[contrib seq2seq] Rename DynamicAttentionWrapper to AttentionWrapper

Change: 151374177

4 files changed, 27 insertions, 27 deletions

diff --git a/tensorflow/contrib/seq2seq/BUILD b/tensorflow/contrib/seq2seq/BUILD
index 0621905705..652bbba85e 100644
--- a/tensorflow/contrib/seq2seq/BUILD
+++ b/tensorflow/contrib/seq2seq/BUILD
@@ -88,9 +88,9 @@ cuda_py_test(
 )
 
 cuda_py_test(
-    name = "dynamic_attention_wrapper_test",
+    name = "attention_wrapper_test",
     size = "medium",
-    srcs = ["python/kernel_tests/dynamic_attention_wrapper_test.py"],
+    srcs = ["python/kernel_tests/attention_wrapper_test.py"],
     additional_deps = [
         ":seq2seq_py",
         "//tensorflow/contrib/layers:layers_py",
diff --git a/tensorflow/contrib/seq2seq/__init__.py b/tensorflow/contrib/seq2seq/__init__.py
index 29bce7bbae..277434c160 100644
--- a/tensorflow/contrib/seq2seq/__init__.py
+++ b/tensorflow/contrib/seq2seq/__init__.py
@@ -35,8 +35,8 @@ See the @{$python/contrib.seq2seq} guide.
 
 @@hardmax
 
-@@DynamicAttentionWrapperState
-@@DynamicAttentionWrapper
+@@AttentionWrapperState
+@@AttentionWrapper
 """
 
 from __future__ import absolute_import
@@ -44,9 +44,9 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import,line-too-long
+from tensorflow.contrib.seq2seq.python.ops.attention_wrapper import *
 from tensorflow.contrib.seq2seq.python.ops.basic_decoder import *
 from tensorflow.contrib.seq2seq.python.ops.decoder import *
-from tensorflow.contrib.seq2seq.python.ops.dynamic_attention_wrapper import *
 from tensorflow.contrib.seq2seq.python.ops.helper import *
 from tensorflow.contrib.seq2seq.python.ops.loss import *
 # pylint: enable=unused-import,widcard-import,line-too-long
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/dynamic_attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
index 47cf102f39..5d952e97b7 100644
--- a/tensorflow/contrib/seq2seq/python/kernel_tests/dynamic_attention_wrapper_test.py
+++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for contrib.seq2seq.python.ops.dynamic_attention_wrapper."""
+"""Tests for contrib.seq2seq.python.ops.attention_wrapper."""
 # pylint: disable=unused-import,g-bad-import-order
 from __future__ import absolute_import
 from __future__ import division
@@ -25,7 +25,7 @@ import numpy as np
 
 from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import decoder
-from tensorflow.contrib.seq2seq.python.ops import dynamic_attention_wrapper as wrapper
+from tensorflow.contrib.seq2seq.python.ops import attention_wrapper as wrapper
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.contrib.seq2seq.python.ops import basic_decoder
 from tensorflow.python.framework import dtypes
@@ -38,12 +38,12 @@ from tensorflow.python.util import nest
 # pylint: enable=g-import-not-at-top
 
 
-class DynamicAttentionWrapperTest(test.TestCase):
+class AttentionWrapperTest(test.TestCase):
 
   def assertAllClose(self, *args, **kwargs):
     kwargs["atol"] = 1e-4  # For GPU tests
     kwargs["rtol"] = 1e-4  # For GPU tests
-    return super(DynamicAttentionWrapperTest, self).assertAllClose(
+    return super(AttentionWrapperTest, self).assertAllClose(
         *args, **kwargs)
 
   def _testWithAttention(self,
@@ -76,7 +76,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
           "root",
           initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
         cell = core_rnn_cell.LSTMCell(cell_depth)
-        cell = wrapper.DynamicAttentionWrapper(
+        cell = wrapper.AttentionWrapper(
             cell, attention_mechanism, attention_size=attention_depth)
         helper = helper_py.TrainingHelper(decoder_inputs,
                                           decoder_sequence_length)
@@ -91,7 +91,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
       self.assertTrue(
           isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
       self.assertTrue(
-          isinstance(final_state, wrapper.DynamicAttentionWrapperState))
+          isinstance(final_state, wrapper.AttentionWrapperState))
       self.assertTrue(
           isinstance(final_state.cell_state, core_rnn_cell.LSTMStateTuple))
 
@@ -178,7 +178,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
             [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
             dtype=int32))
 
