[misc] lint (#9710)

src/llamafactory/hparams/parser.py

@@ -142,6 +142,7 @@ def _verify_model_args(
         logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
         model_args.use_fast_tokenizer = False
 
+
 def _check_extra_dependencies(
     model_args: "ModelArguments",
     finetuning_args: "FinetuningArguments",

src/llamafactory/hparams/training_args.py

@@ -94,6 +94,7 @@ class RayArguments:
 @dataclass
 class Fp8Arguments:
     r"""Arguments pertaining to the FP8 training."""
+
     fp8: bool = field(
         default=False,
         metadata={

src/llamafactory/model/patcher.py

@@ -139,14 +139,13 @@ def patch_config(
         setattr(config.text_config, "topk_method", "greedy")
 
     architectures = getattr(config, "architectures", None)
-
+    if isinstance(architectures, list) and "InternVLChatModel" in architectures:
-    if isinstance(architectures, (list, tuple)) and "InternVLChatModel" in architectures:
         raise ValueError(
             "Please download the internvl models in a Hugging Face–compatible format "
             "(for example, https://huggingface.co/OpenGVLab/InternVL3-8B-hf)."
         )
 
-    if isinstance(architectures, (list, tuple)) and "LlavaLlamaForCausalLM" in architectures:
+    if isinstance(architectures, list) and "LlavaLlamaForCausalLM" in architectures:
         raise ValueError("Please download llava models with hf-compatible format: https://huggingface.co/llava-hf")
 
     if getattr(config, "model_type", None) == "internlm3" and not is_transformers_version_greater_than("4.47.1"):

src/llamafactory/train/fp8_utils.py

@@ -93,7 +93,10 @@ def create_fp8_kwargs(training_args: "TrainingArguments") -> list[Any]:
             return True
 
         # Map FSDP all-gather setting if available (this affects the underlying implementation)
-        if hasattr(training_args, "fp8_enable_fsdp_float8_all_gather") and training_args.fp8_enable_fsdp_float8_all_gather:
+        if (
+            hasattr(training_args, "fp8_enable_fsdp_float8_all_gather")
+            and training_args.fp8_enable_fsdp_float8_all_gather
+        ):
             logger.info_rank0("FSDP float8 all-gather optimization requested")
 
         return [AORecipeKwargs(config=config, module_filter_func=module_filter_func)]

src/llamafactory/train/pt/trainer.py

@@ -19,7 +19,6 @@ import torch
 from transformers import Trainer
 from typing_extensions import override
 
-from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..fp8_utils import configure_fp8_environment, patch_accelerator_for_fp8, verify_fp8_status
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@@ -28,7 +27,7 @@ from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 if TYPE_CHECKING:
     from transformers import ProcessorMixin
 
-    from ...hparams import FinetuningArguments, ModelArguments
+    from ...hparams import FinetuningArguments, ModelArguments, TrainingArguments
 
 
 class CustomTrainer(Trainer):
@@ -43,7 +42,7 @@ class CustomTrainer(Trainer):
     ) -> None:
         kwargs["processing_class"] = kwargs.pop("tokenizer")
         # Configure FP8 environment if enabled
-        training_args = kwargs.get("args")
+        training_args: TrainingArguments = kwargs.get("args")
         if training_args.fp8:
             configure_fp8_environment(training_args)
             if getattr(training_args, "fp8_backend", "auto") == "te":
@@ -66,7 +65,7 @@ class CustomTrainer(Trainer):
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
-        if training_args.fp8 and hasattr(self, "accelerator"): # verify FP8 status after trainer initialization
+        if training_args.fp8 and hasattr(self, "accelerator"):  # verify FP8 status after trainer initialization
             verify_fp8_status(self.accelerator, training_args)
 
     @override

src/llamafactory/train/sft/trainer.py

@@ -27,7 +27,6 @@ from typing_extensions import override
 
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
-from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..fp8_utils import configure_fp8_environment, patch_accelerator_for_fp8, verify_fp8_status
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@@ -35,10 +34,10 @@ from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
 if TYPE_CHECKING:
     from torch.utils.data import Dataset
-    from transformers import PreTrainedTokenizer, ProcessorMixin
+    from transformers import ProcessorMixin
     from transformers.trainer import PredictionOutput
 
-    from ...hparams import FinetuningArguments, ModelArguments
+    from ...hparams import FinetuningArguments, ModelArguments, TrainingArguments
 
 
 logger = logging.get_logger(__name__)
@@ -57,7 +56,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
     ) -> None:
         kwargs["processing_class"] = kwargs.pop("tokenizer")
         # Configure FP8 environment if enabled
-        training_args = kwargs.get("args")
+        training_args: TrainingArguments = kwargs.get("args")
         if training_args.fp8:
             configure_fp8_environment(training_args)
             if getattr(training_args, "fp8_backend", "auto") == "te":
@@ -88,7 +87,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
 
             self.compute_loss_func = dft_loss_func
 
-        if training_args.fp8 and hasattr(self, "accelerator"): # verify FP8 status after trainer initialization
+        if training_args.fp8 and hasattr(self, "accelerator"):  # verify FP8 status after trainer initialization
             verify_fp8_status(self.accelerator, training_args)
 
     @override