Add typings for evaluation_loop.py and remove some dead code (#7015)

.github/workflows/ci_pkg-install.yml

@@ -60,4 +60,4 @@ jobs:
           pip install dist/*.whl
           cd ..
           python -c "import pytorch_lightning as pl ; print(pl.__version__)"
-          pip uninstall -y pytorch-lightning
+          pip uninstall -y pytorch-lightning

.github/workflows/events-recurrent.yml

@@ -39,4 +39,4 @@ jobs:
           echo $jobs_to_delete
           if [ ${#jobs_to_delete} -gt 1 ];
             then kubectl delete job $(kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $1}');
-          fi
+          fi

pytorch_lightning/accelerators/tpu.py

@@ -56,7 +56,10 @@ def run_optimizer_step(
         xm.optimizer_step(optimizer, barrier=False, optimizer_args={'closure': lambda_closure, **kwargs})
 
     def clip_gradients(
-        self, optimizer: Optimizer, clip_val: Union[float, int], norm_type: float = 2.0,
+        self,
+        optimizer: Optimizer,
+        clip_val: Union[float, int],
+        norm_type: float = 2.0,
         gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM
     ) -> None:
         assert gradient_clip_algorithm is GradClipAlgorithmType.NORM, \

pytorch_lightning/callbacks/gpu_stats_monitor.py

@@ -118,9 +118,7 @@ def on_train_epoch_start(self, trainer, pl_module) -> None:
         self._snap_inter_step_time = None
 
     @rank_zero_only
-    def on_train_batch_start(
-        self, trainer, pl_module, batch: Any, batch_idx: int, dataloader_idx: int
-    ) -> None:
+    def on_train_batch_start(self, trainer, pl_module, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
         if self._log_stats.intra_step_time:
             self._snap_intra_step_time = time.time()
 

pytorch_lightning/callbacks/pruning.py

@@ -422,9 +422,8 @@ def sanitize_parameters_to_prune(
         current_modules = [m for m in pl_module.modules() if not isinstance(m, _MODULE_CONTAINERS)]
 
         if parameters_to_prune is None:
-            parameters_to_prune = [
-                (m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
-            ]
+            parameters_to_prune = [(m, p) for p in parameters for m in current_modules
+                                   if getattr(m, p, None) is not None]
         elif (
             isinstance(parameters_to_prune, (list, tuple)) and len(parameters_to_prune) > 0
             and all(len(p) == 2 for p in parameters_to_prune)

pytorch_lightning/loggers/mlflow.py

@@ -29,17 +29,17 @@
 _MLFLOW_AVAILABLE = _module_available("mlflow")
 try:
     import mlflow
-    from mlflow.tracking import MlflowClient
-    from mlflow.tracking import context
+    from mlflow.tracking import context, MlflowClient
 # todo: there seems to be still some remaining import error with Conda env
 except ImportError:
     _MLFLOW_AVAILABLE = False
     mlflow, MlflowClient, context = None, None, None
 
-
 # before v1.1.0
 if hasattr(context, 'resolve_tags'):
     from mlflow.tracking.context import resolve_tags
+
+
 # since v1.1.0
 elif hasattr(context, 'registry'):
     from mlflow.tracking.context.registry import resolve_tags

pytorch_lightning/plugins/precision/sharded_native_amp.py

@@ -31,6 +31,6 @@ def __init__(self) -> None:
         super().__init__()
         self.scaler = ShardedGradScaler()
 
-    def clip_grad_by_norm(self, optimizer: Optimizer, clip_val: Union[int, float], norm_type: float = 2.0) -> None:
+    def clip_grad_by_norm(self, optimizer: 'Optimizer', clip_val: Union[int, float], norm_type: float = 2.0) -> None:
         optimizer = cast(OSS, optimizer)
         optimizer.clip_grad_norm(clip_val, norm_type=norm_type)

pytorch_lightning/trainer/connectors/training_trick_connector.py

@@ -35,9 +35,7 @@ def on_trainer_init(
 
         # gradient clipping
         if gradient_clip_algorithm not in list(GradClipAlgorithmType):
-            raise MisconfigurationException(
-                f"gradient_clip_algorithm should be in {list(GradClipAlgorithmType)}"
-            )
+            raise MisconfigurationException(f"gradient_clip_algorithm should be in {list(GradClipAlgorithmType)}")
         self.trainer.gradient_clip_val = gradient_clip_val
         self.trainer.gradient_clip_algorithm = GradClipAlgorithmType(gradient_clip_algorithm)
 

pytorch_lightning/trainer/data_loading.py

@@ -15,7 +15,7 @@
 import multiprocessing
 from abc import ABC
 from copy import deepcopy
-from typing import Iterable, List, Tuple, Union
+from typing import Iterable, List, Optional, Tuple, Union
 
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
@@ -41,9 +41,9 @@ class TrainerDataLoadingMixin(ABC):
     train_dataloader: DataLoader
     num_training_batches: Union[int, float]
     val_check_batch: float
-    val_dataloaders: List[DataLoader]
+    val_dataloaders: Optional[List[DataLoader]]
     num_val_batches: List[Union[int, float]]
-    test_dataloaders: List[DataLoader]
+    test_dataloaders: Optional[List[DataLoader]]
     num_test_batches: List[Union[int, float]]
     limit_train_batches: Union[int, float]
     overfit_batches: Union[int, float]