forked from patrick-tssn/CDBert
-
Notifications
You must be signed in to change notification settings - Fork 0
/
clue_c3.py
executable file
·475 lines (376 loc) · 14.9 KB
/
clue_c3.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
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
import os
import collections
from pathlib import Path
from packaging import version
import numpy as np
from tqdm import tqdm
import logging
import shutil
from pprint import pprint
import random
import wandb
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast
from transformers import BertConfig, BertModel
from utils.param import parse_args
from utils.dist_utils import all_gather
from utils.utils import load_state_dict, LossMeter, set_global_logging_level, init_logger, logger
from models.trainer_base import TrainerBase
from clue.clue_mrc_data import get_loader
from clue.clue_mrc_model import BertDictMRC
# set_global_logging_level(logging.ERROR, ["transformers"])
proj_dir = Path(__file__).resolve().parent.parent
def seed_everything(seed=42):
'''
设置整个开发环境的seed
:param seed:
:param device:
:return:
'''
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# some cudnn methods can be random even after fixing the seed
# unless you tell it to be deterministic
torch.backends.cudnn.deterministic = True
class Trainer(TrainerBase):
def __init__(self, args, train_loader=None, val_loader=None, test_loader=None, train=True, num_labels=0):
super().__init__(
args,
train_loader=train_loader,
val_loader=val_loader,
test_loader=test_loader,
train=train)
if not self.verbose:
set_global_logging_level(logging.ERROR, ["transformers"])
model_kwargs = {}
model_class = BertDictMRC
self.num_labels = num_labels
config = self.create_config()
self.model = BertDictMRC(config)
self.tokenizer = self.create_tokenizer()
self.model.bert = self.create_model(BertModel, config, **model_kwargs)
self.model.tokenizer = self.tokenizer
# Load Checkpoint
self.start_epoch = None
if args.load is not None:
ckpt_path = args.load + '.pth'
self.load_checkpoint(ckpt_path)
if self.args.from_scratch:
self.init_weights()
# GPU Options
print(f'Model Launching at GPU {self.args.gpu}')
if self.verbose:
from time import time
start = time()
if not self.args.debug:
self.model = self.model.to(args.gpu)
# Optimizer
if train:
self.optim, self.lr_scheduler = self.create_optimizer_and_scheduler()
if self.args.fp16:
self.scaler = torch.cuda.amp.GradScaler()
if args.multiGPU:
if args.distributed:
self.model = DDP(self.model, device_ids=[args.gpu],
find_unused_parameters=True
)
if self.verbose:
print(f'It took {time() - start:.1f}s')
def load_checkpoint(self, ckpt_path):
state_dict = load_state_dict(ckpt_path, 'cpu')
original_keys = list(state_dict.keys())
for key in original_keys:
if key.startswith("vis_encoder."):
new_key = 'encoder.' + key[len("vis_encoder."):]
state_dict[new_key] = state_dict.pop(key)
if key.startswith("model.vis_encoder."):
new_key = 'model.encoder.' + key[len("model.vis_encoder."):]
state_dict[new_key] = state_dict.pop(key)
results = self.model.dict_bert.load_state_dict(state_dict, strict=False)
if self.verbose:
print('Model loaded from ', ckpt_path)
pprint(results)
def create_config(self):
config_class = BertConfig
config = config_class.from_pretrained(self.args.backbone, num_labels=self.num_labels)
config.radical_vocab_size = self.args.rid+1
config.fuse = self.args.fuse
config.glyph = self.args.glyph
return config
def create_model(self, model_class, config=None, **kwargs):
print(f'Building Model at GPU {self.args.gpu}')
model_name = self.args.backbone
model = model_class.from_pretrained(
model_name,
config=config,
**kwargs
)
return model
def train(self):
seed_everything()
if self.verbose:
loss_meter = LossMeter()
best_valid = 0.
best_test = 0.
best_epoch = 0
src_dir = Path(__file__).resolve().parent
base_path = str(src_dir.parent)
src_dir = str(src_dir)
if self.args.distributed:
dist.barrier()
if self.args.evaluate_start:
score_dict = self.evaluate(self.test_loader)
test_score = score_dict['accuracy'] * 100.
