allow non-packing pretraining

Former-commit-id: 3fee5cc5a3db9ce874ad90f2500ec092d904bd4e

src/llmtuner/hparams/data_args.py

@@ -78,9 +78,11 @@ class DataArguments:
         default=0.0,
         metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."},
     )
-    sft_packing: bool = field(
-        default=False,
-        metadata={"help": "Packing the questions and answers in the supervised fine-tuning stage."},
+    packing: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether or not to pack the sequences in training. Will automatically enable in pre-training."
+        },
     )
     cache_path: Optional[str] = field(
         default=None,

src/llmtuner/hparams/model_args.py

@@ -135,7 +135,6 @@ class ModelArguments:
     )
 
     def __post_init__(self):
-        self.aqlm_optimization = None
         self.compute_dtype = None
         self.device_map = None
         self.model_max_length = None

src/llmtuner/hparams/parser.py

@@ -230,7 +230,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
         model_args.compute_dtype = torch.float16
 
     model_args.model_max_length = data_args.cutoff_len
-    model_args.aqlm_optimization = not training_args.predict_with_generate
+    data_args.packing = data_args.packing if data_args.packing is not None else finetuning_args.stage == "pt"
 
     # Log on each process the small summary:
     logger.info(
@@ -253,7 +253,6 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
 
     _set_transformers_logging()
     _verify_model_args(model_args, finetuning_args)
-    model_args.aqlm_optimization = False
     model_args.device_map = "auto"
 
     if data_args.template is None:
@@ -267,7 +266,6 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
 
     _set_transformers_logging()
     _verify_model_args(model_args, finetuning_args)
-    model_args.aqlm_optimization = True
     model_args.device_map = "auto"
 
     if data_args.template is None: