Renamed the DDPSpawnPlugin to DDPSpawnStrategy

CHANGELOG.md

@@ -145,8 +145,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - DeepSpeed does not require lightning module zero 3 partitioning ([#10655](https://github.com/PyTorchLightning/pytorch-lightning/pull/10655))
 
 
+- Renamed the `DDPSpawnPlugin` to `DDPSpawnStrategy` ([#11145](https://github.com/PyTorchLightning/pytorch-lightning/pull/11145))
+
+
 - Renamed the `DDPFullyShardedPlugin` to `DDPFullyShardedStrategy` ([#11143](https://github.com/PyTorchLightning/pytorch-lightning/pull/11143))
 
+
 ### Deprecated
 
 - Deprecated `ClusterEnvironment.master_{address,port}` in favor of `ClusterEnvironment.main_{address,port}` ([#10103](https://github.com/PyTorchLightning/pytorch-lightning/issues/10103))

docs/source/advanced/training_tricks.rst

@@ -178,7 +178,7 @@ For example, when training Graph Neural Networks, a common strategy is to load t
 
 A simple way to prevent redundant dataset replicas is to rely on :obj:`torch.multiprocessing` to share the `data automatically between spawned processes via shared memory <https://pytorch.org/docs/stable/notes/multiprocessing.html>`_.
 For this, all data pre-loading should be done on the main process inside :meth:`DataModule.__init__`.
-As a result, all tensor-data will get automatically shared when using the :class:`~pytorch_lightning.plugins.DDPSpawnPlugin` training type plugin:
+As a result, all tensor-data will get automatically shared when using the :class:`~pytorch_lightning.plugins.DDPSpawnStrategy` training type strategy:
 
 .. warning::
 

docs/source/api_references.rst

@@ -154,7 +154,7 @@ Training Type Plugins
     DDP2Plugin
     DDPShardedPlugin
     DDPSpawnShardedPlugin
-    DDPSpawnPlugin
+    DDPSpawnStrategy
     DeepSpeedPlugin
     HorovodPlugin
     SingleTPUPlugin

docs/source/extensions/plugins.rst

@@ -113,7 +113,7 @@ Training Type Plugins
     DDP2Plugin
     DDPShardedPlugin
     DDPSpawnShardedPlugin
-    DDPSpawnPlugin
+    DDPSpawnStrategy
     DeepSpeedPlugin
     HorovodPlugin
     SingleTPUPlugin

docs/source/guides/speed.rst

@@ -95,11 +95,11 @@ This by default comes with a performance hit, and can be disabled in most cases.
 
 .. code-block:: python
 
-    from pytorch_lightning.plugins import DDPSpawnPlugin
+    from pytorch_lightning.plugins import DDPSpawnStrategy
 
     trainer = pl.Trainer(
         gpus=2,
-        strategy=DDPSpawnPlugin(find_unused_parameters=False),
+        strategy=DDPSpawnStrategy(find_unused_parameters=False),
     )
 
 When using DDP on a multi-node cluster, set NCCL parameters

pytorch_lightning/core/lightning.py

@@ -1926,7 +1926,7 @@ def add_to_queue(self, queue: pl.plugins.training_type.ddp_spawn._FakeQueue) ->
             queue: the instance of the queue to append the data.
 
         .. deprecated:: v1.5
-            This method was deprecated in v1.5 in favor of `DDPSpawnPlugin.add_to_queue`
+            This method was deprecated in v1.5 in favor of `DDPSpawnStrategy.add_to_queue`
             and will be removed in v1.7.
         """
 
@@ -1938,7 +1938,7 @@ def get_from_queue(self, queue: pl.plugins.training_type.ddp_spawn._FakeQueue) -
             queue: the instance of the queue from where to get the data.
 
