1 files changed, 2 insertions, 52 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn.py b/tensorflow/contrib/estimator/python/estimator/rnn.py
index 98660bb731..c595f47395 100644
--- a/tensorflow/contrib/estimator/python/estimator/rnn.py
+++ b/tensorflow/contrib/estimator/python/estimator/rnn.py
@@ -30,7 +30,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.layers import core as core_layers
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import partitioned_variables
@@ -92,55 +91,6 @@ def _make_rnn_cell_fn(num_units, cell_type='basic_rnn'):
   return rnn_cell_fn
 
 
-def _concatenate_context_input(sequence_input, context_input):
-  """Replicates `context_input` across all timesteps of `sequence_input`.
-
-  Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
-  This value is appended to `sequence_input` on dimension 2 and the result is
-  returned.
-
-  Args:
-    sequence_input: A `Tensor` of dtype `float32` and shape `[batch_size,
-      padded_length, d0]`.
-    context_input: A `Tensor` of dtype `float32` and shape `[batch_size, d1]`.
-
-  Returns:
-    A `Tensor` of dtype `float32` and shape `[batch_size, padded_length,
-    d0 + d1]`.
-
-  Raises:
-    ValueError: If `sequence_input` does not have rank 3 or `context_input` does
-      not have rank 2.
-  """
-  seq_rank_check = check_ops.assert_rank(
-      sequence_input,
-      3,
-      message='sequence_input must have rank 3',
-      data=[array_ops.shape(sequence_input)])
-  seq_type_check = check_ops.assert_type(
-      sequence_input,
-      dtypes.float32,
-      message='sequence_input must have dtype float32; got {}.'.format(
-          sequence_input.dtype))
-  ctx_rank_check = check_ops.assert_rank(
-      context_input,
-      2,
-      message='context_input must have rank 2',
-      data=[array_ops.shape(context_input)])
-  ctx_type_check = check_ops.assert_type(
-      context_input,
-      dtypes.float32,
-      message='context_input must have dtype float32; got {}.'.format(
-          context_input.dtype))
-  with ops.control_dependencies(
-      [seq_rank_check, seq_type_check, ctx_rank_check, ctx_type_check]):
-    padded_length = array_ops.shape(sequence_input)[1]
-    tiled_context_input = array_ops.tile(
-        array_ops.expand_dims(context_input, 1),
-        array_ops.concat([[1], [padded_length], [1]], 0))
-  return array_ops.concat([sequence_input, tiled_context_input], 2)
-
-
 def _select_last_activations(activations, sequence_lengths):
   """Selects the nth set of activations for each n in `sequence_length`.
 
@@ -222,8 +172,8 @@ def _rnn_logit_fn_builder(output_units, rnn_cell_fn, sequence_feature_columns,
         context_input = feature_column_lib.input_layer(
             features=features,
             feature_columns=context_feature_columns)
-        sequence_input = _concatenate_context_input(sequence_input,
-                                                    context_input)
+        sequence_input = seq_fc.concatenate_context_input(
+            context_input, sequence_input)
 
     cell = rnn_cell_fn(mode)
     # Ignore output state.