add option to disable version check

Former-commit-id: fd769cb2de696aee3c5e882237e16eace6a9d675

src/llmtuner/hparams/parser.py

@@ -8,8 +8,10 @@ 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
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -28,6 +30,14 @@ _EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArgu
 _EVAL_CLS = Tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
 
 
+def _check_dependencies():
+    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>=0.21.0", "To fix: pip install accelerate>=0.21.0")
+    require_version("peft>=0.7.0", "To fix: pip install peft>=0.7.0")
+    require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
+
+
 def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
     if args is not None:
         return parser.parse_dict(args)
@@ -123,8 +133,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
         raise ValueError("Please specify `lora_target` in LoRA training.")
 
+    if training_args.do_train and model_args.use_unsloth and not is_unsloth_available:
+        raise ValueError("Install Unsloth: https://github.com/unslothai/unsloth")
+
     _verify_model_args(model_args, finetuning_args)
 
+    if not finetuning_args.disable_version_checking:
+        _check_dependencies()
+
     if (
         training_args.do_train
         and finetuning_args.finetuning_type == "lora"
@@ -145,7 +161,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
         logger.warning("Specify `ref_model` for computing rewards at evaluation.")
 
-    # postprocess training_args
+    # Post-process training arguments
     if (
         training_args.local_rank != -1
         and training_args.ddp_find_unused_parameters is None
@@ -158,7 +174,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
 
     if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
         can_resume_from_checkpoint = False
-        training_args.resume_from_checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            logger.warning("Cannot resume from checkpoint in current stage.")
+            training_args.resume_from_checkpoint = None
     else:
         can_resume_from_checkpoint = True
 
@@ -194,7 +212,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
             )
         )
 
-    # postprocess model_args
+    # Post-process model arguments
     model_args.compute_dtype = (
         torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None)
     )
@@ -212,7 +230,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     )
     logger.info(f"Training/evaluation parameters {training_args}")
 
-    # Set seed before initializing model.
     transformers.set_seed(training_args.seed)
 
     return model_args, data_args, training_args, finetuning_args, generating_args
@@ -220,24 +237,30 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
 
 def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
     model_args, data_args, finetuning_args, generating_args = _parse_infer_args(args)
+
     _set_transformers_logging()
+    _verify_model_args(model_args, finetuning_args)
 
     if data_args.template is None:
         raise ValueError("Please specify which `template` to use.")
 
-    _verify_model_args(model_args, finetuning_args)
+    if not finetuning_args.disable_version_checking:
+        _check_dependencies()
 
     return model_args, data_args, finetuning_args, generating_args
 
 
 def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
     model_args, data_args, eval_args, finetuning_args = _parse_eval_args(args)
+
     _set_transformers_logging()
+    _verify_model_args(model_args, finetuning_args)
 
     if data_args.template is None:
         raise ValueError("Please specify which `template` to use.")
 
-    _verify_model_args(model_args, finetuning_args)
+    if not finetuning_args.disable_version_checking:
+        _check_dependencies()
 
     transformers.set_seed(eval_args.seed)