class Trainer(BaseTrainer):
"""
Trainer class
Note:
Inherited from BaseTrainer.
"""
def __init__(self, model, metrics, optimizer, config, train_dataset, valid_datasets, lr_scheduler=None):
super().__init__(model, metrics, optimizer, config, train_dataset)
self.config = config
self.config['data_loaders']['valid']['args']['batch_size'] = self.data_loader.batch_size
self.valid_data_loaders = {}
for corpus, valid_dataset in valid_datasets.items():
self.valid_data_loaders[corpus] = config.init_obj('data_loaders.valid', module_loader, valid_dataset)
self.lr_scheduler = lr_scheduler
self.log_step = math.ceil(len(self.data_loader.dataset) / np.sqrt(self.data_loader.batch_size) / 200)
self.writer = TensorboardWriter(config.log_dir)
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Current training epoch.
:return: A log that contains all information you want to save.
Note:
If you have additional information to record, for example:
> additional_log = {"x": x, "y": y}
merge it with log before return. i.e.
> log = {**log, **additional_log}
> return log
The metrics in log must have the key 'metrics'.
"""
self.model.train()
self.optimizer.zero_grad()
total_loss = 0
accum_count = 0
for step, batch in enumerate(self.data_loader):
# (input_ids, input_mask, segment_ids, ng_token_mask, target, deps, task)
batch = {label: t.to(self.device, non_blocking=True) for label, t in batch.items()}
current_step = (epoch - 1) * len(self.data_loader) + step
loss, *_ = self.model(**batch, progress=current_step / self.total_step)
if len(loss.size()) > 0:
loss = loss.mean() # mean() to average on multi-gpu parallel training
loss_value = loss.item() # mean loss for this step
total_loss += loss_value * next(iter(batch.values())).size(0)
if step % self.log_step == 0:
self.logger.info('Train Epoch: {} [{}/{} ({:.0f}%)] Time: {} Loss: {:.6f}'.format(
epoch,
step * self.data_loader.batch_size,
len(self.data_loader.dataset),
100.0 * step / len(self.data_loader),
datetime.datetime.now().strftime('%H:%M:%S'),
loss_value))
gradient_accumulation_steps = self.gradient_accumulation_steps
if step >= (len(self.data_loader) // self.gradient_accumulation_steps) * self.gradient_accumulation_steps:
gradient_accumulation_steps = len(self.data_loader) % self.gradient_accumulation_steps # fraction
if gradient_accumulation_steps > 1:
loss = loss / gradient_accumulation_steps # scale loss
loss.backward()
accum_count += 1
if accum_count == gradient_accumulation_steps:
self.writer.set_step(self.writer.step + 1)
self.writer.add_scalar('lr', self.lr_scheduler.get_last_lr()[0])
self.writer.add_scalar('loss', loss_value)
self.writer.add_scalar('progress', current_step / self.total_step)
self.optimizer.step()
self.optimizer.zero_grad()
accum_count = 0
if self.lr_scheduler is not None:
self.lr_scheduler.step()
log = {
'loss': total_loss / len(self.data_loader.dataset),
}
corpus2result = {}
for corpus, valid_data_loader in self.valid_data_loaders.items():
val_log = self._valid_epoch(valid_data_loader, corpus)
log[f'val_{corpus}_loss'] = val_log['loss']
corpus2result[corpus] = val_log['result']
if 'all' not in corpus2result:
corpus2result['all'] = val_log['result']
else:
corpus2result['all'] += val_log['result']
for corpus, result in corpus2result.items():
val_metrics = self._eval_metrics(result.to_dict(), corpus)
log.update({f'val_{corpus}_{met.__name__}': v for met, v in zip(self.metrics, val_metrics)})
return log
def _valid_epoch(self, data_loader, corpus):
"""
Validate after training an epoch
:return: A log that contains information about validation
Note:
The validation metrics in log must have the key 'val_metrics'.
