test-docker / serving_completion.py
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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 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,
)
]).model_dump_json(exclude_unset=True)
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,
).model_dump_json(exclude_unset=True)
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.model_dump_json()
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