-
Notifications
You must be signed in to change notification settings - Fork 5
/
run_down_triplecls.py
231 lines (182 loc) · 8.69 KB
/
run_down_triplecls.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
from tkinter import N
from torch.utils.data import DataLoader
from data import KGDataset_down_triplecls, KGTokenizer, KGDataset, Config
from setup_parser import setup_parser
from kg_bert import KGBert, KGBert_down_tirplecls
import torch
from trainer import KGBertTrainer_down_triplecls
import logging
from utils import save_best_model
import glob
import os
def pad_sequence(batch_data, sentences_ft, masks_ft, batch_token_types, batch_label, visible_matrixs = None):
# Make all tensor in a batch the same length by padding with zeros
max_len = 0
for item in batch_data:
max_len = max(max_len, len(item))
mask = torch.zeros((len(batch_data),max_len))
if visible_matrixs is not None:
final_visible_matrix = torch.zeros((len(batch_data), max_len, max_len))
for index, item in enumerate(batch_data):
mask[index][0:len(item)] = 1
pad_length = max_len-len(item)
batch_data[index] = batch_data[index] + [config.tokenizer.token2id['[PAD]']]*pad_length
sentences_ft[index] = sentences_ft[index] + [config.tokenizer.token2id['[PAD]']]*pad_length
masks_ft[index] = masks_ft[index] + [0] * pad_length
batch_token_types[index] = batch_token_types[index] + [2] * pad_length
visible_matrix_len=visible_matrixs[index].shape[0]
final_visible_matrix[index][0:visible_matrix_len,0:visible_matrix_len] = visible_matrixs[index]
else:
for index, item in enumerate(batch_data):
mask[index][0:len(item)] = 1
pad_length = max_len-len(item)
batch_data[index] = batch_data[index] + [config.tokenizer.token2id['[PAD]']]*pad_length
sentences_ft[index] = sentences_ft[index] + [config.tokenizer.token2id['[PAD]']]*pad_length
masks_ft[index] = masks_ft[index] + [0] * pad_length
batch_token_types[index] = batch_token_types[index] + [0]*pad_length
final_visible_matrix = None
batch = torch.tensor(batch_data)
batch_sent_ft = torch.tensor(sentences_ft)
batch_mask_ft = torch.tensor(masks_ft)
label = torch.tensor(batch_label)
token_type_ids = torch.tensor(batch_token_types)
# import pdb; pdb.set_trace()
return batch, batch_sent_ft, batch_mask_ft, mask.int(), token_type_ids, label, final_visible_matrix
def collate_fn(batch):
sentences, sentence_fts, mask_fts, labels, visible_matrixs, task_indexs, token_types = [], [], [], [], [], [], []
for senten, senten_ft, mask_ft, label, visible_matrix, task_index, token_type in batch:
sentences += senten
sentence_fts += senten_ft
mask_fts += mask_ft
labels += label
visible_matrixs += visible_matrix
task_indexs += task_index
token_types += token_type
batch_sentences, batch_sent_ft, batch_mask_ft, mask, token_type_ids, batch_labels, final_visible_matrix = pad_sequence(sentences, sentence_fts, mask_fts, token_types, labels, visible_matrixs)
return batch_sentences, batch_sent_ft, batch_mask_ft, mask, token_type_ids, batch_labels, final_visible_matrix, torch.tensor(task_indexs)
# ------------------------------------
# setup parser
# ------------------------------------
args = setup_parser()
tokenizer = KGTokenizer(args)
config = Config(tokenizer)
# ------------------------------------
# logging
# ------------------------------------
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=args.log_file_down_task,
filemode='a')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
logger = logging.getLogger('logger')
# ------------------------------------
# process data
# ------------------------------------
tokenizer.down_data_triplecls()
train_dataset = KGDataset_down_triplecls(args.seq_len_down, tokenizer, tokenizer.down_data_train[0:], args)
valid_dataset = KGDataset_down_triplecls(args.seq_len_down, tokenizer, tokenizer.down_data_valid[0:], args)
test_dataset = KGDataset_down_triplecls(args.seq_len_down, tokenizer, tokenizer.down_data_test[0:], args)
train_loader = DataLoader(
train_dataset,
batch_size=args.train_bs,
shuffle=True,
collate_fn=collate_fn,
#num_workers=num_workers,
)
valid_loader = DataLoader(
valid_dataset,
batch_size=args.test_bs,
shuffle=False,
drop_last=False,
collate_fn=collate_fn,
#num_workers=num_workers,
)
test_loader = DataLoader(
test_dataset,
batch_size=args.test_bs,
shuffle=False,
drop_last=False,
collate_fn=collate_fn,
#num_workers=num_workers,
)
# ------------------------------------
# init model and load parameters
# ------------------------------------
KGModel = KGBert_down_tirplecls(tokenizer, args)
assert KGModel.encoder.embeddings.word_embeddings.weight.requires_grad == True
logger.info(f"KGModel.encoder.embeddings.word_embeddings.weight.requires_grad == {KGModel.encoder.embeddings.word_embeddings.weight.requires_grad}")
if args.direct_ft:
logger.info(f"Directly ft, no pretrained parameters.")
