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Do not require lr_scheduler for all other recipes #2051

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Do not require lr_scheduler for all other recipes #2051

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14 changes: 10 additions & 4 deletions recipes/knowledge_distillation_distributed.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -315,7 +315,7 @@ def setup(self, cfg: DictConfig) -> None:
# Learning rate scheduler can only be set up after number of steps
# has been computed
self._lr_scheduler = self._setup_lr_scheduler(
cfg_lr_scheduler=cfg.lr_scheduler,
cfg_lr_scheduler=cfg.get("lr_scheduler", None),
num_training_steps=self.total_epochs * self._steps_per_epoch,
last_epoch=self.global_step - 1,
)
Expand Down Expand Up @@ -626,10 +626,15 @@ def _setup_optimizer(

def _setup_lr_scheduler(
self,
cfg_lr_scheduler: DictConfig,
cfg_lr_scheduler: Optional[DictConfig],
num_training_steps: int,
last_epoch: int,
) -> Optimizer:
) -> Optional[Optimizer]:
if cfg_lr_scheduler is None:
log.info(
"No learning rate scheduler configured. Using constant learning rate."
)
return None
lr_scheduler = config.instantiate(
cfg_lr_scheduler,
self._optimizer,
Expand Down Expand Up @@ -886,7 +891,8 @@ def train(self) -> None:
kd_loss_to_log = running_kd_loss.item() / num_tokens
self._optimizer.step()
self._optimizer.zero_grad(set_to_none=True)
self._lr_scheduler.step()
if self._lr_scheduler is not None:
self._lr_scheduler.step()
# Update the number of steps when the weights are updated
self.global_step += 1

Expand Down
15 changes: 11 additions & 4 deletions recipes/knowledge_distillation_single_device.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,7 +306,7 @@ def setup(self, cfg: DictConfig) -> None:
# Learning rate scheduler can only be set up after number of steps
# has been computed
self._lr_scheduler = self._setup_lr_scheduler(
cfg_lr_scheduler=cfg.lr_scheduler,
cfg_lr_scheduler=cfg.get("lr_scheduler", None),
num_training_steps=self.total_epochs * self._steps_per_epoch,
last_epoch=self.global_step - 1,
)
Expand Down Expand Up @@ -495,10 +495,16 @@ def _setup_optimizer(

def _setup_lr_scheduler(
self,
cfg_lr_scheduler: DictConfig,
cfg_lr_scheduler: Optional[DictConfig],
num_training_steps: int,
last_epoch: int,
) -> Optimizer:
) -> Optional[Optimizer]:
if cfg_lr_scheduler is None:
log.info(
"No learning rate scheduler configured. Using constant learning rate."
)
return None

lr_scheduler = config.instantiate(
cfg_lr_scheduler,
self._optimizer,
Expand Down Expand Up @@ -727,7 +733,8 @@ def train(self) -> None:
)
self._optimizer.step()
self._optimizer.zero_grad(set_to_none=True)
self._lr_scheduler.step()
if self._lr_scheduler is not None:
self._lr_scheduler.step()
# Update the number of steps when the weights are updated
self.global_step += 1

Expand Down
15 changes: 11 additions & 4 deletions recipes/lora_dpo_distributed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -284,7 +284,7 @@ def setup(self, cfg: DictConfig) -> None:
# Learning rate scheduler can only be set up after number of steps
# has been computed
self._lr_scheduler = self._setup_lr_scheduler(
cfg_lr_scheduler=cfg.lr_scheduler,
cfg_lr_scheduler=cfg.get("lr_scheduler", None),
num_training_steps=self.total_epochs * self._steps_per_epoch,
last_epoch=self.global_step - 1,
)
Expand Down Expand Up @@ -426,10 +426,16 @@ def _setup_optimizer(

def _setup_lr_scheduler(
self,
cfg_lr_scheduler: DictConfig,
cfg_lr_scheduler: Optional[DictConfig],
num_training_steps: int,
last_epoch: int,
) -> Optimizer:
) -> Optional[Optimizer]:
if cfg_lr_scheduler is None:
log.info(
"No learning rate scheduler configured. Using constant learning rate."
)
return None

lr_scheduler = config.instantiate(
cfg_lr_scheduler,
self._optimizer,
Expand Down Expand Up @@ -679,7 +685,8 @@ def train(self) -> None:
if (idx + 1) % self._gradient_accumulation_steps == 0:
self._optimizer.step()
self._optimizer.zero_grad(set_to_none=True)
self._lr_scheduler.step()
if self._lr_scheduler is not None:
self._lr_scheduler.step()

# Update the number of steps when the weights are updated
self.global_step += 1
Expand Down
15 changes: 11 additions & 4 deletions recipes/lora_finetune_distributed.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -334,7 +334,7 @@ def setup(self, cfg: DictConfig) -> None:
# Learning rate scheduler can only be set up after number of steps
# has been computed
self._lr_scheduler = self._setup_lr_scheduler(
cfg_lr_scheduler=cfg.lr_scheduler,
cfg_lr_scheduler=cfg.get("lr_scheduler", None),
num_training_steps=self.total_epochs * self._steps_per_epoch,
last_epoch=self.global_step - 1,
)
Expand Down Expand Up @@ -563,10 +563,16 @@ def _setup_optimizer(

def _setup_lr_scheduler(
self,
cfg_lr_scheduler: DictConfig,
cfg_lr_scheduler: Optional[DictConfig],
num_training_steps: int,
last_epoch: int,
) -> Optimizer:
) -> Optional[Optimizer]:
if cfg_lr_scheduler is None:
log.info(
"No learning rate scheduler configured. Using constant learning rate."
)
return None

lr_scheduler = config.instantiate(
cfg_lr_scheduler,
self._optimizer,
Expand Down Expand Up @@ -837,7 +843,8 @@ def train(self) -> None:
)
self._optimizer.step()
self._optimizer.zero_grad(set_to_none=True)
self._lr_scheduler.step()
if self._lr_scheduler is not None:
self._lr_scheduler.step()

# Update the number of steps when the weights are updated
self.global_step += 1
Expand Down
15 changes: 11 additions & 4 deletions recipes/lora_finetune_single_device.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -331,7 +331,7 @@ def setup(self, cfg: DictConfig) -> None:
# Learning rate scheduler can only be set up after number of steps
# has been computed
self._lr_scheduler = self._setup_lr_scheduler(
cfg_lr_scheduler=cfg.lr_scheduler,
cfg_lr_scheduler=cfg.get("lr_scheduler", None),
num_training_steps=self.total_epochs * self._steps_per_epoch,
last_epoch=self.global_step - 1,
)
Expand Down Expand Up @@ -497,10 +497,16 @@ def _setup_optimizer(

def _setup_lr_scheduler(
self,
cfg_lr_scheduler: DictConfig,
cfg_lr_scheduler: Optional[DictConfig],
num_training_steps: int,
last_epoch: int,
) -> Optimizer:
) -> Optional[Optimizer]:
if cfg_lr_scheduler is None:
log.info(
"No learning rate scheduler configured. Using constant learning rate."
)
return None

lr_scheduler = config.instantiate(
cfg_lr_scheduler,
self._optimizer,
Expand Down Expand Up @@ -717,7 +723,8 @@ def train(self) -> None:
)
self._optimizer.step()
self._optimizer.zero_grad(set_to_none=True)
self._lr_scheduler.step()
if self._lr_scheduler is not None:
self._lr_scheduler.step()
# Update the number of steps when the weights are updated
self.global_step += 1

Expand Down
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