rnn_v2.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================


"""RNN helpers for TensorFlow models.





@@bidirectional_dynamic_rnn

@@dynamic_rnn

@@raw_rnn

@@static_rnn

@@static_state_saving_rnn

@@static_bidirectional_rnn

"""

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

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.util import nest
import tensorflow as tf


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)


# pylint: disable=protected-access

_concat = rnn_cell_impl._concat
try:
    _like_rnncell = rnn_cell_impl._like_rnncell
except:
    _like_rnncell = _like_rnncell_


# pylint: enable=protected-access


def _transpose_batch_time(x):
    """Transpose the batch and time dimensions of a Tensor.



    Retains as much of the static shape information as possible.



    Args:

      x: A tensor of rank 2 or higher.



    Returns:

      x transposed along the first two dimensions.



    Raises:

      ValueError: if `x` is rank 1 or lower.

    """

    x_static_shape = x.get_shape()

    if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
        raise ValueError(

            "Expected input tensor %s to have rank at least 2, but saw shape: %s" %

            (x, x_static_shape))

    x_rank = array_ops.rank(x)

    x_t = array_ops.transpose(

        x, array_ops.concat(

            ([1, 0], math_ops.range(2, x_rank)), axis=0))

    x_t.set_shape(

        tensor_shape.TensorShape([

            x_static_shape[1], x_static_shape[0]

        ]).concatenate(x_static_shape[2:]))

    return x_t


def _best_effort_input_batch_size(flat_input):
    """Get static input batch size if available, with fallback to the dynamic one.



    Args:

      flat_input: An iterable of time major input Tensors of shape [max_time,

        batch_size, ...]. All inputs should have compatible batch sizes.



    Returns:

      The batch size in Python integer if available, or a scalar Tensor otherwise.



    Raises:

      ValueError: if there is any input with an invalid shape.

    """

    for input_ in flat_input:

        shape = input_.shape

        if shape.ndims is None:
            continue

        if shape.ndims < 2:
            raise ValueError(

                "Expected input tensor %s to have rank at least 2" % input_)

        batch_size = shape[1]

        if batch_size is not None:
            return batch_size

    # Fallback to the dynamic batch size of the first input.

    return array_ops.shape(flat_input[0])[1]


def _infer_state_dtype(explicit_dtype, state):
    """Infer the dtype of an RNN state.



    Args:

      explicit_dtype: explicitly declared dtype or None.

      state: RNN's hidden state. Must be a Tensor or a nested iterable containing

        Tensors.



    Returns:

      dtype: inferred dtype of hidden state.



    Raises:

      ValueError: if `state` has heterogeneous dtypes or is empty.

    """

    if explicit_dtype is not None:

        return explicit_dtype

    elif nest.is_sequence(state):

        inferred_dtypes = [element.dtype for element in nest.flatten(state)]

        if not inferred_dtypes:
            raise ValueError("Unable to infer dtype from empty state.")

        all_same = all([x == inferred_dtypes[0] for x in inferred_dtypes])

        if not all_same:
            raise ValueError(

                "State has tensors of different inferred_dtypes. Unable to infer a "

                "single representative dtype.")

        return inferred_dtypes[0]

    else:

        return state.dtype


# pylint: disable=unused-argument

def _rnn_step(

        time, sequence_length, min_sequence_length, max_sequence_length,

        zero_output, state, call_cell, state_size, skip_conditionals=False):
    """Calculate one step of a dynamic RNN minibatch.



    Returns an (output, state) pair conditioned on the sequence_lengths.

    When skip_conditionals=False, the pseudocode is something like:



    if t >= max_sequence_length:

      return (zero_output, state)

    if t < min_sequence_length:

      return call_cell()



    # Selectively output zeros or output, old state or new state depending

    # on if we've finished calculating each row.

    new_output, new_state = call_cell()

    final_output = np.vstack([

      zero_output if time >= sequence_lengths[r] else new_output_r

      for r, new_output_r in enumerate(new_output)

    ])

    final_state = np.vstack([

      state[r] if time >= sequence_lengths[r] else new_state_r

      for r, new_state_r in enumerate(new_state)

    ])

    return (final_output, final_state)



