensor(test.TestCase):
           constant_op.constant((1, -1000, 11), dtype=dtypes.int64))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, 3), ids.eval())
 
   def test_index_table_from_tensor_with_default_value(self):
@@ -1378,7 +1378,7 @@ class IndexTableFromTensor(test.TestCase):
       ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
 
       self.assertRaises(errors_impl.OpError, ids.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, default_value), ids.eval())
 
   def test_index_table_from_tensor_missing_mapping(self):
@@ -1394,7 +1394,7 @@ class IndexTableFromTensor(test.TestCase):
       self.assertRaises(errors_impl.OpError, ids.eval)
       with self.assertRaisesRegexp(
           errors_impl.OpError, "keys and values cannot be empty"):
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
   def test_index_table_from_tensor_with_invalid_hashers(self):
     with self.test_session():
@@ -1422,7 +1422,7 @@ class StringToIndexTest(test.TestCase):
       indices = lookup.string_to_index(feats, mapping=mapping_strings)
 
       self.assertRaises(errors_impl.OpError, indices.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       self.assertAllEqual((1, 2, -1), indices.eval())
 
@@ -1433,7 +1433,7 @@ class StringToIndexTest(test.TestCase):
       _ = lookup.string_to_index(feats, mapping=mapping_strings)
 
       self.assertRaises(errors_impl.OpError,
-                        data_flow_ops.tables_initializer().run)
+                        lookup_ops.tables_initializer().run)
 
   def test_string_to_index_with_default_value(self):
     default_value = -42
@@ -1444,7 +1444,7 @@ class StringToIndexTest(test.TestCase):
           feats, mapping=mapping_strings, default_value=default_value)
       self.assertRaises(errors_impl.OpError, indices.eval)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((1, 2, default_value), indices.eval())
 
 
@@ -1463,7 +1463,7 @@ class IndexToStringTableFromFileTest(test.TestCase):
           vocabulary_file=vocabulary_file)
       features = table.lookup(constant_op.constant([0, 1, 2, 3], dtypes.int64))
       self.assertRaises(errors_impl.OpError, features.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"brain", b"salad", b"surgery", b"UNK"),
                           features.eval())
 
@@ -1475,7 +1475,7 @@ class IndexToStringTableFromFileTest(test.TestCase):
           vocabulary_file=vocabulary_file, default_value=default_value)
       features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
       self.assertRaises(errors_impl.OpError, features.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"salad", b"surgery", default_value),
                           features.eval())
 
@@ -1489,7 +1489,7 @@ class IndexToStringTableFromFileTest(test.TestCase):
           default_value=default_value)
       features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
       self.assertRaises(errors_impl.OpError, features.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"salad", default_value, default_value),
                           features.eval())
 
@@ -1501,7 +1501,7 @@ class IndexToStringTableFromFileTest(test.TestCase):
       features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
 
       self.assertRaises(errors_impl.OpError, features.eval)
-      init = data_flow_ops.tables_initializer()
+      init = lookup_ops.tables_initializer()
       self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
                               "Invalid vocab_size", init.run)
 
@@ -1513,7 +1513,7 @@ class IndexToStringTableFromFileTest(test.TestCase):
       features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
 
       self.assertRaises(errors_impl.OpError, features.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"salad", b"surgery", b"UNK"), features.eval())
 
 
@@ -1528,7 +1528,7 @@ class IndexToStringTableFromTensorTest(test.TestCase):
       indices = constant_op.constant([0, 1, 2, 3], dtypes.int64)
       features = table.lookup(indices)
       self.assertRaises(errors_impl.OpError, features.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       self.assertAllEqual((b"brain", b"salad", b"surgery", b"UNK"),
                           features.eval())
@@ -1540,7 +1540,7 @@ class IndexToStringTableFromTensorTest(test.TestCase):
           mapping=mapping_strings)
       indices = constant_op.constant([0, 1, 4], dtypes.int64)
       features = table.lookup(indices)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"hello", b"hello", b"UNK"), features.eval())
 
   def test_index_to_string_with_default_value(self):
@@ -1553,7 +1553,7 @@ class IndexToStringTableFromTensorTest(test.TestCase):
       features = table.lookup(indices)
       self.assertRaises(errors_impl.OpError, features.eval)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"salad", b"surgery", default_value),
                           features.eval())
 
@@ -1567,7 +1567,7 @@ class IndexToStringTest(test.TestCase):
       feats = lookup.index_to_string(indices, mapping=mapping_strings)
 
       self.assertRaises(errors_impl.OpError, feats.eval)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       self.assertAllEqual((b"brain", b"salad", b"surgery", b"UNK"),
                           feats.eval())
@@ -1577,11 +1577,11 @@ class IndexToStringTest(test.TestCase):
       mapping_strings = constant_op.constant(["hello", "hello"])
       indices = constant_op.constant([0, 1, 4], dtypes.int64)
       feats = lookup.index_to_string(indices, mapping=mapping_strings)
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"hello", b"hello", b"UNK"), feats.eval())
 
       self.assertRaises(errors_impl.OpError,
-                        data_flow_ops.tables_initializer().run)
+                        lookup_ops.tables_initializer().run)
 
   def test_index_to_string_with_default_value(self):
     default_value = b"NONE"
@@ -1592,7 +1592,7 @@ class IndexToStringTest(test.TestCase):
           indices, mapping=mapping_strings, default_value=default_value)
       self.assertRaises(errors_impl.OpError, feats.eval)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
       self.assertAllEqual((b"salad", b"surgery", default_value), feats.eval())
 
 
@@ -1755,7 +1755,7 @@ class InitializeTableFromFileOpTest(test.TestCase):
           default_value,
           shared_name=shared_name)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       input_string = constant_op.constant(["brain", "salad", "tank"])
 
@@ -2081,7 +2081,7 @@ class IdTableWithHashBucketsTest(test.TestCase):
           hasher_spec=lookup.StrongHashSpec((1, 2)),
           name="table2")
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       input_string = constant_op.constant(
           ["fruit", "brain", "salad", "surgery", "UNK"])
@@ -2167,7 +2167,7 @@ class IdTableWithHashBucketsTest(test.TestCase):
               default_value2),
           oov_buckets)
 
-      data_flow_ops.tables_initializer().run()
+      lookup_ops.tables_initializer().run()
 
       input_string_1 = constant_op.constant(
           ["brain", "salad", "surgery", "UNK"])
diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py
index 5ced8a4f08..b70d612f55 100644
--- a/tensorflow/contrib/slim/python/slim/learning.py
+++ b/tensorflow/contrib/slim/python/slim/learning.py
@@ -261,7 +261,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import clip_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.platform import tf_logging as logging
@@ -657,7 +657,7 @@ def train(train_op,
       if local_init_op == _USE_DEFAULT:
         local_init_op = control_flow_ops.group(
             tf_variables.local_variables_initializer(),
-            data_flow_ops.tables_initializer())
+            lookup_ops.tables_initializer())
 
