-
Notifications
You must be signed in to change notification settings - Fork 7
/
search.py
263 lines (221 loc) · 10.6 KB
/
search.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from utils import util, dtype
from collections import namedtuple
from tensorflow.python.util import nest
class BeamSearchState(namedtuple("BeamSearchState",
("inputs", "state", "finish"))):
pass
def beam_search(features, encoding_fn, decoding_fn, params):
decode_length = params.decode_length
beam_size = params.beam_size
alpha = params.decode_alpha
eos_id = params.tgt_vocab.eos()
pad_id = params.tgt_vocab.pad()
eval_task = params.eval_task
batch_size = tf.shape(features["image"])[0]
if eval_task in ('sign2text', 'sign2gloss'):
model_state = encoding_fn(features["image"], features["mask"])
else:
model_state = encoding_fn(features["source"], None)
src_mask = features["mask"]
source_length = tf.reduce_sum(src_mask, -1)
max_target_length = source_length + decode_length
model_state = nest.map_structure(
lambda x: util.expand_tile_dims(x, beam_size, axis=1),
model_state
)
# in our mixed precision mode, we finally convert logits into tf.float32
# tfdtype = tf.as_dtype(dtype.floatx())
tfdtype = tf.float32
# [batch, beam]
init_log_probs = tf.constant([[0.] + [tfdtype.min] * (beam_size - 1)], dtype=tfdtype)
init_log_probs = tf.tile(init_log_probs, [batch_size, 1])
init_scores = tf.zeros_like(init_log_probs)
# [batch, beam, 1], begin-of-sequence
init_seq = tf.fill([batch_size, beam_size, 1], params.tgt_vocab.pad())
init_finish_seq = tf.zeros_like(init_seq)
# [batch, beam]
init_finish_scores = tf.fill([batch_size, beam_size], tfdtype.min)
init_finish_flags = tf.zeros([batch_size, beam_size], tf.bool)
def cache_init(prev_seq, state):
# used to initialize some caches
# this is because pre-compute these caches is to hard,
# so let's use one dummy run to obtain them.
flat_prev_seqs = util.merge_neighbor_dims(prev_seq, axis=0)
flat_prev_state = nest.map_structure(
lambda x: util.merge_neighbor_dims(x, axis=0),
state
)
_, step_state = decoding_fn(
flat_prev_seqs[:, -1:], flat_prev_state, 0)
new_state = nest.map_structure(
lambda x: util.unmerge_neighbor_dims(x, batch_size, axis=0),
step_state
)
new_state = util.dict_update(new_state, state)
return new_state
model_state = cache_init(init_seq, model_state)
bsstate = BeamSearchState(
inputs=(init_seq, init_log_probs, init_scores),
state=model_state,
finish=(init_finish_seq, init_finish_scores, init_finish_flags)
)
def _not_finished(time, bsstate):
# if the maximum time step is reached, or
# all samples in one batch satisfy that the worst finished sequence
# score is not better than the best alive sequence score
alive_log_probs = bsstate.inputs[1]
finish_scores = bsstate.finish[1]
finish_flags = bsstate.finish[2]
# upper bound of length penality
max_length_penality = tf.pow(
(5. + tf.cast(max_target_length, tfdtype)) / 6., alpha)
best_alive_score = alive_log_probs[:, 0] / max_length_penality
# minimum score among finished sequences alone
worst_finish_score = tf.reduce_min(
finish_scores * tf.cast(finish_flags, tfdtype), 1)
# deal with unfinished instances, which is set to `tf.float32.min`
unfinish_mask = 1. - tf.cast(tf.reduce_any(finish_flags, 1), tfdtype)
worst_finish_score += unfinish_mask * tfdtype.min
# boundary
bound_is_met = tf.reduce_all(tf.greater(worst_finish_score,
best_alive_score))
# length constraint
length_is_met = tf.reduce_any(
tf.less(time, tf.cast(max_target_length, tf.int32)))
return tf.logical_and(tf.logical_not(bound_is_met), length_is_met)
def _step_fn(time, bsstate):
"""one expansion step of beam search process"""
# 1. feed previous predictions, and get the next probabilities
# generating beam * vocab_size predictions
prev_seq, prev_log_probs, prev_scores = bsstate.inputs
flat_prev_seqs = util.merge_neighbor_dims(prev_seq, axis=0)
flat_prev_state = nest.map_structure(
lambda x: util.merge_neighbor_dims(x, axis=0),
bsstate.state
)
# curr_logits: [batch * beam, vocab_size]
step_logits, step_state = decoding_fn(
flat_prev_seqs[:, -1:], flat_prev_state, time)
# add gumbel noise into the logits, simulate gumbel top-k sampling without replacement
if params.enable_noise_beam_search:
step_logits += util.gumbel_noise(util.shape_list(step_logits))
# apply temperature decoding
step_logits /= params.beam_search_temperature
step_log_probs = util.log_prob_from_logits(step_logits)
vocab_size = util.shape_list(step_log_probs)[-1]
