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import time
import codecs
from fastapi import Request
from typing import AsyncGenerator, AsyncIterator, Union
from vllm.logger import init_logger
from vllm.utils import random_uuid
from vllm.engine.async_llm_engine import AsyncLLMEngine
from protocol import (
ChatCompletionRequest, ChatCompletionResponse,
ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
UsageInfo)
from vllm.outputs import RequestOutput
from serving_engine import OpenAIServing
logger = init_logger(__name__)
class OpenAIServingChat(OpenAIServing):
def __init__(self,
engine: AsyncLLMEngine,
served_model: str,
response_role: str,
chat_template=None):
super().__init__(engine=engine, served_model=served_model)
self.response_role = response_role
self._load_chat_template(chat_template)
async def create_chat_completion(
self, request: ChatCompletionRequest, raw_request: Request
) -> Union[ErrorResponse, AsyncGenerator[str, None],
ChatCompletionResponse]:
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/chat/create
for the API specification. This API mimics the OpenAI ChatCompletion API.
NOTE: Currently we do not support the following features:
- function_call (Users should implement this by themselves)
- logit_bias (to be supported by vLLM engine)
"""
error_check_ret = await self._check_model(request)
if error_check_ret is not None:
return error_check_ret
if request.logit_bias is not None and len(request.logit_bias) > 0:
# TODO: support logit_bias in vLLM engine.
return self.create_error_response(
"logit_bias is not currently supported")
try:
prompt = self.tokenizer.apply_chat_template(
conversation=request.messages,
tokenize=False,
add_generation_prompt=request.add_generation_prompt)
except Exception as e:
logger.error(
f"Error in applying chat template from request: {str(e)}")
return self.create_error_response(str(e))
request_id = f"cmpl-{random_uuid()}"
try:
token_ids = self._validate_prompt_and_tokenize(request,
prompt=prompt)
sampling_params = request.to_sampling_params()
except ValueError as e:
return self.create_error_response(str(e))
result_generator = self.engine.generate(prompt, sampling_params,
request_id, token_ids)
# Streaming response
if request.stream:
return self.chat_completion_stream_generator(
request, result_generator, request_id)
else:
return await self.chat_completion_full_generator(
request, raw_request, result_generator, request_id)
def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
if request.add_generation_prompt:
return self.response_role
else:
return request.messages[-1].role
async def chat_completion_stream_generator(
self, request: ChatCompletionRequest,
result_generator: AsyncIterator[RequestOutput], request_id: str
) -> Union[ErrorResponse, AsyncGenerator[str, None]]:
model_name = request.model
created_time = int(time.monotonic())
chunk_object_type = "chat.completion.chunk"
# Send first response for each request.n (index) with the role
role = self.get_chat_request_role(request)
for i in range(request.n):
choice_data = ChatCompletionResponseStreamChoice(
index=i, delta=DeltaMessage(role=role), finish_reason=None)
chunk = ChatCompletionStreamResponse(id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
# Send response to echo the input portion of the last message
if request.echo:
last_msg_content = ""
if request.messages and isinstance(
request.messages, list) and request.messages[-1].get(
"content") and request.messages[-1].get(
"role") == role:
last_msg_content = request.messages[-1]["content"]
if last_msg_content:
for i in range(request.n):
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(content=last_msg_content),
finish_reason=None)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
# Send response for each token for each request.n (index)
previous_texts = [""] * request.n
previous_num_tokens = [0] * request.n
finish_reason_sent = [False] * request.n
async for res in result_generator:
res: RequestOutput
for output in res.outputs:
i = output.index
if finish_reason_sent[i]:
continue
delta_text = output.text[len(previous_texts[i]):]
previous_texts[i] = output.text
previous_num_tokens[i] = len(output.token_ids)
if output.finish_reason is None:
# Send token-by-token response for each request.n
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(content=delta_text),
finish_reason=None)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
else:
# Send the finish response for each request.n only once
prompt_tokens = len(res.prompt_token_ids)
final_usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=previous_num_tokens[i],
total_tokens=prompt_tokens + previous_num_tokens[i],
)
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(content=delta_text),
finish_reason=output.finish_reason)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
if final_usage is not None:
chunk.usage = final_usage
data = chunk.model_dump_json(exclude_unset=True,
exclude_none=True)
yield f"data: {data}\n\n"
finish_reason_sent[i] = True
# Send the final done message after all response.n are finished
yield "data: [DONE]\n\n"
async def chat_completion_full_generator(
self, request: ChatCompletionRequest, raw_request: Request,
result_generator: AsyncIterator[RequestOutput],
request_id: str) -> Union[ErrorResponse, ChatCompletionResponse]:
model_name = request.model
created_time = int(time.monotonic())
final_res: RequestOutput = None
async for res in result_generator:
if await raw_request.is_disconnected():
# Abort the request if the client disconnects.
await self.engine.abort(request_id)
return self.create_error_response("Client disconnected")
final_res = res
assert final_res is not None
choices = []
role = self.get_chat_request_role(request)
for output in final_res.outputs:
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=ChatMessage(role=role, content=output.text),
finish_reason=output.finish_reason,
)
choices.append(choice_data)
if request.echo:
last_msg_content = ""
if request.messages and isinstance(
request.messages, list) and request.messages[-1].get(
"content") and request.messages[-1].get(
"role") == role:
last_msg_content = request.messages[-1]["content"]
for choice in choices:
full_message = last_msg_content + choice.message.content
choice.message.content = full_message
num_prompt_tokens = len(final_res.prompt_token_ids)
num_generated_tokens = sum(
len(output.token_ids) for output in final_res.outputs)
usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
)
response = ChatCompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
)
return response
def _load_chat_template(self, chat_template):
if chat_template is not None:
try:
with open(chat_template, "r") as f:
self.tokenizer.chat_template = f.read()
except OSError:
# If opening a file fails, set chat template to be args to
# ensure we decode so our escape are interpreted correctly
self.tokenizer.chat_template = codecs.decode(
chat_template, "unicode_escape")
logger.info(
f"Using supplied chat template:\n{self.tokenizer.chat_template}"
)
elif self.tokenizer.chat_template is not None:
logger.info(
f"Using default chat template:\n{self.tokenizer.chat_template}"
)
else:
logger.warning(
"No chat template provided. Chat API will not work.") |