fix #649

src/llmtuner/tuner/core/parser.py

@@ -156,10 +156,9 @@ def get_train_args(
         and finetuning_args.finetuning_type == "lora"
     ):
         logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
-        training_args.ddp_find_unused_parameters = False
-
-    if training_args.optim == "adamw_hf":
-        training_args.optim = "adamw_torch" # suppress warning
+        training_args_dict = training_args.to_dict()
+        training_args_dict.update(dict(ddp_find_unused_parameters=False))
+        training_args = Seq2SeqTrainingArguments(**training_args_dict)
 
     if (
         training_args.resume_from_checkpoint is None
@@ -172,7 +171,9 @@ def get_train_args(
             raise ValueError("Output directory already exists and is not empty. Use `overwrite_output_dir`.")
 
         if last_checkpoint is not None:
-            training_args.resume_from_checkpoint = last_checkpoint
+            training_args_dict = training_args.to_dict()
+            training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
+            training_args = Seq2SeqTrainingArguments(**training_args_dict)
             logger.info(
                 "Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid."
             )

src/llmtuner/tuner/dpo/workflow.py

@@ -31,13 +31,14 @@ def run_dpo(
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
     )
 
-    training_args.remove_unused_columns = False # important for pairwise dataset
-    ref_model = deepcopy(model) if not isinstance(model, PeftModel) else None
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
 
     # Initialize our Trainer
     trainer = DPOPeftTrainer(
         finetuning_args=finetuning_args,
-        ref_model=ref_model,
+        ref_model=deepcopy(model) if not isinstance(model, PeftModel) else None,
         model=model,
         args=training_args,
         tokenizer=tokenizer,

src/llmtuner/tuner/rm/workflow.py

@@ -28,7 +28,9 @@ def run_rm(
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
     data_collator = PairwiseDataCollatorWithPadding(tokenizer)
 
-    training_args.remove_unused_columns = False # important for pairwise dataset
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
 
     # Initialize our Trainer
     trainer = PairwisePeftTrainer(

src/llmtuner/tuner/sft/workflow.py

@@ -33,10 +33,12 @@ def run_sft(
     )
 
     # Override the decoding parameters of Seq2SeqTrainer
-    training_args.generation_max_length = training_args.generation_max_length if \
-                training_args.generation_max_length is not None else data_args.max_target_length
-    training_args.generation_num_beams = data_args.eval_num_beams if \
-                data_args.eval_num_beams is not None else training_args.generation_num_beams
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(
+        generation_max_length=training_args.generation_max_length or data_args.max_target_length,
+        generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams
+    ))
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
 
     # Initialize our Trainer
     trainer = Seq2SeqPeftTrainer(