       if sync_optimizer is not None and isinstance(
           sync_optimizer, sync_replicas_optimizer.SyncReplicasOptimizer):
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 119bc0f899..435618ace7 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -506,6 +506,7 @@ tf_gen_op_libs(
         "image_ops",
         "io_ops",
         "linalg_ops",
+        "lookup_ops",
         "logging_ops",
         "math_ops",
         "nn_ops",
@@ -582,6 +583,7 @@ cc_library(
         ":image_ops_op_lib",
         ":io_ops_op_lib",
         ":linalg_ops_op_lib",
+        ":lookup_ops_op_lib",
         ":logging_ops_op_lib",
         ":math_ops_op_lib",
         ":nn_ops_op_lib",
@@ -708,6 +710,7 @@ cc_library(
         "//tensorflow/core/kernels:image",
         "//tensorflow/core/kernels:io",
         "//tensorflow/core/kernels:linalg",
+        "//tensorflow/core/kernels:lookup",
         "//tensorflow/core/kernels:logging",
         "//tensorflow/core/kernels:math",
         "//tensorflow/core/kernels:multinomial_op",
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 0847d1279b..02ab30a04f 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -1327,6 +1327,14 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "lookup",
+    deps = [
+        ":lookup_table_init_op",
+        ":lookup_table_op",
+    ],
+)
+
 DATA_FLOW_DEPS = [
     ":bounds_check",
     ":concat_lib",
@@ -1450,10 +1458,10 @@ LOOKUP_DEPS = [
     ":initializable_lookup_table",
     ":lookup_util",
     "//tensorflow/core:core_cpu",
-    "//tensorflow/core:data_flow_ops_op_lib",
     "//tensorflow/core:framework",
     "//tensorflow/core:lib",
     "//tensorflow/core:lib_internal",
+    "//tensorflow/core:lookup_ops_op_lib",
 ]
 
 tf_kernel_library(
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index f35a1bb648..032ede6459 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -1876,604 +1876,6 @@ size: The number of incomplete elements (i.e. those with some of their value
 
 // --------------------------------------------------------------------------
 
-REGISTER_OP("LookupTableFind")
-    .Input("table_handle: Ref(string)")
-    .Input("keys: Tin")
-    .Input("default_value: Tout")
-    .Output("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      // Default value must be scalar or vector.
-      ShapeHandle unused;
-      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(2), 1, &unused));
-      c->set_output(0, c->UnknownShape());
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Looks up keys in a table, outputs the corresponding values.
-
-The tensor `keys` must of the same type as the keys of the table.
-The output `values` is of the type of the table values.
-
-The scalar `default_value` is the value output for keys not present in the
-table. It must also be of the same type as the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Same shape as `keys`.  Values found in the table, or `default_values`
-   for missing keys.
-)doc");
-
-REGISTER_OP("LookupTableFindV2")
-    .Input("table_handle: resource")
-    .Input("keys: Tin")
-    .Input("default_value: Tout")
-    .Output("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      // Default value must be scalar or vector.
-      ShapeHandle unused;
-      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(2), 1, &unused));
-      c->set_output(0, c->UnknownShape());
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Looks up keys in a table, outputs the corresponding values.
-
-The tensor `keys` must of the same type as the keys of the table.
-The output `values` is of the type of the table values.
-
-The scalar `default_value` is the value output for keys not present in the
-table. It must also be of the same type as the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Same shape as `keys`.  Values found in the table, or `default_values`
-   for missing keys.
-)doc");
-
-REGISTER_OP("LookupTableInsert")
-    .Input("table_handle: Ref(string)")
-    .Input("keys: Tin")
-    .Input("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      // TODO(ebrevdo): Validate keys and values shape.
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Updates the table to associates keys with values.
-
-The tensor `keys` must be of the same type as the keys of the table.
-The tensor `values` must be of the type of the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Values to associate with keys.
-)doc");
-
-REGISTER_OP("LookupTableInsertV2")
-    .Input("table_handle: resource")
-    .Input("keys: Tin")
-    .Input("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      // TODO: Validate keys and values shape.
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Updates the table to associates keys with values.
-
-The tensor `keys` must be of the same type as the keys of the table.
-The tensor `values` must be of the type of the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Values to associate with keys.
-)doc");
-
-REGISTER_OP("LookupTableSize")
-    .Input("table_handle: Ref(string)")
-    .Output("size: int64")
-    .SetShapeFn(TwoElementVectorInputsAndScalarOutputs)
-    .Doc(R"doc(
-Computes the number of elements in the given table.
-
-table_handle: Handle to the table.
-size: Scalar that contains number of elements in the table.
-)doc");
-
-REGISTER_OP("LookupTableSizeV2")
-    .Input("table_handle: resource")
-    .Output("size: int64")
-    .SetShapeFn(ScalarAndTwoElementVectorInputsAndScalarOutputs)
-    .Doc(R"doc(
-Computes the number of elements in the given table.
-
-table_handle: Handle to the table.
-size: Scalar that contains number of elements in the table.
-)doc");
-
-REGISTER_OP("LookupTableExport")
-    .Input("table_handle: Ref(string)")
-    .Output("keys: Tkeys")
-    .Output("values: Tvalues")
-    .Attr("Tkeys: type")
-    .Attr("Tvalues: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      ShapeHandle values = c->UnknownShape();
-      TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
-      ShapeHandle keys = c->Vector(c->Dim(values, 0));
-      c->set_output(0, keys);
-      c->set_output(1, values);
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs all keys and values in the table.
-
-table_handle: Handle to the table.
-keys: Vector of all keys present in the table.
-values: Tensor of all values in the table. Indexed in parallel with `keys`.
-)doc");
-
-REGISTER_OP("LookupTableExportV2")
-    .Input("table_handle: resource")
-    .Output("keys: Tkeys")
-    .Output("values: Tvalues")
-    .Attr("Tkeys: type")
-    .Attr("Tvalues: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      ShapeHandle values = c->UnknownShape();
-      TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
-      ShapeHandle keys = c->Vector(c->Dim(values, 0));
-      c->set_output(0, keys);
-      c->set_output(1, values);
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs all keys and values in the table.
-
-table_handle: Handle to the table.
-keys: Vector of all keys present in the table.
-values: Tensor of all values in the table. Indexed in parallel with `keys`.
-)doc");
-
-REGISTER_OP("LookupTableImport")
-    .Input("table_handle: Ref(string)")
-    .Input("keys: Tin")
-    .Input("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      // TODO(ebrevdo): Validate keys and values shape.
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Replaces the contents of the table with the specified keys and values.
-
-The tensor `keys` must be of the same type as the keys of the table.
-The tensor `values` must be of the type of the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Values to associate with keys.
-)doc");
-
-REGISTER_OP("LookupTableImportV2")
-    .Input("table_handle: resource")
-    .Input("keys: Tin")
-    .Input("values: Tout")
-    .Attr("Tin: type")
-    .Attr("Tout: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      // TODO: Validate keys and values shape.
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Replaces the contents of the table with the specified keys and values.
-
-The tensor `keys` must be of the same type as the keys of the table.
-The tensor `values` must be of the type of the table values.
-
-table_handle: Handle to the table.
-keys:  Any shape.  Keys to look up.
-values: Values to associate with keys.
-)doc");
-
-REGISTER_OP("HashTable")
-    .Output("table_handle: Ref(string)")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .SetIsStateful()
-    .SetShapeFn(TwoElementOutput)
-    .Doc(R"doc(
-Creates a non-initialized hash table.
-
-This op creates a hash table, specifying the type of its keys and values.
-Before using the table you will have to initialize it.  After initialization the
-table will be immutable.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-use_node_name_sharing: If true and shared_name is empty, the table is shared
-  using the node name.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("HashTableV2")
-    .Output("table_handle: resource")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .SetIsStateful()
-    .SetShapeFn(ScalarOutput)
-    .Doc(R"doc(
-Creates a non-initialized hash table.
-
-This op creates a hash table, specifying the type of its keys and values.
-Before using the table you will have to initialize it.  After initialization the
-table will be immutable.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-use_node_name_sharing: If true and shared_name is empty, the table is shared
-  using the node name.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("MutableHashTable")
-    .Output("table_handle: Ref(string)")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .SetIsStateful()
-    .SetShapeFn(TwoElementOutput)
-    .Doc(R"doc(
-Creates an empty hash table.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a scalar. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-use_node_name_sharing: If true and shared_name is empty, the table is shared
-  using the node name.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("MutableHashTableV2")
-    .Output("table_handle: resource")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .SetIsStateful()
-    .SetShapeFn(ScalarOutput)
-    .Doc(R"doc(
-Creates an empty hash table.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a scalar. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-use_node_name_sharing: If true and shared_name is empty, the table is shared
-  using the node name.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("MutableHashTableOfTensors")
-    .Output("table_handle: Ref(string)")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .Attr("value_shape: shape = {}")
-    .SetIsStateful()
-    .SetShapeFn(TwoElementOutput)
-    .Doc(R"doc(
-Creates an empty hash table.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a vector. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("MutableHashTableOfTensorsV2")
-    .Output("table_handle: resource")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .Attr("value_shape: shape = {}")
-    .SetIsStateful()
-    .SetShapeFn(ScalarOutput)
-    .Doc(R"doc(
-Creates an empty hash table.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a vector. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-)doc");
-
-REGISTER_OP("MutableDenseHashTable")
-    .Input("empty_key: key_dtype")
-    .Output("table_handle: Ref(string)")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .Attr("value_shape: shape = {}")
-    .Attr("initial_num_buckets: int = 131072")  // 2^17
-    .Attr("max_load_factor: float = 0.8")
-    .SetIsStateful()
-    .SetShapeFn(TwoElementOutput)
-    .Doc(R"doc(
-Creates an empty hash table that uses tensors as the backing store. It uses
-"open addressing" with quadratic reprobing to resolve collisions.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a scalar. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-empty_key: The key used to represent empty key buckets internally. Must not
-  be used in insert or lookup operations.
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-value_shape: The shape of each value.
-initial_num_buckets: The initial number of hash table buckets. Must be a power
-  to 2.
-max_load_factor: The maximum ratio between number of entries and number of
-  buckets before growing the table. Must be between 0 and 1.
-)doc");
-
-REGISTER_OP("MutableDenseHashTableV2")
-    .Input("empty_key: key_dtype")
-    .Output("table_handle: resource")
-    .Attr("container: string = ''")
-    .Attr("shared_name: string = ''")
-    .Attr("use_node_name_sharing: bool = false")
-    .Attr("key_dtype: type")
-    .Attr("value_dtype: type")
-    .Attr("value_shape: shape = {}")
-    .Attr("initial_num_buckets: int = 131072")  // 2^17
-    .Attr("max_load_factor: float = 0.8")
-    .SetIsStateful()
-    .SetShapeFn(ScalarOutput)
-    .Doc(R"doc(
-Creates an empty hash table that uses tensors as the backing store. It uses
-"open addressing" with quadratic reprobing to resolve collisions.
-
-This op creates a mutable hash table, specifying the type of its keys and
-values. Each value must be a scalar. Data can be inserted into the table using
-the insert operations. It does not support the initialization operation.
-
-empty_key: The key used to represent empty key buckets internally. Must not