print(f'The ACC. of the zero shot is: {test_score}')
exit(0)
# return
global_step = 0
for epoch in range(self.args.epochs):
if self.start_epoch is not None:
epoch += self.start_epoch
self.model.train()
if self.args.distributed:
self.train_loader.sampler.set_epoch(epoch)
if self.verbose:
pbar = tqdm(total=len(self.train_loader), ncols=80)
epoch_results = {
'loss': 0.,
}
# quesid2ans = {}
textid2ans = {}
for step_i, batch in enumerate(self.train_loader):
if self.args.fp16:
with autocast():
if self.args.distributed:
results = self.model.module.train_step(batch)
else:
results = self.model.train_step(batch)
else:
if self.args.distributed:
results = self.model.module.train_step(batch)
else:
results = self.model.train_step(batch)
loss = results['loss']
if self.args.fp16:
self.scaler.scale(loss).backward()
else:
loss.backward()
loss = loss.detach()
# Update Parameters
if self.args.clip_grad_norm > 0:
if self.args.fp16:
self.scaler.unscale_(self.optim)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.clip_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.clip_grad_norm)
if self.args.fp16:
self.scaler.step(self.optim)
self.scaler.update()
else:
self.optim.step()
if self.lr_scheduler:
self.lr_scheduler.step()
for param in self.model.parameters():
param.grad = None
global_step += 1
for k, v in results.items():
if k in epoch_results:
epoch_results[k] += v.item()
if self.lr_scheduler:
if version.parse(torch.__version__) >= version.parse("1.4"):
lr = self.lr_scheduler.get_last_lr()[0]
else:
lr = self.lr_scheduler.get_lr()[0]
else:
try:
lr = self.optim.get_lr()[0]
except AttributeError:
lr = self.args.lr
if self.verbose:
loss_meter.update(loss.item())
desc_str = f'Epoch {epoch} | LR {lr:.6f}'
desc_str += f' | Loss {loss_meter.val:4f}'
pbar.set_description(desc_str)
pbar.update(1)
if self.args.distributed:
dist.barrier()
if self.verbose:
pbar.close()
# Validation
score_dict = self.evaluate(self.val_loader)
if self.verbose:
valid_score = score_dict['accuracy'] * 100.
valid_score_raw = score_dict['accuracy']
# valid_score = score_dict['topk_score'] * 100.
# valid_score_raw = score_dict['overall']
if valid_score_raw > best_valid or epoch == 0:
best_valid = valid_score_raw
best_epoch = epoch
self.save("BEST")
log_str = ''
log_str += "\nEpoch %d: Valid Raw %0.2f Topk %0.2f" % (epoch, valid_score_raw, valid_score)
log_str += "\nEpoch %d: Best Raw %0.2f\n" % (best_epoch, best_valid)
wandb_log_dict = {}
wandb_log_dict['Train/Loss'] = epoch_results['loss'] / len(self.train_loader)
wandb_log_dict['Valid/score'] = valid_score
print(log_str)
logger.info(log_str)
if self.args.distributed:
dist.barrier()
if self.verbose:
self.save("LAST")
# Test Set
best_path = os.path.join(self.args.output, 'BEST')
self.load(best_path)
quesid2ans = self.predict(self.test_loader)
if self.verbose:
evaluator = self.test_loader.evaluator
score_dict = evaluator.evaluate(quesid2ans)
print(f'The ACC. of the best ckpt. for predict result is: {score_dict}')
evaluator.dump_result(quesid2ans, os.path.join(self.args.output, 'predict.json'))
if self.args.submit:
dump_path = os.path.join(self.args.output, 'submit.json')
self.predict(self.submit_test_loader, dump_path)
if self.args.distributed:
dist.barrier()
exit()
def predict(self, loader, dump_path=None):
self.model.eval()
with torch.no_grad():
# quesid2ans = {}
textid2ans = {}
# res = []
if self.verbose:
pbar = tqdm(total=len(loader), ncols=80, desc="Prediction")
for i, batch in enumerate(loader):
if self.args.distributed:
results = self.model.module.test_step(batch)
else:
results = self.model.test_step(batch)
pred_ans = results['pred_ans']
text_id = batch['text_id']
# ques_ids = batch['question_ids']
# label = batch['label']
for tid, ans in zip(text_id, pred_ans):
textid2ans[tid] = ans
# for qid, ans in zip(ques_ids, pred_ans):
# quesid2ans[qid] = ans
if self.verbose:
pbar.update(1)
if self.verbose:
pbar.close()
if self.args.distributed:
dist.barrier()
tid2ans_list = all_gather(textid2ans)
if self.verbose:
textid2ans = {}
for tid2ans in tid2ans_list:
for k, v in tid2ans.items():
textid2ans[k] = v
if dump_path is not None:
evaluator = loader.evaluator
evaluator.dump_result(textid2ans, dump_path)
return textid2ans
def evaluate(self, loader, dump_path=None):
textid2ans = self.predict(loader, dump_path)
if self.verbose:
evaluator = loader.evaluator
# acc_dict = evaluator.evaluate_raw(textid2ans)
topk_score = evaluator.evaluate(textid2ans)
# acc_dict['topk_score'] = topk_score
acc_dict = {'accuracy':topk_score}
return acc_dict
def main_worker(gpu, args):
seed_everything()
# GPU is assigned
args.gpu = gpu
args.rank = gpu
print(f'Process Launching at GPU {gpu}')
if args.distributed:
torch.cuda.set_device(args.gpu)
dist.init_process_group(backend='nccl')
else:
torch.cuda.set_device(args.gpu)
print(f'Building train loader at GPU {gpu}')
train_loader, num_labels = get_loader(
args,
split=args.train, mode='train', batch_size=args.batch_size,
distributed=args.distributed, gpu=args.gpu,
workers=args.num_workers,
topk=args.train_topk,
)
if args.valid_batch_size is not None:
valid_batch_size = args.valid_batch_size
else:
valid_batch_size = args.batch_size
print(f'Building val loader at GPU {gpu}')
val_loader, num_labels = get_loader(
args,
split=args.valid, mode='val', batch_size=valid_batch_size,
distributed=args.distributed, gpu=args.gpu,
workers=4,
topk=args.valid_topk,
)
print(f'Building test loader at GPU {gpu}')
test_loader, num_labels = get_loader(
args,
split=args.test, mode='test', batch_size=valid_batch_size,
distributed=args.distributed, gpu=args.gpu,
workers=4,
topk=args.valid_topk,
)
trainer = Trainer(args, train_loader, val_loader, test_loader, train=True, num_labels=num_labels)
if args.submit:
print(f'Building test submit loader at GPU {gpu}')
submit_test_loader = get_loader(
args,
split='test', mode='val', batch_size=valid_batch_size,
distributed=args.distributed, gpu=args.gpu,
workers=4,
topk=args.valid_topk,
)
trainer.submit_test_loader = submit_test_loader
trainer.train()
if __name__ == "__main__":
cudnn.benchmark = True
args = parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
init_logger(log_file=args.output + '/{}-{}.log'.format(args.model_name, args.task_name))
ngpus_per_node = torch.cuda.device_count()
args.world_size = ngpus_per_node
if args.local_rank in [0, -1]:
print(args)
comments = []
if args.load is not None:
ckpt_str = "_".join(args.load.split('/')[-3:])
comments.append(ckpt_str)
elif args.load_lxmert_qa is not None:
ckpt_str = "_".join(args.load_lxmert_qa.split('/')[-3:])
comments.append(ckpt_str)
if args.comment != '':
comments.append(args.comment)
comment = '_'.join(comments)
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M')
run_name = f'{current_time}_GPU{args.world_size}'
if len(comments) > 0:
run_name += f'_{comment}'
args.run_name = run_name
if args.distributed:
main_worker(args.local_rank, args)
if args.debug:
main_worker(0, args)
else:
main_worker(0, args)