attention_gru.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.
# ==============================================================================
"""Module implementing RNN Cells.

This module provides a number of basic commonly used RNN cells, such as LSTM
(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
operators that allow adding dropouts, projections, or embeddings for inputs.
Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by
calling the `rnn` ops several times.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import hashlib
import numbers

from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.layers import base as base_layer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_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 partitioned_variables
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
from tensorflow.python.ops.rnn_cell_impl import RNNCell


_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"


class AttentionGRUCell(RNNCell):
  """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).

  Args:
    num_units: int, The number of units in the GRU cell.
    activation: Nonlinearity to use.  Default: `tanh`.
    reuse: (optional) Python boolean describing whether to reuse variables
     in an existing scope.  If not `True`, and the existing scope already has
     the given variables, an error is raised.
    kernel_initializer: (optional) The initializer to use for the weight and
    projection matrices.
    bias_initializer: (optional) The initializer to use for the bias.
  """

  def __init__(self,
               num_units,
               activation=None,
               reuse=None,
               name=None,
               kernel_initializer=None,
               bias_initializer=None):
    super(AttentionGRUCell, self).__init__(_reuse=reuse, name=name)
    self._num_units = num_units
    self._activation = activation or math_ops.tanh
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._gate_linear = None
    self._candidate_linear = None

  @property
  def state_size(self):
    return self._num_units

  @property
  def output_size(self):
    return self._num_units

  def call(self, inputs, state):
    # extract input vector and attention (gate)
    if inputs.get_shape()[-1] != self._num_units + 1:
      raise ValueError("Input should be passed as word input concatenated with 1D attention on end axis")
    inputs, g = array_ops.split(inputs,
      num_or_size_splits=[self._num_units,1],
      axis=1)

    """Gated recurrent unit (GRU) with nunits cells."""
    if self._gate_linear is None:
      bias_ones = self._bias_initializer
      if self._bias_initializer is None:
        bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
      with vs.variable_scope("gates"):  # Reset gate and update gate.
        self._gate_linear = _Linear(
            [inputs, state],
            self._num_units,
            True,
            bias_initializer=bias_ones,
            kernel_initializer=self._kernel_initializer)

    r = math_ops.sigmoid(self._gate_linear([inputs, state]))

    r_state = r * state
    if self._candidate_linear is None:
      with vs.variable_scope("candidate"):
        self._candidate_linear = _Linear(
            [inputs, r_state],
            self._num_units,
            True,
            bias_initializer=self._bias_initializer,
            kernel_initializer=self._kernel_initializer)
    c = self._activation(self._candidate_linear([inputs, r_state]))
    new_h = (1 - g) * state + g * c
    return new_h, new_h


class _Linear(object):
  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.

  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of weight variable.
    dtype: data type for variables.
    build_bias: boolean, whether to build a bias variable.
    bias_initializer: starting value to initialize the bias
      (default is all zeros).
    kernel_initializer: starting value to initialize the weight.

  Raises:
    ValueError: if inputs_shape is wrong.
  """

  def __init__(self,
               args,
               output_size,
               build_bias,
               bias_initializer=None,
               kernel_initializer=None):
    self._build_bias = build_bias

    if args is None or (nest.is_sequence(args) and not args):
      raise ValueError("`args` must be specified")
    if not nest.is_sequence(args):
      args = [args]
      self._is_sequence = False
    else:
      self._is_sequence = True

    # Calculate the total size of arguments on dimension 1.
    total_arg_size = 0
    shapes = [a.get_shape() for a in args]
    for shape in shapes:
      if shape.ndims != 2:
        raise ValueError("linear is expecting 2D arguments: %s" % shapes)
      if shape[1].value is None:
        raise ValueError("linear expects shape[1] to be provided for shape %s, "
                         "but saw %s" % (shape, shape[1]))
      else:
        total_arg_size += shape[1].value

    dtype = [a.dtype for a in args][0]

    scope = vs.get_variable_scope()
    with vs.variable_scope(scope) as outer_scope:
      self._weights = vs.get_variable(
          _WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
          dtype=dtype,
          initializer=kernel_initializer)
      if build_bias:
        with vs.variable_scope(outer_scope) as inner_scope:
          inner_scope.set_partitioner(None)
          if bias_initializer is None:
            bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
          self._biases = vs.get_variable(
              _BIAS_VARIABLE_NAME, [output_size],
              dtype=dtype,
              initializer=bias_initializer)

  def __call__(self, args):
    if not self._is_sequence:
      args = [args]

    if len(args) == 1:
      res = math_ops.matmul(args[0], self._weights)
    else:
      res = math_ops.matmul(array_ops.concat(args, 1), self._weights)
    if self._build_bias:
      res = nn_ops.bias_add(res, self._biases)
    return res