attention_wrapper.py

# -*- coding: utf-8 -*-
#/usr/bin/python2

"""Attention wrapper object for location-based attention."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import functools
import math

import numpy as np
import tensorflow as tf 

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.layers import core as layers_core
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import functional_ops
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 tensor_array_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.util import nest
from hyperparams_tacotron2 import Hyperparams as hp

#from hyperparams import Hyperparams as hp

_zero_state_tensors = rnn_cell_impl._zero_state_tensors  # pylint: disable=protected-access


class AttentionMechanism(object):
	pass


def _prepare_memory(memory, memory_sequence_length, check_inner_dims_defined):
	"""Convert to tensor and possibly mask `memory`.
	Args:
		memory: `Tensor`, shaped `[batch_size, max_time, ...]`.
		memory_sequence_length: `int32` `Tensor`, shaped `[batch_size]`.
		check_inner_dims_defined: Python boolean.  If `True`, the `memory`
			argument's shape is checked to ensure all but the two outermost
			dimensions are fully defined.
	Returns:
		A (possibly masked), checked, new `memory`.
	Raises:
		ValueError: If `check_inner_dims_defined` is `True` and not
			`memory.shape[2:].is_fully_defined()`.
	"""
	memory = nest.map_structure(
			lambda m: ops.convert_to_tensor(m, name="memory"), memory)
	if memory_sequence_length is not None:
		memory_sequence_length = ops.convert_to_tensor(
				memory_sequence_length, name="memory_sequence_length")
	if check_inner_dims_defined:
		def _check_dims(m):
			if not m.get_shape()[2:].is_fully_defined():
				raise ValueError("Expected memory %s to have fully defined inner dims, "
												 "but saw shape: %s" % (m.name, m.get_shape()))
		nest.map_structure(_check_dims, memory)
	if memory_sequence_length is None:
		seq_len_mask = None
	else:
		seq_len_mask = array_ops.sequence_mask(
				memory_sequence_length,
				maxlen=array_ops.shape(nest.flatten(memory)[0])[1],
				dtype=nest.flatten(memory)[0].dtype)
		seq_len_batch_size = (
				memory_sequence_length.shape[0].value
				or array_ops.shape(memory_sequence_length)[0])
	def _maybe_mask(m, seq_len_mask):
		rank = m.get_shape().ndims
		rank = rank if rank is not None else array_ops.rank(m)
		extra_ones = array_ops.ones(rank - 2, dtype=dtypes.int32)
		m_batch_size = m.shape[0].value or array_ops.shape(m)[0]
		if memory_sequence_length is not None:
			message = ("memory_sequence_length and memory tensor batch sizes do not "
								 "match.")
			with ops.control_dependencies([
					check_ops.assert_equal(
							seq_len_batch_size, m_batch_size, message=message)]):
				seq_len_mask = array_ops.reshape(
						seq_len_mask,
						array_ops.concat((array_ops.shape(seq_len_mask), extra_ones), 0))
				return m * seq_len_mask
		else:
			return m
	return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask), memory)


def _maybe_mask_score(score, memory_sequence_length, score_mask_value):
	if memory_sequence_length is None:
		return score
	message = ("All values in memory_sequence_length must greater than zero.")
	with ops.control_dependencies(
			[check_ops.assert_positive(memory_sequence_length, message=message)]):
		score_mask = array_ops.sequence_mask(
				memory_sequence_length, maxlen=array_ops.shape(score)[1])
		score_mask_values = score_mask_value * array_ops.ones_like(score)
		return array_ops.where(score_mask, score, score_mask_values)

class _BaseAttentionMechanism(AttentionMechanism):
  """A base AttentionMechanism class providing common functionality.
  Common functionality includes:
    1. Storing the query and memory layers.
    2. Preprocessing and storing the memory.
  """