"""
self.model.eval()
total_loss = 0
arguments_set: List[List[List[int]]] = []
contingency_set: List[int] = []
with torch.no_grad():
for step, batch in enumerate(data_loader):
batch = {label: t.to(self.device, non_blocking=True) for label, t in batch.items()}
loss, *output = self.model(**batch)
if len(loss.size()) > 0:
loss = loss.mean()
pas_scores = output[0] # (b, seq, case, seq)
if corpus != 'commonsense':
arguments_set += torch.argmax(pas_scores, dim=3).tolist() # (b, seq, case)
total_loss += loss.item() * pas_scores.size(0)
if step % self.log_step == 0:
self.logger.info('Validation [{}/{} ({:.0f}%)] Time: {}'.format(
step * data_loader.batch_size,
len(data_loader.dataset),
100.0 * step / len(data_loader),
datetime.datetime.now().strftime('%H:%M:%S')))
log = {'loss': total_loss / len(data_loader.dataset)}
self.writer.add_scalar(f'loss/{corpus}', log['loss'])
if corpus != 'commonsense':
dataset = data_loader.dataset
prediction_writer = PredictionKNPWriter(dataset, self.logger)
documents_pred = prediction_writer.write(arguments_set, None, add_pas_tag=False)
targets2label = {tuple(): '', ('pred',): 'pred', ('noun',): 'noun', ('pred', 'noun'): 'all'}
scorer = Scorer(documents_pred, dataset.gold_documents,
target_cases=dataset.target_cases,
target_exophors=dataset.target_exophors,
coreference=dataset.coreference,
bridging=dataset.bridging,
pas_target=targets2label[tuple(dataset.pas_targets)])
result = scorer.run()
log['result'] = result
else:
log['f1'] = self._eval_commonsense(contingency_set)
return log
def _eval_commonsense(self, contingency_set: List[int]) -> float:
valid_data_loader = self.valid_data_loaders['commonsense']
gold = [f.label for f in valid_data_loader.dataset.features]
f1 = f1_score(gold, contingency_set)
self.writer.add_scalar(f'commonsense_f1', f1)
return f1
def _eval_metrics(self, result: dict, corpus: str):
f1_metrics = np.zeros(len(self.metrics))
for i, metric in enumerate(self.metrics):
f1_metrics[i] += metric(result)
self.writer.add_scalar(f'{metric.__name__}/{corpus}', f1_metrics[i])
return f1_metrics
class Trainer:
"""
Implements training and validation logic
"""
def __init__(self,
model,
criterion,
metric_ftns,
optimizer,
config,
data_loader,
valid_data_loader=None,
lr_scheduler=None,
len_epoch=None):
self.config = config
self.logger = config.get_logger('trainer',
config['trainer']['verbosity'])
# setup GPU device if available, move model into configured device
self.device, device_ids = self._prepare_device(config['n_gpu'])
self.model = model.to(self.device)
if len(device_ids) > 1:
self.model = torch.nn.DataParallel(model, device_ids=device_ids)
self.criterion = criterion
self.metric_ftns = metric_ftns
self.optimizer = optimizer
cfg_trainer = config['trainer']
self.epochs = cfg_trainer['epochs']
self.save_period = cfg_trainer['save_period']
self.monitor = cfg_trainer.get('monitor', 'off')
# configuration to monitor model performance and save best
if self.monitor == 'off':
self.mnt_mode = 'off'
self.mnt_best = 0
else:
self.mnt_mode, self.mnt_metric = self.monitor.split()
assert self.mnt_mode in ['min', 'max']
self.mnt_best = inf if self.mnt_mode == 'min' else -inf
self.early_stop = cfg_trainer.get('early_stop', inf)
self.start_epoch = 1
self.checkpoint_dir = config.save_dir
# setup visualization writer instance
self.writer = TensorboardWriter(config.log_dir, self.logger,
cfg_trainer['tensorboard'])
if config.resume is not None:
self._resume_checkpoint(config.resume)
self.config = config
self.data_loader = data_loader
if len_epoch is None:
# epoch-based training
self.len_epoch = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_epoch = len_epoch
self.valid_data_loader = valid_data_loader
self.do_validation = self.valid_data_loader is not None
self.lr_scheduler = lr_scheduler
self.log_step = int(np.sqrt(data_loader.batch_size))
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Integer, current training epoch.
