support returning stop_str in output

lmdeploy/messages.py

@@ -110,6 +110,7 @@ class GenerationConfig:
     logits_processors: Optional[List[LogitsProcessor]] = None
     output_logits: Literal['all', 'generation'] = None
     output_last_hidden_state: Literal['all', 'generation'] = None
+    include_stop_str_in_output: bool = False
 
     # for disaggregation
     with_cache: bool = False

lmdeploy/serve/async_engine.py

@@ -682,7 +682,6 @@ async def generate(
             step: int = 0,
             do_preprocess: bool = True,
             adapter_name: Optional[str] = None,
-            skip_stop_tokens: bool = True,
             rewind_stop_tokens: bool = False,
             input_ids: Optional[List] = None,
             enable_thinking: Optional[bool] = None,
@@ -778,9 +777,8 @@ async def generate(
         def is_error(status):
             return status not in [ResponseType.SUCCESS, ResponseType.FINISH, ResponseType.CANCEL]
 
-        # used to skip / rewind stop words in interactive mode
         stop_ids = []
-        if skip_stop_tokens and not gen_config.ignore_eos:
+        if not gen_config.ignore_eos:
             stop_ids = gen_config.stop_token_ids or []
 
         metrics_processor.increment_total_requests()
@@ -864,11 +862,15 @@ def is_error(status):
 
                 if not is_error(outputs.status):
                     finish_reason = 'stop' if outputs.token_ids[-1] in stop_ids else 'length'
-                    # utf-8 char at the end means it's a potential unfinished
-                    # byte sequence
+                    # utf-8 char at the end means it's a potential unfinished byte sequence
                     if not response.endswith('�'):
                         # avoid returning the last response twice
                         response = ''
+                    token_ids = []
+                    if gen_config.include_stop_str_in_output and finish_reason == 'stop':
+                        # return the eos token id (MUST be in a list) and its string
+                        token_ids = outputs.token_ids[-1:]
+                        response = self.tokenizer.decode(token_ids, skip_special_tokens=False)
                     logger.info(f'session {session_id} finished, reason '
                                 f'"{finish_reason}", input_tokens '
                                 f'{len(input_ids)}, output_tokens {gen_len}')
@@ -877,7 +879,7 @@ def is_error(status):
                                  len(input_ids),
                                  gen_len,
                                  finish_reason,
-                                 token_ids=[],
+                                 token_ids=token_ids,
                                  cache_block_ids=outputs.cache_block_ids)
                     # Update a session's sequence only when it is in finished status
                     if outputs.status == ResponseType.FINISH:

lmdeploy/serve/openai/api_server.py

@@ -406,6 +406,7 @@ async def chat_completions_v1(request: ChatCompletionRequest, raw_request: Reque
         repetition_penalty=request.repetition_penalty,
         ignore_eos=request.ignore_eos,
         stop_words=request.stop,
+        include_stop_str_in_output=request.include_stop_str_in_output,
         skip_special_tokens=request.skip_special_tokens,
         response_format=response_format,
         logits_processors=logits_processors,

lmdeploy/serve/openai/protocol.py

@@ -107,16 +107,15 @@ class ChatCompletionRequest(BaseModel):
     """Chat completion request."""
     model: str
 
-    messages: Union[str, List[Dict[str, Any]]] = Field(examples=[[{'role': 'user', 'content': 'hi'}]])  # noqa
+    messages: Union[str, List[Dict[str, Any]]] = Field(examples=[[{'role': 'user', 'content': 'hi'}]])
     temperature: Optional[float] = 0.7
     top_p: Optional[float] = 1.0
     tools: Optional[List[Tool]] = Field(default=None, examples=[None])
-    tool_choice: Union[ToolChoice, Literal['auto', 'required', 'none']] = Field(default='auto',
-                                                                                examples=['none'])  # noqa
+    tool_choice: Union[ToolChoice, Literal['auto', 'required', 'none']] = Field(default='auto', examples=['none'])
     logprobs: Optional[bool] = False
     top_logprobs: Optional[int] = None
     n: Optional[int] = 1
-    logit_bias: Optional[Dict[str, float]] = Field(default=None, examples=[None])  # noqa
+    logit_bias: Optional[Dict[str, float]] = Field(default=None, examples=[None])
     max_completion_tokens: Optional[int] = Field(
         default=None,
         examples=[None],
@@ -128,15 +127,15 @@ class ChatCompletionRequest(BaseModel):
         examples=[None],
         deprecated='max_tokens is deprecated in favor of the max_completion_tokens field',
     )
-    stop: Optional[Union[str, List[str]]] = Field(default=None, examples=[None])  # noqa
+    stop: Optional[Union[str, List[str]]] = Field(default=None, examples=[None])
 
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = Field(default=None, examples=[None])
     presence_penalty: Optional[float] = 0.0
     frequency_penalty: Optional[float] = 0.0
     user: Optional[str] = None
     reasoning_effort: Optional[Literal['low', 'medium', 'high']] = None
-    response_format: Optional[ResponseFormat] = Field(default=None, examples=[None])  # noqa
+    response_format: Optional[ResponseFormat] = Field(default=None, examples=[None])
     # additional argument of lmdeploy
     do_preprocess: Optional[bool] = True
     repetition_penalty: Optional[float] = 1.0
@@ -150,6 +149,7 @@ class ChatCompletionRequest(BaseModel):
     min_p: float = 0.0
     enable_thinking: Optional[bool] = None
     return_token_ids: Optional[bool] = False
+    include_stop_str_in_output: Optional[bool] = False
 
 
 class FunctionCall(BaseModel):