-    expected_final_state = wrapper.DynamicAttentionWrapperState(
+    expected_final_state = wrapper.AttentionWrapperState(
         cell_state=core_rnn_cell.LSTMStateTuple(
             c=array(
                 [[
@@ -306,7 +306,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
             [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
             dtype=int32))
 
-    expected_final_state = wrapper.DynamicAttentionWrapperState(
+    expected_final_state = wrapper.AttentionWrapperState(
         cell_state=core_rnn_cell.LSTMStateTuple(
             c=array(
                 [[
@@ -434,7 +434,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
         sample_id=array(
             [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
             dtype=int32))
-    expected_final_state = wrapper.DynamicAttentionWrapperState(
+    expected_final_state = wrapper.AttentionWrapperState(
         cell_state=core_rnn_cell.LSTMStateTuple(
             c=array(
                 [[
@@ -566,7 +566,7 @@ class DynamicAttentionWrapperTest(test.TestCase):
         sample_id=array(
             [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
             dtype=int32))
-    expected_final_state = wrapper.DynamicAttentionWrapperState(
+    expected_final_state = wrapper.AttentionWrapperState(
         cell_state=core_rnn_cell.LSTMStateTuple(
             c=array(
                 [[
diff --git a/tensorflow/contrib/seq2seq/python/ops/dynamic_attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index 9fb67ed175..0af001d274 100644
--- a/tensorflow/contrib/seq2seq/python/ops/dynamic_attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -37,8 +37,8 @@ from tensorflow.python.util import nest
 
 
 __all__ = [
-    "DynamicAttentionWrapper",
-    "DynamicAttentionWrapperState",
+    "AttentionWrapper",
+    "AttentionWrapperState",
     "LuongAttention",
     "BahdanauAttention",
     "hardmax",
@@ -376,10 +376,10 @@ class BahdanauAttention(_BaseAttentionMechanism):
     return score
 
 
-class DynamicAttentionWrapperState(
+class AttentionWrapperState(
     collections.namedtuple(
-        "DynamicAttentionWrapperState", ("cell_state", "attention"))):
-  """`namedtuple` storing the state of a `DynamicAttentionWrapper`.
+        "AttentionWrapperState", ("cell_state", "attention"))):
+  """`namedtuple` storing the state of a `AttentionWrapper`.
 
   Contains:
 
@@ -410,7 +410,7 @@ def hardmax(logits, name=None):
         math_ops.argmax(logits, -1), depth, dtype=logits.dtype)
 
 
-class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
+class AttentionWrapper(core_rnn_cell.RNNCell):
   """Wraps another `RNNCell` with attention.
   """
 
@@ -422,7 +422,7 @@ class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
                probability_fn=None,
                output_attention=True,
                name=None):
-    """Construct the `DynamicAttentionWrapper`.
+    """Construct the `AttentionWrapper`.
 
     Args:
       cell: An instance of `RNNCell`.
@@ -484,13 +484,13 @@ class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
 
   @property
   def state_size(self):
-    return DynamicAttentionWrapperState(
+    return AttentionWrapperState(
         cell_state=self._cell.state_size,
         attention=self._attention_size)
 
   def zero_state(self, batch_size, dtype):
     with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
-      return DynamicAttentionWrapperState(
+      return AttentionWrapperState(
           cell_state=self._cell.zero_state(batch_size, dtype),
           attention=_zero_state_tensors(
               self._attention_size, batch_size, dtype))
@@ -511,7 +511,7 @@ class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
 
     Args:
       inputs: (Possibly nested tuple of) Tensor, the input at this time step.
-      state: An instance of `DynamicAttentionWrapperState` containing
+      state: An instance of `AttentionWrapperState` containing
         tensors from the previous time step.
       scope: Must be `None`.
 
@@ -519,7 +519,7 @@ class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
       A tuple `(attention, next_state)`, where:
 
       - `attention` is the attention passed to the layer above.
-      - `next_state` is an instance of `DynamicAttentionWrapperState`
+      - `next_state` is an instance of `AttentionWrapperState`
          containing the state calculated at this time step.
 
     Raises:
@@ -555,7 +555,7 @@ class DynamicAttentionWrapper(core_rnn_cell.RNNCell):
     attention = self._attention_layer(
         array_ops.concat([cell_output, context], 1))
 
-    next_state = DynamicAttentionWrapperState(
+    next_state = AttentionWrapperState(
         cell_state=next_cell_state,
         attention=attention)