  def __init__(self,
               query_layer,
               memory,
               probability_fn,
               memory_sequence_length=None,
               memory_layer=None,
               check_inner_dims_defined=True,
               score_mask_value=float("-inf"),
               name=None):
    """Construct base AttentionMechanism class.
    Args:
      query_layer: Callable.  Instance of `tf.layers.Layer`.  The layer's depth
        must match the depth of `memory_layer`.  If `query_layer` is not
        provided, the shape of `query` must match that of `memory_layer`.
      memory: The memory to query; usually the output of an RNN encoder.  This
        tensor should be shaped `[batch_size, max_time, ...]`.
      probability_fn: A `callable`.  Converts the score and previous alignments
        to probabilities. Its signature should be:
        `probabilities = probability_fn(score, previous_alignments)`.
      memory_sequence_length (optional): Sequence lengths for the batch entries
        in memory.  If provided, the memory tensor rows are masked with zeros
        for values past the respective sequence lengths.
      memory_layer: Instance of `tf.layers.Layer` (may be None).  The layer's
        depth must match the depth of `query_layer`.
        If `memory_layer` is not provided, the shape of `memory` must match
        that of `query_layer`.
      check_inner_dims_defined: Python boolean.  If `True`, the `memory`
        argument's shape is checked to ensure all but the two outermost
        dimensions are fully defined.
      score_mask_value: (optional): The mask value for score before passing into
        `probability_fn`. The default is -inf. Only used if
        `memory_sequence_length` is not None.
      name: Name to use when creating ops.
    """
    if (query_layer is not None
        and not isinstance(query_layer, layers_base.Layer)):
      raise TypeError(
          "query_layer is not a Layer: %s" % type(query_layer).__name__)
    if (memory_layer is not None
        and not isinstance(memory_layer, layers_base.Layer)):
      raise TypeError(
          "memory_layer is not a Layer: %s" % type(memory_layer).__name__)
    self._query_layer = query_layer
    self._memory_layer = memory_layer
    if not callable(probability_fn):
      raise TypeError("probability_fn must be callable, saw type: %s" %
                      type(probability_fn).__name__)
    self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
        probability_fn(
            _maybe_mask_score(score, memory_sequence_length, score_mask_value),
            prev))
    with ops.name_scope(
        name, "BaseAttentionMechanismInit", nest.flatten(memory)):
      self._values = _prepare_memory(
          memory, memory_sequence_length,
          check_inner_dims_defined=check_inner_dims_defined)
      self._keys = (
          self.memory_layer(self._values) if self.memory_layer  # pylint: disable=not-callable
          else self._values)
      self._batch_size = (
          self._keys.shape[0].value or array_ops.shape(self._keys)[0])
      self._alignments_size = (self._keys.shape[1].value or
                               array_ops.shape(self._keys)[1])

  @property
  def memory_layer(self):
    return self._memory_layer

  @property
  def query_layer(self):
    return self._query_layer

  @property
  def values(self):
    return self._values

  @property
  def keys(self):
    return self._keys

  @property
  def batch_size(self):
    return self._batch_size

  @property
  def alignments_size(self):
    return self._alignments_size

  def initial_alignments(self, batch_size, dtype):
    """Creates the initial alignment values for the `AttentionWrapper` class.
    This is important for AttentionMechanisms that use the previous alignment
    to calculate the alignment at the next time step (e.g. monotonic attention).
    The default behavior is to return a tensor of all zeros.
    Args:
      batch_size: `int32` scalar, the batch_size.
      dtype: The `dtype`.
    Returns:
      A `dtype` tensor shaped `[batch_size, alignments_size]`
      (`alignments_size` is the values' `max_time`).
    """
    max_time = self._alignments_size
    return _zero_state_tensors(max_time, batch_size, dtype)

def _bahdanau_score(processed_query, keys, normalize):
  """Implements Bahdanau-style (additive) scoring function.
  This attention has two forms.  The first is Bhandanau attention,
  as described in:
  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
  "Neural Machine Translation by Jointly Learning to Align and Translate."
  ICLR 2015. https://arxiv.org/abs/1409.0473
  The second is the normalized form.  This form is inspired by the
  weight normalization article:
  Tim Salimans, Diederik P. Kingma.
  "Weight Normalization: A Simple Reparameterization to Accelerate
   Training of Deep Neural Networks."
  https://arxiv.org/abs/1602.07868
  To enable the second form, set `normalize=True`.
  Args:
    processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
    normalize: Whether to normalize the score function.
  Returns:
    A `[batch_size, max_time]` tensor of unnormalized score values.
  """
  dtype = processed_query.dtype
  # Get the number of hidden units from the trailing dimension of keys
  num_units = keys.shape[2].value or array_ops.shape(keys)[2]
  # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
  processed_query = array_ops.expand_dims(processed_query, 1)
  v = variable_scope.get_variable(
      "attention_v", [num_units], dtype=dtype)
  if normalize:
    # Scalar used in weight normalization
    g = variable_scope.get_variable(
        "attention_g", dtype=dtype,
        initializer=math.sqrt((1. / num_units)))
    # Bias added prior to the nonlinearity
    b = variable_scope.get_variable(
        "attention_b", [num_units], dtype=dtype,
        initializer=init_ops.zeros_initializer())
    # normed_v = g * v / ||v||
    normed_v = g * v * math_ops.rsqrt(
        math_ops.reduce_sum(math_ops.square(v)))
    return math_ops.reduce_sum(
        normed_v * math_ops.tanh(keys + processed_query + b), [2])
  else:
    return math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query), [2])



