Remove Trainer._strategy_type

pytorch_lightning/core/lightning.py

@@ -95,7 +95,6 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         # pointer to the trainer object
         self.trainer = None
 
-        self._strategy_type = None
         self._device_type = None
 
         # true if using amp

pytorch_lightning/loops/epoch/prediction_epoch_loop.py

@@ -164,7 +164,11 @@ def _get_batch_indices(self, dataloader_idx: int) -> List[List[int]]:
         """Returns a reference to the seen batch indices if the dataloader has a batch sampler wrapped by our
         :class:`~pytorch_lightning.overrides.distributed.IndexBatchSamplerWrapper`."""
         # the batch_sampler is not be defined in case of CombinedDataLoaders
-        batch_sampler = getattr(self.trainer.predict_dataloaders[dataloader_idx], "batch_sampler", None)
+        batch_sampler = getattr(
+            self.trainer.predict_dataloaders[dataloader_idx],  # type: ignore[has-type]
+            "batch_sampler",
+            None,
+        )
         if isinstance(batch_sampler, IndexBatchSamplerWrapper) and self.should_store_predictions:
             return batch_sampler.seen_batch_indices
 

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -65,7 +65,6 @@
     TPUSpawnStrategy,
 )
 from pytorch_lightning.utilities import (
-    _StrategyType,
     AMPType,
     device_parser,
     LightningEnum,
@@ -855,7 +854,3 @@ def has_tpu(self) -> bool:
     @property
     def use_dp(self) -> bool:
         return isinstance(self.strategy, DataParallelStrategy)
-
-    @property
-    def _strategy_type(self) -> _StrategyType:
-        return self.strategy.strategy_name

pytorch_lightning/trainer/connectors/data_connector.py

@@ -22,6 +22,7 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.overrides.distributed import UnrepeatedDistributedSampler
+from pytorch_lightning.strategies import DDPSpawnStrategy
 from pytorch_lightning.trainer.states import RunningStage, TrainerFn
 from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator
 from pytorch_lightning.utilities.apply_func import apply_to_collection
@@ -34,7 +35,6 @@
     has_iterable_dataset,
     has_len_all_ranks,
 )
-from pytorch_lightning.utilities.enums import _StrategyType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.model_helpers import is_overridden
@@ -217,7 +217,7 @@ def _worker_check(self, dataloader: DataLoader, name: str) -> None:
         if not isinstance(dataloader, DataLoader):
             return
 
-        using_spawn = self.trainer._accelerator_connector._strategy_type == _StrategyType.DDP_SPAWN
+        using_spawn = isinstance(self.trainer.strategy, DDPSpawnStrategy)
         num_cpus = multiprocessing.cpu_count()
 
         # ddp_spawn + num_workers > 0 don't mix! tell the user

pytorch_lightning/trainer/trainer.py

@@ -77,7 +77,6 @@
 from pytorch_lightning.utilities import (
     _AcceleratorType,
     _IPU_AVAILABLE,
-    _StrategyType,
     _TPU_AVAILABLE,
     AMPType,
     device_parser,
@@ -1963,10 +1962,6 @@ def should_rank_save_checkpoint(self) -> bool:
             isinstance(strategy, pl.strategies.TPUSpawnStrategy) and strategy.local_rank == 0 or strategy.is_global_zero
         )
 
-    @property
-    def _strategy_type(self) -> str:
-        return self.strategy.strategy_name
-
     @property
     def _device_type(self) -> _AcceleratorType:
         return self._accelerator_connector.device_type
@@ -2126,12 +2121,7 @@ def distributed_sampler_kwargs(self) -> Optional[dict]:
 
     @property
     def data_parallel(self) -> bool:
-        return self._strategy_type in (
-            _StrategyType.DP,
-            _StrategyType.DDP,
-            _StrategyType.DDP_SPAWN,
-            _StrategyType.DDP2,
-        )
+        return isinstance(self.strategy, ParallelStrategy)
 
     @property
     def progress_bar_dict(self) -> dict:

tests/accelerators/test_accelerator_connector.py

@@ -42,7 +42,7 @@
     ParallelStrategy,
     SingleDeviceStrategy,
 )
-from pytorch_lightning.utilities import _AcceleratorType, _StrategyType
+from pytorch_lightning.utilities import _AcceleratorType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers.runif import RunIf
 
@@ -580,11 +580,9 @@ def test_devices_with_cpu_only_supports_integer():
 
 @pytest.mark.parametrize("training_type", ["ddp2", "dp"])
 def test_unsupported_strategy_types_on_cpu(training_type):
-
     with pytest.warns(UserWarning, match="is not supported on CPUs, hence setting `strategy='ddp"):
         trainer = Trainer(accelerator=training_type, num_processes=2)
-
-    assert trainer._strategy_type == _StrategyType.DDP
+    assert isinstance(trainer.strategy, DDPStrategy)
 
 
 def test_accelerator_ddp_for_cpu(tmpdir):

tests/trainer/test_data_loading.py

@@ -19,10 +19,10 @@
 from torch.utils.data import BatchSampler, DataLoader, DistributedSampler, Sampler, SequentialSampler
 
 from pytorch_lightning import Trainer
+from pytorch_lightning.strategies import DDPSpawnStrategy
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.trainer.supporters import CombinedLoader
 from pytorch_lightning.utilities.data import _update_dataloader
-from pytorch_lightning.utilities.enums import _StrategyType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from tests.helpers import BoringModel, RandomDataset
@@ -133,7 +133,7 @@ def _get_warning_msg():
 @pytest.mark.parametrize("num_workers", [0, 1])
 def test_dataloader_warnings(tmpdir, num_workers):
     trainer = Trainer(default_root_dir=tmpdir, accelerator="cpu", devices=2, strategy="ddp_spawn", fast_dev_run=4)
-    assert trainer._accelerator_connector._strategy_type == _StrategyType.DDP_SPAWN
+    assert isinstance(trainer.strategy, DDPSpawnStrategy)
     trainer.fit(TestSpawnBoringModel(num_workers))