Move block_backward_sync from ParallelPlugin to DDPPlugins

CHANGELOG.md

@@ -205,6 +205,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed `InterBatchProcessor` in favor of `DataLoaderIterDataFetcher` ([#9052](https://github.com/PyTorchLightning/pytorch-lightning/pull/9052))
 
 
+- Removed `block_backward_sync` from `ParallelPlugin` and added to `DDPPlugin` and `DDPSpawnPlugin` ([#9101](https://github.com/PyTorchLightning/pytorch-lightning/pull/9101))
+
+
 ### Fixed
 
 - Fixed save/load/resume from checkpoint for DeepSpeed Plugin (

pytorch_lightning/loops/batch/training_batch_loop.py

@@ -31,7 +31,7 @@
     _process_training_step_output,
     check_finite_loss,
 )
-from pytorch_lightning.plugins import ParallelPlugin
+from pytorch_lightning.plugins import DDPPlugin, DDPSpawnPlugin
 from pytorch_lightning.trainer.progress import OptimizationProgress
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.utilities import AMPType, AttributeDict, DeviceType, grad_norm
@@ -430,9 +430,10 @@ def block_ddp_sync_behaviour(self, should_block_sync: bool = False) -> Generator
         Returns:
             context manager with sync behaviour off
         """
-        if isinstance(self.trainer.training_type_plugin, ParallelPlugin) and (
-            self.trainer.lightning_module.automatic_optimization or should_block_sync
-        ):
+        if (
+            isinstance(self.trainer.training_type_plugin, DDPPlugin)
+            or isinstance(self.trainer.training_type_plugin, DDPPlugin)
+        ) and (self.trainer.lightning_module.automatic_optimization or should_block_sync):
             with self.trainer.training_type_plugin.block_backward_sync():
                 yield None
         else:

pytorch_lightning/plugins/training_type/ddp.py

@@ -19,6 +19,7 @@
 import sys
 import tempfile
 import time
+from contextlib import contextmanager
 from pathlib import Path
 from time import sleep
 from typing import Any, Dict, List, Optional, Union
@@ -442,3 +443,16 @@ def reconciliate_processes(self, trace: str):
                 os.kill(pid, signal.SIGKILL)
         shutil.rmtree(sync_dir)
         raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
+
+    @contextmanager
+    def block_backward_sync(self):
+        """
+        Blocks ddp sync gradients behaviour on backwards pass.
+        This is useful for skipping sync when accumulating gradients, reducing communication overhead
+        Returns: context manager with sync behaviour off
+        """
+        if isinstance(self.model, DistributedDataParallel):
+            with self.model.no_sync():
+                yield None
+        else:
+            yield None

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -14,6 +14,7 @@
 import logging
 import os
 import re
+from contextlib import contextmanager
 from multiprocessing.queues import SimpleQueue
 from typing import Any, Dict, List, Optional, Union
 
@@ -364,3 +365,16 @@ def register_plugins(cls, plugin_registry: Dict) -> None:
             description="DDPSpawn Plugin with `find_unused_parameters` as False",
             find_unused_parameters=False,
         )
+
+    @contextmanager
+    def block_backward_sync(self):
+        """
+        Blocks ddp sync gradients behaviour on backwards pass.
+        This is useful for skipping sync when accumulating gradients, reducing communication overhead
+        Returns: context manager with sync behaviour off
+        """
+        if isinstance(self.model, DistributedDataParallel):
+            with self.model.no_sync():
+                yield None
+        else:
+            yield None

pytorch_lightning/plugins/training_type/parallel.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 import os
 from abc import ABC, abstractmethod
-from contextlib import contextmanager
 from typing import Any, List, Optional
 
 import torch
@@ -121,19 +120,6 @@ def configure_sync_batchnorm(model: "pl.LightningModule") -> "pl.LightningModule
         """
         return torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
 
-    @contextmanager
-    def block_backward_sync(self):
-        """
-        Blocks ddp sync gradients behaviour on backwards pass.
-        This is useful for skipping sync when accumulating gradients, reducing communication overhead
-        Returns: context manager with sync behaviour off
-        """
-        if isinstance(self.model, DistributedDataParallel):
-            with self.model.no_sync():
-                yield None
-        else:
-            yield None
-
     def teardown(self) -> None:
         # Un-reference the wrapper if any was used.
         # todo (tchaton): Add support for all plugins.

pytorch_lightning/trainer/trainer.py

@@ -32,7 +32,7 @@
 from pytorch_lightning.loops.dataloader.evaluation_loop import EvaluationLoop
 from pytorch_lightning.loops.dataloader.prediction_loop import PredictionLoop
 from pytorch_lightning.loops.fit_loop import FitLoop
-from pytorch_lightning.plugins import DDPSpawnPlugin, Plugin
+from pytorch_lightning.plugins import DDPPlugin, DDPSpawnPlugin, Plugin
 from pytorch_lightning.plugins.environments import ClusterEnvironment
 from pytorch_lightning.profiler import (
     AdvancedProfiler,