class BahdanauAttention(_BaseAttentionMechanism):
  """Implements Bahdanau-style (additive) attention.
  This attention has two forms.  The first is Bahdanau attention,
  as described in:
  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
  "Neural Machine Translation by Jointly Learning to Align and Translate."
  ICLR 2015. https://arxiv.org/abs/1409.0473
  The second is the normalized form.  This form is inspired by the
  weight normalization article:
  Tim Salimans, Diederik P. Kingma.
  "Weight Normalization: A Simple Reparameterization to Accelerate
   Training of Deep Neural Networks."
  https://arxiv.org/abs/1602.07868
  To enable the second form, construct the object with parameter
  `normalize=True`.
  """

  def __init__(self,
               num_units,
               memory,
               memory_sequence_length=None,
               normalize=False,
               probability_fn=None,
               score_mask_value=float("-inf"),
               name="BahdanauAttention"):
    """Construct the Attention mechanism.
    Args:
      num_units: The depth of the query mechanism.
      memory: The memory to query; usually the output of an RNN encoder.  This
        tensor should be shaped `[batch_size, max_time, ...]`.
      memory_sequence_length (optional): Sequence lengths for the batch entries
        in memory.  If provided, the memory tensor rows are masked with zeros
        for values past the respective sequence lengths.
      normalize: Python boolean.  Whether to normalize the energy term.
      probability_fn: (optional) A `callable`.  Converts the score to
        probabilities.  The default is @{tf.nn.softmax}. Other options include
        @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
        Its signature should be: `probabilities = probability_fn(score)`.
      score_mask_value: (optional): The mask value for score before passing into
        `probability_fn`. The default is -inf. Only used if
        `memory_sequence_length` is not None.
      name: Name to use when creating ops.
    """
    if probability_fn is None:
      probability_fn = nn_ops.softmax
    wrapped_probability_fn = lambda score, _: probability_fn(score)
    super(BahdanauAttention, self).__init__(
        query_layer=layers_core.Dense(
            num_units, name="query_layer", use_bias=False),
        memory_layer=layers_core.Dense(
            num_units, name="memory_layer", use_bias=False),
        memory=memory,
        probability_fn=wrapped_probability_fn,
        memory_sequence_length=memory_sequence_length,
        score_mask_value=score_mask_value,
        name=name)
    self._num_units = num_units
    self._normalize = normalize
    self._name = name

  def __call__(self, query, previous_alignments):
    """Score the query based on the keys and values.
    Args:
      query: Tensor of dtype matching `self.values` and shape
        `[batch_size, query_depth]`.
      previous_alignments: Tensor of dtype matching `self.values` and shape
        `[batch_size, alignments_size]`
        (`alignments_size` is memory's `max_time`).
    Returns:
      alignments: Tensor of dtype matching `self.values` and shape
        `[batch_size, alignments_size]` (`alignments_size` is memory's
        `max_time`).
    """
    with variable_scope.variable_scope(None, "bahdanau_attention", [query]):
      processed_query = self.query_layer(query) if self.query_layer else query
      score = _bahdanau_score(processed_query, self._keys, self._normalize)
    alignments = self._probability_fn(score, previous_alignments)
    return alignments

class _BaseAttentionMechanism(AttentionMechanism):
	"""A base AttentionMechanism class providing common functionality.
	Common functionality includes:
		1. Storing the query and memory layers.
		2. Preprocessing and storing the memory.
	"""

