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attn_decoder.py
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attn_decoder.py
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"""Attention-enabled decoder class of seq2seq model.
Author: Shubham Toshniwal
Contact: shtoshni@ttic.edu
Date: February, 2018
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.rnn.python.ops.core_rnn_cell import _linear
from decoder import Decoder
from base_params import BaseParams
class AttnDecoder(Decoder, BaseParams):
"""Implements the attention decoder of encoder-decoder framework."""
@classmethod
def class_params(cls):
"""Defines params of the class."""
params = super(AttnDecoder, cls).class_params()
params['attention_vec_size'] = 128
params['lm_hidden_size'] = 256
params['ind_softmax'] = False
return params
def __init__(self, isTraining, params=None, scope=None):
"""Initializer."""
super(AttnDecoder, self).__init__(isTraining=isTraining, params=params)
# No output projection required in attention decoder
self.scope = scope
self.cell = self.get_cell()
def __call__(self, decoder_inp, seq_len,
encoder_hidden_states, seq_len_inp):
# First prepare the decoder input - Embed the input and obtain the
# relevant loop function
params = self.params
scope = "rnn_decoder" + ("" if self.scope is None else "_" + self.scope)
with tf.variable_scope(scope):
decoder_inputs, loop_function = self.prepare_decoder_input(decoder_inp)
lm_cell = self.get_cell(hidden_size=params.lm_hidden_size)
# TensorArray is used to do dynamic looping over decoder input
inputs_ta = tf.TensorArray(size=params.max_output,
dtype=tf.float32)
inputs_ta = inputs_ta.unstack(decoder_inputs)
batch_size = tf.shape(decoder_inputs)[1]
attn_length = tf.shape(encoder_hidden_states)[1]
emb_size = decoder_inputs.get_shape()[2].value
attn_size = encoder_hidden_states.get_shape()[2].value
# Attention variables
attn_mask = tf.sequence_mask(tf.cast(seq_len_inp, tf.int32), dtype=tf.float32)
batch_attn_size = tf.stack([batch_size, attn_size])
attn = tf.zeros(batch_attn_size, dtype=tf.float32)
batch_alpha_size = tf.stack([batch_size, attn_length, 1, 1])
alpha = tf.zeros(batch_alpha_size, dtype=tf.float32)
with tf.variable_scope(scope):
# Calculate the W*h_enc component
hidden = tf.expand_dims(encoder_hidden_states, 2)
W_attn = tf.get_variable(
"AttnW", [1, 1, attn_size, params.attention_vec_size])
hidden_features = tf.nn.conv2d(hidden, W_attn, [1, 1, 1, 1], "SAME")
v = tf.get_variable("AttnV", [params.attention_vec_size])
def raw_loop_function(time, cell_output, state, loop_state):
def attention(query, prev_alpha):
"""Put attention masks on hidden using hidden_features and query."""
with tf.variable_scope("Attention"):
y = _linear(query, params.attention_vec_size, True)
y = tf.reshape(y, [-1, 1, 1, params.attention_vec_size])
s = tf.reduce_sum(
v * tf.tanh(hidden_features + y), [2, 3])
alpha = tf.nn.softmax(s) * attn_mask
sum_vec = tf.reduce_sum(alpha, reduction_indices=[1], keepdims=True)
norm_term = tf.tile(sum_vec, tf.stack([1, tf.shape(alpha)[1]]))
alpha = alpha / norm_term
alpha = tf.expand_dims(alpha, 2)
alpha = tf.expand_dims(alpha, 3)
context_vec = tf.reduce_sum(alpha * hidden, [1, 2])
return tuple([context_vec, alpha])
# If loop_function is set, we use it instead of decoder_inputs.
elements_finished = (time >= tf.cast(seq_len, tf.int32))
finished = tf.reduce_all(elements_finished)
if cell_output is None:
next_state = self.cell.zero_state(batch_size, dtype=tf.float32)
# This output is not used but is just used to tell the shape
# without the batch dimension
# Check here - https://www.tensorflow.org/api_docs/python/tf/nn/raw_rnn
output = tf.zeros((self.params.vocab_size))
lm_input = inputs_ta.read(time)
attn_state = tuple([attn, alpha])
lm_state = lm_cell.zero_state(batch_size, dtype=tf.float32)
else:
next_state = state
#loop_state = attention(cell_output, loop_state[1])
lm_state, attn_state = loop_state
attn_state = attention(self.get_state(state), attn_state[1])
with tf.variable_scope("AttnProjection"):
proj_output = _linear([self.get_state(state), attn_state[0]],
self.params.hidden_size_dec, True)
if params.ind_softmax:
# Don't share parameters with LM model
with tf.variable_scope("OutputProjection2"):
output = _linear([proj_output], self.params.vocab_size, True)
else:
with tf.variable_scope("OutputProjection"):
output = _linear([proj_output], self.params.vocab_size, True)
if not self.isTraining:
lm_input = loop_function(output)
else:
if loop_function is not None:
random_prob = tf.random_uniform([])
lm_input = tf.cond(
finished,
lambda: tf.zeros([batch_size, emb_size], dtype=tf.float32),
lambda: tf.cond(tf.less(random_prob, 1 - params.samp_prob),
lambda: inputs_ta.read(time),
lambda: loop_function(output))
)
else:
lm_input = tf.cond(
finished,
lambda: tf.zeros([batch_size, emb_size], dtype=tf.float32),
lambda: inputs_ta.read(time)
)
# Common calculations
lm_output, next_lm_state = lm_cell(lm_input, lm_state)
if params.lm_hidden_size != params.hidden_size_dec:
with tf.variable_scope("SimpleProjection", reuse=tf.AUTO_REUSE):
lm_output = _linear([lm_output], params.hidden_size_dec, True)
# Merge input and previous attentions into one vector of the right size.
input_size = lm_input.get_shape().with_rank(2)[1]
if input_size.value is None:
raise ValueError("Could not infer input size from input")
with tf.variable_scope("InputProjection", reuse=tf.AUTO_REUSE):
next_input = _linear([lm_output, attn_state[0]], input_size, True)
loop_state = tuple([next_lm_state, attn_state])
return (elements_finished, next_input, next_state, output, loop_state)
# outputs is a TensorArray with T=max(sequence_length) entries
# of shape Bx|V|
outputs, state, _ = tf.nn.raw_rnn(self.cell, raw_loop_function)
# Concatenate the output across timesteps to get a tensor of TxBx|V|
# shape
outputs = outputs.concat()
return outputs
@classmethod
def add_parse_options(cls, parser):
"""Add decoder specific arguments."""
# Decoder params
super(AttnDecoder, cls).add_parse_options(parser)
parser.add_argument("-samp_prob", "--samp_prob", default=0.1, type=float,
help="Scheduled sampling probability")
parser.add_argument("-attn_vec_size", "--attention_vec_size", default=128,
type=int, help="Attention vector size")
parser.add_argument("-lm_hsize", "--lm_hidden_size", default=256,
type=int, help="Hidden Size of LM layer")
parser.add_argument('-ind_softmax', "--ind_softmax", default=False,
action="store_true", help="Independent (from LM) softmax params")