Update Trainer's ckpt_path type for pathlib Path (#19362)

Author: awaelchli

src/lightning/pytorch/trainer/trainer.py

@@ -506,7 +506,7 @@ def fit(
         train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
         val_dataloaders: Optional[EVAL_DATALOADERS] = None,
         datamodule: Optional[LightningDataModule] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
     ) -> None:
         r"""Runs the full optimization routine.
 
@@ -550,7 +550,7 @@ def _fit_impl(
         train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
         val_dataloaders: Optional[EVAL_DATALOADERS] = None,
         datamodule: Optional[LightningDataModule] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
     ) -> None:
         log.debug(f"{self.__class__.__name__}: trainer fit stage")
 
@@ -586,7 +586,7 @@ def validate(
         self,
         model: Optional["pl.LightningModule"] = None,
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
         verbose: bool = True,
         datamodule: Optional[LightningDataModule] = None,
     ) -> _EVALUATE_OUTPUT:
@@ -649,7 +649,7 @@ def _validate_impl(
         self,
         model: Optional["pl.LightningModule"] = None,
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
         verbose: bool = True,
         datamodule: Optional[LightningDataModule] = None,
     ) -> Optional[Union[_PREDICT_OUTPUT, _EVALUATE_OUTPUT]]:
@@ -694,7 +694,7 @@ def test(
         self,
         model: Optional["pl.LightningModule"] = None,
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
         verbose: bool = True,
         datamodule: Optional[LightningDataModule] = None,
     ) -> _EVALUATE_OUTPUT:
@@ -758,7 +758,7 @@ def _test_impl(
         self,
         model: Optional["pl.LightningModule"] = None,
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
         verbose: bool = True,
         datamodule: Optional[LightningDataModule] = None,
     ) -> Optional[Union[_PREDICT_OUTPUT, _EVALUATE_OUTPUT]]:
@@ -805,7 +805,7 @@ def predict(
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
         datamodule: Optional[LightningDataModule] = None,
         return_predictions: Optional[bool] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
     ) -> Optional[_PREDICT_OUTPUT]:
         r"""Run inference on your data. This will call the model forward function to compute predictions. Useful to
         perform distributed and batched predictions. Logging is disabled in the predict hooks.
@@ -870,7 +870,7 @@ def _predict_impl(
         dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
         datamodule: Optional[LightningDataModule] = None,
         return_predictions: Optional[bool] = None,
-        ckpt_path: Optional[str] = None,
+        ckpt_path: Optional[_PATH] = None,
     ) -> Optional[_PREDICT_OUTPUT]:
         # --------------------
         # SETUP HOOK