	def __init__(self,
							 query_layer,
							 memory,
							 probability_fn,
							 memory_sequence_length=None,
							 memory_layer=None,
							 check_inner_dims_defined=True,
							 score_mask_value=float("-inf"),
							 name=None):
		"""Construct base AttentionMechanism class.
		Args:
			query_layer: Callable.  Instance of `tf.layers.Layer`.  The layer's depth
				must match the depth of `memory_layer`.  If `query_layer` is not
				provided, the shape of `query` must match that of `memory_layer`.
			memory: The memory to query; usually the output of an RNN encoder.  This
				tensor should be shaped `[batch_size, max_time, ...]`.
			probability_fn: A `callable`.  Converts the score and previous alignments
				to probabilities. Its signature should be:
				`probabilities = probability_fn(score, previous_alignments)`.
			memory_sequence_length (optional): Sequence lengths for the batch entries
				in memory.  If provided, the memory tensor rows are masked with zeros
				for values past the respective sequence lengths.
			memory_layer: Instance of `tf.layers.Layer` (may be None).  The layer's
				depth must match the depth of `query_layer`.
				If `memory_layer` is not provided, the shape of `memory` must match
				that of `query_layer`.
			check_inner_dims_defined: Python boolean.  If `True`, the `memory`
				argument's shape is checked to ensure all but the two outermost
				dimensions are fully defined.
			score_mask_value: (optional): The mask value for score before passing into
				`probability_fn`. The default is -inf. Only used if
				`memory_sequence_length` is not None.
			name: Name to use when creating ops.
		"""
		if (query_layer is not None
				and not isinstance(query_layer, layers_base.Layer)):
			raise TypeError(
					"query_layer is not a Layer: %s" % type(query_layer).__name__)
		if (memory_layer is not None
				and not isinstance(memory_layer, layers_base.Layer)):
			raise TypeError(
					"memory_layer is not a Layer: %s" % type(memory_layer).__name__)
		self._query_layer = query_layer
		self._memory_layer = memory_layer
		if not callable(probability_fn):
			raise TypeError("probability_fn must be callable, saw type: %s" %
											type(probability_fn).__name__)
		self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
				probability_fn(
						_maybe_mask_score(score, memory_sequence_length, score_mask_value),
						prev))
		with ops.name_scope(
				name, "BaseAttentionMechanismInit", nest.flatten(memory)):
			self._values = _prepare_memory(
					memory, memory_sequence_length,
					check_inner_dims_defined=check_inner_dims_defined)
			self._keys = (
					self.memory_layer(self._values) if self.memory_layer  # pylint: disable=not-callable
					else self._values)
			self._batch_size = (
					self._keys.shape[0].value or array_ops.shape(self._keys)[0])
			self._alignments_size = (self._keys.shape[1].value or
															 array_ops.shape(self._keys)[1])

	@property
	def memory_layer(self):
		return self._memory_layer

	@property
	def query_layer(self):
		return self._query_layer

	@property
	def values(self):
		return self._values

	@property
	def keys(self):
		return self._keys

	@property
	def batch_size(self):
		return self._batch_size

	@property
	def alignments_size(self):
		return self._alignments_size

	def initial_alignments(self, batch_size, dtype):
		"""Creates the initial alignment values for the `AttentionWrapper` class.
		This is important for AttentionMechanisms that use the previous alignment
		to calculate the alignment at the next time step (e.g. monotonic attention).
		The default behavior is to return a tensor of all zeros.
		Args:
			batch_size: `int32` scalar, the batch_size.
			dtype: The `dtype`.
		Returns:
			A `dtype` tensor shaped `[batch_size, alignments_size]`
			(`alignments_size` is the values' `max_time`).
		"""
		max_time = self._alignments_size
		return _zero_state_tensors(max_time, batch_size, dtype)


def _location_based_score(W_query, attention_weights):
	"""Impelements Bahdanau-style (cumulative) scoring function.

  ############################################################
                    location-based attention
                        f = F * α_{i-1}
        energy = dot(v_a, tanh(W_query(h_dec) + W_fil(f)))
  ############################################################

  Args:
  	W_query: Tensor, shape '[batch_size, num_units]' to compare to location features.
  	attention_weights (alignments): previous attention weights, shape '[batch_size, max_time]'
  Returns:
  	A '[batch_size, max_time]'
	"""
	dtype = W_query.dtype
	# Get the number of hidden units from the trailing dimension of query
	num_units = W_query.shape[-1].value or array_ops.shape(W_query)[-1]
	# [batch_size, max_time] -> [batch_size, max_time, 1]
	attention_weights = tf.expand_dims(attention_weights, axis=2)
	# location features [batch_size, max_time, filters]
	f = tf.layers.conv1d(attention_weights, filters=32,
											 kernel_size=31, padding='same',
											 name='location_features')

	# Projected location features [batch_size, max_time, attention_dim]
	W_fil = tf.contrib.layers.fully_connected(
		f,
		num_outputs=num_units,
		activation_fn=None,
		weights_initializer=tf.truncated_normal_initializer(
			stddev=hp.att_parameter_init),
		biases_initializer=tf.zeros_initializer(),
		scope='W_filter')

	v_a = tf.get_variable(
		'v_a', shape=[num_units], dtype=tf.float32)

	return tf.reduce_sum(v_a * tf.tanh(tf.expand_dims(W_query, axis=1) + W_fil), axis=2)


class LocationBasedAttention(_BaseAttentionMechanism):
	"""Impelements Bahdanau-style (cumulative) scoring function.
	This attention is described in:
	J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Ben-
  gio, “Attention-based models for speech recognition,” in Ad-
  vances in Neural Information Processing Systems, 2015, pp.
  577–585.
	"""

