Change the return type of tune() in trainer.py to TypedDict

pyproject.toml

@@ -88,7 +88,6 @@ module = [
     "pytorch_lightning.trainer.supporters",
     "pytorch_lightning.trainer.trainer",
     "pytorch_lightning.tuner.batch_size_scaling",
-    "pytorch_lightning.tuner.tuning",
     "pytorch_lightning.utilities.auto_restart",
     "pytorch_lightning.utilities.data",
     "pytorch_lightning.utilities.distributed",

src/pytorch_lightning/trainer/trainer.py

@@ -85,8 +85,7 @@
 from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
 from pytorch_lightning.trainer.states import RunningStage, TrainerFn, TrainerState, TrainerStatus
 from pytorch_lightning.trainer.supporters import CombinedLoader
-from pytorch_lightning.tuner.lr_finder import _LRFinder
-from pytorch_lightning.tuner.tuning import Tuner
+from pytorch_lightning.tuner.tuning import _TunerResult, Tuner
 from pytorch_lightning.utilities import (
     _HPU_AVAILABLE,
     _IPU_AVAILABLE,
@@ -1015,7 +1014,7 @@ def tune(
         datamodule: Optional[LightningDataModule] = None,
         scale_batch_size_kwargs: Optional[Dict[str, Any]] = None,
         lr_find_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Dict[str, Optional[Union[int, _LRFinder]]]:
+    ) -> _TunerResult:
         r"""
         Runs routines to tune hyperparameters before training.
 

src/pytorch_lightning/tuner/tuning.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 from typing import Any, Dict, Optional, Union
 
+from typing_extensions import NotRequired, TypedDict
+
 import pytorch_lightning as pl
 from pytorch_lightning.trainer.states import TrainerStatus
 from pytorch_lightning.tuner.batch_size_scaling import scale_batch_size
@@ -21,6 +23,11 @@
 from pytorch_lightning.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
 
 
+class _TunerResult(TypedDict):
+    lr_find: NotRequired[Optional[_LRFinder]]
+    scale_batch_size: NotRequired[Optional[int]]
+
+
 class Tuner:
     """Tuner class to tune your model."""
 
@@ -36,11 +43,11 @@ def _tune(
         model: "pl.LightningModule",
         scale_batch_size_kwargs: Optional[Dict[str, Any]] = None,
         lr_find_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Dict[str, Optional[Union[int, _LRFinder]]]:
+    ) -> _TunerResult:
         scale_batch_size_kwargs = scale_batch_size_kwargs or {}
         lr_find_kwargs = lr_find_kwargs or {}
         # return a dict instead of a tuple so BC is not broken if a new tuning procedure is added
-        result = {}
+        result = _TunerResult()
 
         self.trainer.strategy.connect(model)