1 files changed, 99 insertions, 0 deletions
diff --git a/tensorflow/python/kernel_tests/seq2seq_test.py b/tensorflow/python/kernel_tests/seq2seq_test.py
index 77ff0571b7..a6f017f22f 100644
--- a/tensorflow/python/kernel_tests/seq2seq_test.py
+++ b/tensorflow/python/kernel_tests/seq2seq_test.py
@@ -495,6 +495,105 @@ class Seq2SeqTest(tf.test.TestCase):
         if len(perplexities[bucket]) > 1:  # Assert that perplexity went down.
           self.assertLess(perplexities[bucket][-1], perplexities[bucket][0])
 
+  def testModelWithBooleanFeedPrevious(self):
+    """Test the model behavior when feed_previous is True.
+
+    For example, the following two cases have the same effect:
+      - Train `embedding_rnn_seq2seq` with `feed_previous=True`, which contains
+        a `embedding_rnn_decoder` with `feed_previous=True` and
+        `update_embedding_for_previous=True`. The decoder is fed with "<Go>"
+        and outputs "A, B, C".
+      - Train `embedding_rnn_seq2seq` with `feed_previous=False`. The decoder
+        is fed with "<Go>, A, B".
+    """
+    num_encoder_symbols = 3
+    num_decoder_symbols = 5
+    batch_size = 2
+    num_enc_timesteps = 2
+    num_dec_timesteps = 3
+
+    def TestModel(seq2seq):
+      with self.test_session(graph=tf.Graph()) as sess:
+        tf.set_random_seed(111)
+        random.seed(111)
+        np.random.seed(111)
+
+        enc_inp = [tf.constant(i + 1, tf.int32, shape=[batch_size])
+                     for i in range(num_enc_timesteps)]
+        dec_inp_fp_true = [tf.constant(i, tf.int32, shape=[batch_size])
+                           for i in range(num_dec_timesteps)]
+        dec_inp_holder_fp_false = [tf.placeholder(tf.int32, shape=[batch_size])
+                                   for _ in range(num_dec_timesteps)]
+        targets = [tf.constant(i + 1, tf.int32, shape=[batch_size])
+                   for i in range(num_dec_timesteps)]
+        weights = [tf.constant(1.0, shape=[batch_size])
+                   for i in range(num_dec_timesteps)]
+
+        def ForwardBackward(enc_inp, dec_inp, feed_previous):
+          scope_name = "fp_{}".format(feed_previous)
+          with tf.variable_scope(scope_name):
+            dec_op, _ = seq2seq(enc_inp, dec_inp, feed_previous=feed_previous)
+            net_variables = tf.get_collection(tf.GraphKeys.VARIABLES,
+                                              scope_name)
+          optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5)
+          update_op = optimizer.minimize(
+              tf.nn.seq2seq.sequence_loss(dec_op, targets, weights),
+              var_list=net_variables)
+          return dec_op, update_op, net_variables
+
+        dec_op_fp_true, update_fp_true, variables_fp_true = ForwardBackward(
+            enc_inp, dec_inp_fp_true, feed_previous=True)
+        dec_op_fp_false, update_fp_false, variables_fp_false = ForwardBackward(
+            enc_inp, dec_inp_holder_fp_false, feed_previous=False)
+
+        sess.run(tf.initialize_all_variables())
+
+        # We only check consistencies between the variables existing in both
+        # the models with True and False feed_previous. Variables created by
+        # the loop_function in the model with True feed_previous are ignored.
+        v_false_name_dict = {v.name.split('/', 1)[-1]: v
+                             for v in variables_fp_false}
+        matched_variables = [(v, v_false_name_dict[v.name.split('/', 1)[-1]])
+                             for v in variables_fp_true]
+        for v_true, v_false in matched_variables:
+          sess.run(tf.assign(v_false, v_true))
+
+        # Take the symbols generated by the decoder with feed_previous=True as
+        # the true input symbols for the decoder with feed_previous=False.
+        dec_fp_true = sess.run(dec_op_fp_true)
+        output_symbols_fp_true = np.argmax(dec_fp_true, axis=2)
+        dec_inp_fp_false = np.vstack((dec_inp_fp_true[0].eval(),
+                                      output_symbols_fp_true[:-1]))
+        sess.run(update_fp_true)
+        sess.run(update_fp_false,
+                 {holder: inp for holder, inp in zip(dec_inp_holder_fp_false,
+                                                     dec_inp_fp_false)})
+
+        for v_true, v_false in matched_variables:
+          self.assertAllClose(v_true.eval(), v_false.eval())
+
+    def EmbeddingRNNSeq2SeqF(enc_inp, dec_inp, feed_previous):
+      cell = tf.nn.rnn_cell.BasicLSTMCell(2)
+      return tf.nn.seq2seq.embedding_rnn_seq2seq(
+          enc_inp, dec_inp, cell, num_encoder_symbols,
+          num_decoder_symbols, feed_previous=feed_previous)
+
+    def EmbeddingTiedRNNSeq2Seq(enc_inp, dec_inp, feed_previous):
+      cell = tf.nn.rnn_cell.BasicLSTMCell(2)
+      return tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
+          enc_inp, dec_inp, cell, num_decoder_symbols,
+          feed_previous=feed_previous)
+
+    def EmbeddingAttentionSeq2Seq(enc_inp, dec_inp, feed_previous):
+      cell = tf.nn.rnn_cell.BasicLSTMCell(2)
+      return tf.nn.seq2seq.embedding_attention_seq2seq(
+          enc_inp, dec_inp, cell, num_encoder_symbols,
+          num_decoder_symbols, feed_previous=feed_previous)
+
+    for model in (EmbeddingRNNSeq2SeqF, EmbeddingTiedRNNSeq2Seq,
+                  EmbeddingAttentionSeq2Seq):
+      TestModel(model)
+
 
 if __name__ == "__main__":
   tf.test.main()