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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications Copyright 2017 Abigail See
#
# 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.
# ==============================================================================
"""This file defines the decoder"""
import tensorflow as tf
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import math_ops
# Note: this function is based on tf.contrib.legacy_seq2seq_attention_decoder, which is now outdated.
# In the future, it would make more sense to write variants on the attention mechanism using the new seq2seq library for tensorflow 1.0: https://www.tensorflow.org/api_guides/python/contrib.seq2seq#Attention
def attention_decoder(decoder_inputs, initial_state, encoder_states, enc_padding_mask, cell, initial_state_attention=False, pointer_gen=True, use_coverage=False, prev_coverage=None):
"""
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor [batch_size x cell.state_size].
encoder_states: 3D Tensor [batch_size x attn_length x attn_size].
enc_padding_mask: 2D Tensor [batch_size x attn_length] containing 1s and 0s; indicates which of the encoder locations are padding (0) or a real token (1).
cell: rnn_cell.RNNCell defining the cell function and size.
initial_state_attention:
Note that this attention decoder passes each decoder input through a linear layer with the previous step's context vector to get a modified version of the input. If initial_state_attention is False, on the first decoder step the "previous context vector" is just a zero vector. If initial_state_attention is True, we use initial_state to (re)calculate the previous step's context vector. We set this to False for train/eval mode (because we call attention_decoder once for all decoder steps) and True for decode mode (because we call attention_decoder once for each decoder step).
pointer_gen: boolean. If True, calculate the generation probability p_gen for each decoder step.
use_coverage: boolean. If True, use coverage mechanism.
prev_coverage:
If not None, a tensor with shape (batch_size, attn_length). The previous step's coverage vector. This is only not None in decode mode when using coverage.
Returns:
outputs: A list of the same length as decoder_inputs of 2D Tensors of
shape [batch_size x cell.output_size]. The output vectors.
state: The final state of the decoder. A tensor shape [batch_size x cell.state_size].
attn_dists: A list containing tensors of shape (batch_size,attn_length).
The attention distributions for each decoder step.
p_gens: List of length input_size, containing tensors of shape [batch_size, 1]. The values of p_gen for each decoder step. Empty list if pointer_gen=False.
coverage: Coverage vector on the last step computed. None if use_coverage=False.
"""
with variable_scope.variable_scope("attention_decoder") as scope:
batch_size = encoder_states.get_shape()[0].value # if this line fails, it's because the batch size isn't defined
attn_size = encoder_states.get_shape()[2].value # if this line fails, it's because the attention length isn't defined
# Reshape encoder_states (need to insert a dim)
encoder_states = tf.expand_dims(encoder_states, axis=2) # now is shape (batch_size, attn_len, 1, attn_size)
# To calculate attention, we calculate
# v^T tanh(W_h h_i + W_s s_t + b_attn)
# where h_i is an encoder state, and s_t a decoder state.
# attn_vec_size is the length of the vectors v, b_attn, (W_h h_i) and (W_s s_t).
# We set it to be equal to the size of the encoder states.
attention_vec_size = attn_size
# Get the weight matrix W_h and apply it to each encoder state to get (W_h h_i), the encoder features
W_h = variable_scope.get_variable("W_h", [1, 1, attn_size, attention_vec_size])
encoder_features = nn_ops.conv2d(encoder_states, W_h, [1, 1, 1, 1], "SAME") # shape (batch_size,attn_length,1,attention_vec_size)
# Get the weight vectors v and w_c (w_c is for coverage)
v = variable_scope.get_variable("v", [attention_vec_size])
if use_coverage:
with variable_scope.variable_scope("coverage"):
w_c = variable_scope.get_variable("w_c", [1, 1, 1, attention_vec_size])
if prev_coverage is not None: # for beam search mode with coverage
# reshape from (batch_size, attn_length) to (batch_size, attn_len, 1, 1)
prev_coverage = tf.expand_dims(tf.expand_dims(prev_coverage,2),3)
def attention(decoder_state, coverage=None):
"""Calculate the context vector and attention distribution from the decoder state.
Args:
decoder_state: state of the decoder
coverage: Optional. Previous timestep's coverage vector, shape (batch_size, attn_len, 1, 1).
