Move result teardown to loops

CHANGELOG.md

@@ -167,6 +167,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Refactored trainer `_run_*` functions and separate evaluation loops ([#8065](https://github.com/PyTorchLightning/pytorch-lightning/pull/8065))
     * Refactored prediction loop interface; added new classes `PredictionLoop`, `PredictionEpochLoop` ([#7700](https://github.com/PyTorchLightning/pytorch-lightning/pull/7700), [#8077](https://github.com/PyTorchLightning/pytorch-lightning/pull/8077))
     * Removed `pytorch_lightning/trainer/predict_loop.py` ([#8094](https://github.com/PyTorchLightning/pytorch-lightning/pull/8094))
+    * Moved result teardown to the loops ([#8245](https://github.com/PyTorchLightning/pytorch-lightning/pull/8245))
 
 
 - Refactored logging

pytorch_lightning/loops/base.py

@@ -100,7 +100,6 @@ def run(self, *args: Any, **kwargs: Any) -> Optional[Any]:
                 break
 
         output = self.on_run_end()
-        self.teardown()
         return output
 
     @abstractmethod
@@ -132,7 +131,7 @@ def on_run_end(self) -> Any:
         """Hook to be called at the end of the run. Its return argument is returned from :attr:`run`."""
 
     def teardown(self) -> None:
-        """The very last method called inside :meth:`run`. Use to release memory etc."""
+        """Use to release memory etc."""
 
     def load_state_dict(self, state_dict: Dict) -> None:
         """Restore the loop state from the provided state_dict."""

pytorch_lightning/loops/dataloader/evaluation_loop.py

@@ -263,3 +263,7 @@ def on_evaluation_epoch_end(self) -> None:
         self.trainer.call_hook(hook_name)
         self.trainer.call_hook("on_epoch_end")
         self.trainer.logger_connector.on_epoch_end()
+
+    def teardown(self) -> None:
+        self._results.cpu()
+        self.epoch_loop.teardown()

pytorch_lightning/loops/epoch/evaluation_epoch_loop.py

@@ -119,11 +119,10 @@ def advance(
 
     def on_run_end(self) -> List[STEP_OUTPUT]:
         """Returns the outputs of the whole run"""
-        return self.outputs
-
-    def teardown(self) -> None:
-        """Frees memory of tracked outputs"""
+        outputs = self.outputs
+        # free memory
         self.outputs = []
+        return outputs
 
     def evaluation_step(self, batch: Any, batch_idx: int, dataloader_idx: int) -> Optional[STEP_OUTPUT]:
         """The evaluation step (validation_step or test_step depending on the trainer's state).

pytorch_lightning/loops/epoch/prediction_epoch_loop.py

@@ -88,12 +88,12 @@ def advance(
 
     def on_run_end(self) -> Tuple[Any, Any]:
         """Returns the predictions and the corresponding batch indices"""
-        return self.predictions, self._all_batch_indices
-
-    def teardown(self) -> None:
-        """Frees memory of collected predictions."""
+        predictions = self.predictions
+        all_batch_indices = self._all_batch_indices
+        # free memory
         self.predictions = []
         self._all_batch_indices = []
+        return predictions, all_batch_indices
 
     def _predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
         """Runs the actual predict step together with all the

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -208,11 +208,16 @@ def on_run_end(self) -> List[List[STEP_OUTPUT]]:
         self._on_train_epoch_end_hook(processed_outputs)
         self.trainer.call_hook('on_epoch_end')
         self.trainer.logger_connector.on_epoch_end()
-        return self._epoch_output
+
+        epoch_output = self._epoch_output
+        # free memory
+        self._epoch_output = None
+        return epoch_output
 
     def teardown(self) -> None:
-        """Frees memory of tracked epoch outputs."""
-        self.epoch_output = None
+        self._results.cpu()
+        self.batch_loop.teardown()
+        self.val_loop.teardown()
 
     def _run_validation(self):
         # reload dataloaders

pytorch_lightning/loops/fit_loop.py

@@ -236,7 +236,7 @@ def on_advance_end(self) -> None:
             self.global_step += 1
 
     def on_run_end(self) -> None:
-        """Runs teardown logic and calls the ``on_train_end`` hook"""
+        """Calls the ``on_train_end`` hook"""
         # NOTE: the iteration_count/current_epoch is already incremented
         # Lightning today does not increment the current epoch at the last epoch run in Trainer.fit
         # To simulate that current behavior, we decrement here.
@@ -265,9 +265,6 @@ def on_run_end(self) -> None:
         # give accelerators a chance to finish
         self.trainer.accelerator.on_train_end()
 
-        # reset bookkeeping
-        self.trainer._running_stage = None
-
     def should_accumulate(self) -> bool:
         """Whether the gradients should be accumulated"""
         return self.epoch_loop.batch_loop.should_accumulate()
@@ -291,3 +288,6 @@ def state_dict(self) -> Dict:
 
     def load_state_dict(self, state_dict: Dict) -> None:
         self.epoch_loop.load_state_dict(state_dict["epoch_loop"])
+
+    def teardown(self) -> None:
+        self.epoch_loop.teardown()

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -312,9 +312,3 @@ def progress_bar_metrics(self) -> Dict[str, float]:
             metrics = self.metrics[MetricSource.PBAR]
             self._progress_bar_metrics.update(metrics)
         return self._progress_bar_metrics
-
-    def teardown(self):
-        self.trainer.fit_loop.epoch_loop._results.cpu()
-        self.trainer.fit_loop.epoch_loop.val_loop._results.cpu()
-        self.trainer.validate_loop._results.cpu()
-        self.trainer.test_loop._results.cpu()

pytorch_lightning/trainer/trainer.py

@@ -900,8 +900,10 @@ def _pre_dispatch(self):
 
     def _post_dispatch(self):
         self.accelerator.post_dispatch(self)
+        # these `teardown` calls are here instead of in `_call_teardown_hook` since they are internal teardowns
+        # which need to happen before.
         self.accelerator.teardown()
-        self.logger_connector.teardown()
+        self._active_loop.teardown()
 
     def _dispatch(self):
         if self.evaluating:
@@ -977,7 +979,6 @@ def _run_train(self) -> None:
                 self.on_keyboard_interrupt()
                 # same treatment as below
                 self.accelerator.on_train_end()
-                self.state.stage = None
         except BaseException:
             self.state.status = TrainerStatus.INTERRUPTED
             if distributed_available() and self.world_size > 1: