Fix relationship between optimizer and LR scheduler.

- Always initialize the LR scheduler before the first step.
- Correctly call LR scheduler only once per iteration.

See pytorch/pytorch#20124

Note: there is still a problem if you set update_interval as
a number of Epochs.

src/refiners/training_utils/clock.py

@@ -22,7 +22,7 @@ def __init__(
         self,
         training_duration: TimeValue,
         gradient_accumulation: Step,
-        lr_scheduler_interval: TimeValue,
+        lr_scheduler_interval: Iteration | Epoch,
         verbose: bool = True,
     ) -> None:
         self.training_duration = training_duration

src/refiners/training_utils/trainer.py

@@ -130,6 +130,10 @@ def __init__(self, config: ConfigType) -> None:
         self._load_models()
         self._call_callbacks(event_name="on_init_end")
 
+        # Ensure the lr_scheduler is initialized before calling `step` on the optimizer.
+        # See `patch_track_step_called` in LRScheduler constructor.
+        assert self.lr_scheduler
+
     @register_callback()
     def clock(self, config: ClockConfig) -> TrainingClock:
         return TrainingClock(
@@ -299,10 +303,10 @@ def backward(self) -> None:
             self.optimizer.step()
             self.optimizer.zero_grad()
             self._call_callbacks(event_name="on_optimizer_step_end")
-        if self.clock.is_due(self.config.lr_scheduler.update_interval):
-            self._call_callbacks(event_name="on_lr_scheduler_step_begin")
-            self.lr_scheduler.step()
-            self._call_callbacks(event_name="on_lr_scheduler_step_end")
+            if self.clock.is_due(self.config.lr_scheduler.update_interval):
+                self._call_callbacks(event_name="on_lr_scheduler_step_begin")
+                self.lr_scheduler.step()
+                self._call_callbacks(event_name="on_lr_scheduler_step_end")
 
     def step(self, batch: Batch) -> None:
         """Perform a single training step."""