fix #2961

src/llmtuner/data/template.py

@@ -689,6 +689,8 @@ def get_template_and_fix_tokenizer(
 
 _register_template(
     name="vanilla",
+    format_separator=EmptyFormatter(slots=["\n"]),
+    efficient_eos=True,
 )
 
 

src/llmtuner/eval/template.py

@@ -1,45 +1,48 @@
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Dict, List, Tuple
+from typing import Dict, List, Sequence, Tuple
 
 from ..data import Role
 from ..extras.constants import CHOICES
 
 
-if TYPE_CHECKING:
-    from datasets import Dataset
-
-
 @dataclass
 class EvalTemplate:
     system: str
     choice: str
     answer: str
     prefix: str
 
-    def parse_example(self, example: Dict[str, str]) -> Tuple[str, str]:
+    def _parse_example(self, example: Dict[str, str]) -> Tuple[str, str]:
+        r"""
+        input: a dict with keys {"question", "A", "B", "C", "D", "answer"}
+        output: a tuple of (prompt, response)
+        """
         candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in CHOICES if ch in example]
         return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
 
     def format_example(
-        self, target_data: Dict[str, str], support_set: "Dataset", subject_name: str
+        self, target_data: Dict[str, str], support_set: Sequence[Dict[str, str]], subject_name: str
     ) -> List[Dict[str, str]]:
+        r"""
+        Converts dataset examples to messages.
+        """
         messages = []
         for k in range(len(support_set)):
-            prompt, response = self.parse_example(support_set[k])
-            messages.append({"role": Role.USER, "content": prompt})
-            messages.append({"role": Role.ASSISTANT, "content": response})
+            prompt, response = self._parse_example(support_set[k])
+            messages.append({"role": Role.USER.value, "content": prompt})
+            messages.append({"role": Role.ASSISTANT.value, "content": response})
 
-        prompt, response = self.parse_example(target_data)
-        messages.append({"role": Role.USER, "content": prompt})
-        messages.append({"role": Role.ASSISTANT, "content": response})
+        prompt, response = self._parse_example(target_data)
+        messages.append({"role": Role.USER.value, "content": prompt})
+        messages.append({"role": Role.ASSISTANT.value, "content": response})
         messages[0]["content"] = self.system.format(subject=subject_name) + messages[0]["content"]
         return messages
 
 
 eval_templates: Dict[str, "EvalTemplate"] = {}
 
 
-def register_eval_template(name: str, system: str, choice: str, answer: str, prefix: str) -> None:
+def _register_eval_template(name: str, system: str, choice: str, answer: str, prefix: str) -> None:
     eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer, prefix=prefix)
 
 
@@ -49,7 +52,7 @@ def get_eval_template(name: str) -> "EvalTemplate":
     return eval_template
 
 
-register_eval_template(
+_register_eval_template(
     name="en",
     system="The following are multiple choice questions (with answers) about {subject}.\n\n",
     choice="\n{choice}. {content}",
@@ -58,10 +61,10 @@ def get_eval_template(name: str) -> "EvalTemplate":
 )
 
 
-register_eval_template(
+_register_eval_template(
     name="zh",
     system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n",
     choice="\n{choice}. {content}",
     answer="\n答案：",
-    prefix="\n",
+    prefix=" ",
 )

src/llmtuner/hparams/finetuning_args.py

@@ -102,6 +102,10 @@ class RLHFArguments:
         default="sigmoid",
         metadata={"help": "The type of DPO loss to use."},
     )
+    dpo_label_smoothing = field(
+        default=0.0,
+        metadata={"help": "The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5."},
+    )
     dpo_ftx: float = field(
         default=0.0,
         metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."},
@@ -248,6 +252,9 @@ def split_arg(arg):
         if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
             raise ValueError("`reward_model_type` cannot be lora for Freeze/Full PPO training.")
 
+        if self.stage == "dpo" and self.dpo_loss != "sigmoid" and self.dpo_label_smoothing > 1e-6:
+            raise ValueError("`dpo_label_smoothing` is only valid for sigmoid loss function.")
+
         if self.use_llama_pro and self.finetuning_type == "full":
             raise ValueError("`use_llama_pro` is only valid for the Freeze or LoRA method.")
 

src/llmtuner/model/patcher.py

@@ -173,7 +173,7 @@ def _configure_quantization(
     """
     if getattr(config, "quantization_config", None):  # ptq
         if is_deepspeed_zero3_enabled():
-            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
+            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantized models.")
 
         init_kwargs["device_map"] = {"": get_current_device()}
         quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)

src/llmtuner/train/dpo/trainer.py

@@ -20,12 +20,9 @@
 class CustomDPOTrainer(DPOTrainer):
     def __init__(
         self,
-        beta: float,
-        loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"],
-        ftx_gamma: float,
         model: Union["PreTrainedModel", torch.nn.Module],
+        ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]],
         finetuning_args: "FinetuningArguments",
-        ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
         disable_dropout: bool = True,
         **kwargs,
     ):
@@ -47,10 +44,10 @@ def __init__(
         self._peft_has_been_casted_to_bf16 = False
 
         self.ref_model = ref_model
-        self.beta = beta
-        self.label_smoothing = 0
-        self.loss_type = loss_type
-        self.ftx_gamma = ftx_gamma
+        self.beta = finetuning_args.dpo_beta
+        self.label_smoothing = finetuning_args.dpo_label_smoothing
+        self.loss_type = finetuning_args.dpo_loss
+        self.ftx_gamma = finetuning_args.dpo_ftx
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
 
         Trainer.__init__(self, model=model, **kwargs)

src/llmtuner/train/dpo/workflow.py

@@ -45,13 +45,10 @@ def run_dpo(
 
     # Initialize our Trainer
     trainer = CustomDPOTrainer(
-        beta=finetuning_args.dpo_beta,
-        loss_type=finetuning_args.dpo_loss,
-        ftx_gamma=finetuning_args.dpo_ftx,
-        finetuning_args=finetuning_args,
         model=model,
         ref_model=ref_model,
         args=training_args,
+        finetuning_args=finetuning_args,
         tokenizer=tokenizer,
         data_collator=data_collator,
         callbacks=callbacks,

src/llmtuner/train/utils.py

@@ -5,7 +5,6 @@
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
-from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available