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trainer.py
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trainer.py
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import os
import logging
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import AdamW, get_linear_schedule_with_warmup
from utils import set_seed, compute_metrics, get_label, MODEL_CLASSES
logger = logging.getLogger(__name__)
class Trainer(object):
def __init__(self, args, train_dataset=None, dev_dataset=None, test_dataset=None):
self.args = args
self.train_dataset = train_dataset
self.dev_dataset = dev_dataset
self.test_dataset = test_dataset
self.label_lst = get_label(args)
self.num_labels = len(self.label_lst)
self.config_class, self.model_class, _ = MODEL_CLASSES[args.model_type]
self.bert_config = self.config_class.from_pretrained(args.model_name_or_path, num_labels=self.num_labels, finetuning_task=args.task)
self.model = self.model_class(self.bert_config, args)
# GPU or CPU
self.device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
self.model.to(self.device)
def train(self):
train_sampler = RandomSampler(self.train_dataset)
train_dataloader = DataLoader(self.train_dataset, sampler=train_sampler, batch_size=self.args.batch_size)
if self.args.max_steps > 0:
t_total = self.args.max_steps
self.args.num_train_epochs = self.args.max_steps // (len(train_dataloader) // self.args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // self.args.gradient_accumulation_steps * self.args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': self.args.weight_decay},
{'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=t_total)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(self.train_dataset))
logger.info(" Num Epochs = %d", self.args.num_train_epochs)
logger.info(" Total train batch size = %d", self.args.batch_size)
logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss = 0.0
self.model.zero_grad()
train_iterator = trange(int(self.args.num_train_epochs), desc="Epoch")
set_seed(self.args)
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
self.model.train()
batch = tuple(t.to(self.device) for t in batch) # GPU or CPU
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'token_type_ids': batch[2],
'labels': batch[3],
'img_features': batch[4]}
outputs = self.model(**inputs)
loss = outputs[0]
if self.args.gradient_accumulation_steps > 1:
loss = loss / self.args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
if (step + 1) % self.args.gradient_accumulation_steps == 0:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
self.model.zero_grad()
global_step += 1
if self.args.logging_steps > 0 and global_step % self.args.logging_steps == 0:
self.evaluate("dev") # Only test set available for NSMC
if self.args.save_steps > 0 and global_step % self.args.save_steps == 0:
self.save_model()
if 0 < self.args.max_steps < global_step:
epoch_iterator.close()
break
if 0 < self.args.max_steps < global_step:
train_iterator.close()
break
return global_step, tr_loss / global_step
def evaluate(self, mode):
# We use test dataset because semeval doesn't have dev dataset
if mode == 'test':
dataset = self.test_dataset
elif mode == 'dev':
dataset = self.dev_dataset
else:
raise Exception("Only dev and test dataset available")
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=self.args.batch_size)
# Eval!
logger.info("***** Running evaluation on %s dataset *****", mode)
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", self.args.batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
self.model.eval()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(self.device) for t in batch)
with torch.no_grad():
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'token_type_ids': batch[2],
'labels': batch[3],
'img_features': batch[4]}
outputs = self.model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs['labels'].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
results = {
"loss": eval_loss
}
# preds = np.argmax(preds, axis=1)
preds[preds >= 0.5] = 1
preds[preds < 0.5] = 0
result = compute_metrics(preds, out_label_ids)
results.update(result)
logger.info("***** Eval results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
return results
def save_model(self):
# Save model checkpoint (Overwrite)
output_dir = os.path.join(self.args.model_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = self.model.module if hasattr(self.model, 'module') else self.model
model_to_save.save_pretrained(output_dir)
torch.save(self.args, os.path.join(output_dir, 'training_config.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
def load_model(self):
# Check whether model exists
if not os.path.exists(self.args.model_dir):
raise Exception("Model doesn't exists! Train first!")
try:
self.bert_config = self.config_class.from_pretrained(self.args.model_dir)
logger.info("***** Config loaded *****")
self.model = self.model_class.from_pretrained(self.args.model_dir, config=self.bert_config, args=self.args)
self.model.to(self.device)
logger.info("***** Model Loaded *****")
except:
raise Exception("Some model files might be missing...")