fix version checking

Former-commit-id: 5780da8d640609cca388f55983d0251e5547209a

src/llmtuner/data/formatter.py

@@ -75,8 +75,7 @@ class Formatter(ABC):
     tool_format: Literal["default"] = "default"
 
     @abstractmethod
-    def apply(self, **kwargs) -> SLOTS:
-        ...
+    def apply(self, **kwargs) -> SLOTS: ...
 
     def extract(self, content: str) -> Union[str, Tuple[str, str]]:
         raise NotImplementedError

src/llmtuner/extras/misc.py

@@ -14,6 +14,7 @@ from transformers.utils import (
     is_torch_npu_available,
     is_torch_xpu_available,
 )
+from transformers.utils.versions import require_version
 
 from .constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from .logging import get_logger
@@ -56,6 +57,17 @@ class AverageMeter:
         self.avg = self.sum / self.count
 
 
+def check_dependencies() -> None:
+    if int(os.environ.get("DISABLE_VERSION_CHECK", "0")):
+        logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
+    else:
+        require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
+        require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
+        require_version("accelerate>=0.27.2", "To fix: pip install accelerate>=0.27.2")
+        require_version("peft>=0.9.0", "To fix: pip install peft>=0.9.0")
+        require_version("trl>=0.7.11", "To fix: pip install trl>=0.7.11")
+
+
 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
     r"""
     Returns the number of trainable parameters and number of all parameters in the model.

src/llmtuner/hparams/finetuning_args.py

@@ -173,10 +173,6 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments):
         default=False,
         metadata={"help": "Whether or not to make only the parameters in the expanded blocks trainable."},
     )
-    disable_version_checking: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Whether or not to disable version checking."},
-    )
     plot_loss: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether or not to save the training loss curves."},

src/llmtuner/hparams/parser.py

@@ -7,7 +7,6 @@ import torch
 import transformers
 from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.packages import is_unsloth_available
@@ -29,17 +28,6 @@ _EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArgu
 _EVAL_CLS = Tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
 
 
-def _check_dependencies(disabled: bool) -> None:
-    if disabled:
-        logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
-    else:
-        require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
-        require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
-        require_version("accelerate>=0.27.2", "To fix: pip install accelerate>=0.27.2")
-        require_version("peft>=0.9.0", "To fix: pip install peft>=0.9.0")
-        require_version("trl>=0.7.11", "To fix: pip install trl>=0.7.11")
-
-
 def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
     if args is not None:
         return parser.parse_dict(args)
@@ -152,7 +140,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
             raise ValueError("Unsloth does not support DoRA.")
 
     _verify_model_args(model_args, finetuning_args)
-    _check_dependencies(disabled=finetuning_args.disable_version_checking)
 
     if (
         training_args.do_train
@@ -249,7 +236,6 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
 
     _set_transformers_logging()
     _verify_model_args(model_args, finetuning_args)
-    _check_dependencies(disabled=finetuning_args.disable_version_checking)
 
     if data_args.template is None:
         raise ValueError("Please specify which `template` to use.")
@@ -262,7 +248,6 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
 
     _set_transformers_logging()
     _verify_model_args(model_args, finetuning_args)
-    _check_dependencies(disabled=finetuning_args.disable_version_checking)
     model_args.aqlm_optimization = True
 
     if data_args.template is None:

src/llmtuner/model/loader.py

@@ -5,7 +5,7 @@ from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 from trl import AutoModelForCausalLMWithValueHead
 
 from ..extras.logging import get_logger
-from ..extras.misc import count_parameters, get_current_device, try_download_model_from_ms
+from ..extras.misc import check_dependencies, count_parameters, get_current_device, try_download_model_from_ms
 from .adapter import init_adapter
 from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
 from .utils import load_valuehead_params, register_autoclass
@@ -20,6 +20,9 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
+check_dependencies()
+
+
 def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
     return {
         "trust_remote_code": True,