Change rnn ops to check types via duck-typing, instead of a private attribute.

PiperOrigin-RevId: 156190878

tensorflow/contrib/rnn/python/ops/core_rnn.py

@@ -19,7 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.rnn.python.ops import core_rnn_cell
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
@@ -32,6 +31,7 @@ from tensorflow.python.util import nest
 
 # pylint: disable=protected-access
 _concat = rnn_cell_impl._concat
+_like_rnncell = rnn_cell_impl._like_rnncell
 _infer_state_dtype = rnn._infer_state_dtype
 _reverse_seq = rnn._reverse_seq
 _rnn_step = rnn._rnn_step
@@ -99,7 +99,7 @@ def static_rnn(cell, inputs, initial_state=None, dtype=None,
       (column size) cannot be inferred from inputs via shape inference.
   """
 
-  if not isinstance(cell, core_rnn_cell.RNNCell):
+  if not _like_rnncell(cell):
     raise TypeError("cell must be an instance of RNNCell")
   if not nest.is_sequence(inputs):
     raise TypeError("inputs must be a sequence")
@@ -319,9 +319,9 @@ def static_bidirectional_rnn(cell_fw, cell_bw, inputs,
     ValueError: If inputs is None or an empty list.
   """
 
-  if not isinstance(cell_fw, core_rnn_cell.RNNCell):
+  if not _like_rnncell(cell_fw):
     raise TypeError("cell_fw must be an instance of RNNCell")
-  if not isinstance(cell_bw, core_rnn_cell.RNNCell):
+  if not _like_rnncell(cell_bw):
     raise TypeError("cell_bw must be an instance of RNNCell")
   if not nest.is_sequence(inputs):
     raise TypeError("inputs must be a sequence")

tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py

@@ -42,16 +42,21 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope as vs
 
 from tensorflow.python.ops.math_ops import sigmoid
 from tensorflow.python.ops.math_ops import tanh
-from tensorflow.python.ops.rnn_cell_impl import _RNNCell as RNNCell
 
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
 
 
+# pylint: disable=protected-access
+RNNCell = rnn_cell_impl._RNNCell  # pylint: disable=invalid-name
+_like_rnncell = rnn_cell_impl._like_rnncell
+# pylint: enable=protected-access
+
 _BIAS_VARIABLE_NAME = "biases"
 _WEIGHTS_VARIABLE_NAME = "weights"
 
@@ -424,7 +429,7 @@ class OutputProjectionWrapper(RNNCell):
       ValueError: if output_size is not positive.
     """
     super(OutputProjectionWrapper, self).__init__(_reuse=reuse)
-    if not isinstance(cell, RNNCell):
+    if not _like_rnncell(cell):
       raise TypeError("The parameter cell is not RNNCell.")
     if output_size < 1:
       raise ValueError("Parameter output_size must be > 0: %d." % output_size)
@@ -480,7 +485,7 @@ class InputProjectionWrapper(RNNCell):
     super(InputProjectionWrapper, self).__init__(_reuse=reuse)
     if input_size is not None:
       logging.warn("%s: The input_size parameter is deprecated.", self)
-    if not isinstance(cell, RNNCell):
+    if not _like_rnncell(cell):
       raise TypeError("The parameter cell is not RNNCell.")
     self._cell = cell
     self._num_proj = num_proj
@@ -556,7 +561,7 @@ class DropoutWrapper(RNNCell):
       TypeError: if cell is not an RNNCell.
       ValueError: if any of the keep_probs are not between 0 and 1.
     """
-    if not isinstance(cell, RNNCell):
+    if not _like_rnncell(cell):
       raise TypeError("The parameter cell is not a RNNCell.")
     with ops.name_scope("DropoutWrapperInit"):
       def tensor_and_const_value(v):
@@ -791,7 +796,7 @@ class EmbeddingWrapper(RNNCell):
       ValueError: if embedding_classes is not positive.
     """
     super(EmbeddingWrapper, self).__init__(_reuse=reuse)
-    if not isinstance(cell, RNNCell):
+    if not _like_rnncell(cell):
       raise TypeError("The parameter cell is not RNNCell.")
     if embedding_classes <= 0 or embedding_size <= 0:
       raise ValueError("Both embedding_classes and embedding_size must be > 0: "

