Fix mypy errors attributed to `pytorch_lightning. strategies.sharded_…

…spawn` (#14102)


Co-authored-by: rohitgr7 <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: awaelchli <[email protected]>

pyproject.toml

@@ -58,7 +58,6 @@ module = [
     "pytorch_lightning.profilers.base",
     "pytorch_lightning.profilers.pytorch",
     "pytorch_lightning.strategies.sharded",
-    "pytorch_lightning.strategies.sharded_spawn",
     "pytorch_lightning.trainer.callback_hook",
     "pytorch_lightning.trainer.connectors.data_connector",
     "pytorch_lightning.trainer.supporters",

src/pytorch_lightning/overrides/base.py

@@ -75,6 +75,7 @@ def forward(self, *inputs: Any, **kwargs: Any) -> Any:
         trainer = pl_module._trainer
 
         if trainer is not None:
+            assert isinstance(self.module, (pl.LightningModule, _LightningPrecisionModuleWrapperBase))
             if trainer.training:
                 output = self.module.training_step(*inputs, **kwargs)
                 # In manual_optimization, we need to prevent DDP reducer as

src/pytorch_lightning/strategies/sharded_spawn.py

@@ -12,13 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from contextlib import contextmanager
-from typing import Dict, Generator, List, Optional, Tuple
+from typing import Any, Dict, Generator, List, Optional, Tuple
 
 from torch import Tensor
 from torch.nn import Module
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
+from pytorch_lightning.overrides.base import _LightningPrecisionModuleWrapperBase
 from pytorch_lightning.overrides.fairscale import _FAIRSCALE_AVAILABLE
 from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
 from pytorch_lightning.trainer.states import TrainerFn
@@ -42,7 +43,9 @@ class DDPSpawnShardedStrategy(DDPSpawnStrategy):
 
     def configure_ddp(self) -> None:
         # set up optimizers after the wrapped module has been moved to the device
+        assert self.lightning_module is not None
         self.setup_optimizers(self.lightning_module.trainer)
+        assert isinstance(self.model, (pl.LightningModule, _LightningPrecisionModuleWrapperBase))
         self.model, self.optimizers = self._setup_model_and_optimizers(
             model=LightningShardedDataParallel(self.model), optimizers=self.optimizers
         )
@@ -69,12 +72,13 @@ def _reinit_optimizers_with_oss(self, optimizers: List[Optimizer]) -> List["OSS"
         return optimizers
 
     def _wrap_optimizers(self, optimizers: List[Optimizer]) -> List["OSS"]:
-        if self.model is not None and self.model.trainer.state.fn != TrainerFn.FITTING:
+        assert self.lightning_module
+        if self.model is not None and self.lightning_module.trainer.state.fn != TrainerFn.FITTING:
             return optimizers
 
         return self._reinit_optimizers_with_oss(optimizers)
 
-    def optimizer_state(self, optimizer: "OSS") -> Optional[dict]:
+    def optimizer_state(self, optimizer: "OSS") -> Dict[str, Any]:
         if isinstance(optimizer, OSS):
             optimizer.consolidate_state_dict()
         return self._optim_state_dict(optimizer)
@@ -93,7 +97,7 @@ def block_backward_sync(self) -> Generator:
             yield None
 
     @rank_zero_only
-    def _optim_state_dict(self, optimizer):
+    def _optim_state_dict(self, optimizer: Optimizer) -> Dict[str, Any]:
         """
         Retrieves state dict only on rank 0, which contains the entire optimizer state after calling
         :meth:`consolidate_state_dict`.
@@ -112,7 +116,7 @@ def lightning_module(self) -> Optional["pl.LightningModule"]:
     def pre_backward(self, closure_loss: Tensor) -> None:
         pass
 
-    def post_training_step(self):
+    def post_training_step(self) -> None:
         pass
 
     @classmethod