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beam_search.py
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beam_search.py
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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 contains code to run beam search decoding"""
import tensorflow as tf
import numpy as np
import data
FLAGS = tf.app.flags.FLAGS
class Hypothesis(object):
"""Class to represent a hypothesis during beam search. Holds all the information needed for the hypothesis."""
def __init__(self, tokens, log_probs, state, attn_dists, p_gens, coverage):
"""Hypothesis constructor.
Args:
tokens: List of integers. The ids of the tokens that form the summary so far.
log_probs: List, same length as tokens, of floats, giving the log probabilities of the tokens so far.
state: Current state of the decoder, a LSTMStateTuple.
attn_dists: List, same length as tokens, of numpy arrays with shape (attn_length). These are the attention distributions so far.
p_gens: List, same length as tokens, of floats, or None if not using pointer-generator model. The values of the generation probability so far.
coverage: Numpy array of shape (attn_length), or None if not using coverage. The current coverage vector.
"""
self.tokens = tokens
self.log_probs = log_probs
self.state = state
self.attn_dists = attn_dists
self.p_gens = p_gens
self.coverage = coverage
def extend(self, token, log_prob, state, attn_dist, p_gen, coverage):
"""Return a NEW hypothesis, extended with the information from the latest step of beam search.
Args:
token: Integer. Latest token produced by beam search.
log_prob: Float. Log prob of the latest token.
state: Current decoder state, a LSTMStateTuple.
attn_dist: Attention distribution from latest step. Numpy array shape (attn_length).
p_gen: Generation probability on latest step. Float.
coverage: Latest coverage vector. Numpy array shape (attn_length), or None if not using coverage.
Returns:
New Hypothesis for next step.
"""
return Hypothesis(tokens = self.tokens + [token],
log_probs = self.log_probs + [log_prob],
state = state,
attn_dists = self.attn_dists + [attn_dist],
p_gens = self.p_gens + [p_gen],
coverage = coverage)
@property
def latest_token(self):
return self.tokens[-1]
@property
def log_prob(self):
# the log probability of the hypothesis so far is the sum of the log probabilities of the tokens so far
return sum(self.log_probs)
@property
def avg_log_prob(self):
# normalize log probability by number of tokens (otherwise longer sequences always have lower probability)
return self.log_prob / len(self.tokens)
def run_beam_search(sess, model, vocab, batch):
"""Performs beam search decoding on the given example.
Args:
sess: a tf.Session
model: a seq2seq model
vocab: Vocabulary object
batch: Batch object that is the same example repeated across the batch
Returns:
best_hyp: Hypothesis object; the best hypothesis found by beam search.
"""
# Run the encoder to get the encoder hidden states and decoder initial state
enc_states, dec_in_state = model.run_encoder(sess, batch)
# dec_in_state is a LSTMStateTuple
# enc_states has shape [batch_size, <=max_enc_steps, 2*hidden_dim].
# Initialize beam_size-many hyptheses
hyps = [Hypothesis(tokens=[vocab.word2id(data.START_DECODING)],
log_probs=[0.0],
state=dec_in_state,
attn_dists=[],
p_gens=[],
coverage=np.zeros([batch.enc_batch.shape[1]]) # zero vector of length attention_length
) for _ in xrange(FLAGS.beam_size)]
results = [] # this will contain finished hypotheses (those that have emitted the [STOP] token)
steps = 0
while steps < FLAGS.max_dec_steps and len(results) < FLAGS.beam_size:
latest_tokens = [h.latest_token for h in hyps] # latest token produced by each hypothesis
latest_tokens = [t if t in xrange(vocab.size()) else vocab.word2id(data.UNKNOWN_TOKEN) for t in latest_tokens] # change any in-article temporary OOV ids to [UNK] id, so that we can lookup word embeddings
states = [h.state for h in hyps] # list of current decoder states of the hypotheses
prev_coverage = [h.coverage for h in hyps] # list of coverage vectors (or None)
# Run one step of the decoder to get the new info
(topk_ids, topk_log_probs, new_states, attn_dists, p_gens, new_coverage) = model.decode_onestep(sess=sess,
batch=batch,
latest_tokens=latest_tokens,
enc_states=enc_states,
dec_init_states=states,
prev_coverage=prev_coverage)
# Extend each hypothesis and collect them all in all_hyps
all_hyps = []
num_orig_hyps = 1 if steps == 0 else len(hyps) # On the first step, we only had one original hypothesis (the initial hypothesis). On subsequent steps, all original hypotheses are distinct.
for i in xrange(num_orig_hyps):
h, new_state, attn_dist, p_gen, new_coverage_i = hyps[i], new_states[i], attn_dists[i], p_gens[i], new_coverage[i] # take the ith hypothesis and new decoder state info
for j in xrange(FLAGS.beam_size * 2): # for each of the top 2*beam_size hyps:
# Extend the ith hypothesis with the jth option
new_hyp = h.extend(token=topk_ids[i, j],
log_prob=topk_log_probs[i, j],
state=new_state,
attn_dist=attn_dist,
p_gen=p_gen,
coverage=new_coverage_i)
all_hyps.append(new_hyp)
# Filter and collect any hypotheses that have produced the end token.
hyps = [] # will contain hypotheses for the next step
for h in sort_hyps(all_hyps): # in order of most likely h
if h.latest_token == vocab.word2id(data.STOP_DECODING): # if stop token is reached...
# If this hypothesis is sufficiently long, put in results. Otherwise discard.
if steps >= FLAGS.min_dec_steps:
results.append(h)
else: # hasn't reached stop token, so continue to extend this hypothesis
hyps.append(h)
if len(hyps) == FLAGS.beam_size or len(results) == FLAGS.beam_size:
# Once we've collected beam_size-many hypotheses for the next step, or beam_size-many complete hypotheses, stop.
break
steps += 1
# At this point, either we've got beam_size results, or we've reached maximum decoder steps
if len(results)==0: # if we don't have any complete results, add all current hypotheses (incomplete summaries) to results
results = hyps
# Sort hypotheses by average log probability
hyps_sorted = sort_hyps(results)
# Return the hypothesis with highest average log prob
return hyps_sorted[0]
def sort_hyps(hyps):
"""Return a list of Hypothesis objects, sorted by descending average log probability"""
return sorted(hyps, key=lambda h: h.avg_log_prob, reverse=True)