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model_handler.py
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model_handler.py
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import torch
import torch.nn as nn
from utils.data_utils import prepare_datasets
from utils.eval_utils import AverageMeter
from model import Model
from transformers import *
import time
import os
MODELS = {'BERT':(BertModel, BertTokenizer, 'bert-base-uncased'),
'DistilBERT':(DistilBertModel, DistilBertTokenizer, 'distilbert-base-uncased'),
'RoBERTa':(RobertaModel, RobertaTokenizer, 'roberta-base'),
'SpanBERT':(BertModel, BertTokenizer, 'bert-base-cased')}
class ModelHandler():
def __init__(self, config):
self.config = config
tokenizer_model = MODELS[config['model_name']]
self.train_loader, self.dev_loader, tokenizer = prepare_datasets(config, tokenizer_model)
self._n_dev_batches = len(self.dev_loader.dataset) // config['batch_size']
self._n_train_batches = len(self.train_loader.dataset) // config['batch_size']
if config['cuda']:
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self._train_loss = AverageMeter()
self._train_f1 = AverageMeter()
self._train_em = AverageMeter()
self._dev_f1 = AverageMeter()
self._dev_em = AverageMeter()
self.model = Model(config, MODELS[config['model_name']], self.device, tokenizer).to(self.device)
t_total = len(self.train_loader) // config['gradient_accumulation_steps'] * config['max_epochs']
self.optimizer = AdamW(self.model.parameters(), lr=config['lr'], eps = config['adam_epsilon'] )
self.optimizer.zero_grad()
self._n_train_examples = 0
self._epoch = self._best_epoch = 0
self._best_f1 = 0
self._best_em = 0
self.restored = False
if config['pretrained_dir'] is not None:
self.restore()
def train(self):
if not self.restored:
print("\n>>> Dev Epoch: [{} / {}]".format(self._epoch, self.config['max_epochs']))
self._run_epoch(self.dev_loader, training=False, verbose=self.config['verbose'], save = False)
format_str = "Validation Epoch {} -- F1: {:0.2f}, EM: {:0.2f} --"
print(format_str.format(self._epoch, self._dev_f1.mean(), self._dev_em.mean()))
self._best_f1 = self._dev_f1.mean()
self._best_em = self._dev_em.mean()
while self._stop_condition(self._epoch):
self._epoch += 1
print("\n>>> Train Epoch: [{} / {}]".format(self._epoch, self.config['max_epochs']))
if not self.restored:
self.train_loader.prepare()
self.restored = False
self._run_epoch(self.train_loader, training=True, verbose=self.config['verbose'])
format_str = "Training Epoch {} -- Loss: {:0.4f}, F1: {:0.2f}, EM: {:0.2f} --"
print(format_str.format(self._epoch, self._train_loss.mean(),
self._train_f1.mean(), self._train_em.mean()))
print("\n>>> Dev Epoch: [{} / {}]".format(self._epoch, self.config['max_epochs']))
self.dev_loader.prepare()
self._run_epoch(self.dev_loader, training=False, verbose=self.config['verbose'], save = False)
format_str = "Validation Epoch {} -- F1: {:0.2f}, EM: {:0.2f} --"
print(format_str.format(self._epoch, self._dev_f1.mean(), self._dev_em.mean()))
if self._best_f1 <= self._dev_f1.mean():
self._best_epoch = self._epoch
self._best_f1 = self._dev_f1.mean()
self._best_em = self._dev_em.mean()
print("!!! Updated: F1: {:0.2f}, EM: {:0.2f}".format(self._best_f1, self._best_em))
self._reset_metrics()
self.save(self._epoch)
def restore(self):
if not os.path.exists(self.config['pretrained_dir']):
print('dir doesn\'t exists, cannot restore')
return
restored_params = torch.load(self.config['pretrained_dir']+'/latest/model.pth')
self.model.load_state_dict(restored_params['model'])
self.optimizer.load_state_dict(restored_params['optimizer'])
self._epoch = restored_params['epoch']
self._best_epoch = restored_params['best_epoch']
self._n_train_examples = restored_params['train_examples']
self._best_f1 = restored_params['best_f1']
self._best_em = restored_params['best_em']
examples = restored_params['dataloader_examples']
