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mtp.py
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from utils.tools import *
from model import BertForModel
from transformers import WEIGHTS_NAME, CONFIG_NAME, AutoTokenizer
class PretrainModelManager:
"""
Multi-task pre-training
"""
def __init__(self, args, data):
set_seed(args.seed)
self.args = args
n_gpu = torch.cuda.device_count()
print(n_gpu)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = BertForModel(args.bert_model, num_labels=data.n_known_cls, device=self.device)
if n_gpu > 1:
self.model = nn.DataParallel(self.model)
self.num_train_optimization_steps = int(len(data.train_labeled_examples) / args.pretrain_batch_size) * args.num_pretrain_epochs
self.optimizer, self.scheduler = self.get_optimizer(args)
self.best_eval_score = 0
def eval(self, args, data):
"""
calculate acc on validation set
"""
self.model.eval()
total_labels = torch.empty(0,dtype=torch.long).to(self.device)
total_logits = torch.empty((0, data.n_known_cls)).to(self.device)
for batch in tqdm(data.eval_dataloader, desc="Iteration"):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
X = {"input_ids":input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
with torch.set_grad_enabled(False):
logits = self.model(X)["logits"]
total_labels = torch.cat((total_labels,label_ids))
total_logits = torch.cat((total_logits, logits))
total_probs, total_preds = F.softmax(total_logits.detach(), dim=1).max(dim = 1)
y_pred = total_preds.cpu().numpy()
y_true = total_labels.cpu().numpy()
acc = round(accuracy_score(y_true, y_pred) * 100, 2)
return acc
def train(self, args, data):
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
wait = 0
best_model = None
mlm_iter = iter(data.train_semi_dataloader) # mlm on semi-dataloader
for epoch in trange(int(args.num_pretrain_epochs), desc="Epoch"):
self.model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(data.train_labeled_dataloader, desc="Iteration")):
# 1. load data
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
X = {"input_ids":input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
try:
# batch = mlm_iter.next()
batch = next(mlm_iter)
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, _ = batch
except StopIteration:
mlm_iter = iter(data.train_semi_dataloader)
# batch = mlm_iter.next()
batch = next(mlm_iter)
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, _ = batch
X_mlm = {"input_ids":input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
# 2. get masked data
mask_ids, mask_lb = mask_tokens(X_mlm['input_ids'].cpu(), tokenizer)
X_mlm["input_ids"] = mask_ids.to(self.device)
# 3. compute loss and update parameters
with torch.set_grad_enabled(True):
logits = self.model(X)["logits"]
if isinstance(self.model, nn.DataParallel):
loss_src = self.model.module.loss_ce(logits, label_ids)
loss_mlm = self.model.module.mlmForward(X_mlm, mask_lb.to(self.device))
else:
loss_src = self.model.loss_ce(logits, label_ids)
loss_mlm = self.model.mlmForward(X_mlm, mask_lb.to(self.device))
lossTOT = loss_src + loss_mlm
lossTOT.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
tr_loss += lossTOT.item()
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
loss = tr_loss / nb_tr_steps
print('train_loss',loss)
eval_score = self.eval(args, data)
print('score', eval_score)
if eval_score > self.best_eval_score:
best_model = copy.deepcopy(self.model)
wait = 0
self.best_eval_score = eval_score
else:
wait += 1
if wait >= args.wait_patient:
break
self.model = best_model
if args.save_premodel:
self.model.save_backbone(args.pretrain_dir)
def get_optimizer(self, args):
num_warmup_steps = int(args.warmup_proportion*self.num_train_optimization_steps)
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.lr_pre)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=self.num_train_optimization_steps)
return optimizer, scheduler
def save_model(self):
if not os.path.exists(self.args.pretrain_dir):
os.makedirs(self.args.pretrain_dir)
self.save_model = self.model.module if hasattr(self.model, 'module') else self.model
model_file = os.path.join(self.args.pretrain_dir, WEIGHTS_NAME)
model_config_file = os.path.join(self.args.pretrain_dir, CONFIG_NAME)
torch.save(self.save_model.state_dict(), model_file)
with open(model_config_file, "w") as f:
f.write(self.save_model.config.to_json_string())
def get_features_labels(self, dataloader, model, args):
model.eval()
total_features = torch.empty((0,args.feat_dim)).to(self.device)
total_labels = torch.empty(0,dtype=torch.long).to(self.device)
for batch in tqdm(dataloader, desc="Extracting representation"):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
X = {"input_ids":input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
with torch.no_grad():
feature = model(X, output_hidden_states=True)["hidden_states"]
total_features = torch.cat((total_features, feature))
total_labels = torch.cat((total_labels, label_ids))
return total_features, total_labels