:return: A log that contains average loss and metric in this epoch.
"""
self.model.train()
epoch_target = []
epoch_scores = []
epoch_word_pairs = []
epoch_loss = []
for batch_idx, batch_data in enumerate(self.data_loader):
if not batch_data:
continue
for field in [
'input_ids', 'label', 'target', 'attention_mask',
'term1_mask', 'term2_mask'
]:
batch_data[field] = batch_data[field].to(self.device)
self.optimizer.zero_grad()
output = self.model(batch_data)
loss = self.criterion(
output, batch_data['target'],
self.data_loader.dataset.class_weights.to(self.device))
loss.backward()
self.optimizer.step()
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
# accumulate epoch quantities
epoch_target += [t.item() for t in batch_data['label']]
epoch_scores += [output.cpu().detach().numpy()]
epoch_word_pairs += batch_data['term_pair']
epoch_loss += [loss.item()]
# update metrics
self.writer.add_scalar("loss", loss.item())
for met in self.metric_ftns:
self.writer.add_scalar(met.__name__,
met(epoch_target, epoch_scores))
if batch_idx % self.log_step == 0:
self.logger.debug('Train Epoch: {} {} Loss: {:.6f}'.format(
epoch, self._progress(batch_idx), loss.item()))
#self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
if batch_idx == self.len_epoch:
break
log = {
m.__name__: m(epoch_target, epoch_scores)
for m in self.metric_ftns
}
log["loss"] = np.sum(epoch_loss) / len(self.data_loader)
if self.do_validation:
val_log = self._valid_epoch(epoch)
log.update(**{'val_' + k: v for k, v in val_log.items()})
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return log
def _valid_epoch(self, epoch):
"""
Validate after training an epoch
:param epoch: Integer, current training epoch.
:return: A log that contains information about validation
"""
self.model.eval()
with torch.no_grad():
epoch_target = []
epoch_scores = []
epoch_word_pairs = []
epoch_loss = []
for batch_idx, batch_data in enumerate(tqdm(
self.valid_data_loader)):
for field in [
'input_ids', 'target', 'label', 'attention_mask',
'term1_mask', 'term2_mask'
]:
batch_data[field] = batch_data[field].to(self.device)
output = self.model(batch_data)
pred = torch.argmax(output, dim=-1)
loss = self.criterion(
output, batch_data['target'].squeeze(-1),
self.data_loader.dataset.class_weights.to(self.device))
self.writer.set_step(
(epoch - 1) * len(self.valid_data_loader) + batch_idx,
'valid')
# accumulate epoch quantities
epoch_target += [t.item() for t in batch_data['label']]
epoch_scores += [output.cpu().detach().numpy()]
epoch_word_pairs += batch_data['term_pair']
epoch_loss += [loss.item()]
# update metrics
self.writer.add_scalar('loss', loss.item())
for met in self.metric_ftns:
self.writer.add_scalar(met.__name__,
met(epoch_target, epoch_scores))
# add histogram of model parameters to the tensorboard
for name, p in self.model.named_parameters():
self.writer.add_histogram(name, p, bins='auto')
log = {
m.__name__: m(epoch_target, epoch_scores)
for m in self.metric_ftns
}
log["loss"] = np.sum(epoch_loss) / len(self.valid_data_loader)
return log
def _progress(self, batch_idx):
base = '[{}/{} ({:.0f}%)]'
if hasattr(self.data_loader, 'n_samples'):
current = batch_idx * self.data_loader.batch_size
total = self.data_loader.n_samples
else:
current = batch_idx
total = self.len_epoch
return base.format(current, total, 100.0 * current / total)
def train(self):
"""
Full training logic
"""
not_improved_count = 0
for epoch in range(self.start_epoch, self.epochs + 1):
result = self._train_epoch(epoch)
# save logged informations into log dict
log = {'epoch': epoch}
log.update(result)
# print logged informations to the screen
for key, value in log.items():
