Move the implementation code of static_rnn, static_bidirectional_rnn and static_state_saving_rnn from core to contrib.

Change: 142148394

1 files changed, 10 insertions, 8 deletions

diff --git a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
index d2018bb219..b04b6ce16a 100644
--- a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
+++ b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
@@ -60,6 +60,7 @@ from __future__ import print_function
 from six.moves import xrange  # pylint: disable=redefined-builtin
 from six.moves import zip  # pylint: disable=redefined-builtin
 
+from tensorflow.contrib.rnn.python.ops import core_rnn as contrib_rnn
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
@@ -67,7 +68,6 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import rnn
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope
@@ -180,7 +180,7 @@ def basic_rnn_seq2seq(encoder_inputs,
         It is a 2D Tensor of shape [batch_size x cell.state_size].
   """
   with variable_scope.variable_scope(scope or "basic_rnn_seq2seq"):
-    _, enc_state = rnn.rnn(cell, encoder_inputs, dtype=dtype)
+    _, enc_state = contrib_rnn.static_rnn(cell, encoder_inputs, dtype=dtype)
     return rnn_decoder(decoder_inputs, enc_state, cell)
 
 
@@ -216,7 +216,8 @@ def tied_rnn_seq2seq(encoder_inputs,
   """
   with variable_scope.variable_scope("combined_tied_rnn_seq2seq"):
     scope = scope or "tied_rnn_seq2seq"
-    _, enc_state = rnn.rnn(cell, encoder_inputs, dtype=dtype, scope=scope)
+    _, enc_state = contrib_rnn.static_rnn(
+        cell, encoder_inputs, dtype=dtype, scope=scope)
     variable_scope.get_variable_scope().reuse_variables()
     return rnn_decoder(
         decoder_inputs,
@@ -355,7 +356,8 @@ def embedding_rnn_seq2seq(encoder_inputs,
         cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype)
+    _, encoder_state = contrib_rnn.static_rnn(
+        encoder_cell, encoder_inputs, dtype=dtype)
 
     # Decoder.
     if output_projection is None:
@@ -844,9 +846,8 @@ def embedding_attention_seq2seq(encoder_inputs,
         cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    encoder_outputs, encoder_state = rnn.rnn(encoder_cell,
-                                             encoder_inputs,
-                                             dtype=dtype)
+    encoder_outputs, encoder_state = contrib_rnn.static_rnn(
+        encoder_cell, encoder_inputs, dtype=dtype)
 
     # First calculate a concatenation of encoder outputs to put attention on.
     top_states = [
@@ -967,7 +968,8 @@ def one2many_rnn_seq2seq(encoder_inputs,
         cell,
         embedding_classes=num_encoder_symbols,
         embedding_size=embedding_size)
-    _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype)
+    _, encoder_state = contrib_rnn.static_rnn(
+        encoder_cell, encoder_inputs, dtype=dtype)
 
     # Decoder.
     for name, decoder_inputs in decoder_inputs_dict.items():