def _auto_step_lr_scheduler_per_batch(self, batch_idx: int, lr_scheduler: LRScheduler) -> None:
"""
This function aims at automatically step a LR scheduler. It should be called per batch.
"""
if lr_scheduler._step_mode == LRScheduler.StepMode.STEP_EVERY_BATCH:
lr_scheduler.step()
elif lr_scheduler._step_mode == LRScheduler.StepMode.STEP_EVERY_EPOCH:
mod = (batch_idx + 1) % len(self.training_loader)
if mod == 0 or mod < self.hvd_config.aggregation_frequency:
lr_scheduler.step()
def _auto_step_lr_scheduler_per_batch(
self, batch_idx: int, lr_scheduler: pytorch.LRScheduler) -> None:
"""
This function aims at automatically step a LR scheduler. It should be called per batch.
"""
# Never step lr when we do not step optimizer.
if not self.context._should_communicate_and_update():
return
if lr_scheduler._step_mode == pytorch.LRScheduler.StepMode.STEP_EVERY_BATCH:
start_idx = batch_idx - self.context._aggregation_frequency + 1
for i in range(start_idx, batch_idx + 1):
if (i + 1) % lr_scheduler._frequency == 0:
lr_scheduler.step()
elif lr_scheduler._step_mode == pytorch.LRScheduler.StepMode.STEP_EVERY_OPTIMIZER_STEP:
if (batch_idx + 1) % lr_scheduler._frequency == 0:
lr_scheduler.step()
elif lr_scheduler._step_mode == pytorch.LRScheduler.StepMode.STEP_EVERY_EPOCH:
# We will step if the next optimizer step will land in the next epoch.
epoch_idx = self.get_epoch_idx(batch_idx)
next_steppable_batch = batch_idx + self.context._aggregation_frequency
next_batch_epoch_idx = self.get_epoch_idx(next_steppable_batch)
for e in range(epoch_idx, next_batch_epoch_idx):
if (e + 1) % lr_scheduler._frequency == 0:
lr_scheduler.step()