Remove TrainingTypePlugin.post_dispatch in favor of teardown

CHANGELOG.md

@@ -116,6 +116,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated the access to the attribute `IndexBatchSamplerWrapper.batch_indices` in favor of `IndexBatchSamplerWrapper.seen_batch_indices` ([#10870](https://github.com/PyTorchLightning/pytorch-lightning/pull/10870))
 
 
+- Deprecated `TrainingTypePlugin.post_dispatch` in favor of `TrainingTypePlugin.teardown` ([#10939](https://github.com/PyTorchLightning/pytorch-lightning/pull/10939))
+
+
 ### Removed
 
 - Removed deprecated parameter `method` in `pytorch_lightning.utilities.model_helpers.is_overridden` ([#10507](https://github.com/PyTorchLightning/pytorch-lightning/pull/10507))

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -207,9 +207,6 @@ def clip_grad_by_norm(self, optimizer: Optimizer, clip_val: Union[int, float]) -
     def dispatch(self, trainer: "pl.Trainer") -> None:
         """Hook to do something when ``TrainingTypePlugin.dispatch()`` gets called."""
 
-    def post_dispatch(self) -> None:
-        """Hook to do something after the training/evaluation/prediction finishes."""
-
     @contextlib.contextmanager
     def forward_context(self) -> Generator[None, None, None]:
         """A contextmanager for managing model forward/training_step/evaluation_step/predict_step."""

pytorch_lightning/plugins/training_type/ddp.py

@@ -356,10 +356,6 @@ def pre_dispatch(self, trainer: "pl.Trainer") -> None:
         if trainer_fn == TrainerFn.FITTING:
             self.configure_ddp()
 
-    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():
             return
@@ -501,6 +497,7 @@ def reconciliate_processes(self, trace: str) -> None:
         raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
 
     def teardown(self) -> None:
+        super().teardown()
         if isinstance(self.model, DistributedDataParallel):
             self.model = self.lightning_module
 

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -395,6 +395,7 @@ def register_plugins(cls, plugin_registry: Dict) -> None:
         )
 
     def teardown(self) -> None:
+        super().teardown()
         if isinstance(self.model, DistributedDataParallel):
             self.model = self.lightning_module
 

pytorch_lightning/plugins/training_type/dp.py

@@ -150,6 +150,7 @@ def test_step_end(self, output):
         return output
 
     def teardown(self) -> None:
+        super().teardown()
         if self.on_gpu:
             # GPU teardown
             self.lightning_module.cpu()

pytorch_lightning/plugins/training_type/horovod.py

@@ -217,6 +217,7 @@ def _filter_named_parameters(model: nn.Module, optimizer: Optimizer) -> List[Tup
         return [(name, p) for name, p in model.named_parameters() if p in opt_params]
 
     def teardown(self) -> None:
+        super().teardown()
         if self.on_gpu:
             # GPU teardown
             self.lightning_module.cpu()

pytorch_lightning/plugins/training_type/ipu.py

@@ -276,6 +276,7 @@ def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
             return self._step(RunningStage.PREDICTING, *args, **kwargs)
 
     def teardown(self) -> None:
+        super().teardown()
         # undo dataloader patching
         pl.trainer.data_loading._update_dataloader = self._update_dataloader_original
 

pytorch_lightning/plugins/training_type/parallel.py

@@ -132,3 +132,7 @@ def block_backward_sync(self):
                 yield None
         else:
             yield None
+
+    def teardown(self) -> None:
+        self.cluster_environment.teardown()
+        super().teardown()

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -28,8 +28,10 @@
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.precision import PrecisionPlugin
 from pytorch_lightning.trainer.states import TrainerFn
+from pytorch_lightning.utilities import rank_zero_deprecation
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
 from pytorch_lightning.utilities.distributed import ReduceOp
+from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
 
 TBroadcast = TypeVar("TBroadcast")
@@ -49,6 +51,11 @@ def __init__(
         self.optimizers: List[Optimizer] = []
         self.lr_schedulers: List[_LRScheduler] = []
         self.optimizer_frequencies: List[int] = []
+        if is_overridden("post_dispatch", self, parent=TrainingTypePlugin):
+            rank_zero_deprecation(
+                f"`{self.__class__.__name__}.post_dispatch()` has been deprecated in v1.6 and will be removed in v1.7."
+                f" Move your implementation to `{self.__class__.__name__}.teardown()` instead."
+            )
 
     @property
     def checkpoint_io(self) -> CheckpointIO:
@@ -486,5 +493,9 @@ def dispatch(self, trainer: "pl.Trainer") -> None:
         self.precision_plugin.dispatch(trainer)
 
     def post_dispatch(self, trainer: "pl.Trainer") -> None:
-        """Hook to do something after the training/evaluation/prediction starts."""
-        self.precision_plugin.post_dispatch()
+        r"""
+        .. deprecated::
+            v1.6 This method has been deprecated in v1.6 and will be removed in v1.7. Use :meth:`teardown` instead.
+
+        Hook to do something after the training/evaluation/prediction finishes.
+        """

pytorch_lightning/trainer/trainer.py

@@ -1224,9 +1224,9 @@ def _log_hyperparams(self) -> None:
             self.logger.save()
 
     def _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.training_type_plugin.post_dispatch(self)
         self.accelerator.teardown()
         self._data_connector.teardown()
         self._active_loop.teardown()

tests/deprecated_api/test_remove_1-7.py

@@ -13,10 +13,12 @@
 # limitations under the License.
 """Test deprecated functionality which will be removed in v1.7.0."""
 import os
+from re import escape
 from unittest import mock
 from unittest.mock import Mock
 
 import pytest
+import torch
 
 from pytorch_lightning import Callback, LightningDataModule, Trainer
 from pytorch_lightning.callbacks.gpu_stats_monitor import GPUStatsMonitor
@@ -25,6 +27,7 @@
 from pytorch_lightning.callbacks.xla_stats_monitor import XLAStatsMonitor
 from pytorch_lightning.loggers import LoggerCollection, TestTubeLogger
 from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper
+from pytorch_lightning.plugins import SingleDevicePlugin
 from pytorch_lightning.plugins.environments import (
     KubeflowEnvironment,
     LightningEnvironment,
@@ -539,3 +542,12 @@ def test_v1_7_0_index_batch_sampler_wrapper_batch_indices():
 
     with pytest.deprecated_call(match="was deprecated in v1.5 and will be removed in v1.7"):
         sampler.batch_indices = []
+
+
+def test_v1_7_0_post_dispatch_hook():
+    class CustomPlugin(SingleDevicePlugin):
+        def post_dispatch(self, trainer):
+            pass
+
+    with pytest.deprecated_call(match=escape("`CustomPlugin.post_dispatch()` has been deprecated in v1.6")):
+        CustomPlugin(torch.device("cpu"))