-  be used in insert or lookup operations.
-table_handle: Handle to a table.
-container: If non-empty, this table is placed in the given container.
-  Otherwise, a default container is used.
-shared_name: If non-empty, this table is shared under the given name across
-  multiple sessions.
-key_dtype: Type of the table keys.
-value_dtype: Type of the table values.
-value_shape: The shape of each value.
-initial_num_buckets: The initial number of hash table buckets. Must be a power
-  to 2.
-max_load_factor: The maximum ratio between number of entries and number of
-  buckets before growing the table. Must be between 0 and 1.
-)doc");
-
-REGISTER_OP("InitializeTable")
-    .Input("table_handle: Ref(string)")
-    .Input("keys: Tkey")
-    .Input("values: Tval")
-    .Attr("Tkey: type")
-    .Attr("Tval: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      ShapeHandle keys;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
-      TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Table initializer that takes two tensors for keys and values respectively.
-
-table_handle: Handle to a table which will be initialized.
-keys: Keys of type Tkey.
-values: Values of type Tval.
-)doc");
-
-REGISTER_OP("InitializeTableV2")
-    .Input("table_handle: resource")
-    .Input("keys: Tkey")
-    .Input("values: Tval")
-    .Attr("Tkey: type")
-    .Attr("Tval: type")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      ShapeHandle keys;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
-      TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Table initializer that takes two tensors for keys and values respectively.
-
-table_handle: Handle to a table which will be initialized.
-keys: Keys of type Tkey.
-values: Values of type Tval.
-)doc");
-
-REGISTER_OP("InitializeTableFromTextFile")
-    .Input("table_handle: Ref(string)")
-    .Input("filename: string")
-    .Attr("key_index: int >= -2")
-    .Attr("value_index: int >= -2")
-    .Attr("vocab_size: int >= -1 = -1")
-    .Attr("delimiter: string = '\t'")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
-      DimensionHandle unused_dim;
-      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
-
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &handle));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Initializes a table from a text file.
-
-It inserts one key-value pair into the table for each line of the file.
-The key and value is extracted from the whole line content, elements from the
-split line based on `delimiter` or the line number (starting from zero).
-Where to extract the key and value from a line is specified by `key_index` and
-`value_index`.
-
-- A value of -1 means use the line number(starting from zero), expects `int64`.
-- A value of -2 means use the whole line content, expects `string`.
-- A value >= 0 means use the index (starting at zero) of the split line based
-  on `delimiter`.
-
-table_handle: Handle to a table which will be initialized.
-filename: Filename of a vocabulary text file.
-key_index: Column index in a line to get the table `key` values from.
-value_index: Column index that represents information of a line to get the table
-  `value` values from.
-vocab_size: Number of elements of the file, use -1 if unknown.
-delimiter: Delimiter to separate fields in a line.
-)doc");
-
-REGISTER_OP("InitializeTableFromTextFileV2")
-    .Input("table_handle: resource")
-    .Input("filename: string")
-    .Attr("key_index: int >= -2")
-    .Attr("value_index: int >= -2")
-    .Attr("vocab_size: int >= -1 = -1")
-    .Attr("delimiter: string = '\t'")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle handle;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
-
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &handle));
-      return Status::OK();
-    })
-    .Doc(R"doc(
-Initializes a table from a text file.
-
-It inserts one key-value pair into the table for each line of the file.
-The key and value is extracted from the whole line content, elements from the
-split line based on `delimiter` or the line number (starting from zero).
-Where to extract the key and value from a line is specified by `key_index` and
-`value_index`.
-
-- A value of -1 means use the line number(starting from zero), expects `int64`.
-- A value of -2 means use the whole line content, expects `string`.
-- A value >= 0 means use the index (starting at zero) of the split line based
-  on `delimiter`.
-
-table_handle: Handle to a table which will be initialized.
-filename: Filename of a vocabulary text file.
-key_index: Column index in a line to get the table `key` values from.
-value_index: Column index that represents information of a line to get the table
-  `value` values from.
-vocab_size: Number of elements of the file, use -1 if unknown.
-delimiter: Delimiter to separate fields in a line.
-)doc");
-
 REGISTER_OP("GetSessionHandle")
     .Input("value: T")
     .Output("handle: string")
diff --git a/tensorflow/core/ops/lookup_ops.cc b/tensorflow/core/ops/lookup_ops.cc
new file mode 100644
index 0000000000..498a65690d
--- /dev/null
+++ b/tensorflow/core/ops/lookup_ops.cc
@@ -0,0 +1,666 @@
+/* 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.
+==============================================================================*/
+
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_def_builder.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+namespace tensorflow {
+
+using shape_inference::DimensionHandle;
+using shape_inference::InferenceContext;
+using shape_inference::ShapeHandle;
+
+// --------------------------------------------------------------------------
+
+namespace {
+Status TwoElementVectorInputsAndScalarOutputs(InferenceContext* c) {
+  ShapeHandle handle;
+  DimensionHandle unused_handle;
+  for (int i = 0; i < c->num_inputs(); ++i) {
+    TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &handle));
+    TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_handle));
+  }
+  for (int i = 0; i < c->num_outputs(); ++i) {
+    c->set_output(i, c->Scalar());
+  }
+  return Status::OK();
+}
+
+Status ScalarAndTwoElementVectorInputsAndScalarOutputs(InferenceContext* c) {
+  ShapeHandle handle;
+  DimensionHandle unused_handle;
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+  for (int i = 1; i < c->num_inputs(); ++i) {
+    TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &handle));
+    TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_handle));
+  }
+  for (int i = 0; i < c->num_outputs(); ++i) {
+    c->set_output(i, c->Scalar());
+  }
+  return Status::OK();
+}
+
+Status TwoElementOutput(InferenceContext* c) {
+  c->set_output(0, c->Vector(2));
+  return Status::OK();
+}
+
+Status ScalarOutput(InferenceContext* c) {
+  c->set_output(0, c->Scalar());
+  return Status::OK();
+}
+}  // namespace
+
+REGISTER_OP("LookupTableFind")
+    .Input("table_handle: Ref(string)")
+    .Input("keys: Tin")
+    .Input("default_value: Tout")
+    .Output("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      // Default value must be scalar or vector.
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(2), 1, &unused));
+      c->set_output(0, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Looks up keys in a table, outputs the corresponding values.
+
+The tensor `keys` must of the same type as the keys of the table.
+The output `values` is of the type of the table values.
+
+The scalar `default_value` is the value output for keys not present in the
+table. It must also be of the same type as the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Same shape as `keys`.  Values found in the table, or `default_values`
+   for missing keys.
+)doc");
+
+REGISTER_OP("LookupTableFindV2")
+    .Input("table_handle: resource")
+    .Input("keys: Tin")
+    .Input("default_value: Tout")
+    .Output("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      // Default value must be scalar or vector.
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(2), 1, &unused));
+      c->set_output(0, c->UnknownShape());
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Looks up keys in a table, outputs the corresponding values.
+
+The tensor `keys` must of the same type as the keys of the table.
+The output `values` is of the type of the table values.
+
+The scalar `default_value` is the value output for keys not present in the
+table. It must also be of the same type as the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Same shape as `keys`.  Values found in the table, or `default_values`
+   for missing keys.
+)doc");
+
+REGISTER_OP("LookupTableInsert")
+    .Input("table_handle: Ref(string)")
+    .Input("keys: Tin")
+    .Input("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      // TODO(ebrevdo): Validate keys and values shape.
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the table to associates keys with values.
+
+The tensor `keys` must be of the same type as the keys of the table.
+The tensor `values` must be of the type of the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Values to associate with keys.
+)doc");
+
+REGISTER_OP("LookupTableInsertV2")
+    .Input("table_handle: resource")
+    .Input("keys: Tin")
+    .Input("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      // TODO: Validate keys and values shape.
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Updates the table to associates keys with values.
+
+The tensor `keys` must be of the same type as the keys of the table.
+The tensor `values` must be of the type of the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Values to associate with keys.
+)doc");
+
+REGISTER_OP("LookupTableSize")
+    .Input("table_handle: Ref(string)")
+    .Output("size: int64")
+    .SetShapeFn(TwoElementVectorInputsAndScalarOutputs)
+    .Doc(R"doc(
+Computes the number of elements in the given table.
+
+table_handle: Handle to the table.
+size: Scalar that contains number of elements in the table.
+)doc");
+
+REGISTER_OP("LookupTableSizeV2")
+    .Input("table_handle: resource")
+    .Output("size: int64")
+    .SetShapeFn(ScalarAndTwoElementVectorInputsAndScalarOutputs)
+    .Doc(R"doc(
+Computes the number of elements in the given table.
+
+table_handle: Handle to the table.
+size: Scalar that contains number of elements in the table.
+)doc");
+
+REGISTER_OP("LookupTableExport")
+    .Input("table_handle: Ref(string)")
+    .Output("keys: Tkeys")
+    .Output("values: Tvalues")
+    .Attr("Tkeys: type")
+    .Attr("Tvalues: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      ShapeHandle values = c->UnknownShape();
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
+      ShapeHandle keys = c->Vector(c->Dim(values, 0));
+      c->set_output(0, keys);
+      c->set_output(1, values);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs all keys and values in the table.
+
+table_handle: Handle to the table.
+keys: Vector of all keys present in the table.
+values: Tensor of all values in the table. Indexed in parallel with `keys`.
+)doc");
+
+REGISTER_OP("LookupTableExportV2")
+    .Input("table_handle: resource")
+    .Output("keys: Tkeys")
+    .Output("values: Tvalues")
+    .Attr("Tkeys: type")
+    .Attr("Tvalues: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      ShapeHandle values = c->UnknownShape();
