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📉 Add learning_rate argument to _maybe_log_save_evaluate #3206

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Merged
merged 1 commit into from
Apr 3, 2025
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📉 Add learning_rate argument to _maybe_log_save_evaluate #3206

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9 changes: 7 additions & 2 deletions trl/trainer/online_dpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -695,7 +695,9 @@ def training_step(

# Same as Trainer._maybe_log_save_evaluate but log our metrics
# start_time defaults to None to allow compatibility with transformers<=4.46
def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time=None):
def _maybe_log_save_evaluate(
self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time=None, learning_rate=None
):
if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
logs: dict[str, float] = {}

Expand All @@ -708,7 +710,10 @@ def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, igno
logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
if grad_norm is not None:
logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm
logs["learning_rate"] = self._get_learning_rate()
if learning_rate is not None:
logs["learning_rate"] = learning_rate
else:
logs["learning_rate"] = self._get_learning_rate()

# Add our metrics
for key, val in self.stats.items():
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