run style check

Former-commit-id: 5ec33baf5f95df9fa2afe5523c825d3eda8a076b

src/llamafactory/integrations/ray/ray_train.py

@@ -1,21 +1,19 @@
-
 from typing import Any, Callable, Dict
 
-from ray.train.torch import TorchTrainer
 from ray.train import ScalingConfig
+from ray.train.torch import TorchTrainer
 
 from .ray_train_args import RayTrainArguments
 
-    
+
 def get_ray_trainer(
     training_function: Callable,
     train_loop_config: Dict[str, Any],
     ray_args: RayTrainArguments,
 ) -> TorchTrainer:
-    
     if not ray_args.use_ray:
         raise ValueError("Ray is not enabled. Please set USE_RAY=1 in your environment.")
-    
+
     trainer = TorchTrainer(
         training_function,
         train_loop_config=train_loop_config,
@@ -25,4 +23,4 @@ def get_ray_trainer(
             use_gpu=True,
         ),
     )
-    return trainer
+    return trainer

src/llamafactory/integrations/ray/ray_train_args.py

@@ -3,14 +3,23 @@ from typing import Any, Dict, Literal, Optional
 
 from .ray_utils import should_use_ray
 
+
 @dataclass
 class RayTrainArguments:
     r"""
     Arguments pertaining to the Ray training.
     """
-    resources_per_worker: Optional[Dict[str, Any]] = field(default_factory=lambda: {"GPU": 1}, metadata={"help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."})
-    num_workers: Optional[int] = field(default=1, metadata={"help": "The number of workers for Ray training. Default is 1 worker."})
-    placement_strategy: Optional[Literal["SPREAD", "PACK", "STRICT_SPREAD", "STRICT_PACK"]] = field(default="PACK", metadata={"help": "The placement strategy for Ray training. Default is PACK."})
+
+    resources_per_worker: Optional[Dict[str, Any]] = field(
+        default_factory=lambda: {"GPU": 1},
+        metadata={"help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."},
+    )
+    num_workers: Optional[int] = field(
+        default=1, metadata={"help": "The number of workers for Ray training. Default is 1 worker."}
+    )
+    placement_strategy: Optional[Literal["SPREAD", "PACK", "STRICT_SPREAD", "STRICT_PACK"]] = field(
+        default="PACK", metadata={"help": "The placement strategy for Ray training. Default is PACK."}
+    )
 
     @property
     def use_ray(self) -> bool:
@@ -19,4 +28,3 @@ class RayTrainArguments:
         This prevents manual setting of use_ray.
         """
         return should_use_ray()
-    

src/llamafactory/integrations/ray/ray_utils.py

@@ -1,9 +1,5 @@
-
 import os
 
 
 def should_use_ray():
     return os.getenv("USE_RAY", "0").lower() in ["true", "1"]
-
-
-