2/n Move Accelerator into strategy - remove dispatch functions from Accelerator

CHANGELOG.md

@@ -214,6 +214,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed method `setup_optimizers_in_pre_dispatch` from the `strategies` and achieve the same logic in `setup` and `pre_dispatch` methods ([#10906](https://github.com/PyTorchLightning/pytorch-lightning/pull/10906))
 
 
+- Removed methods `pre_dispatch`, `dispatch` and `post_dispatch` from the `Accelerator` ([#10885](https://github.com/PyTorchLightning/pytorch-lightning/pull/10885))
+
+
 ### Fixed
 
 - Fixed an issue with `SignalConnector` not restoring the default signal handlers on teardown when running on SLURM or with fault-tolerant training enabled ([#10611](https://github.com/PyTorchLightning/pytorch-lightning/pull/10611))

pytorch_lightning/accelerators/accelerator.py

@@ -70,21 +70,6 @@ def setup(self, trainer: "pl.Trainer") -> None:
         """
         self.training_type_plugin.setup(trainer)
 
-    def pre_dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something before the training/evaluation/prediction starts."""
-        self.training_type_plugin._move_optimizer_state()
-        self.training_type_plugin.pre_dispatch(trainer)
-
-    def dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something before the training/evaluation/prediction starts."""
-        self.training_type_plugin.dispatch(trainer)
-        self.training_type_plugin.precision_plugin.dispatch(trainer)
-
-    def post_dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something after the training/evaluation/prediction starts."""
-        self.training_type_plugin.post_dispatch(trainer)
-        self.training_type_plugin.precision_plugin.post_dispatch()
-
     @property
     def model(self) -> Module:
         """Returns the model.

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -202,7 +202,7 @@ def clip_grad_by_norm(self, optimizer: Optimizer, clip_val: Union[int, float]) -
         torch.nn.utils.clip_grad_norm_(parameters, clip_val)
 
     def dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something when ``Accelerator.dispatch()`` gets called."""
+        """Hook to do something when ``TrainingTypePlugin.dispatch()`` gets called."""
 
     def post_dispatch(self) -> None:
         """Hook to do something after the training/evaluation/prediction finishes."""

pytorch_lightning/plugins/training_type/ddp.py

@@ -339,6 +339,7 @@ def determine_ddp_device_ids(self):
         return [self.root_device.index]
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        super().pre_dispatch(trainer)
         # share ddp pids to all processes
         self._rank_0_has_called_call_children_scripts = self.broadcast(self._rank_0_has_called_call_children_scripts)
         if self._should_run_deadlock_detection():
@@ -357,6 +358,7 @@ def pre_dispatch(self, trainer: "pl.Trainer") -> None:
 
     def post_dispatch(self, trainer: "pl.Trainer") -> None:
         self.cluster_environment.teardown()
+        super().post_dispatch(trainer)
 
     def barrier(self, *args, **kwargs) -> None:
         if not distributed_available():

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -375,6 +375,7 @@ def restore_checkpoint_after_pre_dispatch(self) -> bool:
         return True
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        self._move_optimizer_state()
         self.init_deepspeed()
         self.barrier()
 

pytorch_lightning/plugins/training_type/fully_sharded.py

@@ -161,6 +161,7 @@ def configure_ddp(self) -> None:
         self.setup_optimizers(self.lightning_module.trainer)
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        self._move_optimizer_state()
         if self.sync_batchnorm:
             self.model = self.configure_sync_batchnorm(self.model)
         self.configure_ddp()

pytorch_lightning/plugins/training_type/horovod.py

@@ -79,7 +79,7 @@ def setup(self, trainer: "pl.Trainer") -> None:
         super().setup(trainer)
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
-
+        super().pre_dispatch(trainer)
         if not self.lightning_module.trainer.training:
             # no need to setup optimizers
             return

pytorch_lightning/plugins/training_type/ipu.py

@@ -130,6 +130,7 @@ def setup_optimizers(self, trainer: "pl.Trainer") -> None:
             raise MisconfigurationException("IPUs currently only support one optimizer.")
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        super().pre_dispatch(trainer)
         model = LightningIPUModule(self.lightning_module, self.precision_plugin.precision)
         self.model = model
 

pytorch_lightning/plugins/training_type/single_tpu.py

@@ -65,6 +65,7 @@ def model_to_device(self) -> None:
         self.model.to(self.root_device)
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        super().pre_dispatch(trainer)
         if isinstance(self.device, int):
             self.device = xm.xla_device(self.device)
 

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -119,6 +119,7 @@ def connect(self, model: "pl.LightningModule") -> None:
         return super().connect(model)
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
+        super().pre_dispatch(trainer)
         if self.debug:
             os.environ["PT_XLA_DEBUG"] = str(1)
 

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -459,9 +459,12 @@ def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int =
 
     def pre_dispatch(self, trainer: "pl.Trainer") -> None:
         """Hook to do something before the training/evaluation/prediction starts."""
+        self._move_optimizer_state()
 
     def dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something at trainer run_stage starts."""
+        """Hook to do something before the training/evaluation/prediction starts."""
+        self.precision_plugin.dispatch(trainer)
 
     def post_dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something after the training/evaluation/prediction finishes."""
+        """Hook to do something after the training/evaluation/prediction starts."""
+        self.precision_plugin.post_dispatch()

pytorch_lightning/trainer/trainer.py

@@ -1182,7 +1182,7 @@ def _run(
         return results
 
     def _pre_dispatch(self):
-        self.accelerator.pre_dispatch(self)
+        self.training_type_plugin.pre_dispatch(self)
         self._log_hyperparams()
 
     def _log_hyperparams(self) -> None:
@@ -1223,7 +1223,7 @@ def _log_hyperparams(self) -> None:
             self.logger.save()
 
     def _post_dispatch(self):
-        self.accelerator.post_dispatch(self)
+        self.training_type_plugin.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()
@@ -1241,7 +1241,7 @@ def _dispatch(self) -> Any:
             return self.training_type_plugin.start_training(self)
 
     def run_stage(self):
-        self.accelerator.dispatch(self)
+        self.training_type_plugin.dispatch(self)
         self.__setup_profiler()
 
         if self.evaluating: