291 lines
12 KiB
Python
291 lines
12 KiB
Python
import time
|
|
from fastapi import Request
|
|
from typing import AsyncGenerator, AsyncIterator
|
|
from vllm.logger import init_logger
|
|
from vllm.utils import random_uuid
|
|
from vllm.engine.async_llm_engine import AsyncLLMEngine
|
|
from .protocol import (
|
|
CompletionRequest,
|
|
CompletionResponse,
|
|
CompletionResponseChoice,
|
|
CompletionResponseStreamChoice,
|
|
CompletionStreamResponse,
|
|
LogProbs,
|
|
UsageInfo,
|
|
)
|
|
from vllm.outputs import RequestOutput
|
|
from vllm.entrypoints.openai.serving_engine import OpenAIServing
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
async def completion_stream_generator(
|
|
request: CompletionRequest,
|
|
result_generator: AsyncIterator[RequestOutput],
|
|
echo_without_generation, create_logprobs_fn, request_id, created_time,
|
|
model_name) -> AsyncGenerator[str, None]:
|
|
previous_texts = [""] * request.n
|
|
previous_num_tokens = [0] * request.n
|
|
has_echoed = [False] * request.n
|
|
|
|
async for res in result_generator:
|
|
# TODO: handle client disconnect for streaming
|
|
for output in res.outputs:
|
|
i = output.index
|
|
delta_text = output.text[len(previous_texts[i]):]
|
|
token_ids = output.token_ids[previous_num_tokens[i]:]
|
|
if request.logprobs is not None:
|
|
top_logprobs = output.logprobs[previous_num_tokens[i]:]
|
|
else:
|
|
top_logprobs = None
|
|
offsets = len(previous_texts[i])
|
|
if request.echo and not has_echoed[i]:
|
|
if not echo_without_generation:
|
|
delta_text = res.prompt + delta_text
|
|
token_ids = res.prompt_token_ids + token_ids
|
|
if top_logprobs:
|
|
top_logprobs = res.prompt_logprobs + top_logprobs
|
|
else: # only just return the prompt
|
|
delta_text = res.prompt
|
|
token_ids = res.prompt_token_ids
|
|
if top_logprobs:
|
|
top_logprobs = res.prompt_logprobs
|
|
has_echoed[i] = True
|
|
if request.logprobs is not None:
|
|
logprobs = create_logprobs_fn(
|
|
token_ids=token_ids,
|
|
top_logprobs=top_logprobs,
|
|
num_output_top_logprobs=request.logprobs,
|
|
initial_text_offset=offsets,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
previous_texts[i] = output.text
|
|
previous_num_tokens[i] = len(output.token_ids)
|
|
finish_reason = output.finish_reason
|
|
response_json = CompletionStreamResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=[
|
|
CompletionResponseStreamChoice(
|
|
index=i,
|
|
text=delta_text,
|
|
logprobs=logprobs,
|
|
finish_reason=finish_reason,
|
|
)
|
|
]).json(exclude_unset=True, ensure_ascii=False)
|
|
yield f"data: {response_json}\n\n"
|
|
|
|
if output.finish_reason is not None:
|
|
logprobs = LogProbs() if request.logprobs is not None else None
|
|
prompt_tokens = len(res.prompt_token_ids)
|
|
completion_tokens = len(output.token_ids)
|
|
final_usage = UsageInfo(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=prompt_tokens + completion_tokens,
|
|
)
|
|
response_json = CompletionStreamResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=[
|
|
CompletionResponseStreamChoice(
|
|
index=i,
|
|
text="",
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason,
|
|
)
|
|
],
|
|
usage=final_usage,
|
|
).json(exclude_unset=True, ensure_ascii=False)
|
|
yield f"data: {response_json}\n\n"
|
|
|
|
yield "data: [DONE]\n\n"
|
|
|
|
|
|
def parse_prompt_format(prompt) -> tuple[bool, list]:
