TensorFlow-Models/textsum/seq2seq_lib.py

# Copyright 2016 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.
# ==============================================================================

"""seq2seq library codes copied from elsewhere for customization."""

import tensorflow as tf


# Adapted to support sampled_softmax loss function, which accepts activations
# instead of logits.
def sequence_loss_by_example(inputs, targets, weights, loss_function,
                             average_across_timesteps=True, name=None):
  """Sampled softmax loss for a sequence of inputs (per example).

  Args:
    inputs: List of 2D Tensors of shape [batch_size x hid_dim].
    targets: List of 1D batch-sized int32 Tensors of the same length as logits.
    weights: List of 1D batch-sized float-Tensors of the same length as logits.
    loss_function: Sampled softmax function (inputs, labels) -> loss
    average_across_timesteps: If set, divide the returned cost by the total
      label weight.
    name: Optional name for this operation, default: 'sequence_loss_by_example'.

  Returns:
    1D batch-sized float Tensor: The log-perplexity for each sequence.

  Raises:
    ValueError: If len(inputs) is different from len(targets) or len(weights).
  """
  if len(targets) != len(inputs) or len(weights) != len(inputs):
    raise ValueError('Lengths of logits, weights, and targets must be the same '
                     '%d, %d, %d.' % (len(inputs), len(weights), len(targets)))
  with tf.name_scope(values=inputs + targets + weights, name=name,
                     default_name='sequence_loss_by_example'):
    log_perp_list = []
    for inp, target, weight in zip(inputs, targets, weights):
      crossent = loss_function(inp, target)
      log_perp_list.append(crossent * weight)
    log_perps = tf.add_n(log_perp_list)
    if average_across_timesteps:
      total_size = tf.add_n(weights)
      total_size += 1e-12  # Just to avoid division by 0 for all-0 weights.
      log_perps /= total_size
  return log_perps


def sampled_sequence_loss(inputs, targets, weights, loss_function,
                          average_across_timesteps=True,
                          average_across_batch=True, name=None):
  """Weighted cross-entropy loss for a sequence of logits, batch-collapsed.

  Args:
    inputs: List of 2D Tensors of shape [batch_size x hid_dim].
    targets: List of 1D batch-sized int32 Tensors of the same length as inputs.
    weights: List of 1D batch-sized float-Tensors of the same length as inputs.
    loss_function: Sampled softmax function (inputs, labels) -> loss
    average_across_timesteps: If set, divide the returned cost by the total
      label weight.
    average_across_batch: If set, divide the returned cost by the batch size.
    name: Optional name for this operation, defaults to 'sequence_loss'.

  Returns:
    A scalar float Tensor: The average log-perplexity per symbol (weighted).

  Raises:
    ValueError: If len(inputs) is different from len(targets) or len(weights).
  """
  with tf.name_scope(values=inputs + targets + weights, name=name,
                     default_name='sampled_sequence_loss'):
    cost = tf.reduce_sum(sequence_loss_by_example(
        inputs, targets, weights, loss_function,
        average_across_timesteps=average_across_timesteps))
    if average_across_batch:
      batch_size = tf.shape(targets[0])[0]
      return cost / tf.cast(batch_size, tf.float32)
    else:
      return cost


def linear(args, output_size, bias, bias_start=0.0, scope=None):
  """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 W[i].
    bias: boolean, whether to add a bias term or not.
    bias_start: starting value to initialize the bias; 0 by default.
    scope: VariableScope for the created subgraph; defaults to "Linear".

  Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.

  Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
  """
  if args is None or (isinstance(args, (list, tuple)) and not args):
    raise ValueError('`args` must be specified')
  if not isinstance(args, (list, tuple)):
    args = [args]

  # Calculate the total size of arguments on dimension 1.
  total_arg_size = 0
  shapes = [a.get_shape().as_list() for a in args]
  for shape in shapes:
    if len(shape) != 2:
      raise ValueError('Linear is expecting 2D arguments: %s' % str(shapes))
    if not shape[1]:
      raise ValueError('Linear expects shape[1] of arguments: %s' % str(shapes))
    else:
      total_arg_size += shape[1]

  # Now the computation.
  with tf.variable_scope(scope or 'Linear'):
    matrix = tf.get_variable('Matrix', [total_arg_size, output_size])
    if len(args) == 1:
      res = tf.matmul(args[0], matrix)
    else:
      res = tf.matmul(tf.concat(axis=1, values=args), matrix)
    if not bias:
      return res
    bias_term = tf.get_variable(
        'Bias', [output_size],
        initializer=tf.constant_initializer(bias_start))
  return res + bias_term