add qwen3

vllm-v0.6.2/vllm/entrypoints/openai/serving_tokenization.py

@@ -0,0 +1,144 @@
+from typing import List, Optional, Union
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import load_chat_template
+from vllm.entrypoints.logger import RequestLogger
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (DetokenizeRequest,
+                                              DetokenizeResponse,
+                                              ErrorResponse,
+                                              TokenizeChatRequest,
+                                              TokenizeRequest,
+                                              TokenizeResponse)
+# yapf: enable
+from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+                                                    LoRAModulePath,
+                                                    OpenAIServing)
+from vllm.logger import init_logger
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingTokenization(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]],
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=lora_modules,
+                         prompt_adapters=None,
+                         request_logger=request_logger)
+
+        # If this is None we use the tokenizer's default chat template
+        # the list of commonly-used chat template names for HF named templates
+        hf_chat_templates: List[str] = ['default', 'tool_use']
+        self.chat_template = chat_template \
+            if chat_template in hf_chat_templates \
+            else load_chat_template(chat_template)
+
+    async def create_tokenize(
+        self,
+        request: TokenizeRequest,
+    ) -> Union[TokenizeResponse, ErrorResponse]:
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        request_id = f"tokn-{random_uuid()}"
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if isinstance(request, TokenizeChatRequest):
+                (
+                    _,
+                    request_prompts,
+                    engine_prompts,
+                ) = await self._preprocess_chat(
+                    request,
+                    tokenizer,
+                    request.messages,
+                    chat_template=self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                request_prompts, engine_prompts = self._preprocess_completion(
+                    request,
+                    tokenizer,
+                    request.prompt,
+                    add_special_tokens=request.add_special_tokens,
+                )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        input_ids: List[int] = []
+        for i, engine_prompt in enumerate(engine_prompts):
+            self._log_inputs(request_id,
+                             request_prompts[i],
+                             params=None,
+                             lora_request=lora_request,
+                             prompt_adapter_request=prompt_adapter_request)
+
+            # Silently ignore prompt adapter since it does not affect
+            # tokenization (Unlike in Embeddings API where an error is raised)
+
+            input_ids.extend(engine_prompt["prompt_token_ids"])
+
+        return TokenizeResponse(tokens=input_ids,
+                                count=len(input_ids),
+                                max_model_len=self.max_model_len)
+
+    async def create_detokenize(
+        self,
+        request: DetokenizeRequest,
+    ) -> Union[DetokenizeResponse, ErrorResponse]:
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        request_id = f"tokn-{random_uuid()}"
+
+        (
+            lora_request,
+            prompt_adapter_request,
+        ) = self._maybe_get_adapters(request)
+
+        tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+        self._log_inputs(request_id,
+                         request.tokens,
+                         params=None,
+                         lora_request=lora_request,
+                         prompt_adapter_request=prompt_adapter_request)
+
+        # Silently ignore prompt adapter since it does not affect tokenization
+        # (Unlike in Embeddings API where an error is raised)
+
+        prompt_input = self._tokenize_prompt_input(
+            request,
+            tokenizer,
+            request.tokens,
+        )
+        input_text = prompt_input["prompt"]
+
+        return DetokenizeResponse(prompt=input_text)