self.logger.info(' {:15s}: {}'.format(str(key), value))
# evaluate model performance according to configured metric, save best checkpoint as model_best
best = False
if self.mnt_mode != 'off':
try:
# check whether model performance improved or not, according to specified metric(mnt_metric)
improved = (self.mnt_mode == 'min' and log[self.mnt_metric] <= self.mnt_best) or \
(self.mnt_mode == 'max' and log[self.mnt_metric] >= self.mnt_best)
except KeyError:
self.logger.warning(
"Warning: Metric '{}' is not found. "
"Model performance monitoring is disabled.".format(
self.mnt_metric))
self.mnt_mode = 'off'
improved = False
if improved:
self.mnt_best = log[self.mnt_metric]
not_improved_count = 0
best = True
else:
not_improved_count += 1
if not_improved_count > self.early_stop:
self.logger.info(
"Validation performance didn\'t improve for {} epochs. "
"Training stops.".format(self.early_stop))
break
if epoch % self.save_period == 0:
self._save_checkpoint(epoch, save_best=best)
def _prepare_device(self, n_gpu_use):
"""
setup GPU device if available, move model into configured device
"""
n_gpu = torch.cuda.device_count()
if n_gpu_use > 0 and n_gpu == 0:
self.logger.warning(
"Warning: There\'s no GPU available on this machine,"
"training will be performed on CPU.")
n_gpu_use = 0
if n_gpu_use > n_gpu:
self.logger.warning(
"Warning: The number of GPU\'s configured to use is {}, but only {} are available "
"on this machine.".format(n_gpu_use, n_gpu))
n_gpu_use = n_gpu
device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
list_ids = list(range(n_gpu_use))
return device, list_ids
def _save_checkpoint(self, epoch, save_best=False):
"""
Saving checkpoints
:param epoch: current epoch number
:param log: logging information of the epoch
:param save_best: if True, rename the saved checkpoint to 'model_best.pth'
"""
arch = type(self.model).__name__
state = {
'arch': arch,
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'monitor_best': self.mnt_best,
'config': self.config
}
if save_best:
best_path = str(self.checkpoint_dir / 'model_best.pth')
torch.save(state, best_path)
self.logger.info("Saving current best: model_best.pth ...")
def _resume_checkpoint(self, resume_path):
"""
Resume from saved checkpoints
:param resume_path: Checkpoint path to be resumed
"""
resume_path = str(resume_path)
self.logger.info("Loading checkpoint: {} ...".format(resume_path))
checkpoint = torch.load(resume_path)
self.start_epoch = checkpoint['epoch'] + 1
self.mnt_best = checkpoint['monitor_best']
# load architecture params from checkpoint.
if checkpoint['config']['arch'] != self.config['arch']:
self.logger.warning(
"Warning: Architecture configuration given in config file is different from that of "
"checkpoint. This may yield an exception while state_dict is being loaded."
)
self.model.load_state_dict(checkpoint['state_dict'])
# load optimizer state from checkpoint only when optimizer type is not changed.
if checkpoint['config']['optimizer']['type'] != self.config[
'optimizer']['type']:
self.logger.warning(
"Warning: Optimizer type given in config file is different from that of checkpoint. "
"Optimizer parameters not being resumed.")
else:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.logger.info(
"Checkpoint loaded. Resume training from epoch {}".format(
self.start_epoch))
class Trainer:
"""
Trainer class
"""
def __init__(self,
model,
optimizer,
config,
data_loader,
valid_data_loader=None,
lr_scheduler=None,
max_len_step=None):
'''
:param model:
:param optimizer:
:param config:
:param data_loader:
:param valid_data_loader:
:param lr_scheduler:
:param max_len_step: controls number of batches(steps) in each epoch.