	def __init__(self,
				 num_units,
				 memory,
				 memory_sequence_length=None,
				 probability_fn=None,
				 score_mask_value=tf.float32.min,
				 name='LocationBasedAttention'):
		"""Construct the Attention mechanism.
		Args:
			num_units: The depth of the query mechanism.
			memory: The memory to query; usually the output of an RNN encoder.  This
				tensor should be shaped `[batch_size, max_time, ...]`.
			memory_sequence_length (optional): Sequence lengths for the batch entries
				in memory.  If provided, the memory tensor rows are masked with zeros
				for values past the respective sequence lengths.
			probability_fn: (optional) A `callable`.  Converts the score to
				probabilities.  The default is @{tf.nn.softmax}. Other options include
				@{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
				Its signature should be: `probabilities = probability_fn(score)`.
			score_mask_value: (optional): The mask value for score before passing into
				`probability_fn`. The default is -inf. Only used if
				`memory_sequence_length` is not None.
			name: Name to use when creating ops.
		"""
		if probability_fn is None:
			probability_fn = nn_ops.softmax
		wrapped_probability_fn = lambda score, _: probability_fn(score)
		super(LocationBasedAttention, self).__init__(
				query_layer=layers_core.Dense(
						num_units, name='query_layer', use_bias=False),
				memory_layer=layers_core.Dense(
						num_units, name='memory_layer', use_bias=False),
				memory=memory,
				probability_fn=wrapped_probability_fn,
				memory_sequence_length=memory_sequence_length,
				score_mask_value=score_mask_value,
				name=name)
		self._num_units = num_units
		self._name = name

	def __call__(self, query, previous_alignments):
		"""Score the query based on the keys and values.
		Args:
			query: Tensor of dtype matching `self.values` and shape
				`[batch_size, query_depth]`.
			previous_alignments: Tensor of dtype matching `self.values` and shape
				`[batch_size, alignments_size]`
				(`alignments_size` is memory's `max_time`).
		Returns:
			alignments: Tensor of dtype matching `self.values` and shape
				`[batch_size, alignments_size]` (`alignments_size` is memory's
				`max_time`).
		"""
		with variable_scope.variable_scope(None, "location_based_attention", [query]):
			# processed_query shape [batch_size, query_depth] -> [batch_size, attention_dim]
			processed_query = self.query_layer(query) if self.query_layer else query
			# energy shape [batch_size, max_time]
			energy = _location_based_score(processed_query, previous_alignments)
		# alignments shape = enery shape = [batch_size, max_time]
		alignments = self._probability_fn(energy, previous_alignments)
		return alignments


class AttentionWrapperState(
		collections.namedtuple("AttentionWrapperState",
													 ("cell_state", "attention", "time", "alignments",
														"alignment_history"))):
	"""`namedtuple` storing the state of a `AttentionWrapper`.
	Contains:
		- `cell_state`: The state of the wrapped `RNNCell` at the previous time
			step.
		- `attention`: The attention emitted at the previous time step.
		- `time`: int32 scalar containing the current time step.
		- `alignments`: A single or tuple of `Tensor`(s) containing the alignments
			 emitted at the previous time step for each attention mechanism.
		- `alignment_history`: (if enabled) a single or tuple of `TensorArray`(s)
			 containing alignment matrices from all time steps for each attention
			 mechanism. Call `stack()` on each to convert to a `Tensor`.
	"""

	def clone(self, **kwargs):
		"""Clone this object, overriding components provided by kwargs.
		Example:
		```python
		initial_state = attention_wrapper.zero_state(dtype=..., batch_size=...)
		initial_state = initial_state.clone(cell_state=encoder_state)
		```
		Args:
			**kwargs: Any properties of the state object to replace in the returned
				`AttentionWrapperState`.
		Returns:
			A new `AttentionWrapperState` whose properties are the same as
			this one, except any overridden properties as provided in `kwargs`.
		"""
		return super(AttentionWrapperState, self)._replace(**kwargs)

def _compute_attention(attention_mechanism, cell_output, previous_alignments,
											 attention_layer):
	"""Computes the attention and alignments for a given attention_mechanism."""
	alignments = attention_mechanism(
			cell_output, previous_alignments=previous_alignments)

	# Reshape from [batch_size, memory_time] to [batch_size, 1, memory_time]
	expanded_alignments = array_ops.expand_dims(alignments, 1)
	# Context is the inner product of alignments and values along the
	# memory time dimension.
	# alignments shape is
	#   [batch_size, 1, memory_time]
	# attention_mechanism.values shape is
	#   [batch_size, memory_time, memory_size]
	# the batched matmul is over memory_time, so the output shape is
	#   [batch_size, 1, memory_size].
	# we then squeeze out the singleton dim.
	context = math_ops.matmul(expanded_alignments, attention_mechanism.values)
	context = array_ops.squeeze(context, [1])

	if attention_layer is not None:
		attention = attention_layer(array_ops.concat([cell_output, context], 1))
	else:
		attention = context

	# attention shape [batch_size, memory size]
	return attention, alignments


class AttentionWrapper(rnn_cell_impl.RNNCell):
	"""Wraps TacotronDecoderWrapper with attention.

