init
This commit is contained in:
392
vllm/entrypoints/openai/serving_chat.py
Normal file
392
vllm/entrypoints/openai/serving_chat.py
Normal file
@@ -0,0 +1,392 @@
|
||||
import asyncio
|
||||
import codecs
|
||||
import time
|
||||
from typing import (AsyncGenerator, AsyncIterator, Awaitable, Iterable, List,
|
||||
Optional, Tuple, TypedDict, Union, final)
|
||||
|
||||
from fastapi import Request
|
||||
from openai.types.chat import (ChatCompletionContentPartParam,
|
||||
ChatCompletionRole)
|
||||
|
||||
from vllm.engine.async_llm_engine import AsyncLLMEngine
|
||||
from vllm.entrypoints.openai.protocol import (
|
||||
ChatCompletionRequest, ChatCompletionResponse,
|
||||
ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
|
||||
ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
|
||||
UsageInfo)
|
||||
from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
|
||||
OpenAIServing)
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.guided_decoding import (
|
||||
get_guided_decoding_logits_processor)
|
||||
from vllm.outputs import RequestOutput
|
||||
from vllm.utils import random_uuid
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
@final # So that it should be compatible with Dict[str, str]
|
||||
class ConversationMessage(TypedDict):
|
||||
role: str
|
||||
content: str
|
||||
|
||||
|
||||
class OpenAIServingChat(OpenAIServing):
|
||||
|
||||
def __init__(self,
|
||||
engine: AsyncLLMEngine,
|
||||
served_model_names: List[str],
|
||||
response_role: str,
|
||||
lora_modules: Optional[List[LoRAModulePath]] = None,
|
||||
chat_template: Optional[str] = None):
|
||||
super().__init__(engine=engine,
|
||||
served_model_names=served_model_names,
|
||||
lora_modules=lora_modules,
|
||||
await_post_init=self._load_chat_template(
|
||||
chat_template=chat_template))
|
||||
|
||||
self.response_role = response_role
|
||||
|
||||
def _parse_chat_message_content(
|
||||
self,
|
||||
role: ChatCompletionRole,
|
||||
content: Optional[Union[str,
|
||||
Iterable[ChatCompletionContentPartParam]]],
|
||||
) -> Tuple[List[ConversationMessage], List[Awaitable[object]]]:
|
||||
if content is None:
|
||||
return [], []
|
||||
if isinstance(content, str):
|
||||
return [ConversationMessage(role=role, content=content)], []
|
||||
|
||||
texts: List[str] = []
|
||||
for _, part in enumerate(content):
|
||||
if part["type"] == "text":
|
||||
text = part["text"]
|
||||
|
||||
texts.append(text)
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown part type: {part['type']}")
|
||||
|
||||
return [ConversationMessage(role=role, content="\n".join(texts))], []
|
||||
|
||||
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 feature:
|
||||
- function_call (Users should implement this by themselves)
|
||||
"""
|
||||
error_check_ret = await self._check_model(request)
|
||||
if error_check_ret is not None:
|
||||
return error_check_ret
|
||||
|
||||
try:
|
||||
conversation: List[ConversationMessage] = []
|
||||
|
||||
for m in request.messages:
|
||||
messages, _ = self._parse_chat_message_content(
|
||||
m["role"], m["content"])
|
||||
|
||||
conversation.extend(messages)
|
||||
|
||||
prompt = self.tokenizer.apply_chat_template(
|
||||
conversation=conversation,
|
||||
tokenize=False,
|
||||
add_generation_prompt=request.add_generation_prompt,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error in applying chat template from request: %s", e)
|
||||
return self.create_error_response(str(e))
|
||||
|
||||
request_id = f"cmpl-{random_uuid()}"
|
||||
try:
|
||||
# Tokenize/detokenize depending on prompt format (string/token list)
|
||||
prompt_ids, prompt_text = self._validate_prompt_and_tokenize(
|
||||
request, prompt=prompt)
|
||||
sampling_params = request.to_sampling_params()
|
||||
lora_request = self._maybe_get_lora(request)
|
||||
decoding_config = await self.engine.get_decoding_config()
|
||||
guided_decoding_backend = request.guided_decoding_backend \
|
||||
or decoding_config.guided_decoding_backend
|
||||
guided_decode_logits_processor = (
|
||||
await get_guided_decoding_logits_processor(
|
||||
guided_decoding_backend, request, await
|
||||
self.engine.get_tokenizer()))
|
||||
if guided_decode_logits_processor:
|
||||
if sampling_params.logits_processors is None:
|
||||
sampling_params.logits_processors = []
|
||||
sampling_params.logits_processors.append(
|
||||
guided_decode_logits_processor)
|
||||
except ValueError as e:
|
||||
return self.create_error_response(str(e))
|
||||
|
||||
result_generator = self.engine.generate(prompt_text, sampling_params,
|
||||
request_id, prompt_ids,
|
||||
lora_request)
|
||||
# Streaming response
|
||||
if request.stream:
|
||||
return self.chat_completion_stream_generator(
|
||||
request, result_generator, request_id, conversation)
|
||||
else:
|
||||
try:
|
||||
return await self.chat_completion_full_generator(
|
||||
request, raw_request, result_generator, request_id,
|
||||
conversation)
|
||||
except ValueError as e:
|
||||
# TODO: Use a vllm-specific Validation Error
|
||||
return self.create_error_response(str(e))
|
||||
|
||||
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,
|
||||
conversation: List[ConversationMessage]
|
||||
) -> AsyncGenerator[str, None]:
|
||||
model_name = self.served_model_names[0]
|
||||
created_time = int(time.time())
|
||||
chunk_object_type = "chat.completion.chunk"
|
||||
first_iteration = True
|
||||
|
||||
# Send response for each token for each request.n (index)
|
||||
assert request.n is not None
|
||||
previous_texts = [""] * request.n
|
||||
previous_num_tokens = [0] * request.n
|
||||
finish_reason_sent = [False] * request.n
|
||||
try:
|
||||
async for res in result_generator:
