Fix type hints of tuner/batch_size_scaling.py

pyproject.toml

@@ -59,7 +59,6 @@ module = [
     "pytorch_lightning.callbacks.progress.rich_progress",
     "pytorch_lightning.trainer.trainer",
     "pytorch_lightning.trainer.connectors.checkpoint_connector",
-    "pytorch_lightning.tuner.batch_size_scaling",
     "lightning_app.api.http_methods",
     "lightning_app.api.request_types",
     "lightning_app.cli.app-template.app",

src/pytorch_lightning/callbacks/batch_size_finder.py

@@ -31,6 +31,8 @@
 class BatchSizeFinder(Callback):
     SUPPORTED_MODES = ("power", "binsearch")
 
+    optimal_batch_size: Optional[int]
+
     def __init__(
         self,
         mode: str = "power",

src/pytorch_lightning/tuner/batch_size_scaling.py

@@ -35,10 +35,10 @@ def scale_batch_size(
     init_val: int = 2,
     max_trials: int = 25,
     batch_arg_name: str = "batch_size",
-):
+) -> Optional[int]:
     if trainer.fast_dev_run:
         rank_zero_warn("Skipping batch size scaler since `fast_dev_run` is enabled.")
-        return
+        return None
 
     # Save initial model, that is loaded after batch size is found
     ckpt_path = os.path.join(trainer.default_root_dir, f".scale_batch_size_{uuid.uuid4()}.ckpt")
@@ -141,7 +141,12 @@ def __scale_batch_restore_params(trainer: "pl.Trainer", params: Dict[str, Any])
 
 
 def _run_power_scaling(
-    trainer: "pl.Trainer", pl_module: "pl.LightningModule", new_size: int, batch_arg_name: str, max_trials: int, params
+    trainer: "pl.Trainer",
+    pl_module: "pl.LightningModule",
+    new_size: int,
+    batch_arg_name: str,
+    max_trials: int,
+    params: Dict[str, Any],
 ) -> int:
     """Batch scaling mode where the size is doubled at each iteration until an OOM error is encountered."""
     # this flag is used to determine whether the previously scaled batch size, right before OOM, was a success or not
@@ -179,7 +184,12 @@ def _run_power_scaling(
 
 
 def _run_binary_scaling(
-    trainer: "pl.Trainer", pl_module: "pl.LightningModule", new_size: int, batch_arg_name: str, max_trials: int, params
+    trainer: "pl.Trainer",
+    pl_module: "pl.LightningModule",
+    new_size: int,
+    batch_arg_name: str,
+    max_trials: int,
+    params: Dict[str, Any],
 ) -> int:
     """Batch scaling mode where the size is initially is doubled at each iteration until an OOM error is
     encountered.
@@ -309,7 +319,7 @@ def _reset_dataloaders(trainer: "pl.Trainer", pl_module: "pl.LightningModule") -
         reset_fn(pl_module)
 
 
-def _try_loop_run(trainer: "pl.Trainer", params) -> None:
+def _try_loop_run(trainer: "pl.Trainer", params: Dict[str, Any]) -> None:
     if trainer.state.fn == "fit":
         loop = trainer.fit_loop
     else: