[BugFix][Frontend] Use correct, shared tokenizer in OpenAI server

vllm/engine/async_llm_engine.py

@@ -16,6 +16,7 @@
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import MultiModalData
+from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
 from vllm.usage.usage_lib import UsageContext
 
 logger = init_logger(__name__)
@@ -378,6 +379,12 @@ def _error_callback(self, exc: Exception) -> None:
         self.set_errored(exc)
         self._request_tracker.propagate_exception(exc)
 
+    async def get_tokenizer_group(self) -> BaseTokenizerGroup:
+        if self.engine_use_ray:
+            return await self.engine.get_tokenizer_group.remote()
+        else:
+            return self.engine.get_tokenizer_group()
+
     async def get_tokenizer(self) -> "PreTrainedTokenizer":
         if self.engine_use_ray:
             return await self.engine.get_tokenizer.remote()

vllm/engine/llm_engine.py

@@ -241,6 +241,9 @@ def __reduce__(self):
         # the closure used to initialize Ray worker actors
         raise RuntimeError("LLMEngine should not be pickled!")
 
+    def get_tokenizer_group(self) -> BaseTokenizerGroup:
+        return self.tokenizer
+
     def get_tokenizer(self) -> "PreTrainedTokenizer":
         return self.tokenizer.get_lora_tokenizer(None)
 

vllm/entrypoints/openai/serving_chat.py

@@ -63,10 +63,13 @@ async def create_chat_completion(
 
         request_id = f"cmpl-{random_uuid()}"
         try:
-            token_ids = self._validate_prompt_and_tokenize(request,
-                                                           prompt=prompt)
-            sampling_params = request.to_sampling_params()
             lora_request = self._maybe_get_lora(request)
+            token_ids = await self._validate_prompt_and_tokenize(
+                request,
+                request_id=request_id,
+                lora_request=lora_request,
+                prompt=prompt)
+            sampling_params = request.to_sampling_params()
             guided_decode_logits_processor = (
                 await get_guided_decoding_logits_processor(
                     request, await self.engine.get_tokenizer()))

vllm/entrypoints/openai/serving_completion.py

@@ -135,23 +135,20 @@ async def create_completion(self, request: CompletionRequest,
             prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
 
             for i, prompt in enumerate(prompts):
-                if prompt_is_tokens:
-                    input_ids = self._validate_prompt_and_tokenize(
-                        request,
-                        prompt_ids=prompt,
-                        truncate_prompt_tokens=sampling_params.
-                        truncate_prompt_tokens)
-                else:
-                    input_ids = self._validate_prompt_and_tokenize(
-                        request,
-                        prompt=prompt,
-                        truncate_prompt_tokens=sampling_params.
-                        truncate_prompt_tokens)
+                sub_request_id = f"{request_id}-{i}"
+                prompt_arg = "prompt_ids" if prompt_is_tokens else "prompt"
+                input_ids = await self._validate_prompt_and_tokenize(
+                    request,
+                    request_id=sub_request_id,
+                    lora_request=lora_request,
+                    truncate_prompt_tokens=sampling_params.
+                    truncate_prompt_tokens,
+                    **{prompt_arg: prompt})
 
                 generators.append(
                     self.engine.generate(prompt,
                                          sampling_params,
-                                         f"{request_id}-{i}",
+                                         sub_request_id,
                                          prompt_token_ids=input_ids,
                                          lora_request=lora_request))
         except ValueError as e:

vllm/entrypoints/openai/serving_engine.py

@@ -64,12 +64,11 @@ async def _post_init(self):
         engine_model_config = await self.engine.get_model_config()
         self.max_model_len = engine_model_config.max_model_len
 
-        # A separate tokenizer to map token IDs to strings.
+        # A separate tokenizer for applying the chat template.
         self.tokenizer = get_tokenizer(
             engine_model_config.tokenizer,
             tokenizer_mode=engine_model_config.tokenizer_mode,
-            trust_remote_code=engine_model_config.trust_remote_code,
-            truncation_side="left")
+            trust_remote_code=engine_model_config.trust_remote_code)
 
     async def show_available_models(self) -> ModelList:
         """Show available models. Right now we only have one model."""
@@ -163,25 +162,28 @@ def _maybe_get_lora(self, request) -> Optional[LoRARequest]:
         # if _check_model has been called earlier, this will be unreachable
         raise ValueError("The model `{request.model}` does not exist.")
 
-    def _validate_prompt_and_tokenize(
+    async def _validate_prompt_and_tokenize(
             self,
             request: Union[ChatCompletionRequest, CompletionRequest],
+            request_id: Optional[str] = None,
+            lora_request: Optional[LoRARequest] = None,
             prompt: Optional[str] = None,
             prompt_ids: Optional[List[int]] = None,
             truncate_prompt_tokens: Optional[conint(ge=1)] = None
     ) -> List[int]:
         if not (prompt or prompt_ids):
             raise ValueError("Either prompt or prompt_ids should be provided.")
-        if (prompt and prompt_ids):
+        if prompt and prompt_ids:
             raise ValueError(
                 "Only one of prompt or prompt_ids should be provided.")
 
         if prompt_ids is None:
-            tokenizer_kwargs = {} if truncate_prompt_tokens is None else {
-                "truncation": True,
-                "max_length": truncate_prompt_tokens,
-            }
-            input_ids = self.tokenizer(prompt, **tokenizer_kwargs).input_ids
+            tokenizer = await self.engine.get_tokenizer_group()
+            input_ids = await tokenizer.encode_async(
+                prompt,
+                request_id,
+                lora_request,
+                truncate_to=truncate_prompt_tokens)
         elif truncate_prompt_tokens is not None:
             input_ids = prompt_ids[-truncate_prompt_tokens:]
         else:

vllm/transformers_utils/tokenizer.py

@@ -66,6 +66,9 @@ def get_tokenizer(
                 "Cannot use the fast tokenizer in slow tokenizer mode.")
         kwargs["use_fast"] = False
 
