LRs updates are called at the end of a skipped epoch

src/lightning/fabric/CHANGELOG.md

@@ -27,7 +27,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Learning rate scheduler is stepped at the end of epoch when `on_train_batch_start` returns -1 ([[#21296](https://github.com/Lightning-AI/pytorch-lightning/issues/21296)]).
 
 
 ---

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -325,30 +325,33 @@ def advance(self, data_fetcher: _DataFetcher) -> None:
         trainer._logger_connector.on_batch_start(batch)
 
         batch_output: _BATCH_OUTPUTS_TYPE = None  # for mypy
+        should_skip_rest_of_epoch = False
+
         if batch is None and not using_dataloader_iter:
             self._warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...")
         else:
             # hook
             call._call_callback_hooks(trainer, "on_train_batch_start", batch, batch_idx)
             response = call._call_lightning_module_hook(trainer, "on_train_batch_start", batch, batch_idx)
             call._call_strategy_hook(trainer, "on_train_batch_start", batch, batch_idx)
-            if response == -1:
-                self.batch_progress.increment_processed()
-                raise StopIteration
-
-            self.batch_progress.increment_started()
-
-            kwargs = (
-                self._build_kwargs(OrderedDict(), batch, batch_idx)
-                if not using_dataloader_iter
-                else OrderedDict(any=dataloader_iter)
-            )
-            with trainer.profiler.profile("run_training_batch"):
-                if trainer.lightning_module.automatic_optimization:
-                    # in automatic optimization, there can only be one optimizer
-                    batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
-                else:
-                    batch_output = self.manual_optimization.run(kwargs)
+            should_skip_rest_of_epoch = response == -1
+            # Signal this is the last batch for the current epoch
+            if should_skip_rest_of_epoch:
+                self.batch_progress.increment_by(0, is_last_batch=True)
+            else:
+                self.batch_progress.increment_started()
+
+                kwargs = (
+                    self._build_kwargs(OrderedDict(), batch, batch_idx)
+                    if not using_dataloader_iter
+                    else OrderedDict(any=dataloader_iter)
+                )
+                with trainer.profiler.profile("run_training_batch"):
+                    if trainer.lightning_module.automatic_optimization:
+                        # in automatic optimization, there can only be one optimizer
+                        batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
+                    else:
+                        batch_output = self.manual_optimization.run(kwargs)
 
         self.batch_progress.increment_processed()
 
@@ -358,6 +361,10 @@ def advance(self, data_fetcher: _DataFetcher) -> None:
         if self._num_ready_batches_reached():
             self.update_lr_schedulers("epoch", update_plateau_schedulers=False)
 
+        if should_skip_rest_of_epoch:
+            # Only raise StopIteration now so that the training epoch loop can finish
+            raise StopIteration
+
         if using_dataloader_iter:
             # update the hook kwargs now that the step method might have consumed the iterator
             batch = data_fetcher._batch