+      TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
+      ShapeHandle keys = c->Vector(c->Dim(values, 0));
+      c->set_output(0, keys);
+      c->set_output(1, values);
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Outputs all keys and values in the table.
+
+table_handle: Handle to the table.
+keys: Vector of all keys present in the table.
+values: Tensor of all values in the table. Indexed in parallel with `keys`.
+)doc");
+
+REGISTER_OP("LookupTableImport")
+    .Input("table_handle: Ref(string)")
+    .Input("keys: Tin")
+    .Input("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      // TODO(ebrevdo): Validate keys and values shape.
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Replaces the contents of the table with the specified keys and values.
+
+The tensor `keys` must be of the same type as the keys of the table.
+The tensor `values` must be of the type of the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Values to associate with keys.
+)doc");
+
+REGISTER_OP("LookupTableImportV2")
+    .Input("table_handle: resource")
+    .Input("keys: Tin")
+    .Input("values: Tout")
+    .Attr("Tin: type")
+    .Attr("Tout: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      // TODO: Validate keys and values shape.
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Replaces the contents of the table with the specified keys and values.
+
+The tensor `keys` must be of the same type as the keys of the table.
+The tensor `values` must be of the type of the table values.
+
+table_handle: Handle to the table.
+keys:  Any shape.  Keys to look up.
+values: Values to associate with keys.
+)doc");
+
+REGISTER_OP("HashTable")
+    .Output("table_handle: Ref(string)")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .SetIsStateful()
+    .SetShapeFn(TwoElementOutput)
+    .Doc(R"doc(
+Creates a non-initialized hash table.
+
+This op creates a hash table, specifying the type of its keys and values.
+Before using the table you will have to initialize it.  After initialization the
+table will be immutable.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("HashTableV2")
+    .Output("table_handle: resource")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .SetIsStateful()
+    .SetShapeFn(ScalarOutput)
+    .Doc(R"doc(
+Creates a non-initialized hash table.
+
+This op creates a hash table, specifying the type of its keys and values.
+Before using the table you will have to initialize it.  After initialization the
+table will be immutable.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("MutableHashTable")
+    .Output("table_handle: Ref(string)")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .SetIsStateful()
+    .SetShapeFn(TwoElementOutput)
+    .Doc(R"doc(
+Creates an empty hash table.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a scalar. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("MutableHashTableV2")
+    .Output("table_handle: resource")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .SetIsStateful()
+    .SetShapeFn(ScalarOutput)
+    .Doc(R"doc(
+Creates an empty hash table.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a scalar. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("MutableHashTableOfTensors")
+    .Output("table_handle: Ref(string)")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .Attr("value_shape: shape = {}")
+    .SetIsStateful()
+    .SetShapeFn(TwoElementOutput)
+    .Doc(R"doc(
+Creates an empty hash table.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a vector. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("MutableHashTableOfTensorsV2")
+    .Output("table_handle: resource")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .Attr("value_shape: shape = {}")
+    .SetIsStateful()
+    .SetShapeFn(ScalarOutput)
+    .Doc(R"doc(
+Creates an empty hash table.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a vector. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+)doc");
+
+REGISTER_OP("MutableDenseHashTable")
+    .Input("empty_key: key_dtype")
+    .Output("table_handle: Ref(string)")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .Attr("value_shape: shape = {}")
+    .Attr("initial_num_buckets: int = 131072")  // 2^17
+    .Attr("max_load_factor: float = 0.8")
+    .SetIsStateful()
+    .SetShapeFn(TwoElementOutput)
+    .Doc(R"doc(
+Creates an empty hash table that uses tensors as the backing store. It uses
+"open addressing" with quadratic reprobing to resolve collisions.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a scalar. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+empty_key: The key used to represent empty key buckets internally. Must not
+  be used in insert or lookup operations.
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+value_shape: The shape of each value.
+initial_num_buckets: The initial number of hash table buckets. Must be a power
+  to 2.
+max_load_factor: The maximum ratio between number of entries and number of
+  buckets before growing the table. Must be between 0 and 1.
+)doc");
+
+REGISTER_OP("MutableDenseHashTableV2")
+    .Input("empty_key: key_dtype")
+    .Output("table_handle: resource")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
+    .Attr("key_dtype: type")
+    .Attr("value_dtype: type")
+    .Attr("value_shape: shape = {}")
+    .Attr("initial_num_buckets: int = 131072")  // 2^17
+    .Attr("max_load_factor: float = 0.8")
+    .SetIsStateful()
+    .SetShapeFn(ScalarOutput)
+    .Doc(R"doc(
+Creates an empty hash table that uses tensors as the backing store. It uses
+"open addressing" with quadratic reprobing to resolve collisions.
+
+This op creates a mutable hash table, specifying the type of its keys and
+values. Each value must be a scalar. Data can be inserted into the table using
+the insert operations. It does not support the initialization operation.
+
+empty_key: The key used to represent empty key buckets internally. Must not
+  be used in insert or lookup operations.
+table_handle: Handle to a table.
+container: If non-empty, this table is placed in the given container.
+  Otherwise, a default container is used.
+shared_name: If non-empty, this table is shared under the given name across
+  multiple sessions.
+key_dtype: Type of the table keys.
+value_dtype: Type of the table values.
+value_shape: The shape of each value.
+initial_num_buckets: The initial number of hash table buckets. Must be a power
+  to 2.
+max_load_factor: The maximum ratio between number of entries and number of
+  buckets before growing the table. Must be between 0 and 1.
+)doc");
+
+REGISTER_OP("InitializeTable")
+    .Input("table_handle: Ref(string)")
+    .Input("keys: Tkey")
+    .Input("values: Tval")
+    .Attr("Tkey: type")
+    .Attr("Tval: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      ShapeHandle keys;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
+      TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Table initializer that takes two tensors for keys and values respectively.
+
+table_handle: Handle to a table which will be initialized.
+keys: Keys of type Tkey.
+values: Values of type Tval.
+)doc");
+
+REGISTER_OP("InitializeTableV2")
+    .Input("table_handle: resource")
+    .Input("keys: Tkey")
+    .Input("values: Tval")
+    .Attr("Tkey: type")
+    .Attr("Tval: type")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      ShapeHandle keys;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
+      TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Table initializer that takes two tensors for keys and values respectively.
+
+table_handle: Handle to a table which will be initialized.
+keys: Keys of type Tkey.
+values: Values of type Tval.
+)doc");
+
+REGISTER_OP("InitializeTableFromTextFile")
+    .Input("table_handle: Ref(string)")
+    .Input("filename: string")
+    .Attr("key_index: int >= -2")
+    .Attr("value_index: int >= -2")
+    .Attr("vocab_size: int >= -1 = -1")
+    .Attr("delimiter: string = '\t'")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle));
+      DimensionHandle unused_dim;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_dim));
+
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &handle));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Initializes a table from a text file.
+
+It inserts one key-value pair into the table for each line of the file.
+The key and value is extracted from the whole line content, elements from the
+split line based on `delimiter` or the line number (starting from zero).
+Where to extract the key and value from a line is specified by `key_index` and
+`value_index`.
+
+- A value of -1 means use the line number(starting from zero), expects `int64`.
+- A value of -2 means use the whole line content, expects `string`.
+- A value >= 0 means use the index (starting at zero) of the split line based
+  on `delimiter`.
+
+table_handle: Handle to a table which will be initialized.
+filename: Filename of a vocabulary text file.
+key_index: Column index in a line to get the table `key` values from.
+value_index: Column index that represents information of a line to get the table
+  `value` values from.
+vocab_size: Number of elements of the file, use -1 if unknown.
+delimiter: Delimiter to separate fields in a line.
+)doc");
+
+REGISTER_OP("InitializeTableFromTextFileV2")
+    .Input("table_handle: resource")
+    .Input("filename: string")
+    .Attr("key_index: int >= -2")
+    .Attr("value_index: int >= -2")
+    .Attr("vocab_size: int >= -1 = -1")
+    .Attr("delimiter: string = '\t'")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle handle;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
+
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &handle));
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Initializes a table from a text file.
+
+It inserts one key-value pair into the table for each line of the file.
+The key and value is extracted from the whole line content, elements from the
+split line based on `delimiter` or the line number (starting from zero).
+Where to extract the key and value from a line is specified by `key_index` and
+`value_index`.
+
+- A value of -1 means use the line number(starting from zero), expects `int64`.
+- A value of -2 means use the whole line content, expects `string`.
+- A value >= 0 means use the index (starting at zero) of the split line based
+  on `delimiter`.
+
+table_handle: Handle to a table which will be initialized.
+filename: Filename of a vocabulary text file.
+key_index: Column index in a line to get the table `key` values from.
+value_index: Column index that represents information of a line to get the table
+  `value` values from.
+vocab_size: Number of elements of the file, use -1 if unknown.
+delimiter: Delimiter to separate fields in a line.
+)doc");
+
+}  // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 817d157da2..9fd5ada71e 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1022,7 +1022,6 @@ tf_gen_op_wrapper_private_py(
     require_shape_functions = True,
     visibility = [
         "//learning/brain/python/ops:__pkg__",
-        "//tensorflow/python/feature_column:__pkg__",
         "//tensorflow/python/kernel_tests:__pkg__",
     ],
 )
@@ -1058,6 +1057,16 @@ tf_gen_op_wrapper_private_py(
 )
 
