[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

applications/Chat/coati/trainer/strategies/__init__.py

@@ -2,7 +2,4 @@ from .base import Strategy
 from .colossalai import GeminiStrategy, LowLevelZeroStrategy
 from .ddp import DDPStrategy
 
-__all__ = [
-    'Strategy', 'DDPStrategy',
-    'LowLevelZeroStrategy', 'GeminiStrategy'
-]
+__all__ = ["Strategy", "DDPStrategy", "LowLevelZeroStrategy", "GeminiStrategy"]

applications/Chat/coati/trainer/strategies/base.py

@@ -19,7 +19,7 @@ _BoostArgSpec = Union[nn.Module, Tuple[nn.Module, Optimizer], Dict]
 
 class Strategy(ABC):
     """
-        Base class for training strategies.
+    Base class for training strategies.
     """
 
     def __init__(self, plugin_initializer: Callable[..., Optional[Plugin]] = lambda: None) -> None:
@@ -83,16 +83,18 @@ class Strategy(ABC):
                 rets.append((model, optimizer))
             elif isinstance(arg, Dict):
                 model, optimizer, criterion, dataloader, lr_scheduler = self.booster.boost(**arg)
-                boost_result = dict(model=model,
-                                    optimizer=optimizer,
-                                    criterion=criterion,
-                                    dataloader=dataloader,
-                                    lr_scheduler=lr_scheduler)
+                boost_result = dict(
+                    model=model,
+                    optimizer=optimizer,
+                    criterion=criterion,
+                    dataloader=dataloader,
+                    lr_scheduler=lr_scheduler,
+                )
                 # remove None values
                 boost_result = {key: value for key, value in boost_result.items() if value is not None}
                 rets.append(boost_result)
             else:
-                raise RuntimeError(f'Type {type(arg)} is not supported')
+                raise RuntimeError(f"Type {type(arg)} is not supported")
 
         return rets[0] if len(rets) == 1 else rets
 
@@ -125,11 +127,9 @@ class Strategy(ABC):
         return DistributedSampler(dataset, 1, 0)
 
     @abstractmethod
-    def save_pretrained(self,
-                        model: nn.Module,
-                        path: str,
-                        only_rank0: bool = True,
-                        tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
+    def save_pretrained(
+        self, model: nn.Module, path: str, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None
+    ) -> None:
         pass
 
     @abstractmethod

applications/Chat/coati/trainer/utils.py

@@ -42,7 +42,6 @@ def is_rank_0() -> bool:
 
 
 def to_device(x: Any, device: torch.device) -> Any:
-
     def _to(t: Any):
         if isinstance(t, torch.Tensor):
             return t.to(device)

applications/Chat/evaluate/unieval/__init__.py

@@ -7,6 +7,9 @@ from .utils import (
 )
 
 __all__ = [
-    'get_evaluator', 'convert_data_to_unieval_format', 'calculate_average_score', 'save_unieval_results',
-    'analyze_unieval_results'
+    "get_evaluator",
+    "convert_data_to_unieval_format",
+    "calculate_average_score",
+    "save_unieval_results",
+    "analyze_unieval_results",
 ]