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diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/seq2seq_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/seq2seq_test.py
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+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/seq2seq_test.py
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+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for contrib.seq2seq.python.ops.seq2seq."""
+# pylint: disable=unused-import,g-bad-import-order
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+# pylint: enable=unused-import
+
+import tensorflow as tf
+from tensorflow.contrib import layers
+
+class Seq2SeqTest(tf.test.TestCase):
+
+  # test a default call of rnn_decoder
+  def test_rnn_decoder(self):
+    pass
+
+  # test default call with time_major=True
+  def test_dynamic_rnn_decoder_time_major(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=
+                             tf.constant_initializer(0.5)) as varscope:
+        # Define inputs/outputs to model
+        batch_size = 2
+        encoder_embedding_size = 3
+        decoder_embedding_size = 4
+        encoder_hidden_size = 5
+        decoder_hidden_size = encoder_hidden_size
+        input_sequence_length = 6
+        decoder_sequence_length = 7
+        num_decoder_symbols = 20
+        start_of_sequence_id = end_of_sequence_id = 1
+        decoder_embeddings = tf.get_variable('decoder_embeddings',
+            [num_decoder_symbols, decoder_embedding_size],
+            initializer=tf.random_normal_initializer(stddev=0.1))
+        inputs = tf.constant(0.5, shape=[input_sequence_length, batch_size,
+                                         encoder_embedding_size])
+        decoder_inputs = tf.constant(0.4, shape=[decoder_sequence_length,
+                                                 batch_size,
+                                                 decoder_embedding_size])
+        decoder_length = tf.constant(decoder_sequence_length, dtype=tf.int32,
+                                     shape=[batch_size,])
+        with tf.variable_scope("rnn") as scope:
+          # setting up weights for computing the final output
+          output_fn = lambda x: layers.linear(x, num_decoder_symbols,
+                                              scope=scope)
+
+          # Define model
+          encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
+              cell=tf.nn.rnn_cell.GRUCell(encoder_hidden_size), inputs=inputs,
+              dtype=tf.float32, time_major=True, scope=scope)
+
+
+        with tf.variable_scope("decoder") as scope:
+          # Train decoder
+          decoder_cell = tf.nn.rnn_cell.GRUCell(decoder_hidden_size)
+          decoder_fn_train = tf.contrib.seq2seq.simple_decoder_fn_train(
+              encoder_state=encoder_state)
+          decoder_outputs_train, decoder_state_train = (
+            tf.contrib.seq2seq.dynamic_rnn_decoder(
+                cell=decoder_cell,
+                decoder_fn=decoder_fn_train,
+                inputs=decoder_inputs,
+                sequence_length=decoder_length,
+                time_major=True,
+                scope=scope))
+          decoder_outputs_train = output_fn(decoder_outputs_train)
+
+          # Setup variable reuse
+          scope.reuse_variables()
+
+          # Inference decoder
+          decoder_fn_inference = (
+              tf.contrib.seq2seq.simple_decoder_fn_inference(
+                output_fn=output_fn,
+                encoder_state=encoder_state,
+                embeddings=decoder_embeddings,
+                start_of_sequence_id=start_of_sequence_id,
+                end_of_sequence_id=end_of_sequence_id,
+                #TODO: find out why it goes to +1
+                maximum_length=decoder_sequence_length-1,
+                num_decoder_symbols=num_decoder_symbols,
+                dtype=tf.int32))
+          decoder_outputs_inference, decoder_state_inference = (
+              tf.contrib.seq2seq.dynamic_rnn_decoder(
+                cell=decoder_cell,
+                decoder_fn=decoder_fn_inference,
+                time_major=True,
+                scope=scope))
+
+        # Run model
+        tf.global_variables_initializer().run()
+        decoder_outputs_train_res, decoder_state_train_res = sess.run(
+            [decoder_outputs_train, decoder_state_train])
+        decoder_outputs_inference_res, decoder_state_inference_res = sess.run(
+            [decoder_outputs_inference, decoder_state_inference])
+
+        # Assert outputs
+        self.assertEqual((decoder_sequence_length, batch_size,
+                          num_decoder_symbols),
+                         decoder_outputs_train_res.shape)
+        self.assertEqual((batch_size, num_decoder_symbols),
+                         decoder_outputs_inference_res.shape[1:3])
+        self.assertEqual((batch_size, decoder_hidden_size),
+                         decoder_state_train_res.shape)
+        self.assertEqual((batch_size, decoder_hidden_size),
+                         decoder_state_inference_res.shape)
+        # The dynamic decoder might end earlier than `maximal_length`
+        # under inference
+        true_value = (decoder_sequence_length>=
+                      decoder_state_inference_res.shape[0])
+        self.assertEqual((true_value), True)
+
+if __name__ == '__main__':
+  tf.test.main()