def _logging(
self,
model: MultitaskModel,
dataloaders: List["DictDataLoader"],
batch_size: int,
) -> Metrics:
"""Log and checkpoint if it is time to do so."""
# Switch to eval mode for evaluation
model.eval()
self.log_manager.update(batch_size)
# Log the loss and lr
metric_dict: Metrics = dict()
metric_dict.update(self._aggregate_losses())
# Evaluate the model and log the metric
if self.log_manager.trigger_evaluation():
# Log metrics
metric_dict.update(
self._evaluate(model, dataloaders, self.config.valid_split))
self._log_metrics(metric_dict)
self._reset_losses()
# Checkpoint the model
if self.log_manager.trigger_checkpointing():
self._checkpoint_model(model, metric_dict)
self._reset_losses()
# Switch back to train mode
model.train()
return metric_dict
def fit(self, model: MultitaskModel,
dataloaders: List["DictDataLoader"]) -> None:
"""Train a MultitaskModel.
Parameters
----------
model
The model to train
dataloaders
A list of DataLoaders. These will split into train, valid, and test splits
based on the ``split`` attribute of the DataLoaders.
"""
self._check_dataloaders(dataloaders)
# Identify the dataloaders to train on
train_dataloaders = [
dl for dl in dataloaders
if dl.dataset.split == self.config.train_split # type: ignore
]
# Calculate the total number of batches per epoch
self.n_batches_per_epoch = sum(
[len(dataloader) for dataloader in train_dataloaders])
# Set training helpers
self._set_log_writer()
self._set_checkpointer()
self._set_log_manager()
self._set_optimizer(model)
self._set_lr_scheduler()
self._set_batch_scheduler()
# Set to training mode
model.train()
logging.info("Start training...")
self.metrics: Dict[str, float] = dict()
self._reset_losses()
for epoch_num in range(self.config.n_epochs):
batches = tqdm(
enumerate(self.batch_scheduler.get_batches(train_dataloaders)),
total=self.n_batches_per_epoch,
disable=(not self.config.progress_bar),
desc=f"Epoch {epoch_num}:",
)
for batch_num, (batch, dataloader) in batches:
X_dict, Y_dict = batch
total_batch_num = epoch_num * self.n_batches_per_epoch + batch_num
batch_size = len(next(iter(Y_dict.values())))
# Set gradients of all model parameters to zero
self.optimizer.zero_grad()
# Perform forward pass and calcualte the loss and count
loss_dict, count_dict = model.calculate_loss(X_dict, Y_dict)
# Update running loss and count
for task_name in loss_dict.keys():
identifier = "/".join([
task_name,
dataloader.dataset.name,
dataloader.dataset.split,
"loss",
])
self.running_losses[identifier] += (
loss_dict[task_name].item() * count_dict[task_name])
self.running_counts[task_name] += count_dict[task_name]
# Skip the backward pass if no loss is calcuated
if not loss_dict:
continue
# Calculate the average loss
loss = torch.stack(list(loss_dict.values())).sum()
# Perform backward pass to calculate gradients
loss.backward()
# Clip gradient norm
if self.config.grad_clip:
torch.nn.utils.clip_grad_norm_(model.parameters(),
self.config.grad_clip)
# Update the parameters
self.optimizer.step()
# Update lr using lr scheduler
self._update_lr_scheduler(total_batch_num)
# Update metrics
self.metrics.update(
self._logging(model, dataloaders, batch_size))
batches.set_postfix(self.metrics)
model = self.log_manager.cleanup(model)