 tf_gen_op_wrapper_private_py(
+    name = "lookup_ops_gen",
+    require_shape_functions = True,
+    visibility = [
+        "//learning/brain/python/ops:__pkg__",
+        "//tensorflow/python/feature_column:__pkg__",
+        "//tensorflow/python/kernel_tests:__pkg__",
+    ],
+)
+
+tf_gen_op_wrapper_private_py(
     name = "math_ops_gen",
     require_shape_functions = True,
     visibility = [
@@ -1475,6 +1484,20 @@ py_library(
 )
 
 py_library(
+    name = "lookup_ops",
+    srcs = ["ops/lookup_ops.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":array_ops",
+        ":framework",
+        ":framework_for_generated_wrappers",
+        ":lookup_ops_gen",
+        ":math_ops",
+        "@six_archive//:six",
+    ],
+)
+
+py_library(
     name = "math_grad",
     srcs = ["ops/math_grad.py"],
     srcs_version = "PY2AND3",
@@ -1862,6 +1885,7 @@ py_library(
         ":io_ops",
         ":linalg_ops",
         ":logging_ops",
+        ":lookup_ops",
         ":math_grad",
         ":math_ops",
         ":numerics",
@@ -2269,6 +2293,7 @@ py_library(
         ":io_ops",
         ":io_ops_gen",
         ":lib",
+        ":lookup_ops",
         ":math_ops",
         ":platform",
         ":protos_all_py",
@@ -2991,6 +3016,7 @@ cuda_py_tests(
         ":framework",
         ":framework_for_generated_wrappers",
         ":framework_test_lib",
+        ":lookup_ops",
         ":gradients",
         ":math_ops",
         ":nn_grad",
@@ -3021,7 +3047,7 @@ py_library(
     srcs = ["training/saver_test_utils.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":data_flow_ops_gen",
+        ":lookup_ops_gen",
         ":training",
     ],
 )
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index f70c285f04..b8064f0a77 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -42,8 +42,8 @@ from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import metrics as metrics_lib
 from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import state_ops
@@ -1391,9 +1391,10 @@ class EstimatorExportTest(test.TestCase):
       my_int = variables.Variable(1, name='my_int',
                                   collections=[ops.GraphKeys.LOCAL_VARIABLES])
       scores = constant_op.constant([3.])
-      with ops.control_dependencies(
-          [variables.local_variables_initializer(),
-           data_flow_ops.tables_initializer()]):
+      with ops.control_dependencies([
+          variables.local_variables_initializer(),
+          lookup_ops.tables_initializer()
+      ]):
         assign_op = state_ops.assign(my_int, 12345)
 
       # local_initSop must be an Operation, not a Tensor.
diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD
index d734273845..ac7aef96ac 100644
--- a/tensorflow/python/feature_column/BUILD
+++ b/tensorflow/python/feature_column/BUILD
@@ -80,9 +80,9 @@ py_library(
     deps = [
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
-        "//tensorflow/python:data_flow_ops_gen",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:lookup_ops_gen",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:string_ops",
         "//tensorflow/python:training",
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index d85142abcf..ad67a082dc 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -31,7 +31,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
@@ -41,7 +41,7 @@ from tensorflow.python.platform import test
 def _initialized_session():
   sess = session.Session()
   sess.run(variables_lib.global_variables_initializer())
-  sess.run(data_flow_ops.tables_initializer())
+  sess.run(lookup_ops.tables_initializer())
   return sess
 
 
@@ -1277,7 +1277,7 @@ class VocabularyCategoricalColumnTest(test.TestCase):
     # pylint: enable=protected-access
     with self.assertRaisesRegexp(errors.OpError, 'file_does_not_exist'):
       with self.test_session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
   def test_invalid_vocabulary_size(self):
     with self.assertRaisesRegexp(ValueError, 'Invalid vocabulary_size'):
@@ -1307,7 +1307,7 @@ class VocabularyCategoricalColumnTest(test.TestCase):
     # pylint: enable=protected-access
     with self.assertRaisesRegexp(errors.OpError, 'Invalid vocab_size'):
       with self.test_session():
-        data_flow_ops.tables_initializer().run()
+        lookup_ops.tables_initializer().run()
 
   def test_invalid_num_oov_buckets(self):
     with self.assertRaisesRegexp(ValueError, 'Invalid num_oov_buckets'):
diff --git a/tensorflow/python/feature_column/lookup_ops.py b/tensorflow/python/feature_column/lookup_ops.py
index 13a67fa518..8225b47b20 100644
--- a/tensorflow/python/feature_column/lookup_ops.py
+++ b/tensorflow/python/feature_column/lookup_ops.py
@@ -27,7 +27,7 @@ from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import gen_data_flow_ops
+from tensorflow.python.ops import gen_lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.training.saver import BaseSaverBuilder
@@ -151,7 +151,7 @@ class InitializableLookupTableBase(LookupInterface):
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as scope:
       # pylint: disable=protected-access
-      return gen_data_flow_ops._lookup_table_size(self._table_ref, name=scope)
+      return gen_lookup_ops._lookup_table_size(self._table_ref, name=scope)
       # pylint: enable=protected-access
 
   def lookup(self, keys, name=None):
@@ -182,7 +182,7 @@ class InitializableLookupTableBase(LookupInterface):
         name, "%s_Lookup" % self._name,
         (self._table_ref, key_tensor, self._default_value)) as scope:
       # pylint: disable=protected-access
-      values = gen_data_flow_ops._lookup_table_find(
+      values = gen_lookup_ops._lookup_table_find(
           self._table_ref, key_tensor, self._default_value, name=scope)
       # pylint: enable=protected-access
 
@@ -229,7 +229,7 @@ class HashTable(InitializableLookupTableBase):
     with ops.name_scope(
         name, "hash_table", (initializer, default_value)) as scope:
       # pylint: disable=protected-access
-      table_ref = gen_data_flow_ops._hash_table(
+      table_ref = gen_lookup_ops._hash_table(
           shared_name=shared_name,
           key_dtype=initializer.key_dtype,
           value_dtype=initializer.value_dtype,
@@ -308,10 +308,8 @@ class KeyValueTensorInitializer(TableInitializerBase):
         self._name,
         values=(table.table_ref, self._keys, self._values)) as scope:
       # pylint: disable=protected-access
-      init_op = gen_data_flow_ops._initialize_table(table.table_ref,
-                                                    self._keys,
-                                                    self._values,
-                                                    name=scope)
+      init_op = gen_lookup_ops._initialize_table(
+          table.table_ref, self._keys, self._values, name=scope)
       # pylint: enable=protected-access
     ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
     return init_op
@@ -477,7 +475,7 @@ class TextFileInitializer(TableInitializerBase):
                                        dtypes.string,
                                        name="asset_filepath")
       # pylint: disable=protected-access
-      init_op = gen_data_flow_ops._initialize_table_from_text_file(
+      init_op = gen_lookup_ops._initialize_table_from_text_file(
           table.table_ref,
           filename,
           self._key_index,
@@ -1333,14 +1331,14 @@ class MutableHashTable(LookupInterface):
     use_node_name_sharing = checkpoint and shared_name is None
     # pylint: disable=protected-access
     if self._default_value.get_shape().ndims == 0:
-      self._table_ref = gen_data_flow_ops._mutable_hash_table(
+      self._table_ref = gen_lookup_ops._mutable_hash_table(
           shared_name=shared_name,
           use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
           value_dtype=value_dtype,
           name=name)
     else:
-      self._table_ref = gen_data_flow_ops._mutable_hash_table_of_tensors(
+      self._table_ref = gen_lookup_ops._mutable_hash_table_of_tensors(
           shared_name=shared_name,
           use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
@@ -1368,7 +1366,7 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as name:
       # pylint: disable=protected-access
-      return gen_data_flow_ops._lookup_table_size(self._table_ref, name=name)
+      return gen_lookup_ops._lookup_table_size(self._table_ref, name=name)
 
