update hparams

src/llamafactory/hparams/parser.py

@@ -158,6 +158,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.stage != "sft" and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
 
+    if finetuning_args.stage != "sft" and data_args.neat_packing:
+        raise ValueError("`neat_packing` cannot be set as True except SFT.")
+
     if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
         raise ValueError("Please enable `predict_with_generate` to save model predictions.")
 
@@ -170,9 +173,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.stage == "ppo" and model_args.shift_attn:
         raise ValueError("PPO training is incompatible with S^2-Attn.")
 
-    if finetuning_args.stage != "sft" and model_args.efficient_packing:
-        raise ValueError("`efficient_packing` cannot be set as True except SFT.")
-
     if finetuning_args.stage == "ppo" and finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
         raise ValueError("Unsloth does not support lora reward model.")
 
@@ -314,6 +314,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
 
     model_args.device_map = {"": get_current_device()}
     model_args.model_max_length = data_args.cutoff_len
+    model_args.block_diag_attn = data_args.neat_packing
     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