'''
self.config = config
self.distributed = config['distributed']
if self.distributed:
self.local_master = (config['local_rank'] == 0)
self.global_master = (dist.get_rank() == 0)
else:
self.local_master = True
self.global_master = True
self.logger = config.get_logger(
'trainer',
config['trainer']['log_verbosity']) if self.local_master else None
# setup GPU device if available, move model into configured device
self.device, self.device_ids = self._prepare_device(
config['local_rank'], config['local_world_size'])
self.model = model.to(self.device)
self.optimizer = optimizer
cfg_trainer = config['trainer']
self.epochs = cfg_trainer['epochs']
self.save_period = cfg_trainer['save_period']
monitor_open = cfg_trainer['monitor_open']
if monitor_open:
self.monitor = cfg_trainer.get('monitor', 'off')
else:
self.monitor = 'off'
# configuration to monitor model performance and save best
if self.monitor == 'off':
self.monitor_mode = 'off'
self.monitor_best = 0
else:
self.monitor_mode, self.monitor_metric = self.monitor.split()
assert self.monitor_mode in ['min', 'max']
self.monitor_best = inf if self.monitor_mode == 'min' else -inf
self.early_stop = cfg_trainer.get('early_stop', inf)
self.early_stop = inf if self.early_stop == -1 else self.early_stop
self.start_epoch = 1
if self.local_master:
self.checkpoint_dir = config.save_dir
# setup visualization writer instance
self.writer = TensorboardWriter(config.log_dir, self.logger,
cfg_trainer['tensorboard'])
# load checkpoint for resume training
if config.resume is not None:
self._resume_checkpoint(config.resume)
# load checkpoint following load to multi-gpu, avoid 'module.' prefix
if self.config['trainer']['sync_batch_norm'] and self.distributed:
self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
self.model)
if self.distributed:
self.model = DDP(self.model,
device_ids=self.device_ids,
output_device=self.device_ids[0],
find_unused_parameters=True)
self.data_loader = data_loader
if max_len_step is None: # max length of iteration step of every epoch
# epoch-based training
self.len_step = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_step = max_len_step
self.valid_data_loader = valid_data_loader
self.do_validation = self.valid_data_loader is not None
self.lr_scheduler = lr_scheduler
log_step = self.config['trainer']['log_step_interval']
self.log_step = log_step if log_step != -1 and 0 < log_step < self.len_step else int(
np.sqrt(data_loader.batch_size))
val_step_interval = self.config['trainer']['val_step_interval']
# self.val_step_interval = val_step_interval if val_step_interval!= -1 and 0 < val_step_interval < self.len_step\
# else int(np.sqrt(data_loader.batch_size))
self.val_step_interval = val_step_interval
self.gl_loss_lambda = self.config['trainer']['gl_loss_lambda']
self.train_loss_metrics = MetricTracker(
'loss',
'gl_loss',
'crf_loss',
writer=self.writer if self.local_master else None)
self.valid_f1_metrics = SpanBasedF1MetricTracker(iob_labels_vocab_cls)
def train(self):
"""
Full training logic, including train and validation.
"""
if self.distributed:
dist.barrier() # Syncing machines before training
not_improved_count = 0
for epoch in range(self.start_epoch, self.epochs + 1):
# ensure distribute worker sample different data,
# set different random seed by passing epoch to sampler
if self.distributed:
self.data_loader.sampler.set_epoch(epoch)
result_dict = self._train_epoch(epoch)
# print logged informations to the screen
if self.do_validation:
val_result_dict = result_dict['val_result_dict']
val_res = SpanBasedF1MetricTracker.dict2str(val_result_dict)
else:
val_res = ''
# every epoch log information
self.logger_info(
'[Epoch Validation] Epoch:[{}/{}] Total Loss: {:.6f} '
'GL_Loss: {:.6f} CRF_Loss: {:.6f} \n{}'.format(
epoch, self.epochs, result_dict['loss'],
result_dict['gl_loss'] * self.gl_loss_lambda,
result_dict['crf_loss'], val_res))
# evaluate model performance according to configured metric, check early stop, and
# save best checkpoint as model_best
best = False
if self.monitor_mode != 'off' and self.do_validation:
best, not_improved_count = self._is_best_monitor_metric(
best, not_improved_count, val_result_dict)
if not_improved_count > self.early_stop:
self.logger_info(
"Validation performance didn\'t improve for {} epochs. "
"Training stops.".format(self.early_stop))
break
if epoch % self.save_period == 0:
self._save_checkpoint(epoch, save_best=best)
def _is_best_monitor_metric(self, best, not_improved_count,
val_result_dict):
'''
monitor metric
:param best:
:param not_improved_count:
:param val_result_dict:
:return:
'''
entity_name, metric = self.monitor_metric.split('-')
val_monitor_metric_res = val_result_dict[entity_name][metric]
try:
# check whether model performance improved or not, according to specified metric(monitor_metric)
improved = (self.monitor_mode == 'min' and val_monitor_metric_res <= self.monitor_best) or \
(self.monitor_mode == 'max' and val_monitor_metric_res >= self.monitor_best)
except KeyError:
self.logger_warning(
"Warning: Metric '{}' is not found. "
"Model performance monitoring is disabled.".format(
self.monitor_metric))
self.monitor_mode = 'off'
improved = False
if improved:
self.monitor_best = val_monitor_metric_res
not_improved_count = 0
best = True
else:
not_improved_count += 1
return best, not_improved_count
def _train_epoch(self, epoch):
'''
Training logic for an epoch
:param epoch: Integer, current training epoch.