   def lookup(self, keys, name=None):
     """Looks up `keys` in a table, outputs the corresponding values.
@@ -1394,10 +1392,8 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_find" % self._name,
                         (self._table_ref, keys, self._default_value)) as name:
       # pylint: disable=protected-access
-      values = gen_data_flow_ops._lookup_table_find(self._table_ref,
-                                                    keys,
-                                                    self._default_value,
-                                                    name=name)
+      values = gen_lookup_ops._lookup_table_find(
+          self._table_ref, keys, self._default_value, name=name)
 
     values.set_shape(keys.get_shape().concatenate(self._value_shape))
     return values
@@ -1423,7 +1419,7 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_insert" % self._name,
                         [self._table_ref, keys, values]) as name:
       # pylint: disable=protected-access
-      op = gen_data_flow_ops._lookup_table_insert(
+      op = gen_lookup_ops._lookup_table_insert(
           self._table_ref, keys, values, name=name)
       return op
 
@@ -1440,11 +1436,8 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_export_values" % self._name,
                         [self._table_ref]) as name:
       # pylint: disable=protected-access
-      exported_keys, exported_values = gen_data_flow_ops._lookup_table_export(
-          self._table_ref,
-          self._key_dtype,
-          self._value_dtype,
-          name=name)
+      exported_keys, exported_values = gen_lookup_ops._lookup_table_export(
+          self._table_ref, self._key_dtype, self._value_dtype, name=name)
 
     exported_values.set_shape(exported_keys.get_shape().concatenate(
         self._value_shape))
@@ -1464,7 +1457,7 @@ class MutableHashTable(LookupInterface):
 
     def restore(self, restored_tensors, unused_restored_shapes):
       # pylint: disable=protected-access
-      return gen_data_flow_ops._lookup_table_import(
+      return gen_lookup_ops._lookup_table_import(
           self.op._table_ref, restored_tensors[0], restored_tensors[1])
 
 
@@ -1539,7 +1532,7 @@ class MutableDenseHashTable(LookupInterface):
     use_node_name_sharing = checkpoint and shared_name is None
     empty_key = ops.convert_to_tensor(empty_key, dtype=key_dtype)
     # pylint: disable=protected-access
-    self._table_ref = gen_data_flow_ops._mutable_dense_hash_table(
+    self._table_ref = gen_lookup_ops._mutable_dense_hash_table(
         empty_key=empty_key,
         shared_name=shared_name,
         use_node_name_sharing=use_node_name_sharing,
@@ -1567,7 +1560,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as name:
       # pylint: disable=protected-access
-      return gen_data_flow_ops._lookup_table_size(self._table_ref, name=name)
+      return gen_lookup_ops._lookup_table_size(self._table_ref, name=name)
 
   def lookup(self, keys, name=None):
     """Looks up `keys` in a table, outputs the corresponding values.
@@ -1593,7 +1586,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_find" % self._name,
                         [self._table_ref, keys]) as name:
       # pylint: disable=protected-access
-      values = gen_data_flow_ops._lookup_table_find(
+      values = gen_lookup_ops._lookup_table_find(
           self._table_ref, keys, self._default_value, name=name)
 
     if keys.get_shape().ndims is not None and keys.get_shape().ndims > 0:
@@ -1623,7 +1616,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_insert" % self._name,
                         [self._table_ref, keys, values]) as name:
       # pylint: disable=protected-access
-      op = gen_data_flow_ops._lookup_table_insert(
+      op = gen_lookup_ops._lookup_table_insert(
           self._table_ref, keys, values, name=name)
       return op
 
@@ -1640,7 +1633,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_export_values" % self._name,
                         [self._table_ref]) as name:
       # pylint: disable=protected-access
-      exported_keys, exported_values = gen_data_flow_ops._lookup_table_export(
+      exported_keys, exported_values = gen_lookup_ops._lookup_table_export(
           self._table_ref, self._key_dtype, self._value_dtype, name=name)
 
     exported_values.set_shape(exported_keys.get_shape().concatenate(
@@ -1661,6 +1654,5 @@ class MutableDenseHashTable(LookupInterface):
 
     def restore(self, restored_tensors, unused_restored_shapes):
       # pylint: disable=protected-access
-      return gen_data_flow_ops._lookup_table_import(self.op._table_ref,
-                                                    restored_tensors[0],
-                                                    restored_tensors[1])
+      return gen_lookup_ops._lookup_table_import(
+          self.op._table_ref, restored_tensors[0], restored_tensors[1])
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 95e803e2aa..9a208613ad 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -38,7 +38,6 @@ from tensorflow.python.ops import math_ops
 # pylint: disable=wildcard-import
 from tensorflow.python.ops.gen_data_flow_ops import *
 # pylint: enable=wildcard-import
-from tensorflow.python.util.deprecation import deprecated
 
 
 def _as_type_list(dtypes):
@@ -1037,47 +1036,6 @@ class Barrier(object):
         self._barrier_ref, name=name)
 
 
-@deprecated("2017-03-02", "Use `tf.tables_initializer` instead.")
-def initialize_all_tables(name="init_all_tables"):
-  """Returns an Op that initializes all tables of the default graph.
-
-  Args:
-    name: Optional name for the initialization op.
-
-  Returns:
-    An Op that initializes all tables.  Note that if there are
-    not tables the returned Op is a NoOp.
-  """
-  return tables_initializer(name)
-
-
-def tables_initializer(name="init_all_tables"):
-  """Returns an Op that initializes all tables of the default graph.
-
-  Args:
-    name: Optional name for the initialization op.
-
-  Returns:
-    An Op that initializes all tables.  Note that if there are
-    not tables the returned Op is a NoOp.
-  """
-  initializers = ops.get_collection(ops.GraphKeys.TABLE_INITIALIZERS)
-  if initializers:
-    return control_flow_ops.group(*initializers, name=name)
-  return control_flow_ops.no_op(name=name)
-
-
-ops.NotDifferentiable("LookupTableFind")
-ops.NotDifferentiable("LookupTableInsert")
-ops.NotDifferentiable("LookupTableSize")
-ops.NotDifferentiable("HashTable")
-ops.NotDifferentiable("InitializeTable")
-ops.NotDifferentiable("InitializeTableFromTextFile")
-ops.NotDifferentiable("MutableDenseHashTable")
-ops.NotDifferentiable("MutableHashTable")
-ops.NotDifferentiable("MutableHashTableOfTensors")
-
-
 class ConditionalAccumulatorBase(object):
   """A conditional accumulator for aggregating gradients.
 
