-
Notifications
You must be signed in to change notification settings - Fork 710
/
rollout.py
244 lines (193 loc) · 10.2 KB
/
rollout.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
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
import numpy as np
class ROLLOUT(object):
def __init__(self, lstm, update_rate):
self.lstm = lstm
self.update_rate = update_rate
self.num_emb = self.lstm.num_emb
self.batch_size = self.lstm.batch_size
self.emb_dim = self.lstm.emb_dim
self.hidden_dim = self.lstm.hidden_dim
self.sequence_length = self.lstm.sequence_length
self.start_token = tf.identity(self.lstm.start_token)
self.learning_rate = self.lstm.learning_rate
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit() # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit() # maps h_t to o_t (output token logits)
#####################################################################################################
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length]) # sequence of tokens generated by generator
self.given_num = tf.placeholder(tf.int32)
# processed for batch
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x), perm=[1, 0, 2]) # seq_length x batch_size x emb_dim
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
ta_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length)
ta_x = ta_x.unstack(tf.transpose(self.x, perm=[1, 0]))
#####################################################################################################
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
# When current index i < given_num, use the provided tokens as the input at each time step
def _g_recurrence_1(i, x_t, h_tm1, given_num, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
x_tp1 = ta_emb_x.read(i)
gen_x = gen_x.write(i, ta_x.read(i))
return i + 1, x_tp1, h_t, given_num, gen_x
# When current index i >= given_num, start roll-out, use the output as time step t as the input at time step t+1
def _g_recurrence_2(i, x_t, h_tm1, given_num, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token) # batch x emb_dim
gen_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, given_num, gen_x
i, x_t, h_tm1, given_num, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, given_num, _4: i < given_num,
body=_g_recurrence_1,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, self.given_num, gen_x))
_, _, _, _, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.sequence_length,
body=_g_recurrence_2,
loop_vars=(i, x_t, h_tm1, given_num, self.gen_x))
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
def get_reward(self, sess, input_x, rollout_num, discriminator):
rewards = []
for i in range(rollout_num):
# given_num between 1 to sequence_length - 1 for a part completed sentence
for given_num in range(1, self.sequence_length ):
feed = {self.x: input_x, self.given_num: given_num}
samples = sess.run(self.gen_x, feed)
feed = {discriminator.input_x: samples, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
rewards[given_num - 1] += ypred
# the last token reward
feed = {discriminator.input_x: input_x, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
# completed sentence reward
rewards[self.sequence_length - 1] += ypred
rewards = np.transpose(np.array(rewards)) / (1.0 * rollout_num) # batch_size x seq_length
return rewards
def create_recurrent_unit(self):
# Weights and Bias for input and hidden tensor
self.Wi = tf.identity(self.lstm.Wi)
self.Ui = tf.identity(self.lstm.Ui)
self.bi = tf.identity(self.lstm.bi)
self.Wf = tf.identity(self.lstm.Wf)
self.Uf = tf.identity(self.lstm.Uf)
self.bf = tf.identity(self.lstm.bf)
self.Wog = tf.identity(self.lstm.Wog)
self.Uog = tf.identity(self.lstm.Uog)
self.bog = tf.identity(self.lstm.bog)
self.Wc = tf.identity(self.lstm.Wc)
self.Uc = tf.identity(self.lstm.Uc)
self.bc = tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def update_recurrent_unit(self):
# Weights and Bias for input and hidden tensor
self.Wi = self.update_rate * self.Wi + (1 - self.update_rate) * tf.identity(self.lstm.Wi)
self.Ui = self.update_rate * self.Ui + (1 - self.update_rate) * tf.identity(self.lstm.Ui)
self.bi = self.update_rate * self.bi + (1 - self.update_rate) * tf.identity(self.lstm.bi)
self.Wf = self.update_rate * self.Wf + (1 - self.update_rate) * tf.identity(self.lstm.Wf)
self.Uf = self.update_rate * self.Uf + (1 - self.update_rate) * tf.identity(self.lstm.Uf)
self.bf = self.update_rate * self.bf + (1 - self.update_rate) * tf.identity(self.lstm.bf)
self.Wog = self.update_rate * self.Wog + (1 - self.update_rate) * tf.identity(self.lstm.Wog)
self.Uog = self.update_rate * self.Uog + (1 - self.update_rate) * tf.identity(self.lstm.Uog)
self.bog = self.update_rate * self.bog + (1 - self.update_rate) * tf.identity(self.lstm.bog)
self.Wc = self.update_rate * self.Wc + (1 - self.update_rate) * tf.identity(self.lstm.Wc)
self.Uc = self.update_rate * self.Uc + (1 - self.update_rate) * tf.identity(self.lstm.Uc)
self.bc = self.update_rate * self.bc + (1 - self.update_rate) * tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_output_unit(self):
self.Wo = tf.identity(self.lstm.Wo)
self.bo = tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def update_output_unit(self):
self.Wo = self.update_rate * self.Wo + (1 - self.update_rate) * tf.identity(self.lstm.Wo)
self.bo = self.update_rate * self.bo + (1 - self.update_rate) * tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def update_params(self):
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.update_recurrent_unit()
self.g_output_unit = self.update_output_unit()