	NOTE: this attention wrapper will only work properly if 
	built on top of TactronDecoderWrapper instance.
	In order to use another RNN_cell please use tensorflow AttentionWrapper.
	"""

	def __init__(self,
				 cell,
				 attention_mechanism,
				 attention_layer_size=None,
				 alignment_history=False,
				 cell_input_fn=None,
				 output_attention=True,
				 initial_cell_state=None,
				 name=None):
		"""Construct the `AttentionWrapper`.
		**NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
		`AttentionWrapper`, then you must ensure that:
		- The encoder output has been tiled to `beam_width` via
			@{tf.contrib.seq2seq.tile_batch} (NOT `tf.tile`).
		- The `batch_size` argument passed to the `zero_state` method of this
			wrapper is equal to `true_batch_size * beam_width`.
		- The initial state created with `zero_state` above contains a
			`cell_state` value containing properly tiled final state from the
			encoder.
		An example:
		```
		tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
				encoder_outputs, multiplier=beam_width)
		tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
				encoder_final_state, multiplier=beam_width)
		tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
				sequence_length, multiplier=beam_width)
		attention_mechanism = MyFavoriteAttentionMechanism(
				num_units=attention_depth,
				memory=tiled_inputs,
				memory_sequence_length=tiled_sequence_length)
		attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
		decoder_initial_state = attention_cell.zero_state(
				dtype, batch_size=true_batch_size * beam_width)
		decoder_initial_state = decoder_initial_state.clone(
				cell_state=tiled_encoder_final_state)
		```
		Args:
			cell: An instance of `RNNCell`.
			attention_mechanism: A list of `AttentionMechanism` instances or a single
				instance.
			attention_layer_size: A list of Python integers or a single Python
				integer, the depth of the attention (output) layer(s). If None
				(default), use the context as attention at each time step. Otherwise,
				feed the context and cell output into the attention layer to generate
				attention at each time step. If attention_mechanism is a list,
				attention_layer_size must be a list of the same length.
			alignment_history: Python boolean, whether to store alignment history
				from all time steps in the final output state (currently stored as a
				time major `TensorArray` on which you must call `stack()`).
			cell_input_fn: (optional) A `callable`.  The default is:
				`lambda inputs, attention: array_ops.concat([inputs, attention], -1)`.
			output_attention: Python bool.  If `True` (default), the output at each
				time step is the attention value.  This is the behavior of Luong-style
				attention mechanisms.  If `False`, the output at each time step is
				the output of `cell`.  This is the beahvior of Bhadanau-style
				attention mechanisms.  In both cases, the `attention` tensor is
				propagated to the next time step via the state and is used there.
				This flag only controls whether the attention mechanism is propagated
				up to the next cell in an RNN stack or to the top RNN output.
			initial_cell_state: The initial state value to use for the cell when
				the user calls `zero_state()`.  Note that if this value is provided
				now, and the user uses a `batch_size` argument of `zero_state` which
				does not match the batch size of `initial_cell_state`, proper
				behavior is not guaranteed.
			name: Name to use when creating ops.
		Raises:
			TypeError: `attention_layer_size` is not None and (`attention_mechanism`
				is a list but `attention_layer_size` is not; or vice versa).
			ValueError: if `attention_layer_size` is not None, `attention_mechanism`
				is a list, and its length does not match that of `attention_layer_size`.
		"""
		super(AttentionWrapper, self).__init__(name=name)
		#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 isinstance(attention_mechanism, (list, tuple)):
			self._is_multi = True
			attention_mechanisms = attention_mechanism
			for attention_mechanism in attention_mechanisms:
				if not isinstance(attention_mechanism, AttentionMechanism):
					raise TypeError(
							"attention_mechanism must contain only instances of "
							"AttentionMechanism, saw type: %s"
							% type(attention_mechanism).__name__)
		else:
			self._is_multi = False
			if not isinstance(attention_mechanism, AttentionMechanism):
				raise TypeError(
						"attention_mechanism must be an AttentionMechanism or list of "
						"multiple AttentionMechanism instances, saw type: %s"
						% type(attention_mechanism).__name__)
			attention_mechanisms = (attention_mechanism,)