         .. deprecated:: v1.5
-            This method was deprecated in v1.5 in favor of `DDPSpawnPlugin.get_from_queue`
+            This method was deprecated in v1.5 in favor of `DDPSpawnStrategy.get_from_queue`
             and will be removed in v1.7.
         """
 

pytorch_lightning/distributed/dist.py

@@ -23,7 +23,7 @@ class LightningDistributed:
     """
     .. deprecated:: v1.5
         This class is deprecated in v1.5 and will be removed in v1.7.
-        The broadcast logic will be moved to the :class:`DDPPlugin` and :class`DDPSpawnPlugin` classes.
+        The broadcast logic will be moved to the :class:`DDPPlugin` and :class`DDPSpawnStrategy` classes.
     """
 
     def __init__(self, rank=None, device=None):

pytorch_lightning/lite/lite.py

@@ -26,7 +26,7 @@
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
 from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
-from pytorch_lightning.plugins import DDPSpawnPlugin, DeepSpeedPlugin, PLUGIN_INPUT, Strategy, TPUSpawnPlugin
+from pytorch_lightning.plugins import DDPSpawnStrategy, DeepSpeedPlugin, PLUGIN_INPUT, Strategy, TPUSpawnPlugin
 from pytorch_lightning.plugins.training_type.training_type_plugin import TBroadcast
 from pytorch_lightning.trainer.connectors.accelerator_connector import AcceleratorConnector
 from pytorch_lightning.utilities import _AcceleratorType, _StrategyType, move_data_to_device
@@ -310,7 +310,7 @@ def to_device(self, obj: Union[nn.Module, Tensor, Any]) -> Union[nn.Module, Tens
         """
         if isinstance(obj, nn.Module):
             if self.device.type == "cuda":
-                # need to call this manually here again in case we spawned with DDPSpawnPlugin
+                # need to call this manually here again in case we spawned with DDPSpawnStrategy
                 # TODO: refactor to let plugin handle this cleanly
                 torch.cuda.set_device(self.device)
             return obj.to(self.device)
@@ -403,7 +403,7 @@ def _run_impl(self, run_method: Callable, *args: Any, **kwargs: Any) -> Any:
         # apply sharded context to prevent OOM
         run_method = partial(self._run_with_sharded_context, run_method)
 
-        if isinstance(self._strategy, DDPSpawnPlugin):
+        if isinstance(self._strategy, DDPSpawnStrategy):
             return self._strategy.spawn(run_method, *args, **kwargs)
         else:
             return run_method(*args, **kwargs)

pytorch_lightning/loops/dataloader/prediction_loop.py

@@ -5,7 +5,7 @@
 
 from pytorch_lightning.loops.dataloader.dataloader_loop import DataLoaderLoop
 from pytorch_lightning.loops.epoch.prediction_epoch_loop import PredictionEpochLoop
-from pytorch_lightning.plugins import DDPSpawnPlugin
+from pytorch_lightning.plugins import DDPSpawnStrategy
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.types import _PREDICT_OUTPUT
 
@@ -29,14 +29,14 @@ def return_predictions(self) -> bool:
 
     @return_predictions.setter
     def return_predictions(self, return_predictions: Optional[bool] = None) -> None:
-        # `DDPSpawnPlugin` plugins and derivatives don't support return predictions.
-        is_ddp_spawn = isinstance(self.trainer.training_type_plugin, DDPSpawnPlugin)
+        # `DDPSpawnStrategy` plugins and derivatives don't support return predictions.
+        is_ddp_spawn = isinstance(self.trainer.training_type_plugin, DDPSpawnStrategy)
         if return_predictions and is_ddp_spawn:
             raise MisconfigurationException(
-                "`return_predictions` should be set to `False` when using the `DDPSpawnPlugin` or children class. "
+                "`return_predictions` should be set to `False` when using the `DDPSpawnStrategy` or children class. "
                 f"Found {return_predictions} with training_type_plugin {type(self.trainer.training_type_plugin)}."
             )
-        # For non `DDPSpawnPlugin` plugin, the `return_predictions` is True by default unless user decide otherwise.
+        # For non `DDPSpawnStrategy` plugin, the `return_predictions` is True by default unless user decide otherwise.
         self._return_predictions = not is_ddp_spawn if return_predictions is None else return_predictions
 