Returns:
context_vector: weighted sum of encoder_states
attn_dist: attention distribution
coverage: new coverage vector. shape (batch_size, attn_len, 1, 1)
"""
with variable_scope.variable_scope("Attention"):
# Pass the decoder state through a linear layer (this is W_s s_t + b_attn in the paper)
decoder_features = linear(decoder_state, attention_vec_size, True) # shape (batch_size, attention_vec_size)
decoder_features = tf.expand_dims(tf.expand_dims(decoder_features, 1), 1) # reshape to (batch_size, 1, 1, attention_vec_size)
def masked_attention(e):
"""Take softmax of e then apply enc_padding_mask and re-normalize"""
attn_dist = nn_ops.softmax(e) # take softmax. shape (batch_size, attn_length)
attn_dist *= enc_padding_mask # apply mask
masked_sums = tf.reduce_sum(attn_dist, axis=1) # shape (batch_size)
return attn_dist / tf.reshape(masked_sums, [-1, 1]) # re-normalize
if use_coverage and coverage is not None: # non-first step of coverage
# Multiply coverage vector by w_c to get coverage_features.
coverage_features = nn_ops.conv2d(coverage, w_c, [1, 1, 1, 1], "SAME") # c has shape (batch_size, attn_length, 1, attention_vec_size)
# Calculate v^T tanh(W_h h_i + W_s s_t + w_c c_i^t + b_attn)
e = math_ops.reduce_sum(v * math_ops.tanh(encoder_features + decoder_features + coverage_features), [2, 3]) # shape (batch_size,attn_length)
# Calculate attention distribution
attn_dist = masked_attention(e)
# Update coverage vector
coverage += array_ops.reshape(attn_dist, [batch_size, -1, 1, 1])
else:
# Calculate v^T tanh(W_h h_i + W_s s_t + b_attn)
e = math_ops.reduce_sum(v * math_ops.tanh(encoder_features + decoder_features), [2, 3]) # calculate e
# Calculate attention distribution
attn_dist = masked_attention(e)
if use_coverage: # first step of training
coverage = tf.expand_dims(tf.expand_dims(attn_dist,2),2) # initialize coverage
# Calculate the context vector from attn_dist and encoder_states
context_vector = math_ops.reduce_sum(array_ops.reshape(attn_dist, [batch_size, -1, 1, 1]) * encoder_states, [1, 2]) # shape (batch_size, attn_size).
context_vector = array_ops.reshape(context_vector, [-1, attn_size])
return context_vector, attn_dist, coverage
outputs = []
attn_dists = []
p_gens = []
state = initial_state
coverage = prev_coverage # initialize coverage to None or whatever was passed in
context_vector = array_ops.zeros([batch_size, attn_size])
context_vector.set_shape([None, attn_size]) # Ensure the second shape of attention vectors is set.
if initial_state_attention: # true in decode mode
# Re-calculate the context vector from the previous step so that we can pass it through a linear layer with this step's input to get a modified version of the input
context_vector, _, coverage = attention(initial_state, coverage) # in decode mode, this is what updates the coverage vector
for i, inp in enumerate(decoder_inputs):
tf.logging.info("Adding attention_decoder timestep %i of %i", i, len(decoder_inputs))
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
# Merge input and previous attentions into one vector x of the same size as inp
input_size = inp.get_shape().with_rank(2)[1]
if input_size.value is None:
raise ValueError("Could not infer input size from input: %s" % inp.name)
x = linear([inp] + [context_vector], input_size, True)
# Run the decoder RNN cell. cell_output = decoder state
cell_output, state = cell(x, state)
# Run the attention mechanism.
if i == 0 and initial_state_attention: # always true in decode mode
with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=True): # you need this because you've already run the initial attention(...) call
context_vector, attn_dist, _ = attention(state, coverage) # don't allow coverage to update
else:
context_vector, attn_dist, coverage = attention(state, coverage)
attn_dists.append(attn_dist)
# Calculate p_gen
if pointer_gen:
with tf.variable_scope('calculate_pgen'):
p_gen = linear([context_vector, state.c, state.h, x], 1, True) # Tensor shape (batch_size, 1)
p_gen = tf.sigmoid(p_gen)
p_gens.append(p_gen)
# Concatenate the cell_output (= decoder state) and the context vector, and pass them through a linear layer
# This is V[s_t, h*_t] + b in the paper
with variable_scope.variable_scope("AttnOutputProjection"):
output = linear([cell_output] + [context_vector], cell.output_size, True)
outputs.append(output)
# If using coverage, reshape it
if coverage is not None:
coverage = array_ops.reshape(coverage, [batch_size, -1])
return outputs, state, attn_dists, p_gens, coverage
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
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