:return: A log dict that contains average loss and metric in this epoch.
'''
self.model.train()
self.train_loss_metrics.reset()
## step iteration start ##
for step_idx, input_data_item in enumerate(self.data_loader):
try:
step_idx += 1
for key, input_value in input_data_item.items():
if input_value is not None and isinstance(
input_value, torch.Tensor):
input_data_item[key] = input_value.to(
self.device, non_blocking=True)
if self.config['trainer']['anomaly_detection']:
# This mode will increase the runtime and should only be enabled for debugging
with torch.autograd.detect_anomaly():
self.optimizer.zero_grad()
# model forward
output = self.model(**input_data_item)
# calculate loss
gl_loss = output['gl_loss']
crf_loss = output['crf_loss']
total_loss = torch.sum(
crf_loss
) + self.gl_loss_lambda * torch.sum(gl_loss)
# backward
total_loss.backward()
# self.average_gradients(self.model)
self.optimizer.step()
else:
self.optimizer.zero_grad()
# model forward
output = self.model(**input_data_item)
# calculate loss
gl_loss = output['gl_loss']
crf_loss = output['crf_loss']
total_loss = torch.sum(
crf_loss) + self.gl_loss_lambda * torch.sum(gl_loss)
# backward
total_loss.backward()
# self.average_gradients(self.model)
self.optimizer.step()
# Use a barrier() to make sure that all process have finished forward and backward
if self.distributed:
dist.barrier()
# obtain the sum of all total_loss at all processes
dist.all_reduce(total_loss, op=dist.reduce_op.SUM)
size = dist.get_world_size()
else:
size = 1
gl_loss /= size # averages gl_loss across the whole world
crf_loss /= size # averages crf_loss across the whole world
# calculate average loss across the batch size
avg_gl_loss = torch.mean(gl_loss)
avg_crf_loss = torch.mean(crf_loss)
avg_loss = avg_crf_loss + self.gl_loss_lambda * avg_gl_loss
# update metrics
self.writer.set_step((epoch - 1) * self.len_step + step_idx -
1) if self.local_master else None
self.train_loss_metrics.update('loss', avg_loss.item())
self.train_loss_metrics.update(
'gl_loss',
avg_gl_loss.item() * self.gl_loss_lambda)
self.train_loss_metrics.update('crf_loss', avg_crf_loss.item())
# log messages
if step_idx % self.log_step == 0:
self.logger_info(
'Train Epoch:[{}/{}] Step:[{}/{}] Total Loss: {:.6f} GL_Loss: {:.6f} CRF_Loss: {:.6f}'
.format(
epoch, self.epochs, step_idx, self.len_step,
self.train_loss_metrics.avg('loss'),
self.train_loss_metrics.avg('gl_loss') *
self.gl_loss_lambda,
self.train_loss_metrics.avg('crf_loss')))
# self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
# do validation after val_step_interval iteration
if self.do_validation and step_idx % self.val_step_interval == 0:
val_result_dict = self._valid_epoch(epoch)
self.logger_info(
'[Step Validation] Epoch:[{}/{}] Step:[{}/{}] \n{}'.
