145 lines
5.4 KiB
Python
145 lines
5.4 KiB
Python
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)
|