else:
try:
parameter_paths = [int(i.split('.ep')[-1].split('_delWE')[0]) for i in list(glob.iglob(args.petrain_save_path + '.ep*_delWE'))]
parameter_paths.sort()
parameter_path = args.petrain_save_path + '.ep' + str(parameter_paths[-1]) + '_delWE'
concept_dict = torch.load(parameter_path)
KGModel.load_state_dict(concept_dict, strict=False)
logger.info(f"load pretrained parameters from {parameter_path}.")
except:
logger.info(f"cannot load pretrained parameters.")
if args.fixedT:
for name, p in KGModel.named_parameters():
if 'encoder.encoder.layer.' in name:
p.requires_grad = False
logger.info('freeze parameters of encoder.encoder.layer.')
for name, p in KGModel.named_parameters():
if 'encoder.encoder.layer.' in name:
assert p.requires_grad == False, 'error'
# import pdb; pdb.set_trace()
# ------------------------------------
# train model
# ------------------------------------
logger.info("Creating BERT Trainer")
test_type = 'test'
if test_type == 'valid':
trainer = KGBertTrainer_down_triplecls(KGModel, args, logger, tokenizer, train_dataloader=train_loader, test_dataloader=valid_loader,cuda_devices=args.cuda_devices, log_freq=args.log_freq, test_type='valid')
elif test_type == 'test':
trainer = KGBertTrainer_down_triplecls(KGModel, args, logger, tokenizer, train_dataloader=train_loader, test_dataloader=test_loader,cuda_devices=args.cuda_devices, log_freq=args.log_freq, test_type='test')
logger.info("Training Start")
last_best_metric = 0
last_best_epoch = -1
metric_type = 'f1'
def test_current(epoch, metric_type):
trainer.test(epoch, args.down_task_model_path)
last_best_metric = trainer.current_metric
logger.info(f'test epoch={epoch}, now_best_{metric_type}={last_best_metric}')
return last_best_metric
def load_best_model():
parameter_paths = list(glob.iglob(args.down_task_model_path + '.ep*_'+metric_type+'-*'))
models_max = max([float(i.split(metric_type+'-')[-1]) for i in parameter_paths])
for each_path in parameter_paths:
if metric_type+'-'+str(models_max) in each_path:
parameter_path = each_path
parameter_dict = torch.load(parameter_path)
try:
KGModel.load_state_dict(parameter_dict, strict=False)
except Exception as e:
print(parameter_path)
print(e)
logger.info(f"Load best parameters from {parameter_path}.")
if args.continue_pretrain:
logger.info(f"Load pretrained parameters and continue train.")
load_best_model()
last_best_metric = test_current(epoch = last_best_epoch, metric_type=metric_type)
for epoch in range(args.epochs):
if epoch - last_best_epoch > 10:
break
# now_metric = test_current(epoch, metric_type)
trainer.train(epoch)
now_metric = test_current(epoch, metric_type)
if now_metric > last_best_metric:
logger.info(f"Epoch {epoch}: current_test_metric={now_metric}, better than last_best={last_best_metric}, update model.")
save_best_model(file_save_path=args.down_task_model_path, logger=logger, metric=metric_type)
trainer.save(epoch, args.down_task_model_path, metric=metric_type, value=now_metric)
last_best_metric = now_metric
last_best_epoch = epoch
else:
logger.info(f"Epoch {epoch}: current_test_metric={now_metric}, not better than last_best={last_best_metric}.")