		if cell_input_fn is None:
			cell_input_fn = (
					lambda inputs, attention: array_ops.concat([inputs, attention], -1))
		else:
			if not callable(cell_input_fn):
				raise TypeError(
						"cell_input_fn must be callable, saw type: %s"
						% type(cell_input_fn).__name__)

		if attention_layer_size is not None:
			attention_layer_sizes = tuple(
					attention_layer_size
					if isinstance(attention_layer_size, (list, tuple))
					else (attention_layer_size,))
			if len(attention_layer_sizes) != len(attention_mechanisms):
				raise ValueError(
						"If provided, attention_layer_size must contain exactly one "
						"integer per attention_mechanism, saw: %d vs %d"
						% (len(attention_layer_sizes), len(attention_mechanisms)))
			self._attention_layers = tuple(
					layers_core.Dense(
							attention_layer_size, name="attention_layer", use_bias=False)
					for attention_layer_size in attention_layer_sizes)
			self._attention_layer_size = sum(attention_layer_sizes)
		else:
			self._attention_layers = None
			self._attention_layer_size = sum(
					attention_mechanism.values.get_shape()[-1].value
					for attention_mechanism in attention_mechanisms)

		self._cell = cell
		self._attention_mechanisms = attention_mechanisms
		self._cell_input_fn = cell_input_fn
		self._output_attention = output_attention
		self._alignment_history = alignment_history
		with ops.name_scope(name, "AttentionWrapperInit"):
			if initial_cell_state is None:
				self._initial_cell_state = None
			else:
				final_state_tensor = nest.flatten(initial_cell_state)[-1]
				state_batch_size = (
						final_state_tensor.shape[0].value
						or array_ops.shape(final_state_tensor)[0])
				error_message = (
						"When constructing AttentionWrapper %s: " % self._base_name +
						"Non-matching batch sizes between the memory "
						"(encoder output) and initial_cell_state.  Are you using "
						"the BeamSearchDecoder?  You may need to tile your initial state "
						"via the tf.contrib.seq2seq.tile_batch function with argument "
						"multiple=beam_width.")
				with ops.control_dependencies(
						self._batch_size_checks(state_batch_size, error_message)):
					self._initial_cell_state = nest.map_structure(
							lambda s: array_ops.identity(s, name="check_initial_cell_state"),
							initial_cell_state)

	def _batch_size_checks(self, batch_size, error_message):
		return [check_ops.assert_equal(batch_size,
																	 attention_mechanism.batch_size,
																	 message=error_message)
						for attention_mechanism in self._attention_mechanisms]

	def _item_or_tuple(self, seq):
		"""Returns `seq` as tuple or the singular element.
		Which is returned is determined by how the AttentionMechanism(s) were passed
		to the constructor.
		Args:
			seq: A non-empty sequence of items or generator.
		Returns:
			 Either the values in the sequence as a tuple if AttentionMechanism(s)
			 were passed to the constructor as a sequence or the singular element.
		"""
		t = tuple(seq)
		if self._is_multi:
			return t
		else:
			return t[0]

	@property
	def output_size(self):
		if self._output_attention:
			return self._attention_layer_size
		else:
			return self._cell.output_size

	@property
	def state_size(self):
		"""The `state_size` property of `AttentionWrapper`.
		Returns:
			An `AttentionWrapperState` tuple containing shapes used by this object.
		"""
		return AttentionWrapperState(
				cell_state=self._cell.state_size,
				time=tensor_shape.TensorShape([]),
				attention=self._attention_layer_size,
				alignments=self._item_or_tuple(
						a.alignments_size for a in self._attention_mechanisms),
				alignment_history=self._item_or_tuple(
						() for _ in self._attention_mechanisms))  # sometimes a TensorArray

	def zero_state(self, batch_size, dtype):
		"""Return an initial (zero) state tuple for this `AttentionWrapper`.
		**NOTE** Please see the initializer documentation for details of how
		to call `zero_state` if using an `AttentionWrapper` with a
		`BeamSearchDecoder`.
		Args:
			batch_size: `0D` integer tensor: the batch size.
			dtype: The internal state data type.
		Returns:
			An `AttentionWrapperState` tuple containing zeroed out tensors and,
			possibly, empty `TensorArray` objects.
		Raises:
			ValueError: (or, possibly at runtime, InvalidArgument), if
				`batch_size` does not match the output size of the encoder passed
				to the wrapper object at initialization time.
		"""
		with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
			if self._initial_cell_state is not None:
				cell_state = self._initial_cell_state
			else:
				cell_state = self._cell.zero_state(batch_size, dtype)
			error_message = (
					"When calling zero_state of AttentionWrapper %s: " % self._base_name +
					"Non-matching batch sizes between the memory "
					"(encoder output) and the requested batch size.  Are you using "
					"the BeamSearchDecoder?  If so, make sure your encoder output has "
					"been tiled to beam_width via tf.contrib.seq2seq.tile_batch, and "
					"the batch_size= argument passed to zero_state is "
					"batch_size * beam_width.")
			with ops.control_dependencies(
					self._batch_size_checks(batch_size, error_message)):
				cell_state = nest.map_structure(
						lambda s: array_ops.identity(s, name="checked_cell_state"),
						cell_state)
			return AttentionWrapperState(
					cell_state=cell_state,
					time=array_ops.zeros([], dtype=dtypes.int32),
					attention=_zero_state_tensors(self._attention_layer_size, batch_size,
																				dtype),
					alignments=self._item_or_tuple(
							attention_mechanism.initial_alignments(batch_size, dtype)
							for attention_mechanism in self._attention_mechanisms),
					alignment_history=self._item_or_tuple(
							tensor_array_ops.TensorArray(dtype=dtype, size=0,
																					 dynamic_size=True)
							if self._alignment_history else ()
							for _ in self._attention_mechanisms))

	def call(self, inputs, state):
		"""Perform a step of attention-wrapped RNN.
		- Step 1: Mix the `inputs` and previous step's `attention` output via
			`cell_input_fn`.
		- Step 2: Call the wrapped `cell` with this input and its previous state.
		- Step 3: Score the cell's output with `attention_mechanism`.
		- Step 4: Calculate the alignments by passing the score through the
			`normalizer`.
		- Step 5: Calculate the context vector as the inner product between the
			alignments and the attention_mechanism's values (memory).
		- Step 6: Calculate the attention output by concatenating the cell output
			and context through the attention layer (a linear layer with
			`attention_layer_size` outputs).
		Args:
			inputs: (Possibly nested tuple of) Tensor, the input at this time step.
			state: An instance of `AttentionWrapperState` containing
				tensors from the previous time step.
		Returns:
			A tuple `(attention_or_cell_output, next_state)`, where:
			- `attention_or_cell_output` depending on `output_attention`.
			- `next_state` is an instance of `AttentionWrapperState`
				 containing the state calculated at this time step.
		Raises:
			TypeError: If `state` is not an instance of `AttentionWrapperState`.
		"""
		if not isinstance(state, AttentionWrapperState):
			raise TypeError("Expected state to be instance of AttentionWrapperState. "
											"Received type %s instead."  % type(state))

		# Step 1: Calculate the true inputs to the cell based on the
		# previous attention value.
		cell_inputs = self._cell_input_fn(inputs, state.attention)
		#cell_state = state.cell_state
		(cell_output, LSTM_output), next_cell_state, concat_output_LSTM = self._cell(cell_inputs, state)

		cell_batch_size = (
				cell_output.shape[0].value or array_ops.shape(cell_output)[0])
		error_message = (
				"When applying AttentionWrapper %s: " % self.name +
				"Non-matching batch sizes between the memory "
				"(encoder output) and the query (decoder output).  Are you using "
				"the BeamSearchDecoder?  You may need to tile your memory input via "
				"the tf.contrib.seq2seq.tile_batch function with argument "
				"multiple=beam_width.")
		with ops.control_dependencies(
				self._batch_size_checks(cell_batch_size, error_message)):
			cell_output = array_ops.identity(
					cell_output, name="checked_cell_output")

		if self._is_multi:
			previous_alignments = state.alignments
			previous_alignment_history = state.alignment_history
		else:
			previous_alignments = [state.alignments]
			previous_alignment_history = [state.alignment_history]

		all_alignments = []
		all_attentions = []
		all_histories = []
		for i, attention_mechanism in enumerate(self._attention_mechanisms):
			attention, alignments = _compute_attention(
					attention_mechanism, cell_output, previous_alignments[i],
					self._attention_layers[i] if self._attention_layers else None)
			alignment_history = previous_alignment_history[i].write(
					state.time, alignments) if self._alignment_history else ()

			all_alignments.append(alignments)
			all_histories.append(alignment_history)
			all_attentions.append(attention)

		attention = array_ops.concat(all_attentions, 1)
		next_state = AttentionWrapperState(
				time=state.time + 1,
				cell_state=next_cell_state,
				attention=attention,
				alignments=self._item_or_tuple(all_alignments),
				alignment_history=self._item_or_tuple(all_histories))

		if self._output_attention:
			return attention, next_state, LSTM_output, concat_output_LSTM
		else:
			return cell_output, next_state, LSTM_output, concat_output_LSTM