Rename the TrainingTypePlugin base to Strategy

CHANGELOG.md

@@ -122,6 +122,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Moved ownership of the `Accelerator` instance to the `TrainingTypePlugin`; all training-type plugins now take an optional parameter `accelerator` ([#11022](https://github.com/PyTorchLightning/pytorch-lightning/pull/11022))
 
+
+- Renamed the `TrainingTypePlugin` to `Strategy` ([#11120](https://github.com/PyTorchLightning/pytorch-lightning/pull/11120))
+
+
 ### Deprecated
 
 - Deprecated `ClusterEnvironment.master_{address,port}` in favor of `ClusterEnvironment.main_{address,port}` ([#10103](https://github.com/PyTorchLightning/pytorch-lightning/issues/10103))

docs/source/api_references.rst

@@ -146,7 +146,7 @@ Training Type Plugins
     :nosignatures:
     :template: classtemplate.rst
 
-    TrainingTypePlugin
+    Strategy
     SingleDevicePlugin
     ParallelPlugin
     DataParallelPlugin

docs/source/common/checkpointing.rst

@@ -318,7 +318,7 @@ Customize Checkpointing
 
 
 Lightning supports modifying the checkpointing save/load functionality through the ``CheckpointIO``. This encapsulates the save/load logic
-that is managed by the ``TrainingTypePlugin``. ``CheckpointIO`` is different from :meth:`~pytorch_lightning.core.hooks.CheckpointHooks.on_save_checkpoint`
+that is managed by the ``Strategy``. ``CheckpointIO`` is different from :meth:`~pytorch_lightning.core.hooks.CheckpointHooks.on_save_checkpoint`
 and :meth:`~pytorch_lightning.core.hooks.CheckpointHooks.on_load_checkpoint` methods as it determines how the checkpoint is saved/loaded to storage rather than
 what's saved in the checkpoint.
 
@@ -342,7 +342,7 @@ Built-in Checkpoint IO Plugins
 Custom Checkpoint IO Plugin
 ===========================
 
-``CheckpointIO`` can be extended to include your custom save/load functionality to and from a path. The ``CheckpointIO`` object can be passed to either a ``Trainer`` directly or a ``TrainingTypePlugin`` as shown below:
+``CheckpointIO`` can be extended to include your custom save/load functionality to and from a path. The ``CheckpointIO`` object can be passed to either a ``Trainer`` directly or a ``Strategy`` as shown below:
 
 .. code-block:: python
 
@@ -372,7 +372,7 @@ Custom Checkpoint IO Plugin
     )
     trainer.fit(model)
 
-    # or pass into TrainingTypePlugin
+    # or pass into Strategy
     model = MyModel()
     device = torch.device("cpu")
     trainer = Trainer(

docs/source/extensions/plugins.rst

@@ -29,8 +29,8 @@ We expose Accelerators and Plugins mainly for expert users that want to extend L
 
 There are two types of Plugins in Lightning with different responsibilities:
 
-TrainingTypePlugin
-------------------
+Strategy
+--------
 
 - Launching and teardown of training processes (if applicable)
 - Setup communication between processes (NCCL, GLOO, MPI, ...)
@@ -70,7 +70,7 @@ Expert users may choose to extend an existing plugin by overriding its methods .
                 device_ids=...,
             )
 
-or by subclassing the base classes :class:`~pytorch_lightning.plugins.training_type.TrainingTypePlugin` or
+or by subclassing the base classes :class:`~pytorch_lightning.plugins.training_type.Strategy` or
 :class:`~pytorch_lightning.plugins.precision.PrecisionPlugin` to create new ones. These custom plugins
 can then be passed into the Trainer directly or via a (custom) accelerator:
 
@@ -105,7 +105,7 @@ Training Type Plugins
     :nosignatures:
     :template: classtemplate.rst
 
