-
Notifications
You must be signed in to change notification settings - Fork 26
/
train.py
270 lines (237 loc) · 8.67 KB
/
train.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
264
265
266
267
268
269
270
import numpy as np
import torch
import argparse
import logging
import time
import pdb
import os
import json
import random
from utils import (
evaluate
)
from tqdm import tqdm
from src.biosyn import (
QueryDataset,
CandidateDataset,
DictionaryDataset,
TextPreprocess,
RerankNet,
BioSyn
)
LOGGER = logging.getLogger()
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Biosyn train')
# Required
parser.add_argument('--model_name_or_path', required=True,
help='Directory for pretrained model')
parser.add_argument('--train_dictionary_path', type=str, required=True,
help='train dictionary path')
parser.add_argument('--train_dir', type=str, required=True,
help='training set directory')
parser.add_argument('--output_dir', type=str, required=True,
help='Directory for output')
# Tokenizer settings
parser.add_argument('--max_length', default=25, type=int)
# Train config
parser.add_argument('--seed', type=int,
default=0)
parser.add_argument('--use_cuda', action="store_true")
parser.add_argument('--draft', action="store_true")
parser.add_argument('--topk', type=int,
default=20)
parser.add_argument('--learning_rate',
help='learning rate',
default=0.0001, type=float)
parser.add_argument('--weight_decay',
help='weight decay',
default=0.01, type=float)
parser.add_argument('--train_batch_size',
help='train batch size',
default=16, type=int)
parser.add_argument('--epoch',
help='epoch to train',
default=10, type=int)
parser.add_argument('--initial_sparse_weight',
default=0, type=float)
parser.add_argument('--dense_ratio', type=float,
default=0.5)
parser.add_argument('--save_checkpoint_all', action="store_true")
args = parser.parse_args()
return args
def init_logging():
LOGGER.setLevel(logging.INFO)
fmt = logging.Formatter('%(asctime)s: [ %(message)s ]',
'%m/%d/%Y %I:%M:%S %p')
console = logging.StreamHandler()
console.setFormatter(fmt)
LOGGER.addHandler(console)
def init_seed(seed=None):
if seed is None:
seed = int(round(time.time() * 1000)) % 10000
LOGGER.info("Using seed={}, pid={}".format(seed, os.getpid()))
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def load_dictionary(dictionary_path):
"""
load dictionary
Parameters
----------
dictionary_path : str
a path of dictionary
"""
dictionary = DictionaryDataset(
dictionary_path = dictionary_path
)
return dictionary.data
def load_queries(data_dir, filter_composite, filter_duplicate, filter_cuiless):
"""
load query data
Parameters
----------
is_train : bool
train or dev
filter_composite : bool
filter composite mentions
filter_duplicate : bool
filter duplicate queries
filter_cuiless : bool
filter samples with cuiless
"""
dataset = QueryDataset(
data_dir=data_dir,
filter_composite=filter_composite,
filter_duplicate=filter_duplicate,
filter_cuiless=filter_cuiless
)
return dataset.data
def train(args, data_loader, model):
LOGGER.info("train!")
train_loss = 0
train_steps = 0
model.train()
for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
model.optimizer.zero_grad()
batch_x, batch_y = data
batch_pred = model(batch_x)
loss = model.get_loss(batch_pred, batch_y)
loss.backward()
model.optimizer.step()
train_loss += loss.item()
train_steps += 1
train_loss /= (train_steps + 1e-9)
return train_loss
def main(args):
init_logging()
init_seed(args.seed)
print(args)
# prepare for output
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# load dictionary and queries
train_dictionary = load_dictionary(dictionary_path=args.train_dictionary_path)
train_queries = load_queries(
data_dir = args.train_dir,
filter_composite=True,
filter_duplicate=True,
filter_cuiless=True
)
if args.draft:
train_dictionary = train_dictionary[:100]
train_queries = train_queries[:10]
args.output_dir = args.output_dir + "_draft"
# filter only names
names_in_train_dictionary = train_dictionary[:,0]
names_in_train_queries = train_queries[:,0]
# load BERT tokenizer, dense_encoder, sparse_encoder
biosyn = BioSyn(
max_length=args.max_length,
use_cuda=args.use_cuda,
initial_sparse_weight=args.initial_sparse_weight
)
biosyn.init_sparse_encoder(corpus=names_in_train_dictionary)
biosyn.load_dense_encoder(
model_name_or_path=args.model_name_or_path
)
# load rerank model
model = RerankNet(
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
encoder = biosyn.get_dense_encoder(),
sparse_weight=biosyn.get_sparse_weight(),
use_cuda=args.use_cuda
)
# embed sparse representations for query and dictionary
# Important! This is one time process because sparse represenation never changes.
LOGGER.info("Sparse embedding")
train_query_sparse_embeds = biosyn.embed_sparse(names=names_in_train_queries)
train_dict_sparse_embeds = biosyn.embed_sparse(names=names_in_train_dictionary)
train_sparse_score_matrix = biosyn.get_score_matrix(
query_embeds=train_query_sparse_embeds,
dict_embeds=train_dict_sparse_embeds
)
train_sparse_candidate_idxs = biosyn.retrieve_candidate(
score_matrix=train_sparse_score_matrix,
topk=args.topk
)
# prepare for data loader of train and dev
train_set = CandidateDataset(
queries = train_queries,
dicts = train_dictionary,
tokenizer = biosyn.get_dense_tokenizer(),
s_score_matrix=train_sparse_score_matrix,
s_candidate_idxs=train_sparse_candidate_idxs,
topk = args.topk,
d_ratio=args.dense_ratio,
max_length=args.max_length
)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=args.train_batch_size,
shuffle=True,
)
start = time.time()
for epoch in range(1,args.epoch+1):
# embed dense representations for query and dictionary for train
# Important! This is iterative process because dense represenation changes as model is trained.
LOGGER.info("Epoch {}/{}".format(epoch,args.epoch))
LOGGER.info("train_set dense embedding for iterative candidate retrieval")
train_query_dense_embeds = biosyn.embed_dense(names=names_in_train_queries, show_progress=True)
train_dict_dense_embeds = biosyn.embed_dense(names=names_in_train_dictionary, show_progress=True)
train_dense_score_matrix = biosyn.get_score_matrix(
query_embeds=train_query_dense_embeds,
dict_embeds=train_dict_dense_embeds
)
train_dense_candidate_idxs = biosyn.retrieve_candidate(
score_matrix=train_dense_score_matrix,
topk=args.topk
)
# replace dense candidates in the train_set
train_set.set_dense_candidate_idxs(d_candidate_idxs=train_dense_candidate_idxs)
# train
train_loss = train(args, data_loader=train_loader, model=model)
LOGGER.info('loss/train_per_epoch={}/{}'.format(train_loss,epoch))
# save model every epoch
if args.save_checkpoint_all:
checkpoint_dir = os.path.join(args.output_dir, "checkpoint_{}".format(epoch))
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
biosyn.save_model(checkpoint_dir)
# save model last epoch
if epoch == args.epoch:
biosyn.save_model(args.output_dir)
end = time.time()
training_time = end-start
training_hour = int(training_time/60/60)
training_minute = int(training_time/60 % 60)
training_second = int(training_time % 60)
LOGGER.info("Training Time!{} hours {} minutes {} seconds".format(training_hour, training_minute, training_second))
if __name__ == '__main__':
args = parse_args()
main(args)