     @property

pytorch_lightning/plugins/__init__.py

@@ -21,7 +21,7 @@
 from pytorch_lightning.plugins.precision.tpu_bf16 import TPUBf16PrecisionPlugin
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
 from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin
-from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
+from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnStrategy
 from pytorch_lightning.plugins.training_type.deepspeed import DeepSpeedPlugin
 from pytorch_lightning.plugins.training_type.dp import DataParallelPlugin
 from pytorch_lightning.plugins.training_type.fully_sharded import DDPFullyShardedStrategy
@@ -46,7 +46,7 @@
     "DataParallelPlugin",
     "DDP2Plugin",
     "DDPPlugin",
-    "DDPSpawnPlugin",
+    "DDPSpawnStrategy",
     "DDPFullyShardedStrategy",
     "DeepSpeedPlugin",
     "DeepSpeedPrecisionPlugin",

pytorch_lightning/plugins/training_type/__init__.py

@@ -1,6 +1,6 @@
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin  # noqa: F401
 from pytorch_lightning.plugins.training_type.ddp2 import DDP2Plugin  # noqa: F401
-from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnStrategy  # noqa: F401
 from pytorch_lightning.plugins.training_type.deepspeed import DeepSpeedPlugin  # noqa: F401
 from pytorch_lightning.plugins.training_type.dp import DataParallelPlugin  # noqa: F401
 from pytorch_lightning.plugins.training_type.fully_sharded import DDPFullyShardedStrategy  # noqa: F401

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -54,7 +54,7 @@
 log = logging.getLogger(__name__)
 
 
-class DDPSpawnPlugin(ParallelPlugin):
+class DDPSpawnStrategy(ParallelPlugin):
     """Spawns processes using the :func:`torch.multiprocessing.spawn` method and joins processes after training
     finishes."""
 

pytorch_lightning/plugins/training_type/sharded_spawn.py

@@ -19,7 +19,7 @@
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
-from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
+from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnStrategy
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, rank_zero_only
 from pytorch_lightning.utilities.enums import _StrategyType
@@ -32,7 +32,7 @@
     from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel, unwrap_lightning_module_sharded
 
 
-class DDPSpawnShardedPlugin(DDPSpawnPlugin):
+class DDPSpawnShardedPlugin(DDPSpawnStrategy):
     """Optimizer sharded training provided by FairScale."""
 
     distributed_backend = _StrategyType.DDP_SHARDED_SPAWN

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -26,7 +26,7 @@
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
 from pytorch_lightning.plugins.precision import PrecisionPlugin
-from pytorch_lightning.plugins.training_type.ddp_spawn import _FakeQueue, _SpawnOutput, DDPSpawnPlugin
+from pytorch_lightning.plugins.training_type.ddp_spawn import _FakeQueue, _SpawnOutput, DDPSpawnStrategy
 from pytorch_lightning.trainer.connectors.data_connector import DataConnector
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, set_shared_parameters
@@ -48,7 +48,7 @@
     xm, xmp, MpDeviceLoader, rendezvous = [None] * 4
 
 
-class TPUSpawnPlugin(DDPSpawnPlugin):
+class TPUSpawnPlugin(DDPSpawnStrategy):
     """Plugin for training multiple TPU devices using the :func:`torch.multiprocessing.spawn` method."""
 
     def __init__(
@@ -340,7 +340,7 @@ def should_rank_save_checkpoint(self) -> bool:
     def register_plugins(cls, plugin_registry: Dict) -> None:
         plugin_registry.register("tpu_spawn_debug", cls, description="TPUSpawn Plugin with `debug` as True", debug=True)
 