    Args:

      time: Python int, the current time step

      sequence_length: int32 `Tensor` vector of size [batch_size]

      min_sequence_length: int32 `Tensor` scalar, min of sequence_length

      max_sequence_length: int32 `Tensor` scalar, max of sequence_length

      zero_output: `Tensor` vector of shape [output_size]

      state: Either a single `Tensor` matrix of shape `[batch_size, state_size]`,

        or a list/tuple of such tensors.

      call_cell: lambda returning tuple of (new_output, new_state) where

        new_output is a `Tensor` matrix of shape `[batch_size, output_size]`.

        new_state is a `Tensor` matrix of shape `[batch_size, state_size]`.

      state_size: The `cell.state_size` associated with the state.

      skip_conditionals: Python bool, whether to skip using the conditional

        calculations.  This is useful for `dynamic_rnn`, where the input tensor

        matches `max_sequence_length`, and using conditionals just slows

        everything down.



    Returns:

      A tuple of (`final_output`, `final_state`) as given by the pseudocode above:

        final_output is a `Tensor` matrix of shape [batch_size, output_size]

        final_state is either a single `Tensor` matrix, or a tuple of such

          matrices (matching length and shapes of input `state`).



    Raises:

      ValueError: If the cell returns a state tuple whose length does not match

        that returned by `state_size`.

    """

    # Convert state to a list for ease of use

    flat_state = nest.flatten(state)

    flat_zero_output = nest.flatten(zero_output)

    def _copy_one_through(output, new_output):

        # If the state contains a scalar value we simply pass it through.

        if output.shape.ndims == 0:
            return new_output

        copy_cond = (time >= sequence_length)

        with ops.colocate_with(new_output):
            return array_ops.where(copy_cond, output, new_output)

    def _copy_some_through(flat_new_output, flat_new_state):

        # Use broadcasting select to determine which values should get

        # the previous state & zero output, and which values should get

        # a calculated state & output.

        flat_new_output = [

            _copy_one_through(zero_output, new_output)

            for zero_output, new_output in zip(flat_zero_output, flat_new_output)]

        flat_new_state = [

            _copy_one_through(state, new_state)

            for state, new_state in zip(flat_state, flat_new_state)]

        return flat_new_output + flat_new_state

    def _maybe_copy_some_through():

        """Run RNN step.  Pass through either no or some past state."""

        new_output, new_state = call_cell()

        nest.assert_same_structure(state, new_state)

        flat_new_state = nest.flatten(new_state)

        flat_new_output = nest.flatten(new_output)

        return control_flow_ops.cond(

            # if t < min_seq_len: calculate and return everything

            time < min_sequence_length, lambda: flat_new_output + flat_new_state,

            # else copy some of it through

            lambda: _copy_some_through(flat_new_output, flat_new_state))

    # TODO(ebrevdo): skipping these conditionals may cause a slowdown,

    # but benefits from removing cond() and its gradient.  We should

    # profile with and without this switch here.

    if skip_conditionals:

        # Instead of using conditionals, perform the selective copy at all time

        # steps.  This is faster when max_seq_len is equal to the number of unrolls

        # (which is typical for dynamic_rnn).

        new_output, new_state = call_cell()

        nest.assert_same_structure(state, new_state)

        new_state = nest.flatten(new_state)

        new_output = nest.flatten(new_output)

        final_output_and_state = _copy_some_through(new_output, new_state)

    else:

        empty_update = lambda: flat_zero_output + flat_state

        final_output_and_state = control_flow_ops.cond(

            # if t >= max_seq_len: copy all state through, output zeros

            time >= max_sequence_length, empty_update,

            # otherwise calculation is required: copy some or all of it through

            _maybe_copy_some_through)

    if len(final_output_and_state) != len(flat_zero_output) + len(flat_state):
        raise ValueError("Internal error: state and output were not concatenated "

                         "correctly.")

    final_output = final_output_and_state[:len(flat_zero_output)]

    final_state = final_output_and_state[len(flat_zero_output):]

    for output, flat_output in zip(final_output, flat_zero_output):
        output.set_shape(flat_output.get_shape())

    for substate, flat_substate in zip(final_state, flat_state):
        substate.set_shape(flat_substate.get_shape())

    final_output = nest.pack_sequence_as(

        structure=zero_output, flat_sequence=final_output)

    final_state = nest.pack_sequence_as(

        structure=state, flat_sequence=final_state)

    return final_output, final_state


def _reverse_seq(input_seq, lengths):
    """Reverse a list of Tensors up to specified lengths.



    Args:

      input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)

                 or nested tuples of tensors.

      lengths:   A `Tensor` of dimension batch_size, containing lengths for each

                 sequence in the batch. If "None" is specified, simply reverses

                 the list.



    Returns:

      time-reversed sequence

    """

    if lengths is None:
        return list(reversed(input_seq))

    flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)

    flat_results = [[] for _ in range(len(input_seq))]

    for sequence in zip(*flat_input_seq):

        input_shape = tensor_shape.unknown_shape(

            ndims=sequence[0].get_shape().ndims)

        for input_ in sequence:
            input_shape.merge_with(input_.get_shape())

            input_.set_shape(input_shape)

        # Join into (time, batch_size, depth)

        s_joined = array_ops.stack(sequence)

        # Reverse along dimension 0

        s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)

        # Split again into list

        result = array_ops.unstack(s_reversed)

        for r, flat_result in zip(result, flat_results):
            r.set_shape(input_shape)

            flat_result.append(r)

    results = [nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)

               for input_, flat_result in zip(input_seq, flat_results)]

    return results


#
# def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
#
#                               initial_state_fw=None, initial_state_bw=None,
#
#                               dtype=None, parallel_iterations=None,
#
#                               swap_memory=False, time_major=False, scope=None):
#
#   """Creates a dynamic version of bidirectional recurrent neural network.
#
#
#
#   Takes input and builds independent forward and backward RNNs. The input_size
#
#   of forward and backward cell must match. The initial state for both directions
#
#   is zero by default (but can be set optionally) and no intermediate states are
#
#   ever returned -- the network is fully unrolled for the given (passed in)
#
#   length(s) of the sequence(s) or completely unrolled if length(s) is not
#
#   given.
#
#
#
#   Args:
#
#     cell_fw: An instance of RNNCell, to be used for forward direction.
#
#     cell_bw: An instance of RNNCell, to be used for backward direction.
#
#     inputs: The RNN inputs.
#
#       If time_major == False (default), this must be a tensor of shape:
#
#         `[batch_size, max_time, ...]`, or a nested tuple of such elements.
#
#       If time_major == True, this must be a tensor of shape:
#
#         `[max_time, batch_size, ...]`, or a nested tuple of such elements.
#
#     sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
#
#       containing the actual lengths for each of the sequences in the batch.
#
#       If not provided, all batch entries are assumed to be full sequences; and
#
#       time reversal is applied from time `0` to `max_time` for each sequence.
#
#     initial_state_fw: (optional) An initial state for the forward RNN.
#
#       This must be a tensor of appropriate type and shape
#
#       `[batch_size, cell_fw.state_size]`.
#
#       If `cell_fw.state_size` is a tuple, this should be a tuple of
#
#       tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
#
#     initial_state_bw: (optional) Same as for `initial_state_fw`, but using
#
#       the corresponding properties of `cell_bw`.
#
#     dtype: (optional) The data type for the initial states and expected output.
#
#       Required if initial_states are not provided or RNN states have a
#
#       heterogeneous dtype.
#
#     parallel_iterations: (Default: 32).  The number of iterations to run in
#
#       parallel.  Those operations which do not have any temporal dependency
#
#       and can be run in parallel, will be.  This parameter trades off
#
#       time for space.  Values >> 1 use more memory but take less time,
#
#       while smaller values use less memory but computations take longer.
#
#     swap_memory: Transparently swap the tensors produced in forward inference
#
#       but needed for back prop from GPU to CPU.  This allows training RNNs
#
#       which would typically not fit on a single GPU, with very minimal (or no)
#
#       performance penalty.
#
#     time_major: The shape format of the `inputs` and `outputs` Tensors.
#
#       If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
#
#       If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
#
#       Using `time_major = True` is a bit more efficient because it avoids
#
#       transposes at the beginning and end of the RNN calculation.  However,
#
#       most TensorFlow data is batch-major, so by default this function
#
#       accepts input and emits output in batch-major form.