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
new file mode 100644
index 0000000000..54dba9e38e
--- /dev/null
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -0,0 +1,77 @@
+# 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.
+#==============================================================================
+"""Data Flow Operations."""
+# pylint: disable=g-bad-name
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+# go/tf-wildcard-import
+# pylint: disable=wildcard-import
+from tensorflow.python.ops.gen_lookup_ops import *
+# pylint: enable=wildcard-import
+from tensorflow.python.util.deprecation import deprecated
+
+
+@deprecated("2017-03-02", "Use `tf.tables_initializer` instead.")
+def initialize_all_tables(name="init_all_tables"):
+  """Returns an Op that initializes all tables of the default graph.
+
+  Args:
+    name: Optional name for the initialization op.
+
+  Returns:
+    An Op that initializes all tables.  Note that if there are
+    not tables the returned Op is a NoOp.
+  """
+  return tables_initializer(name)
+
+
+def tables_initializer(name="init_all_tables"):
+  """Returns an Op that initializes all tables of the default graph.
+
+  Args:
+    name: Optional name for the initialization op.
+
+  Returns:
+    An Op that initializes all tables.  Note that if there are
+    not tables the returned Op is a NoOp.
+  """
+  initializers = ops.get_collection(ops.GraphKeys.TABLE_INITIALIZERS)
+  if initializers:
+    return control_flow_ops.group(*initializers, name=name)
+  return control_flow_ops.no_op(name=name)
+
+
+ops.NotDifferentiable("LookupTableFind")
+ops.NotDifferentiable("LookupTableFindV2")
+ops.NotDifferentiable("LookupTableInsert")
+ops.NotDifferentiable("LookupTableInsertV2")
+ops.NotDifferentiable("LookupTableSize")
+ops.NotDifferentiable("LookupTableSizeV2")
+ops.NotDifferentiable("HashTable")
+ops.NotDifferentiable("HashTableV2")
+ops.NotDifferentiable("InitializeTable")
+ops.NotDifferentiable("InitializeTableV2")
+ops.NotDifferentiable("InitializeTableFromTextFile")
+ops.NotDifferentiable("InitializeTableFromTextFileV2")
+ops.NotDifferentiable("MutableDenseHashTable")
+ops.NotDifferentiable("MutableDenseHashTableV2")
+ops.NotDifferentiable("MutableHashTable")
+ops.NotDifferentiable("MutableHashTableV2")
+ops.NotDifferentiable("MutableHashTableOfTensors")
+ops.NotDifferentiable("MutableHashTableOfTensorsV2")
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index 09e04d4247..a39d28490c 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -57,6 +57,7 @@ from tensorflow.python.ops.io_ops import *
 from tensorflow.python.ops.linalg_ops import *
 from tensorflow.python.ops.logging_ops import Print
 from tensorflow.python.ops.logging_ops import get_summary_op
+from tensorflow.python.ops.lookup_ops import *
 from tensorflow.python.ops.math_ops import *
 from tensorflow.python.ops.numerics import *
 from tensorflow.python.ops.parsing_ops import *
diff --git a/tensorflow/python/saved_model/main_op_impl.py b/tensorflow/python/saved_model/main_op_impl.py
index 66cf9d4d8a..355fd57bf1 100644
--- a/tensorflow/python/saved_model/main_op_impl.py
+++ b/tensorflow/python/saved_model/main_op_impl.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops as tf_data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 
 
@@ -35,7 +35,7 @@ def main_op():
   """
   init = variables.global_variables_initializer()
   init_local = variables.local_variables_initializer()
-  init_tables = tf_data_flow_ops.tables_initializer()
+  init_tables = lookup_ops.tables_initializer()
   return control_flow_ops.group(init, init_local, init_tables)
 
 
diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 4c81af56ad..fcec3ed97c 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -26,7 +26,7 @@ from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import resources
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
@@ -238,7 +238,7 @@ class Scaffold(object):
   @staticmethod
   def _default_local_init_op():
     return control_flow_ops.group(variables.local_variables_initializer(),
-                                  data_flow_ops.tables_initializer())
+                                  lookup_ops.tables_initializer())
 
 
 def MonitoredTrainingSession(master='',  # pylint: disable=invalid-name
diff --git a/tensorflow/python/training/saver_test_utils.py b/tensorflow/python/training/saver_test_utils.py
index 5f31e2aa53..6a73565f82 100644
--- a/tensorflow/python/training/saver_test_utils.py
+++ b/tensorflow/python/training/saver_test_utils.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops as ops_lib
-from tensorflow.python.ops import gen_data_flow_ops
+from tensorflow.python.ops import gen_lookup_ops
 from tensorflow.python.training import saver as saver_module
 
 
@@ -34,7 +34,7 @@ class CheckpointedOp(object):
   # pylint: disable=protected-access
   def __init__(self, name, table_ref=None):
     if table_ref is None:
-      self.table_ref = gen_data_flow_ops._mutable_hash_table(
+      self.table_ref = gen_lookup_ops._mutable_hash_table(
           key_dtype=dtypes.string, value_dtype=dtypes.float32, name=name)
     else:
       self.table_ref = table_ref
@@ -52,10 +52,10 @@ class CheckpointedOp(object):
     return self._saveable
 
   def insert(self, keys, values):
-    return gen_data_flow_ops._lookup_table_insert(self.table_ref, keys, values)
+    return gen_lookup_ops._lookup_table_insert(self.table_ref, keys, values)
 
   def lookup(self, keys, default):
-    return gen_data_flow_ops._lookup_table_find(self.table_ref, keys, default)
+    return gen_lookup_ops._lookup_table_find(self.table_ref, keys, default)
 
   def keys(self):
     return self._export()[0]
@@ -64,8 +64,8 @@ class CheckpointedOp(object):
     return self._export()[1]
 
   def _export(self):
-    return gen_data_flow_ops._lookup_table_export(self.table_ref, dtypes.string,
-                                                  dtypes.float32)
+    return gen_lookup_ops._lookup_table_export(self.table_ref, dtypes.string,
+                                               dtypes.float32)
 
   class CustomSaveable(saver_module.BaseSaverBuilder.SaveableObject):
     """A custom saveable for CheckpointedOp."""
@@ -81,6 +81,6 @@ class CheckpointedOp(object):
       super(CheckpointedOp.CustomSaveable, self).__init__(table, specs, name)
 
     def restore(self, restore_tensors, shapes):
-      return gen_data_flow_ops._lookup_table_import(
+      return gen_lookup_ops._lookup_table_import(
           self.op.table_ref, restore_tensors[0], restore_tensors[1])
   # pylint: enable=protected-access
diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py
index 277c11386d..230ed1db68 100644
--- a/tensorflow/python/training/supervisor.py
+++ b/tensorflow/python/training/supervisor.py
@@ -27,7 +27,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import meta_graph
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary as _summary
@@ -426,8 +426,10 @@ class Supervisor(object):
       local_init_op = self._get_first_op_from_collection(
           ops.GraphKeys.LOCAL_INIT_OP)
       if local_init_op is None:
-        op_list = [variables.local_variables_initializer(),
-                   data_flow_ops.tables_initializer()]
+        op_list = [
+            variables.local_variables_initializer(),
+            lookup_ops.tables_initializer()
+        ]
         if op_list:
           local_init_op = control_flow_ops.group(*op_list)
           ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)