|
|
# get the prompt, openai supports the following
|
|
# "a string, array of strings, array of tokens, or array of token arrays."
|
|
prompt_is_tokens = False
|
|
prompts = [prompt] # case 1: a string
|
|
if isinstance(prompt, list):
|
|
if len(prompt) == 0:
|
|
raise ValueError("please provide at least one prompt")
|
|
elif isinstance(prompt[0], str):
|
|
prompt_is_tokens = False
|
|
prompts = prompt # case 2: array of strings
|
|
elif isinstance(prompt[0], int):
|
|
prompt_is_tokens = True
|
|
prompts = [prompt] # case 3: array of tokens
|
|
elif isinstance(prompt[0], list) and isinstance(prompt[0][0], int):
|
|
prompt_is_tokens = True
|
|
prompts = prompt # case 4: array of token arrays
|
|
else:
|
|
raise ValueError(
|
|
"prompt must be a string, array of strings, array of tokens, or array of token arrays"
|
|
)
|
|
return prompt_is_tokens, prompts
|
|
|
|
|
|
def request_output_to_completion_response(final_res: RequestOutput, request,
|
|
echo_without_generation,
|
|
create_logprobs_fn, request_id,
|
|
created_time,
|
|
model_name) -> CompletionResponse:
|
|
assert final_res is not None
|
|
choices = []
|
|
prompt_token_ids = final_res.prompt_token_ids
|
|
prompt_logprobs = final_res.prompt_logprobs
|
|
prompt_text = final_res.prompt
|
|
for output in final_res.outputs:
|
|
if request.logprobs is not None:
|
|
if not echo_without_generation:
|
|
token_ids = output.token_ids
|
|
top_logprobs = output.logprobs
|
|
if request.echo:
|
|
token_ids = prompt_token_ids + token_ids
|
|
top_logprobs = prompt_logprobs + top_logprobs
|
|
else:
|
|
token_ids = prompt_token_ids
|
|
top_logprobs = prompt_logprobs
|
|
logprobs = create_logprobs_fn(
|
|
token_ids=token_ids,
|
|
top_logprobs=top_logprobs,
|
|
num_output_top_logprobs=request.logprobs,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
if not echo_without_generation:
|
|
output_text = output.text
|
|
if request.echo:
|
|
output_text = prompt_text + output_text
|
|
else:
|
|
output_text = prompt_text
|
|
choice_data = CompletionResponseChoice(
|
|
index=output.index,
|
|
text=output_text,
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason,
|
|
)
|
|
choices.append(choice_data)
|
|
|
|
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,
|
|
)
|
|
|
|
return CompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
)
|
|
|
|
|
|
class OpenAIServingCompletion(OpenAIServing):
|
|
|
|
def __init__(self, engine: AsyncLLMEngine, served_model: str):
|
|
super().__init__(engine=engine, served_model=served_model)
|
|
|
|
async def create_completion(self, request: CompletionRequest,
|
|
raw_request: Request):
|
|
"""Completion API similar to OpenAI's API.
|
|
|
|
See https://platform.openai.com/docs/api-reference/completions/create
|
|
for the API specification. This API mimics the OpenAI Completion API.
|
|
|
|
NOTE: Currently we do not support the following features:
|
|
- suffix (the language models we currently support do not support
|
|
suffix)
|
|
- 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
|
|
|
|
# OpenAI API supports echoing the prompt when max_tokens is 0.
|
|
echo_without_generation = request.echo and request.max_tokens == 0
|
|
|
|
# Return error for unsupported features.
|
|
if request.suffix is not None:
|
|
return self.create_error_response(
|
|
"suffix is not currently supported")
|
|
if request.logit_bias is not None and len(request.logit_bias) > 0:
|
|
return self.create_error_response(
|
|
"logit_bias is not currently supported")
|
|
|
|
model_name = request.model
|
|
request_id = f"cmpl-{random_uuid()}"
|
|
created_time = int(time.monotonic())
|
|
|
|
# Schedule the request and get the result generator.
|
|
try:
|
|
sampling_params = request.to_sampling_params()
|
|
|
|
prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
|
|
|
|
if len(prompts) > 1:
|
|
raise ValueError(
|
|
"Batching in completion API is not supported.")
|
|
prompt = prompts[0]
|
|
|
|
if prompt_is_tokens:
|
|
input_ids = self._validate_prompt_and_tokenize(
|
|
request, prompt_ids=prompt)
|
|
else:
|
|
input_ids = self._validate_prompt_and_tokenize(request,
|
|
prompt=prompt)
|
|
|
|
result_generator = self.engine.generate(None,
|
|
sampling_params,
|
|
request_id,
|
|
prompt_token_ids=input_ids)
|
|
except ValueError as e:
|
|
return self.create_error_response(str(e))
|
|
|
|
# Similar to the OpenAI API, when n != best_of, we do not stream the
|
|
# results. In addition, we do not stream the results when use beam search.
|
|
stream = (request.stream
|
|
and (request.best_of is None or request.n == request.best_of)
|
|
and not request.use_beam_search)
|
|
|
|
# Streaming response
|
|
if stream:
|
|
return completion_stream_generator(request, result_generator,
|
|
echo_without_generation,
|
|
self._create_logprobs,
|
|
request_id, created_time,
|
|
model_name)
|
|
|
|
# Non-streaming response
|
|
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
|
|
response = request_output_to_completion_response(
|
|
final_res, request, echo_without_generation, self._create_logprobs,
|
|
request_id, created_time, model_name)
|
|
|
|
# When user requests streaming but we don't stream, we still need to
|
|
# return a streaming response with a single event.
|
|
if request.stream:
|
|
response_json = response.json(ensure_ascii=False)
|
|
|
|
async def fake_stream_generator() -> AsyncGenerator[str, None]:
|
|
yield f"data: {response_json}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
|
|
return fake_stream_generator()
|
|
|
|
return response
|