#
#     scope: VariableScope for the created subgraph; defaults to
#
#       "bidirectional_rnn"
#
#
#
#   Returns:
#
#     A tuple (outputs, output_states) where:
#
#       outputs: A tuple (output_fw, output_bw) containing the forward and
#
#         the backward rnn output `Tensor`.
#
#         If time_major == False (default),
#
#           output_fw will be a `Tensor` shaped:
#
#           `[batch_size, max_time, cell_fw.output_size]`
#
#           and output_bw will be a `Tensor` shaped:
#
#           `[batch_size, max_time, cell_bw.output_size]`.
#
#         If time_major == True,
#
#           output_fw will be a `Tensor` shaped:
#
#           `[max_time, batch_size, cell_fw.output_size]`
#
#           and output_bw will be a `Tensor` shaped:
#
#           `[max_time, batch_size, cell_bw.output_size]`.
#
#         It returns a tuple instead of a single concatenated `Tensor`, unlike
#
#         in the `bidirectional_rnn`. If the concatenated one is preferred,
#
#         the forward and backward outputs can be concatenated as
#
#         `tf.concat(outputs, 2)`.
#
#       output_states: A tuple (output_state_fw, output_state_bw) containing
#
#         the forward and the backward final states of bidirectional rnn.
#
#
#
#   Raises:
#
#     TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
#
#   """
#
#
#
#   if not _like_rnncell(cell_fw):
#
#     raise TypeError("cell_fw must be an instance of RNNCell")
#
#   if not _like_rnncell(cell_bw):
#
#     raise TypeError("cell_bw must be an instance of RNNCell")
#
#
#
#   with vs.variable_scope(scope or "bidirectional_rnn"):
#
#     # Forward direction
#
#     with vs.variable_scope("fw") as fw_scope:
#
#       output_fw, output_state_fw = dynamic_rnn(
#
#           cell=cell_fw, inputs=inputs, sequence_length=sequence_length,
#
#           initial_state=initial_state_fw, dtype=dtype,
#
#           parallel_iterations=parallel_iterations, swap_memory=swap_memory,
#
#           time_major=time_major, scope=fw_scope)
#
#
#
#     # Backward direction
#
#     if not time_major:
#
#       time_dim = 1
#
#       batch_dim = 0
#
#     else:
#
#       time_dim = 0
#
#       batch_dim = 1
#
#
#
#     def _reverse(input_, seq_lengths, seq_dim, batch_dim):
#
#       if seq_lengths is not None:
#
#         return array_ops.reverse_sequence(
#
#             input=input_, seq_lengths=seq_lengths,
#
#             seq_dim=seq_dim, batch_dim=batch_dim)
#
#       else:
#
#         return array_ops.reverse(input_, axis=[seq_dim])
#
#
#
#     with vs.variable_scope("bw") as bw_scope:
#
#       inputs_reverse = _reverse(
#
#           inputs, seq_lengths=sequence_length,
#
#           seq_dim=time_dim, batch_dim=batch_dim)
#
#       tmp, output_state_bw = dynamic_rnn(
#
#           cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length,
#
#           initial_state=initial_state_bw, dtype=dtype,
#
#           parallel_iterations=parallel_iterations, swap_memory=swap_memory,
#
#           time_major=time_major, scope=bw_scope)
#
#
#
#   output_bw = _reverse(
#
#       tmp, seq_lengths=sequence_length,
#
#       seq_dim=time_dim, batch_dim=batch_dim)
#
#
#
#   outputs = (output_fw, output_bw)
#
#   output_states = (output_state_fw, output_state_bw)
#
#
#
#   return (outputs, output_states)
#


def dynamic_rnn(cell, inputs, att_scores=None, sequence_length=None, initial_state=None,

                dtype=None, parallel_iterations=None, swap_memory=False,

                time_major=False, scope=None):
    """Creates a recurrent neural network specified by RNNCell `cell`.



    Performs fully dynamic unrolling of `inputs`.



    Example:



    ```python

    # create a BasicRNNCell

    rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)



    # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]



    # defining initial state

    initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)



    # 'state' is a tensor of shape [batch_size, cell_state_size]

    outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,

                                       initial_state=initial_state,

                                       dtype=tf.float32)

    ```



    ```python

    # create 2 LSTMCells

    rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]



    # create a RNN cell composed sequentially of a number of RNNCells

    multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)



    # 'outputs' is a tensor of shape [batch_size, max_time, 256]

    # 'state' is a N-tuple where N is the number of LSTMCells containing a

    # tf.contrib.rnn.LSTMStateTuple for each cell

    outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,

                                       inputs=data,

                                       dtype=tf.float32)

    ```





    Args:

      cell: An instance of RNNCell.

      inputs: The RNN inputs.

        If `time_major == False` (default), this must be a `Tensor` of shape:

          `[batch_size, max_time, ...]`, or a nested tuple of such

          elements.

        If `time_major == True`, this must be a `Tensor` of shape:

          `[max_time, batch_size, ...]`, or a nested tuple of such

          elements.

        This may also be a (possibly nested) tuple of Tensors satisfying

        this property.  The first two dimensions must match across all the inputs,

        but otherwise the ranks and other shape components may differ.

        In this case, input to `cell` at each time-step will replicate the

        structure of these tuples, except for the time dimension (from which the

        time is taken).

        The input to `cell` at each time step will be a `Tensor` or (possibly

        nested) tuple of Tensors each with dimensions `[batch_size, ...]`.

      sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.

        Used to copy-through state and zero-out outputs when past a batch

        element's sequence length.  So it's more for correctness than performance.

      initial_state: (optional) An initial state for the RNN.

        If `cell.state_size` is an integer, this must be

        a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.

        If `cell.state_size` is a tuple, this should be a tuple of

        tensors having shapes `[batch_size, s] for s in cell.state_size`.

      dtype: (optional) The data type for the initial state and expected output.

        Required if initial_state is not provided or RNN state has a heterogeneous

        dtype.

      parallel_iterations: (Default: 32).  The number of iterations to run in

        parallel.  Those operations which do not have any temporal dependency

        and can be run in parallel, will be.  This parameter trades off

        time for space.  Values >> 1 use more memory but take less time,

        while smaller values use less memory but computations take longer.

      swap_memory: Transparently swap the tensors produced in forward inference

        but needed for back prop from GPU to CPU.  This allows training RNNs

        which would typically not fit on a single GPU, with very minimal (or no)

        performance penalty.

      time_major: The shape format of the `inputs` and `outputs` Tensors.

        If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.

        If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.

        Using `time_major = True` is a bit more efficient because it avoids

        transposes at the beginning and end of the RNN calculation.  However,

        most TensorFlow data is batch-major, so by default this function

        accepts input and emits output in batch-major form.

      scope: VariableScope for the created subgraph; defaults to "rnn".



    Returns:

      A pair (outputs, state) where:



      outputs: The RNN output `Tensor`.



        If time_major == False (default), this will be a `Tensor` shaped:

          `[batch_size, max_time, cell.output_size]`.



        If time_major == True, this will be a `Tensor` shaped:

          `[max_time, batch_size, cell.output_size]`.



        Note, if `cell.output_size` is a (possibly nested) tuple of integers

        or `TensorShape` objects, then `outputs` will be a tuple having the

        same structure as `cell.output_size`, containing Tensors having shapes

        corresponding to the shape data in `cell.output_size`.



      state: The final state.  If `cell.state_size` is an int, this

        will be shaped `[batch_size, cell.state_size]`.  If it is a

        `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.

        If it is a (possibly nested) tuple of ints or `TensorShape`, this will

        be a tuple having the corresponding shapes. If cells are `LSTMCells`

        `state` will be a tuple containing a `LSTMStateTuple` for each cell.



    Raises:

      TypeError: If `cell` is not an instance of RNNCell.

      ValueError: If inputs is None or an empty list.

    """

    if not _like_rnncell(cell):
        raise TypeError("cell must be an instance of RNNCell")

    # By default, time_major==False and inputs are batch-major: shaped

    #   [batch, time, depth]

    # For internal calculations, we transpose to [time, batch, depth]

    flat_input = nest.flatten(inputs)

    if not time_major:
        # (B,T,D) => (T,B,D)

        flat_input = [ops.convert_to_tensor(input_) for input_ in flat_input]

        flat_input = tuple(_transpose_batch_time(input_) for input_ in flat_input)

    parallel_iterations = parallel_iterations or 32

    if sequence_length is not None:

        sequence_length = math_ops.to_int32(sequence_length)

        if sequence_length.get_shape().ndims not in (None, 1):
            raise ValueError(

