Address pre-commit hook failures and add comment to explain checkpointing logic with time based validation.

Author: Sohaib-Ahmed21

src/lightning/pytorch/callbacks/model_checkpoint.py

@@ -137,6 +137,8 @@ class ModelCheckpoint(Checkpoint):
             If ``True``, checkpoints are saved at the end of every training epoch.
             If ``False``, checkpoints are saved at the end of validation.
             If ``None`` (default), checkpointing behavior is determined based on training configuration.
+            If ``val_check_interval`` is a str, dict, or `timedelta` (time-based), checkpointing is performed after
+            validation.
             If ``check_val_every_n_epoch != 1``, checkpointing will not be performed at the end of
             every training epoch. If there are no validation batches of data, checkpointing will occur at the
             end of the training epoch. If there is a non-default number of validation runs per training epoch

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -538,10 +538,8 @@ def _should_check_val_fx(self, data_fetcher: _DataFetcher) -> bool:
         interval = self.trainer._val_check_time_interval
         if interval is not None:
             now = time.monotonic()
-            if now - self.trainer._last_val_time >= interval:
-                # time’s up → tell Trainer to validate
-                return True
-            return False
+            # if time’s up → tell Trainer to validate
+            return now - self.trainer._last_val_time >= interval
         # TODO: let training/eval loop handle logic around limit_*_batches and val_check_batch
         is_val_check_batch = is_last_batch
         if isinstance(self.trainer.limit_train_batches, int) and is_infinite_dataset:

src/lightning/pytorch/trainer/trainer.py

@@ -216,7 +216,8 @@ def __init__(
                 ``check_val_every_n_epoch`` > 1, validation is aligned to epoch multiples: if the interval elapses
                 before the next multiple-N epoch, validation runs at the start of that epoch (after the first batch)
                 and the timer resets; if it elapses during a multiple-N epoch, validation runs after the current batch.
-                For ``None`` or ``1``, the time-based behavior of ``val_check_interval`` applies without additional alignment.
+                For ``None`` or ``1``, the time-based behavior of ``val_check_interval`` applies without additional
+                alignment.
                 Default: ``1``.
 
             num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.