format(
epoch, self.epochs, step_idx, self.len_step,
SpanBasedF1MetricTracker.dict2str(
val_result_dict)))
# check if best metric, if true, then save as model_best checkpoint.
best, not_improved_count = self._is_best_monitor_metric(
False, 0, val_result_dict)
if best:
self._save_checkpoint(epoch, best)
# decide whether continue iter
if step_idx == self.len_step + 1:
break
except Exception as e:
print('OOM')
## step iteration end ##
# {'loss': avg_loss, 'gl_loss': avg_gl_loss, 'crf_loss': avg_crf_loss}
log = self.train_loss_metrics.result()
self.writer.set_step(epoch, 'train')
self.writer.add_scalar('total_loss_epoch', log['loss'])
self.writer.add_scalar('gl_loss_epoch', log['gl_loss'])
self.writer.add_scalar('crf_loss_epoch', log['crf_loss'])
# do validation after training an epoch
if self.do_validation:
val_result_dict = self._valid_epoch(epoch)
log['val_result_dict'] = val_result_dict
self.writer.set_step(epoch, 'valid')
self.writer.add_scalars(
'mEF_valid', {
'total': val_result_dict['total']['mEF'],
'date': val_result_dict['date']['mEF'],
'company': val_result_dict['company']['mEF'],
'address': val_result_dict['address']['mEF']
})
# self.writer.add_scalars('mEF_valid',{'total':val_result_dict['total']['mEF'],
# 'product':val_result_dict['product']['mEF'],
# 'price':val_result_dict['price']['mEF']})
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return log
def _valid_epoch(self, epoch):
'''
Validate after training an epoch or regular step, this is a time-consuming procedure if validation data is big.
:param epoch: Integer, current training epoch.
:return: A dict that contains information about validation
'''
self.model.eval()
self.valid_f1_metrics.reset()
with torch.no_grad():
for step_idx, input_data_item in enumerate(self.valid_data_loader):
for key, input_value in input_data_item.items():
if input_value is not None and isinstance(
input_value, torch.Tensor):
input_data_item[key] = input_value.to(
self.device, non_blocking=True)
output = self.model(**input_data_item)
logits = output['logits']
new_mask = output['new_mask']
if hasattr(self.model, 'module'):
# List[(List[int], torch.Tensor)] contain the tag indices of the maximum likelihood tag sequence.
# and the score of the viterbi path.
best_paths = self.model.module.decoder.crf_layer.viterbi_tags(
logits, mask=new_mask, logits_batch_first=True)
else:
best_paths = self.model.decoder.crf_layer.viterbi_tags(
logits, mask=new_mask, logits_batch_first=True)
predicted_tags = []
for path, score in best_paths:
predicted_tags.append(path)
self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + step_idx, 'valid') \
if self.local_master else None
# calculate and update f1 metrics
# (B, N*T, out_dim)
predicted_tags_hard_prob = logits * 0
for i, instance_tags in enumerate(predicted_tags):
for j, tag_id in enumerate(instance_tags):
predicted_tags_hard_prob[i, j, tag_id] = 1
golden_tags = input_data_item['iob_tags_label']
mask = input_data_item['mask']
union_iob_tags = iob_tags_to_union_iob_tags(golden_tags, mask)
if self.distributed:
dist.barrier() #
self.valid_f1_metrics.update(predicted_tags_hard_prob.long(),
union_iob_tags, new_mask)
# add histogram of model parameters to the tensorboard
# for name, p in self.model.named_parameters():
# self.writer.add_histogram(name, p, bins='auto')
f1_result_dict = self.valid_f1_metrics.result()
# rollback to train mode
self.model.train()
return f1_result_dict
def average_gradients(self, model):
'''
Gradient averaging
:param model:
:return:
'''
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)
param.grad.data /= size
def logger_info(self, msg):
self.logger.info(msg) if self.local_master else None
def logger_warning(self, msg):
self.logger.warning(msg) if self.local_master else None
def _prepare_device(self, local_rank, local_world_size):
'''
setup GPU device if available, move model into configured device
:param local_rank:
:param local_world_size:
:return:
'''
if self.distributed:
ngpu_per_process = torch.cuda.device_count() // local_world_size
device_ids = list(
range(local_rank * ngpu_per_process,
(local_rank + 1) * ngpu_per_process))
if torch.cuda.is_available() and local_rank != -1:
torch.cuda.set_device(
device_ids[0]
) # device_ids[0] =local_rank if local_world_size = n_gpu per node
device = 'cuda'
self.logger_info(
f"[Process {os.getpid()}] world_size = {dist.get_world_size()}, "
+
f"rank = {dist.get_rank()}, n_gpu/process = {ngpu_per_process}, device_ids = {device_ids}"
)
else:
self.logger_warning('Training will be using CPU!')
device = 'cpu'
device = torch.device(device)
return device, device_ids
else:
n_gpu = torch.cuda.device_count()
n_gpu_use = local_world_size
if n_gpu_use > 0 and n_gpu == 0:
self.logger_warning(
"Warning: There\'s no GPU available on this machine,"
"training will be performed on CPU.")
n_gpu_use = 0
if n_gpu_use > n_gpu:
self.logger_warning(
"Warning: The number of GPU\'s configured to use is {}, but only {} are available "
"on this machine.".format(n_gpu_use, n_gpu))
n_gpu_use = n_gpu
list_ids = list(range(n_gpu_use))
if n_gpu_use > 0:
torch.cuda.set_device(
list_ids[0]) # only use first available gpu as devices
self.logger_warning(f'Training is using GPU {list_ids[0]}!')
device = 'cuda'
else:
self.logger_warning('Training is using CPU!')
device = 'cpu'
device = torch.device(device)
return device, list_ids
def _save_checkpoint(self, epoch, save_best=False):
'''
Saving checkpoints
:param epoch: current epoch number
:param save_best: if True, rename the saved checkpoint to 'model_best.pth'
:return:
'''
# only local master process do save model
if not self.local_master:
return
if hasattr(self.model, 'module'):
arch = type(self.model.module).__name__
state_dict = self.model.module.state_dict()
else:
arch = type(self.model).__name__
state_dict = self.model.state_dict()
state = {
'arch': arch,
'epoch': epoch,
'state_dict': state_dict,
'optimizer': self.optimizer.state_dict(),
'monitor_best': self.monitor_best,
'config': self.config
}
if save_best:
best_path = str(self.checkpoint_dir / 'model_best.pth')
torch.save(state, best_path)
self.logger_info("Saving current best: model_best.pth ...")
else:
filename = str(self.checkpoint_dir /
'checkpoint-epoch{}.pth'.format(epoch))
torch.save(state, filename)
self.logger_info("Saving checkpoint: {} ...".format(filename))
def _resume_checkpoint(self, resume_path):
'''
Resume from saved checkpoints
:param resume_path: Checkpoint path to be resumed
:return:
'''
resume_path = str(resume_path)
self.logger_info("Loading checkpoint: {} ...".format(resume_path))
# map_location = {'cuda:%d' % 0: 'cuda:%d' % self.config['local_rank']}
checkpoint = torch.load(resume_path, map_location=self.device)
self.start_epoch = checkpoint['epoch'] + 1
self.monitor_best = checkpoint['monitor_best']
# load architecture params from checkpoint.
if checkpoint['config']['model_arch'] != self.config['model_arch']:
self.logger_warning(
"Warning: Architecture configuration given in config file is different from that of "
"checkpoint. This may yield an exception while state_dict is being loaded."
)
self.model.load_state_dict(checkpoint['state_dict'])
# load optimizer state from checkpoint only when optimizer type is not changed.
if checkpoint['config']['optimizer']['type'] != self.config[
'optimizer']['type']:
self.logger_warning(
"Warning: Optimizer type given in config file is different from that of checkpoint. "
"Optimizer parameters not being resumed.")
else:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.logger_info(
"Checkpoint loaded. Resume training from epoch {}".format(
self.start_epoch))