-    @DDPSpawnPlugin.checkpoint_io.setter
+    @DDPSpawnStrategy.checkpoint_io.setter
     def checkpoint_io(self, io: Optional[XLACheckpointIO]) -> None:
         if io is not None and not isinstance(io, XLACheckpointIO):
             raise MisconfigurationException(f"{self.__class__.__name__}.checkpoint_io` must be a `XLACheckpointIO`.")

pytorch_lightning/trainer/configuration_validator.py

@@ -263,12 +263,12 @@ def _check_add_get_queue(model: "pl.LightningModule") -> None:
     if is_overridden("add_to_queue", model):
         rank_zero_deprecation(
             "The `LightningModule.add_to_queue` method was deprecated in v1.5 and will be removed in v1.7 in "
-            "favor of `DDPSpawnPlugin.add_to_queue`"
+            "favor of `DDPSpawnStrategy.add_to_queue`"
         )
     if is_overridden("get_from_queue", model):
         rank_zero_deprecation(
             "The `LightningModule.get_from_queue` method was deprecated in v1.5 and will be removed in v1.7 in "
-            "favor of `DDPSpawnPlugin.get_from_queue`"
+            "favor of `DDPSpawnStrategy.get_from_queue`"
         )
 
 

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -32,8 +32,8 @@
     DDPFullyShardedStrategy,
     DDPPlugin,
     DDPShardedPlugin,
-    DDPSpawnPlugin,
     DDPSpawnShardedPlugin,
+    DDPSpawnStrategy,
     DeepSpeedPlugin,
     DeepSpeedPrecisionPlugin,
     DoublePrecisionPlugin,
@@ -735,7 +735,7 @@ def select_training_type_plugin(self) -> Strategy:
             ):
                 ddp_plugin_cls = DDPPlugin
             elif use_ddp_spawn or use_ddp_cpu_spawn:
-                ddp_plugin_cls = DDPSpawnPlugin
+                ddp_plugin_cls = DDPSpawnStrategy
             elif use_ddp_fully_sharded:
                 ddp_plugin_cls = DDPFullyShardedStrategy
             else:

pytorch_lightning/trainer/trainer.py

@@ -41,7 +41,7 @@
 from pytorch_lightning.loops.utilities import _parse_loop_limits
 from pytorch_lightning.plugins import (
     ApexMixedPrecisionPlugin,
-    DDPSpawnPlugin,
+    DDPSpawnStrategy,
     NativeMixedPrecisionPlugin,
     ParallelPlugin,
     PLUGIN_INPUT,
@@ -669,7 +669,7 @@ def _call_and_handle_interrupt(self, trainer_fn: Callable, *args: Any, **kwargs:
             **kwargs: keyword arguments to be passed to `trainer_fn`
         """
         try:
-            if isinstance(self.training_type_plugin, DDPSpawnPlugin):
+            if isinstance(self.training_type_plugin, DDPSpawnStrategy):
                 spawn_output: _SpawnOutput = self.training_type_plugin.spawn(trainer_fn, *args, **kwargs)
                 self.training_type_plugin._recover_results_in_main_process(spawn_output, self)
                 return spawn_output.trainer_results
@@ -1179,7 +1179,7 @@ def _run(
             self.state.status = TrainerStatus.FINISHED
         self.state.stage = None
 
-        if isinstance(self.training_type_plugin, DDPSpawnPlugin):
+        if isinstance(self.training_type_plugin, DDPSpawnStrategy):
             results = self.training_type_plugin._collect_rank_zero_results(self, results)
 
         return results
@@ -1416,7 +1416,7 @@ def _handle_meta_model(self) -> None:
         if not is_on_meta_device(self.lightning_module):
             return
 
-        if isinstance(self.training_type_plugin, DDPSpawnPlugin):
+        if isinstance(self.training_type_plugin, DDPSpawnStrategy):
             raise MisconfigurationException("LightningModule on meta device isn't supported with spawn.")
 
         materialize_module(self.lightning_module)