                "sequence_length must be a vector of length batch_size, "

                "but saw shape: %s" % sequence_length.get_shape())

        sequence_length = array_ops.identity(  # Just to find it in the graph.

            sequence_length, name="sequence_length")

    # Create a new scope in which the caching device is either

    # determined by the parent scope, or is set to place the cached

    # Variable using the same placement as for the rest of the RNN.

    try:
        resue = tf.AUTO_REUSE
    except:
        resue = tf.compat.v1.AUTO_REUSE

    with vs.variable_scope(scope or "rnn",reuse=resue) as varscope:#TODO:user defined reuse

        if varscope.caching_device is None:
            varscope.set_caching_device(lambda op: op.device)

        batch_size = _best_effort_input_batch_size(flat_input)

        if initial_state is not None:

            state = initial_state

        else:

            if not dtype:
                raise ValueError("If there is no initial_state, you must give a dtype.")

            state = cell.zero_state(batch_size, dtype)

        def _assert_has_shape(x, shape):

            x_shape = array_ops.shape(x)

            packed_shape = array_ops.stack(shape)

            return control_flow_ops.Assert(

                math_ops.reduce_all(math_ops.equal(x_shape, packed_shape)),

                ["Expected shape for Tensor %s is " % x.name,

                 packed_shape, " but saw shape: ", x_shape])

        if sequence_length is not None:
            # Perform some shape validation

            with ops.control_dependencies(

                    [_assert_has_shape(sequence_length, [batch_size])]):
                sequence_length = array_ops.identity(

                    sequence_length, name="CheckSeqLen")

        inputs = nest.pack_sequence_as(structure=inputs, flat_sequence=flat_input)

        (outputs, final_state) = _dynamic_rnn_loop(

            cell,

            inputs,

            state,

            parallel_iterations=parallel_iterations,

            swap_memory=swap_memory,

            att_scores=att_scores,

            sequence_length=sequence_length,

            dtype=dtype)

        # Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth].

        # If we are performing batch-major calculations, transpose output back

        # to shape [batch, time, depth]

        if not time_major:
            # (T,B,D) => (B,T,D)

            outputs = nest.map_structure(_transpose_batch_time, outputs)

        return (outputs, final_state)


def _dynamic_rnn_loop(cell,

                      inputs,

                      initial_state,

                      parallel_iterations,

                      swap_memory,

                      att_scores=None,

                      sequence_length=None,

                      dtype=None):
    """Internal implementation of Dynamic RNN.



    Args:

      cell: An instance of RNNCell.

      inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested

        tuple of such elements.

      initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if

        `cell.state_size` is a tuple, then this should be a tuple of

        tensors having shapes `[batch_size, s] for s in cell.state_size`.

      parallel_iterations: Positive Python int.

      swap_memory: A Python boolean

      sequence_length: (optional) An `int32` `Tensor` of shape [batch_size].

      dtype: (optional) Expected dtype of output. If not specified, inferred from

        initial_state.



    Returns:

      Tuple `(final_outputs, final_state)`.

      final_outputs:

        A `Tensor` of shape `[time, batch_size, cell.output_size]`.  If

        `cell.output_size` is a (possibly nested) tuple of ints or `TensorShape`

        objects, then this returns a (possibly nsted) tuple of Tensors matching

        the corresponding shapes.

      final_state:

        A `Tensor`, or possibly nested tuple of Tensors, matching in length

        and shapes to `initial_state`.



    Raises:

      ValueError: If the input depth cannot be inferred via shape inference

        from the inputs.

    """

    state = initial_state

    assert isinstance(parallel_iterations, int), "parallel_iterations must be int"

    state_size = cell.state_size

    flat_input = nest.flatten(inputs)

    flat_output_size = nest.flatten(cell.output_size)

    # Construct an initial output

    input_shape = array_ops.shape(flat_input[0])

    time_steps = input_shape[0]

    batch_size = _best_effort_input_batch_size(flat_input)

    inputs_got_shape = tuple(input_.get_shape().with_rank_at_least(3)

                             for input_ in flat_input)

    const_time_steps, const_batch_size = inputs_got_shape[0].as_list()[:2]

    for shape in inputs_got_shape:

        if not shape[2:].is_fully_defined():
            raise ValueError(

                "Input size (depth of inputs) must be accessible via shape inference,"

                " but saw value None.")

        got_time_steps = shape[0]

        got_batch_size = shape[1]

        if const_time_steps != got_time_steps:
            raise ValueError(

                "Time steps is not the same for all the elements in the input in a "

                "batch.")

        if const_batch_size != got_batch_size:
            raise ValueError(

                "Batch_size is not the same for all the elements in the input.")

    # Prepare dynamic conditional copying of state & output

    def _create_zero_arrays(size):

        size = _concat(batch_size, size)

        return array_ops.zeros(

            array_ops.stack(size), _infer_state_dtype(dtype, state))

    flat_zero_output = tuple(_create_zero_arrays(output)

                             for output in flat_output_size)

    zero_output = nest.pack_sequence_as(structure=cell.output_size,

                                        flat_sequence=flat_zero_output)

    if sequence_length is not None:
        min_sequence_length = math_ops.reduce_min(sequence_length)

        max_sequence_length = math_ops.reduce_max(sequence_length)

    time = array_ops.constant(0, dtype=dtypes.int32, name="time")

    with ops.name_scope("dynamic_rnn") as scope:

        base_name = scope

    def _create_ta(name, dtype):

        return tensor_array_ops.TensorArray(dtype=dtype,

                                            size=time_steps,

                                            tensor_array_name=base_name + name)

    output_ta = tuple(_create_ta("output_%d" % i,

                                 _infer_state_dtype(dtype, state))

                      for i in range(len(flat_output_size)))

    input_ta = tuple(_create_ta("input_%d" % i, flat_input[i].dtype)

                     for i in range(len(flat_input)))

    input_ta = tuple(ta.unstack(input_)

                     for ta, input_ in zip(input_ta, flat_input))

    def _time_step(time, output_ta_t, state, att_scores=None):

        """Take a time step of the dynamic RNN.



        Args:

          time: int32 scalar Tensor.

          output_ta_t: List of `TensorArray`s that represent the output.

          state: nested tuple of vector tensors that represent the state.



        Returns:

          The tuple (time + 1, output_ta_t with updated flow, new_state).

        """

        input_t = tuple(ta.read(time) for ta in input_ta)

        # Restore some shape information

        for input_, shape in zip(input_t, inputs_got_shape):
            input_.set_shape(shape[1:])

        input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)

        if att_scores is not None:

            att_score = att_scores[:, time, :]

            call_cell = lambda: cell(input_t, state, att_score)

        else:

            call_cell = lambda: cell(input_t, state)

        if sequence_length is not None:

            (output, new_state) = _rnn_step(

                time=time,

                sequence_length=sequence_length,

                min_sequence_length=min_sequence_length,

                max_sequence_length=max_sequence_length,

                zero_output=zero_output,

                state=state,

                call_cell=call_cell,

                state_size=state_size,

                skip_conditionals=True)

        else:

            (output, new_state) = call_cell()

        # Pack state if using state tuples

        output = nest.flatten(output)

        output_ta_t = tuple(

            ta.write(time, out) for ta, out in zip(output_ta_t, output))

        if att_scores is not None:

            return (time + 1, output_ta_t, new_state, att_scores)

        else:

            return (time + 1, output_ta_t, new_state)

    if att_scores is not None:

        _, output_final_ta, final_state, _ = control_flow_ops.while_loop(

            cond=lambda time, *_: time < time_steps,

            body=_time_step,

            loop_vars=(time, output_ta, state, att_scores),

            parallel_iterations=parallel_iterations,

            swap_memory=swap_memory)

    else:

        _, output_final_ta, final_state = control_flow_ops.while_loop(

            cond=lambda time, *_: time < time_steps,

            body=_time_step,

            loop_vars=(time, output_ta, state),

            parallel_iterations=parallel_iterations,

            swap_memory=swap_memory)

    # Unpack final output if not using output tuples.

    final_outputs = tuple(ta.stack() for ta in output_final_ta)

    # Restore some shape information

    for output, output_size in zip(final_outputs, flat_output_size):
        shape = _concat(

            [const_time_steps, const_batch_size], output_size, static=True)

        output.set_shape(shape)

    final_outputs = nest.pack_sequence_as(

        structure=cell.output_size, flat_sequence=final_outputs)

    return (final_outputs, final_state)