run style check

Former-commit-id: 5ec33baf5f95df9fa2afe5523c825d3eda8a076b

examples/train_lora/llama3_lora_sft.yaml

@@ -9,7 +9,7 @@ finetuning_type: lora
 lora_target: all
 
 ### dataset
-dataset_dir: /home/ray/default/lf/data/
+dataset_dir: /home/ray/default/LLaMA-Factory/data/
 dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 2048

src/llamafactory/cli.py

@@ -25,9 +25,10 @@ from .eval.evaluator import run_eval
 from .extras import logging
 from .extras.env import VERSION, print_env
 from .extras.misc import get_device_count
+from .integrations.ray.ray_utils import should_use_ray
 from .train.tuner import export_model, run_exp
 from .webui.interface import run_web_demo, run_web_ui
-from .integrations.ray.ray_utils import should_use_ray
+
 
 USAGE = (
     "-" * 70

src/llamafactory/hparams/parser.py

@@ -15,16 +15,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import os
 import sys
-from typing import Any, Dict, Optional, Tuple
-
-import json
-import yaml
 from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
 
 import torch
 import transformers
+import yaml
 from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.trainer_utils import get_last_checkpoint
@@ -35,21 +34,35 @@ from transformers.utils.versions import require_version
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES
 from ..extras.misc import check_dependencies, get_current_device
+from ..integrations.ray.ray_train_args import RayTrainArguments
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
 from .generating_args import GeneratingArguments
 from .model_args import ModelArguments
 
-from ..integrations.ray.ray_train_args import RayTrainArguments
 
 logger = logging.get_logger(__name__)
 
 check_dependencies()
 
 
-_TRAIN_ARGS = [ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments, RayTrainArguments]
-_TRAIN_CLS = Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments, RayTrainArguments]
+_TRAIN_ARGS = [
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    RayTrainArguments,
+]
+_TRAIN_CLS = Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    RayTrainArguments,
+]
 _INFER_ARGS = [ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
 _INFER_CLS = Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
 _EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
@@ -70,14 +83,17 @@ def _read_args(args: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
         return {}
 
 
-def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None, allow_extra_keys: bool = False) -> Tuple[Any]:
-
+def _parse_args(
+    parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None, allow_extra_keys: bool = False
+) -> Tuple[Any]:
     args_dict = _read_args(args)
-    
+
     if args_dict:
         return parser.parse_dict(args_dict, allow_extra_keys=allow_extra_keys)
     else:
-        (*parsed_args, unknown_args) = parser.parse_args_into_dataclasses(args=args_dict, return_remaining_strings=True)
+        (*parsed_args, unknown_args) = parser.parse_args_into_dataclasses(
+            args=args_dict, return_remaining_strings=True
+        )
 
         if unknown_args:
             print(parser.format_help())
@@ -85,7 +101,6 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
             raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
 
         return (*parsed_args,)
-    
 
 
 def _set_transformers_logging() -> None:
@@ -187,7 +202,7 @@ def _parse_ray_args(args: Optional[Dict[str, Any]] = None) -> RayTrainArguments:
 
 def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     model_args, data_args, training_args, finetuning_args, generating_args, _ = _parse_train_args(args)
-    
+
     # Setup logging
     if training_args.should_log:
         _set_transformers_logging()

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"]
-
-
-

src/llamafactory/train/tuner.py

@@ -37,14 +37,15 @@ from .trainer_utils import get_swanlab_callback
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
-    
+
 
 logger = logging.get_logger(__name__)
 
+
 def training_function(config: Dict[str, Any]) -> None:
     args = config.get("args", None)
     callbacks = config.get("callbacks", [])
-    
+
     callbacks.append(LogCallback())
     model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
 
@@ -71,15 +72,15 @@ def training_function(config: Dict[str, Any]) -> None:
     else:
         raise ValueError(f"Unknown task: {finetuning_args.stage}.")
 
+
 def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
-    
     args_dict = _read_args(args)
     ray_args = _parse_ray_args(args_dict)
-    
-    if ray_args.use_ray: 
+
+    if ray_args.use_ray:
         # Import lazily to avoid ray not installed error
         from ..integrations.ray.ray_train import get_ray_trainer
-        
+
         # Initialize ray trainer
         trainer = get_ray_trainer(
             training_function=training_function,