+    if "truncation_side" not in kwargs:
+        kwargs["truncation_side"] = "left"
+
     try:
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name,

vllm/transformers_utils/tokenizer_group/base_tokenizer_group.py

@@ -25,16 +25,17 @@ def get_max_input_len(self,
     def encode(self,
                prompt: str,
                request_id: Optional[str] = None,
-               lora_request: Optional[LoRARequest] = None) -> List[int]:
+               lora_request: Optional[LoRARequest] = None,
+               truncate_to: Optional[int] = None) -> List[int]:
         """Encode a prompt using the tokenizer group."""
         pass
 
     @abstractmethod
-    async def encode_async(
-            self,
-            prompt: str,
-            request_id: Optional[str] = None,
-            lora_request: Optional[LoRARequest] = None) -> List[int]:
+    async def encode_async(self,
+                           prompt: str,
+                           request_id: Optional[str] = None,
+                           lora_request: Optional[LoRARequest] = None,
+                           truncate_to: Optional[int] = None) -> List[int]:
         """Encode a prompt using the tokenizer group."""
         pass
 

vllm/transformers_utils/tokenizer_group/ray_tokenizer_group.py

@@ -81,7 +81,8 @@ def _ensure_queue_initialized(self):
     def encode(self,
                prompt: str,
                request_id: Optional[str] = None,
-               lora_request: Optional[LoRARequest] = None) -> List[int]:
+               lora_request: Optional[LoRARequest] = None,
+               truncate_to: Optional[int] = None) -> List[int]:
         """Encode a prompt using the tokenizer group.
 
         We pick an idle actor and use it to encode the prompt.
@@ -97,7 +98,8 @@ def encode(self,
             ret = ray.get(
                 actor.encode.remote(request_id=request_id,
                                     prompt=prompt,
-                                    lora_request=lora_request))
+                                    lora_request=lora_request,
+                                    truncate_to=truncate_to))
         finally:
             # Put the actor back in the queue.
             # This is done in a finally block to ensure that the actor is
@@ -106,11 +108,11 @@ def encode(self,
             self._idle_actors.put_nowait(actor)
         return ret
 
-    async def encode_async(
-            self,
-            prompt: str,
-            request_id: Optional[str] = None,
-            lora_request: Optional[LoRARequest] = None) -> List[int]:
+    async def encode_async(self,
+                           prompt: str,
+                           request_id: Optional[str] = None,
+                           lora_request: Optional[LoRARequest] = None,
+                           truncate_to: Optional[int] = None) -> List[int]:
         """Encode a prompt using the tokenizer group.
 
         We pick an idle actor and use it to encode the prompt.
@@ -125,7 +127,8 @@ async def encode_async(
         try:
             ret = await actor.encode.remote(request_id=request_id,
                                             prompt=prompt,
-                                            lora_request=lora_request)
+                                            lora_request=lora_request,
+                                            truncate_to=truncate_to)
         finally:
             # Put the actor back in the queue.
             # This is done in a finally block to ensure that the actor is

vllm/transformers_utils/tokenizer_group/tokenizer_group.py

@@ -37,17 +37,27 @@ def get_max_input_len(self,
     def encode(self,
                prompt: str,
                request_id: Optional[str] = None,
-               lora_request: Optional[LoRARequest] = None) -> List[int]:
+               lora_request: Optional[LoRARequest] = None,
+               truncate_to: Optional[int] = None) -> List[int]:
         tokenizer = self.get_lora_tokenizer(lora_request)
-        return tokenizer.encode(prompt)
+        return self._encode(tokenizer, prompt, truncate_to)
 
-    async def encode_async(
-            self,
-            prompt: str,
-            request_id: Optional[str] = None,
-            lora_request: Optional[LoRARequest] = None) -> List[int]:
+    async def encode_async(self,
+                           prompt: str,
+                           request_id: Optional[str] = None,
+                           lora_request: Optional[LoRARequest] = None,
+                           truncate_to: Optional[int] = None) -> List[int]:
         tokenizer = await self.get_lora_tokenizer_async(lora_request)
-        return tokenizer.encode(prompt)
+        return self._encode(tokenizer, prompt, truncate_to)
+
+    @staticmethod
+    def _encode(tokenizer: PreTrainedTokenizer, prompt: str,
+                truncate_to: Optional[int]) -> List[int]:
+        tokenizer_kwargs = {} if truncate_to is None else {
+            "truncation": True,
+            "max_length": truncate_to,
+        }
+        return tokenizer.encode(prompt, **tokenizer_kwargs)
 
     def get_lora_tokenizer(
             self,
@@ -60,8 +70,7 @@ def get_lora_tokenizer(
                 lora_request, **self.tokenizer_config) or self.tokenizer)
             self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
             return tokenizer
-        else:
-            return self.lora_tokenizers.get(lora_request.lora_int_id)
+        return self.lora_tokenizers.get(lora_request.lora_int_id)
 
     async def get_lora_tokenizer_async(
             self,
@@ -74,5 +83,4 @@ async def get_lora_tokenizer_async(
                 lora_request, **self.tokenizer_config) or self.tokenizer)
             self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
             return tokenizer
-        else:
-            return self.lora_tokenizers.get(lora_request.lora_int_id)
+        return self.lora_tokenizers.get(lora_request.lora_int_id)