fix #5411

Former-commit-id: c7e51ff187658eb472c2b234f75d8934c6f7c782

src/llamafactory/api/chat.py

@@ -229,8 +229,9 @@ async def create_stream_chat_completion_response(
 async def create_score_evaluation_response(
     request: "ScoreEvaluationRequest", chat_model: "ChatModel"
 ) -> "ScoreEvaluationResponse":
+    score_id = "scoreval-{}".format(uuid.uuid4().hex)
     if len(request.messages) == 0:
         raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
 
     scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
-    return ScoreEvaluationResponse(model=request.model, scores=scores)
+    return ScoreEvaluationResponse(id=score_id, model=request.model, scores=scores)

src/llamafactory/chat/hf_engine.py

@@ -246,29 +246,18 @@ class HuggingfaceEngine(BaseEngine):
         batch_input: List[str],
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> List[float]:
-        max_length = input_kwargs.pop("max_length", None)
+        max_length: Optional[int] = input_kwargs.pop("max_length", None)
         device = getattr(model.pretrained_model, "device", "cuda")
-        inputs = tokenizer(
+        inputs: Dict[str, "torch.Tensor"] = tokenizer(
             batch_input,
             padding=True,
             truncation=True,
             max_length=max_length or getattr(model.config, "max_position_embeddings", 1024),
             return_tensors="pt",
-            add_special_tokens=True,
+            add_special_tokens=False,
         ).to(device)
-
-        input_ids: torch.Tensor = inputs["input_ids"]
-        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
-
-        if getattr(model.config, "model_type", None) == "chatglm":
-            values = torch.transpose(values, 0, 1)
-
-        scores = []
-        for i in range(input_ids.size(0)):
-            end_indexes = (input_ids[i] != tokenizer.pad_token_id).nonzero()
-            end_index = end_indexes[-1].item() if len(end_indexes) else 0
-            scores.append(values[i, end_index].nan_to_num().item())
-
+        values: "torch.Tensor" = model(**inputs, return_dict=True, use_cache=False)[-1]
+        scores = values.gather(dim=-1, index=(inputs["attention_mask"].sum(dim=-1, keepdim=True) - 1))
         return scores
 
     @override

src/llamafactory/train/ppo/ppo_utils.py

@@ -31,7 +31,7 @@ if TYPE_CHECKING:
     from trl import AutoModelForCausalLMWithValueHead
 
 
-def get_rewards_from_server(server_url: str, messages: List[str]) -> List[torch.Tensor]:
+def get_rewards_from_server(server_url: str, messages: List[str]) -> List["torch.Tensor"]:
     r"""
     Gets reward scores from the API server.
     """
@@ -66,7 +66,7 @@ def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["d
         v_head_layer.bias.data = model.get_buffer("{}_head_bias".format(target)).detach().clone().to(device)
 
 
-def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]:
+def dump_layernorm(model: "PreTrainedModel") -> Dict[str, "torch.Tensor"]:
     r"""
     Dumps the layernorm parameters in the model. The model is already unwrapped (and gathered).
     """
@@ -79,7 +79,7 @@ def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]:
     return layer_norm_params
 
 
-def restore_layernorm(model: "PreTrainedModel", layernorm_params: Optional[Dict[str, torch.Tensor]] = None) -> None:
+def restore_layernorm(model: "PreTrainedModel", layernorm_params: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
     r"""
     Restores the layernorm parameters in the model. The model is already unwrapped (and gathered).
     """

src/llamafactory/train/ppo/trainer.py

@@ -392,7 +392,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         """
         if self.finetuning_args.reward_model_type == "api":
             token_ids = [torch.cat((q, r), dim=-1).tolist() for q, r in zip(queries, responses)]
-            messages = self.tokenizer.batch_decode(token_ids, skip_special_tokens=True)
+            messages = self.tokenizer.batch_decode(token_ids, skip_special_tokens=False)
             return get_rewards_from_server(self.reward_model, messages)
 
         batch: Dict[str, "torch.Tensor"] = self.prepare_model_inputs(queries, responses)
@@ -405,7 +405,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             reward_model = self.reward_model
 
         with unwrap_model_for_generation(reward_model, self.accelerator), self.amp_context:  # support bf16
-            _, _, values = reward_model(**batch, return_dict=True, use_cache=False)
+            values: "torch.Tensor" = reward_model(**batch, return_dict=True, use_cache=False)[-1]
 
         if self.finetuning_args.reward_model_type == "lora":
             replace_model(unwrapped_model, target="default")