batch_state = restored_params['dataloader_batch_state']
state = restored_params['dataloader_state']
self.train_loader.restore(examples, state, batch_state)
self.restored = True
def save(self, save_epoch_val):
if not os.path.exists(self.config['save_state_dir']):
os.mkdir(self.config['save_state_dir'])
if self._best_epoch == self._epoch:
if not os.path.exists(self.config['save_state_dir']+'/best'):
os.mkdir(self.config['save_state_dir']+'/best')
save_dic = {'epoch':self._epoch,
'best_epoch': self._best_epoch,
'train_examples':self._n_train_examples,
'model':self.model.state_dict(),
'optimizer':self.optimizer.state_dict(),
'best_f1':self._best_f1,
'best_em':self._best_em}
torch.save(save_dic, self.config['save_state_dir']+'/best/model.pth')
if not os.path.exists(self.config['save_state_dir']+'/latest'):
os.mkdir(self.config['save_state_dir']+'/latest')
save_dic = {'epoch':save_epoch_val,
'best_epoch': self._best_epoch,
'train_examples':self._n_train_examples,
'model':self.model.state_dict(),
'optimizer':self.optimizer.state_dict(),
'best_f1':self._best_f1,
'best_em':self._best_em,
'dataloader_batch_state': self.train_loader.batch_state,
'dataloader_state':self.train_loader.state,
'dataloader_examples':self.train_loader.examples}
torch.save(save_dic, self.config['save_state_dir']+'/latest/model.pth')
def _run_epoch(self, data_loader, training=True, verbose=10, out_predictions=False, save = True):
start_time = time.time()
while data_loader.batch_state < len(data_loader):
input_batch = data_loader.get()
res = self.model(input_batch, training)
tr_loss = 0
if training:
loss = res['loss']
if self.config['gradient_accumulation_steps'] > 1:
loss = loss / self.config['gradient_accumulation_steps']
tr_loss = loss.mean().item()
start_logits = res['start_logits']
end_logits = res['end_logits']
if training:
self.model.update(loss, self.optimizer, data_loader.batch_state)
paragraphs = [inp['tokens'] for inp in input_batch]
answers = [inp['answer'] for inp in input_batch]
f1, em = self.model.evaluate(start_logits, end_logits, paragraphs, answers)
self._update_metrics(tr_loss, f1, em, len(paragraphs), training=training)
if training:
self._n_train_examples += len(paragraphs)
if (verbose > 0) and (data_loader.batch_state % verbose == 0):
if save:
self.save(self._epoch - 1)
mode = "train" if training else "dev"
print(self.report(data_loader.batch_state, tr_loss, f1 * 100, em * 100, mode))
print('used_time: {:0.2f}s'.format(time.time() - start_time))
def _update_metrics(self, loss, f1, em, batch_size, training=True):
if training:
self._train_loss.update(loss)
self._train_f1.update(f1 * 100, batch_size)
self._train_em.update(em * 100, batch_size)
else:
self._dev_f1.update(f1 * 100, batch_size)
self._dev_em.update(em * 100, batch_size)
def _reset_metrics(self):
self._train_loss.reset()
self._train_f1.reset()
self._train_em.reset()
self._dev_f1.reset()
self._dev_em.reset()
def report(self, step, loss, f1, em, mode='train'):
if mode == "train":
format_str = "[train-{}] step: [{} / {}] | exs = {} | loss = {:0.4f} | f1 = {:0.2f} | em = {:0.2f}"
return format_str.format(self._epoch, step, self._n_train_batches, self._n_train_examples, loss, f1, em)
elif mode == "dev":
return "[predict-{}] step: [{} / {}] | f1 = {:0.2f} | em = {:0.2f}".format(
self._epoch, step, self._n_dev_batches, f1, em)
elif mode == "test":
return "[test] | test_exs = {} | step: [{} / {}] | f1 = {:0.2f} | em = {:0.2f}".format(
self._n_test_examples, step, self._n_test_batches, f1, em)
else:
raise ValueError('mode = {} not supported.' % mode)
def _stop_condition(self, epoch):
"""
Checks have not exceeded max epochs and has not gone 10 epochs without improvement.
"""
no_improvement = epoch >= self._best_epoch + 10
exceeded_max_epochs = epoch >= self.config['max_epochs']
return False if exceeded_max_epochs or no_improvement else True