tensorflow/contrib/rnn/python/ops/rnn_cell.py

@@ -34,6 +34,7 @@ from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
@@ -1057,7 +1058,7 @@ class AttentionCellWrapper(core_rnn_cell.RNNCell):
           `state_is_tuple` is `False` or if attn_length is zero or less.
     """
     super(AttentionCellWrapper, self).__init__(_reuse=reuse)
-    if not isinstance(cell, core_rnn_cell.RNNCell):
+    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
       raise TypeError("The parameter cell is not RNNCell.")
     if nest.is_sequence(cell.state_size) and not state_is_tuple:
       raise ValueError("Cell returns tuple of states, but the flag "

tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py

@@ -543,7 +543,7 @@ class AttentionWrapper(core_rnn_cell.RNNCell):
       name: Name to use when creating ops.
     """
     super(AttentionWrapper, self).__init__(name=name)
-    if not isinstance(cell, core_rnn_cell.RNNCell):
+    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
       raise TypeError(
           "cell must be an RNNCell, saw type: %s" % type(cell).__name__)
     if not isinstance(attention_mechanism, AttentionMechanism):

tensorflow/contrib/seq2seq/python/ops/basic_decoder.py

@@ -21,13 +21,13 @@ from __future__ import print_function
 
 import collections
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.layers import base as layers_base
+from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.util import nest
 
 
@@ -60,7 +60,7 @@ class BasicDecoder(decoder.Decoder):
     Raises:
       TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
     """
-    if not isinstance(cell, core_rnn_cell.RNNCell):
+    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
       raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
     if not isinstance(helper, helper_py.Helper):
       raise TypeError("helper must be a Helper, received: %s" % type(helper))

tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py

@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import collections
 
-from tensorflow.contrib.rnn import core_rnn_cell
 from tensorflow.contrib.seq2seq.python.ops import beam_search_ops
 from tensorflow.contrib.seq2seq.python.ops import decoder
 from tensorflow.python.framework import dtypes
@@ -33,6 +32,7 @@ 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_cell_impl
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.util import nest
 
@@ -143,7 +143,7 @@ class BeamSearchDecoder(decoder.Decoder):
       ValueError: If `start_tokens` is not a vector or
         `end_token` is not a scalar.
     """
-    if not isinstance(cell, core_rnn_cell.RNNCell):
+    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
       raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
     if (output_layer is not None
         and not isinstance(output_layer, layers_base.Layer)):

tensorflow/python/ops/rnn.py

@@ -34,6 +34,7 @@ from tensorflow.python.util import nest
 
 # pylint: disable=protected-access
 _concat = rnn_cell_impl._concat
+_like_rnncell = rnn_cell_impl._like_rnncell
 # pylint: enable=protected-access
 
 
@@ -361,12 +362,10 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
     TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
   """
 
-  # pylint: disable=protected-access
-  if not isinstance(cell_fw, rnn_cell_impl._RNNCell):
+  if not _like_rnncell(cell_fw):
     raise TypeError("cell_fw must be an instance of RNNCell")
-  if not isinstance(cell_bw, rnn_cell_impl._RNNCell):
+  if not _like_rnncell(cell_bw):
     raise TypeError("cell_bw must be an instance of RNNCell")
-  # pylint: enable=protected-access
 
   with vs.variable_scope(scope or "bidirectional_rnn"):
     # Forward direction
@@ -507,10 +506,8 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
     ValueError: If inputs is None or an empty list.
   """
 
-  # pylint: disable=protected-access
-  if not isinstance(cell, rnn_cell_impl._RNNCell):
+  if not _like_rnncell(cell):
     raise TypeError("cell must be an instance of RNNCell")
-  # pylint: enable=protected-access
 
   # By default, time_major==False and inputs are batch-major: shaped
   #   [batch, time, depth]
@@ -921,10 +918,8 @@ def raw_rnn(cell, loop_fn,
       a `callable`.
   """
 
-  # pylint: disable=protected-access
-  if not isinstance(cell, rnn_cell_impl._RNNCell):
+  if not _like_rnncell(cell):
     raise TypeError("cell must be an instance of RNNCell")
-  # pylint: enable=protected-access
   if not callable(loop_fn):
     raise TypeError("loop_fn must be a callable")
 

tensorflow/python/ops/rnn_cell_impl.py

@@ -36,6 +36,13 @@ from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.util import nest
 
 
+def _like_rnncell(cell):
+  """Checks that a given object is an RNNCell by using duck typing."""
+  conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
+                hasattr(cell, "zero_state"), callable(cell)]
+  return all(conditions)
+
+
 def _concat(prefix, suffix, static=False):
   """Concat that enables int, Tensor, or TensorShape values.