-    TrainingTypePlugin
+    Strategy
     SingleDevicePlugin
     ParallelPlugin
     DataParallelPlugin

pytorch_lightning/distributed/dist.py

@@ -29,7 +29,7 @@ class LightningDistributed:
     def __init__(self, rank=None, device=None):
         rank_zero_deprecation(
             "LightningDistributed is deprecated in v1.5 and will be removed in v1.7."
-            "Broadcast logic is implemented directly in the :class:`TrainingTypePlugin` implementations."
+            "Broadcast logic is implemented directly in the :class:`Strategy` implementations."
         )
         self.rank = rank
         self.device = device

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, TPUSpawnPlugin, TrainingTypePlugin
+from pytorch_lightning.plugins import DDPSpawnPlugin, 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
@@ -69,7 +69,7 @@ class LightningLite(ABC):
     def __init__(
         self,
         accelerator: Optional[Union[str, Accelerator]] = None,
-        strategy: Optional[Union[str, TrainingTypePlugin]] = None,
+        strategy: Optional[Union[str, Strategy]] = None,
         devices: Optional[Union[List[int], str, int]] = None,
         num_nodes: int = 1,
         precision: Union[int, str] = 32,
@@ -451,13 +451,13 @@ def _check_accelerator_support(self, accelerator: Optional[Union[str, Accelerato
                 f" Choose one of {supported} or pass in a `Accelerator` instance."
             )
 
-    def _check_strategy_support(self, strategy: Optional[Union[str, TrainingTypePlugin]]) -> None:
+    def _check_strategy_support(self, strategy: Optional[Union[str, Strategy]]) -> None:
         supported = [t.lower() for t in self._supported_strategy_types()]
-        valid = strategy is None or isinstance(strategy, TrainingTypePlugin) or strategy in supported
+        valid = strategy is None or isinstance(strategy, Strategy) or strategy in supported
         if not valid:
             raise MisconfigurationException(
                 f"`strategy={repr(strategy)}` is not a valid choice."
-                f" Choose one of {supported} or pass in a `TrainingTypePlugin` instance."
+                f" Choose one of {supported} or pass in a `Strategy` instance."
             )
 
     @staticmethod

pytorch_lightning/lite/wrappers.py

@@ -20,7 +20,7 @@
 from torch.utils.data import DataLoader
 
 from pytorch_lightning.core.mixins import DeviceDtypeModuleMixin
-from pytorch_lightning.plugins import PrecisionPlugin, TrainingTypePlugin
+from pytorch_lightning.plugins import PrecisionPlugin, Strategy
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
 
 
@@ -29,7 +29,7 @@ def _do_nothing_closure() -> None:
 
 
 class _LiteOptimizer:
-    def __init__(self, optimizer: Optimizer, strategy: TrainingTypePlugin) -> None:
+    def __init__(self, optimizer: Optimizer, strategy: Strategy) -> None:
         """LiteOptimizer is a thin wrapper around the :class:`~torch.optim.Optimizer` that delegates the optimizer
         step calls to the strategy plugin.
 

pytorch_lightning/plugins/__init__.py

@@ -33,9 +33,9 @@
 from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin
 from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
 from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.plugins.training_type.training_type_plugin import Strategy
 
-PLUGIN = Union[TrainingTypePlugin, PrecisionPlugin, ClusterEnvironment, CheckpointIO]
+PLUGIN = Union[Strategy, PrecisionPlugin, ClusterEnvironment, CheckpointIO]
 PLUGIN_INPUT = Union[PLUGIN, str]
 
 __all__ = [
@@ -63,7 +63,7 @@
     "TPUPrecisionPlugin",
     "TPUBf16PrecisionPlugin",
     "TPUSpawnPlugin",
-    "TrainingTypePlugin",
+    "Strategy",
     "ParallelPlugin",
     "DDPShardedPlugin",
     "DDPSpawnShardedPlugin",

pytorch_lightning/plugins/io/checkpoint_plugin.py

@@ -18,7 +18,7 @@
 
 
 class CheckpointIO(ABC):
-    """Interface to save/load checkpoints as they are saved through the ``TrainingTypePlugin``.
+    """Interface to save/load checkpoints as they are saved through the ``Strategy``.
 
     Typically most plugins either use the Torch based IO Plugin; ``TorchCheckpointIO`` but may
     require particular handling depending on the plugin.

pytorch_lightning/plugins/plugins_registry.py

@@ -17,7 +17,7 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional
 
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.plugins.training_type.training_type_plugin import Strategy
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -122,7 +122,7 @@ def is_register_plugins_overridden(plugin: type) -> bool:
     plugin_attr = getattr(plugin, method_name)
     previous_super_cls = inspect.getmro(plugin)[1]
 
-    if issubclass(previous_super_cls, TrainingTypePlugin):
+    if issubclass(previous_super_cls, Strategy):
         super_attr = getattr(previous_super_cls, method_name)
     else:
         return False
@@ -133,5 +133,5 @@ def is_register_plugins_overridden(plugin: type) -> bool:
 def call_training_type_register_plugins(root: Path, base_module: str) -> None:
     module = importlib.import_module(base_module)
     for _, mod in getmembers(module, isclass):
-        if issubclass(mod, TrainingTypePlugin) and is_register_plugins_overridden(mod):
+        if issubclass(mod, Strategy) and is_register_plugins_overridden(mod):
             mod.register_plugins(TrainingTypePluginsRegistry)

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -205,7 +205,7 @@ 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 ``TrainingTypePlugin.dispatch()`` gets called."""
+        """Hook to do something when ``Strategy.dispatch()`` gets called."""
 
     @contextlib.contextmanager
     def forward_context(self) -> Generator[None, None, None]:

pytorch_lightning/plugins/training_type/__init__.py

@@ -11,4 +11,4 @@
 from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin  # noqa: F401
 from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin  # noqa: F401
 from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin  # noqa: F401
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin  # noqa: F401
+from pytorch_lightning.plugins.training_type.training_type_plugin import Strategy  # noqa: F401

pytorch_lightning/plugins/training_type/parallel.py

@@ -24,12 +24,12 @@
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.precision import PrecisionPlugin
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.plugins.training_type.training_type_plugin import Strategy
 from pytorch_lightning.utilities import _XLA_AVAILABLE
 from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, ReduceOp
 
 
-class ParallelPlugin(TrainingTypePlugin, ABC):
+class ParallelPlugin(Strategy, ABC):
     """Plugin for training with multiple processes in parallel."""
 
     def __init__(

pytorch_lightning/plugins/training_type/single_device.py

@@ -18,11 +18,11 @@
 import pytorch_lightning as pl
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.precision import PrecisionPlugin
-from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.plugins.training_type.training_type_plugin import Strategy
 from pytorch_lightning.utilities import _XLA_AVAILABLE
 
 
-class SingleDevicePlugin(TrainingTypePlugin):
+class SingleDevicePlugin(Strategy):
     """Plugin that handles communication on a single device."""
 
     def __init__(

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -227,7 +227,7 @@ def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[
         _invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
         if _invalid_reduce_op or _invalid_reduce_op_str:
             raise MisconfigurationException(
-                "Currently, TPUSpawn TrainingTypePlugin only support `sum`, `mean`, `avg` reduce operation."
+                "Currently, TPUSpawn Strategy only support `sum`, `mean`, `avg` reduce operation."
             )
 
         output = xm.mesh_reduce("reduce", output, sum)

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -37,7 +37,7 @@
 TBroadcast = TypeVar("TBroadcast")
 
 
-class TrainingTypePlugin(ABC):
+class Strategy(ABC):
     """Base class for all training type plugins that change the behaviour of the training, validation and test-
     loop."""
 
@@ -55,7 +55,7 @@ def __init__(
         self.optimizers: List[Optimizer] = []
         self.lr_schedulers: List[_LRScheduler] = []
         self.optimizer_frequencies: List[int] = []
-        if is_overridden("post_dispatch", self, parent=TrainingTypePlugin):
+        if is_overridden("post_dispatch", self, parent=Strategy):
             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."