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fix: saving model weights #556

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merged 2 commits into from
Jul 26, 2022
Merged

fix: saving model weights #556

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a451331
fix: saving model weights
csarron Jul 22, 2022
8341dab
fix: saveing model weights for optimizer and scheduler
csarron Jul 23, 2022
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13 changes: 7 additions & 6 deletions src/accelerate/accelerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -446,16 +446,13 @@ def _prepare_one(self, obj, first_pass=False):
if isinstance(obj, torch.utils.data.DataLoader):
return self.prepare_data_loader(obj)
elif isinstance(obj, torch.nn.Module):
self._models.append(obj)
return self.prepare_model(obj)
elif isinstance(obj, torch.optim.Optimizer):
optimizer = self.prepare_optimizer(obj)
self._optimizers.append(optimizer)
return optimizer
# Second pass of preparation: LR scheduler (which need the full list of optimizers)
elif isinstance(obj, torch.optim.lr_scheduler._LRScheduler):
scheduler = self.prepare_scheduler(obj)
self._schedulers.append(scheduler)
return scheduler
# Return the unprocessed object if previous criteria was not met
return obj
Expand Down Expand Up @@ -570,6 +567,7 @@ def prepare(self, *args):
return result if len(result) > 1 else result[0]

def prepare_model(self, model):
self._models.append(model)
if self.device_placement and self.distributed_type != DistributedType.FSDP:
model = model.to(self.device)
if self.distributed_type == DistributedType.MULTI_GPU:
Expand Down Expand Up @@ -782,7 +780,9 @@ def prepare_data_loader(self, data_loader):
)

def prepare_optimizer(self, optimizer):
return AcceleratedOptimizer(optimizer, device_placement=self.device_placement, scaler=self.scaler)
optimizer = AcceleratedOptimizer(optimizer, device_placement=self.device_placement, scaler=self.scaler)
self._optimizers.append(optimizer)
return optimizer

def prepare_scheduler(self, scheduler):
# We try to find the optimizer associated with `scheduler`, the default is the full list.
Expand All @@ -791,13 +791,14 @@ def prepare_scheduler(self, scheduler):
if getattr(scheduler, "optimizer", None) == opt.optimizer:
optimizer = opt
break

return AcceleratedScheduler(
scheduler = AcceleratedScheduler(
scheduler,
optimizer,
step_with_optimizer=self.step_scheduler_with_optimizer,
split_batches=self.split_batches,
)
self._schedulers.append(scheduler)
return scheduler

def backward(self, loss, **kwargs):
"""
Expand Down