|
||||
# We need to do it here, because if there are exceptions in
|
||||
# the result_generator, it needs to be sent as the FIRST
|
||||
# response (by the try...catch).
|
||||
if first_iteration:
|
||||
# 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),
|
||||
logprobs=None,
|
||||
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 conversation and conversation[-1].get(
|
||||
"content") and conversation[-1].get(
|
||||
"role") == role:
|
||||
last_msg_content = conversation[-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],
|
||||
logprobs=None,
|
||||
model=model_name)
|
||||
data = chunk.model_dump_json(
|
||||
exclude_unset=True)
|
||||
yield f"data: {data}\n\n"
|
||||
first_iteration = False
|
||||
|
||||
for output in res.outputs:
|
||||
i = output.index
|
||||
|
||||
if finish_reason_sent[i]:
|
||||
continue
|
||||
|
||||
delta_token_ids = output.token_ids[previous_num_tokens[i]:]
|
||||
top_logprobs = output.logprobs[
|
||||
previous_num_tokens[i]:] if output.logprobs else None
|
||||
|
||||
if request.logprobs:
|
||||
logprobs = self._create_logprobs(
|
||||
token_ids=delta_token_ids,
|
||||
top_logprobs=top_logprobs,
|
||||
num_output_top_logprobs=request.logprobs,
|
||||
initial_text_offset=len(previous_texts[i]),
|
||||
)
|
||||
else:
|
||||
logprobs = None
|
||||
|
||||
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),
|
||||
logprobs=logprobs,
|
||||
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),
|
||||
logprobs=logprobs,
|
||||
finish_reason=output.finish_reason,
|
||||
stop_reason=output.stop_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
|
||||
except ValueError as e:
|
||||
# TODO: Use a vllm-specific Validation Error
|
||||
data = self.create_streaming_error_response(str(e))
|
||||
yield f"data: {data}\n\n"
|
||||
# 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,
|
||||
conversation: List[ConversationMessage]
|
||||
) -> Union[ErrorResponse, ChatCompletionResponse]:
|
||||
|
||||
model_name = self.served_model_names[0]
|
||||
created_time = int(time.time())
|
||||
final_res: Optional[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:
|
||||
token_ids = output.token_ids
|
||||
top_logprobs = output.logprobs
|
||||
|
||||
if request.logprobs:
|
||||
logprobs = self._create_logprobs(
|
||||
token_ids=token_ids,
|
||||
top_logprobs=top_logprobs,
|
||||
num_output_top_logprobs=request.logprobs,
|
||||
)
|
||||
else:
|
||||
logprobs = None
|
||||
|
||||
choice_data = ChatCompletionResponseChoice(
|
||||
index=output.index,
|
||||
message=ChatMessage(role=role, content=output.text),
|
||||
logprobs=logprobs,
|
||||
finish_reason=output.finish_reason,
|
||||
stop_reason=output.stop_reason,
|
||||
)
|
||||
choices.append(choice_data)
|
||||
|
||||
if request.echo:
|
||||
last_msg_content = ""
|
||||
if conversation and conversation[-1].get(
|
||||
"content") and conversation[-1].get("role") == role:
|
||||
last_msg_content = conversation[-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
|
||||
|
||||
async def _load_chat_template(self, chat_template: Optional[str]):
|
||||
while self.tokenizer is None:
|
||||
# Give the parent class time to load the tokenizer
|
||||
await asyncio.sleep(0.1)
|
||||
tokenizer = self.tokenizer
|
||||
|
||||
if chat_template is not None:
|
||||
try:
|
||||
with open(chat_template, "r") as f:
|
||||
tokenizer.chat_template = f.read()
|
||||
except OSError as e:
|
||||
JINJA_CHARS = "{}\n"
|
||||
if not any(c in chat_template for c in JINJA_CHARS):
|
||||
msg = (f"The supplied chat template ({chat_template}) "
|
||||
f"looks like a file path, but it failed to be "
|
||||
f"opened. Reason: {e}")
|
||||
raise ValueError(msg) from e
|
||||
|
||||
# If opening a file fails, set chat template to be args to
|
||||
# ensure we decode so our escape are interpreted correctly
|
||||
tokenizer.chat_template = codecs.decode(
|
||||
chat_template, "unicode_escape")
|
||||
|
||||
logger.info("Using supplied chat template:\n%s",
|
||||
tokenizer.chat_template)
|
||||
elif tokenizer.chat_template is not None:
|
||||
logger.info("Using default chat template:\n%s",
|
||||
tokenizer.chat_template)
|
||||
else:
|
||||
logger.warning(
|
||||
"No chat template provided. Chat API will not work.")
|
||||
Reference in New Issue
Block a user