# force decoding: disable empty output
eos_mask = tf.cast(tf.equal(tf.range(vocab_size), eos_id), tfdtype)
step_log_probs = tf.cond(dtype.tf_to_float(time) < dtype.tf_to_float(1.),
lambda: step_log_probs + tf.expand_dims(eos_mask, 0) * - dtype.inf(),
lambda: step_log_probs)
# expand to [batch, beam, vocab_size]
step_log_probs = util.unmerge_neighbor_dims(step_log_probs,
batch_size, axis=0)
step_state = nest.map_structure(
lambda x: util.unmerge_neighbor_dims(x, batch_size, axis=0),
step_state
)
# 2. compute top-k scored next predictions
# reducing beam * vocab_size to 2 * beam
# [batch, beam, 1] + [batch, beam, vocab_size]
curr_log_probs = tf.expand_dims(prev_log_probs, 2) + step_log_probs
length_penality = tf.pow((5.0 + tf.cast(time + 1, tfdtype)) / 6., alpha)
curr_scores = curr_log_probs / length_penality
# [batch, beam * vocab_size]
curr_flat_scores = util.merge_neighbor_dims(curr_scores, axis=1)
# [batch, 2 * beam]
topk_scores, topk_indices = tf.nn.top_k(
curr_flat_scores, 2 * beam_size)
# index manipulation, [batch, 2 * beam]
curr_beam_indices = topk_indices // vocab_size
curr_symbol_indices = topk_indices % vocab_size
beam2_pos = util.batch_coordinates(batch_size, 2 * beam_size)
curr_coordinates = tf.stack([beam2_pos, curr_beam_indices], axis=2)
# extract candidate sequences
# [batch, 2 * beam, time + 1]
curr_seq = tf.gather_nd(prev_seq, curr_coordinates)
curr_seq = tf.concat([curr_seq,
tf.expand_dims(curr_symbol_indices, 2)], 2)
# 3. handling alive sequences
# reducing 2 * beam to beam
curr_fin_flags = tf.logical_or(
tf.equal(curr_symbol_indices, eos_id),
# if time step exceeds the maximum decoding length, should stop
tf.expand_dims(
tf.greater_equal(time, tf.cast(max_target_length, tf.int32)), 1)
)
alive_scores = topk_scores + tf.cast(curr_fin_flags, tfdtype) * tfdtype.min
# [batch, 2 * beam] -> [batch, beam]
alive_scores, alive_indices = tf.nn.top_k(alive_scores, beam_size)
beam_pos = util.batch_coordinates(batch_size, beam_size)
alive_coordinates = tf.stack([beam_pos, alive_indices], axis=2)
alive_seq = tf.gather_nd(curr_seq, alive_coordinates)
alive_beam_indices = tf.gather_nd(curr_beam_indices, alive_coordinates)
beam_coordinates = tf.stack([beam_pos, alive_beam_indices], axis=2)
alive_state = nest.map_structure(
lambda x: tf.gather_nd(x, beam_coordinates),
step_state
)
alive_log_probs = alive_scores * length_penality
# 4. handle finished sequences
# reducing 3 * beam to beam
prev_fin_seq, prev_fin_scores, prev_fin_flags = bsstate.finish
# [batch, 2 * beam]
curr_fin_scores = topk_scores + (1.0 - tf.cast(curr_fin_flags, tfdtype)) * tfdtype.min
# [batch, 3 * beam]
fin_flags = tf.concat([prev_fin_flags, curr_fin_flags], axis=1)
fin_scores = tf.concat([prev_fin_scores, curr_fin_scores], axis=1)
# [batch, beam]
fin_scores, fin_indices = tf.nn.top_k(fin_scores, beam_size)
fin_coordinates = tf.stack([beam_pos, fin_indices], axis=2)
fin_flags = tf.gather_nd(fin_flags, fin_coordinates)
pad_seq = tf.fill([batch_size, beam_size, 1],
tf.constant(pad_id, tf.int32))
prev_fin_seq = tf.concat([prev_fin_seq, pad_seq], axis=2)
fin_seq = tf.concat([prev_fin_seq, curr_seq], axis=1)
fin_seq = tf.gather_nd(fin_seq, fin_coordinates)
next_state = BeamSearchState(
inputs=(alive_seq, alive_log_probs, alive_scores),
state=alive_state,
finish=(fin_seq, fin_scores, fin_flags)
)
return time + 1, next_state
time = tf.constant(0, tf.int32, name="time")
shape_invariants = BeamSearchState(
inputs=(tf.TensorShape([None, None, None]),
tf.TensorShape([None, None]),
tf.TensorShape([None, None])),
state=nest.map_structure(
lambda x: util.get_shape_invariants(x),
bsstate.state
),
finish=(tf.TensorShape([None, None, None]),
tf.TensorShape([None, None]),
tf.TensorShape([None, None]))
)
outputs = tf.while_loop(_not_finished, _step_fn, [time, bsstate],
shape_invariants=[tf.TensorShape([]),
shape_invariants],
parallel_iterations=32,
back_prop=False)
final_state = outputs[1]
alive_seqs = final_state.inputs[0]
init_scores = final_state.inputs[2]
final_seqs = final_state.finish[0]
final_scores = final_state.finish[1]
final_flags = final_state.finish[2]
alive_seqs.set_shape([None, beam_size, None])
final_seqs.set_shape([None, beam_size, None])
final_seqs = tf.where(tf.reduce_any(final_flags, 1), final_seqs,
alive_seqs)
final_scores = tf.where(tf.reduce_any(final_flags, 1), final_scores,
init_scores)
return {
'seq': final_seqs[:, :, 1:],
'score': final_scores
}