Never pickle the Trainer with the LightningModule (#17133)

Author: carmocca

src/lightning/pytorch/CHANGELOG.md

@@ -14,7 +14,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
--
+- Pickling the `LightningModule` no longer pickles the `Trainer` ([#17133](https://github.com/Lightning-AI/lightning/pull/17133))
 
 
 ### Depercated
@@ -29,7 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+
 
 
 ## [2.0.0] - 2023-03-15

src/lightning/pytorch/callbacks/stochastic_weight_avg.py

@@ -150,8 +150,7 @@ def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: s
             raise MisconfigurationException("SWA does not currently support sharded models.")
 
         # copy the model before moving it to accelerator device.
-        with pl_module._prevent_trainer_and_dataloaders_deepcopy():
-            self._average_model = deepcopy(pl_module)
+        self._average_model = deepcopy(pl_module)
 
     def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         if len(trainer.optimizers) != 1:

src/lightning/pytorch/core/module.py

@@ -124,7 +124,6 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         self._automatic_optimization: bool = True
         self._param_requires_grad_state: Dict[str, bool] = {}
         self._metric_attributes: Optional[Dict[int, str]] = None
-        self._should_prevent_trainer_and_dataloaders_deepcopy: bool = False
         self._register_sharded_tensor_state_dict_hooks_if_available()
         self._compiler_ctx: Optional[Dict[str, Any]] = None
 
@@ -1539,20 +1538,9 @@ def load_from_checkpoint(
         )
         return cast(Self, loaded)
 
-    @contextmanager
-    def _prevent_trainer_and_dataloaders_deepcopy(self) -> Generator[None, None, None]:
-        self._should_prevent_trainer_and_dataloaders_deepcopy = True
-        yield
-        self._should_prevent_trainer_and_dataloaders_deepcopy = False
-
     def __getstate__(self) -> Dict[str, Any]:
         state = dict(self.__dict__)
-        if self._should_prevent_trainer_and_dataloaders_deepcopy:
-            state["_trainer"] = None
-            state.pop("train_dataloader", None)
-            state.pop("val_dataloader", None)
-            state.pop("test_dataloader", None)
-            state.pop("predict_dataloader", None)
+        state["_trainer"] = None
         return state
 
     def _register_sharded_tensor_state_dict_hooks_if_available(self) -> None: