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clue_qa.py
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clue_qa.py
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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_qa_data import get_loader
from clue.clue_qa_model import BertDictQA
import os
os.environ["TOKENIZERS_PARALLELISM"] = "true"
# 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):
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 = BertDictQA
self.num_labels = 0
config = self.create_config()
self.model = BertDictQA(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
)
self.topk = args.topk
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)
if key.startswith('bert'):
new_key = key[len("bert."):]
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 = 369
config.fuse = self.args.fuse
# config.type_vocab_size = 3
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
project_name = 'Bert_QA'
src_dir = Path(__file__).resolve().parent
base_path = str(src_dir.parent)
src_dir = str(src_dir)
# wandb.save(os.path.join(src_dir + "/*.py"), base_path=base_path)
if self.args.distributed:
dist.barrier()
if self.args.evaluate_start:
score_dict = self.evaluate(self.test_loader)
test_score = score_dict['em'] * 100.
print(f'The EM. of the zero shot is: {test_score}')
# 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.,
}
total_steps = len(self.train_loader)
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 step_i == int(total_steps*self.args.eval_epochs):
# Validation
score_dict = self.evaluate(self.val_loader)
if self.verbose:
valid_score = score_dict['em'] * 100.
valid_score_raw = score_dict['em']
valid_score_raw_f1 = score_dict['f1']
# valid_score = score_dict['topk_score'] * 100.
# valid_score_raw = score_dict['overall']
if valid_score_raw > best_valid:
best_valid = valid_score_raw
best_epoch = round(epoch+step_i/total_steps, 1)
self.save("BEST")
log_str = ''
log_str += "\nEpoch %0.1f: Valid Raw EM %0.2f Valid Raw F1 %0.2f Topk %0.2f" % (step_i/total_steps+epoch, valid_score_raw, valid_score_raw_f1, valid_score)
log_str += "\nEpoch %0.1f: Best Raw %0.2f\n" % (best_epoch, best_valid)
print(log_str)
logger.info(log_str)
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['em'] * 100.
valid_score_raw = score_dict['em']
valid_score_raw_f1 = score_dict['f1']
# 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 EM %0.2f Valid Raw F1 %0.2f Topk %0.2f" % (epoch, valid_score_raw, valid_score_raw_f1, valid_score)
log_str += "\nEpoch %d: Best Raw %0.2f\n" % (best_epoch, best_valid)
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 SCORE. 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()
idx2ans = []
previous_question_id = -1
current_answer = (-1, -1, -1, -100.0)
with torch.no_grad():
idx2ans = {}
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)
start_logits = results['start_logits'] # B, L
end_logits = results['end_logits']
start_prob = nn.Softmax(dim=1)(start_logits)
end_prob = nn.Softmax(dim=1)(end_logits)
question_ids = batch['item_idxs']
question_lengths = batch['question_lengths']
span_indexs = batch['span_indexs']
start_offsets = batch['start_offsets']
token_is_max_contexts = batch['token_is_max_contexts']
context_spans = batch['context_spans']
for j in range(start_prob.size()[0]):
question_id = question_ids[j]
question_length = question_lengths[j]
span_index = span_indexs[j]
start_offset = start_offsets[j]
token_is_max_context = token_is_max_contexts[j]
context_span = context_spans[j]
start_score = start_prob[j] # L
end_score = end_prob[j] # L
best_score = 0
start_pred_topk = torch.topk(start_score[question_length+2: ], 20, dim=0).indices
end_pred_topk = torch.topk(end_score[question_length+2: ], 20, dim=0).indices
for start_index in start_pred_topk:
for end_index in end_pred_topk:
if start_index > end_index: continue
if end_index - start_index >= 50: continue # max answer length
if not token_is_max_context.get(start_index.item()+question_length+2, False): continue
if start_index + start_offset >= len(context_span): continue
if end_index + start_offset >= len(context_span): continue
if start_score[start_index+question_length+2] + end_score[end_index+question_length+2] > best_score:
# print('find better span: ', start_index, end_index)
best_score = start_score[start_index+question_length+2] + end_score[end_index+question_length+2]
start_pred = start_index+question_length+2
end_pred = end_index+question_length+2
# if best_score == 0:
# start_pred = end_pred = question_length + 2
score = best_score
start_pred_absolute = start_pred + start_offset - question_length - 2
end_pred_absolute = end_pred + start_offset - question_length - 2
if question_id == previous_question_id:
if score > current_answer[3]:
current_answer = (span_index, start_pred_absolute, end_pred_absolute, score)
else:
if current_answer[0] != -1:
idx2ans[previous_question_id] = current_answer
previous_question_id = question_id
current_answer = (span_index, start_pred_absolute, end_pred_absolute, score)
if self.verbose:
pbar.update(1)
idx2ans[question_id] = current_answer
if self.verbose:
pbar.close()
if self.args.distributed:
dist.barrier()
tid2ans_list = all_gather(idx2ans)
if self.verbose:
idx2ans = {}
for tid2ans in tid2ans_list:
for k, v in tid2ans.items():
idx2ans[k] = v
if dump_path is not None:
evaluator = loader.evaluator
evaluator.dump_result(idx2ans, dump_path)
return idx2ans
def evaluate(self, loader, dump_path=None):
idx2ans = self.predict(loader, dump_path)
if self.verbose:
evaluator = loader.evaluator
em_score, f1_score = evaluator.evaluate(idx2ans)
acc_dict = {'em':em_score, 'f1':f1_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 = 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 = 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 = 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)
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(-1, args)
else:
main_worker(0, args)