mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-09 09:45:34 +08:00
546 lines
22 KiB
Python
546 lines
22 KiB
Python
import asyncio
|
|
import time
|
|
from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional
|
|
from typing import Sequence as GenericSequence
|
|
from typing import Tuple, Union, cast
|
|
|
|
from fastapi import Request
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.engine.protocol import EngineClient
|
|
from vllm.entrypoints.logger import RequestLogger
|
|
# yapf conflicts with isort for this block
|
|
# yapf: disable
|
|
from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
|
|
CompletionRequest,
|
|
CompletionResponse,
|
|
CompletionResponseChoice,
|
|
CompletionResponseStreamChoice,
|
|
CompletionStreamResponse,
|
|
ErrorResponse,
|
|
RequestResponseMetadata,
|
|
UsageInfo)
|
|
# yapf: enable
|
|
from vllm.entrypoints.openai.serving_engine import OpenAIServing
|
|
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
|
|
from vllm.logger import init_logger
|
|
from vllm.outputs import RequestOutput
|
|
from vllm.sampling_params import BeamSearchParams, SamplingParams
|
|
from vllm.sequence import Logprob
|
|
from vllm.transformers_utils.tokenizer import AnyTokenizer
|
|
from vllm.utils import merge_async_iterators
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class OpenAIServingCompletion(OpenAIServing):
|
|
|
|
def __init__(
|
|
self,
|
|
engine_client: EngineClient,
|
|
model_config: ModelConfig,
|
|
models: OpenAIServingModels,
|
|
*,
|
|
request_logger: Optional[RequestLogger],
|
|
return_tokens_as_token_ids: bool = False,
|
|
):
|
|
super().__init__(engine_client=engine_client,
|
|
model_config=model_config,
|
|
models=models,
|
|
request_logger=request_logger,
|
|
return_tokens_as_token_ids=return_tokens_as_token_ids)
|
|
diff_sampling_param = self.model_config.get_diff_sampling_param()
|
|
if diff_sampling_param:
|
|
logger.info(
|
|
"Overwriting default completion sampling param with: %s",
|
|
diff_sampling_param)
|
|
|
|
async def create_completion(
|
|
self,
|
|
request: CompletionRequest,
|
|
raw_request: Request,
|
|
) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
|
|
"""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 feature:
|
|
- suffix (the language models we currently support do not support
|
|
suffix)
|
|
"""
|
|
error_check_ret = await self._check_model(request)
|
|
if error_check_ret is not None:
|
|
return error_check_ret
|
|
|
|
# If the engine is dead, raise the engine's DEAD_ERROR.
|
|
# This is required for the streaming case, where we return a
|
|
# success status before we actually start generating text :).
|
|
if self.engine_client.errored:
|
|
raise self.engine_client.dead_error
|
|
|
|
# Return error for unsupported features.
|
|
if request.suffix is not None:
|
|
return self.create_error_response(
|
|
"suffix is not currently supported")
|
|
|
|
request_id = f"cmpl-{self._base_request_id(raw_request)}"
|
|
created_time = int(time.time())
|
|
|
|
request_metadata = RequestResponseMetadata(request_id=request_id)
|
|
if raw_request:
|
|
raw_request.state.request_metadata = request_metadata
|
|
|
|
try:
|
|
(
|
|
lora_request,
|
|
prompt_adapter_request,
|
|
) = self._maybe_get_adapters(request)
|
|
|
|
tokenizer = await self.engine_client.get_tokenizer(lora_request)
|
|
|
|
request_prompts, engine_prompts = await self._preprocess_completion(
|
|
request,
|
|
tokenizer,
|
|
request.prompt,
|
|
truncate_prompt_tokens=request.truncate_prompt_tokens,
|
|
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))
|
|
|
|
# Schedule the request and get the result generator.
|
|
generators: List[AsyncGenerator[RequestOutput, None]] = []
|
|
try:
|
|
for i, engine_prompt in enumerate(engine_prompts):
|
|
sampling_params: Union[SamplingParams, BeamSearchParams]
|
|
default_max_tokens = self.max_model_len - len(
|
|
engine_prompt["prompt_token_ids"])
|
|
# Build default sampling params
|
|
default_sampling_params = (
|
|
self.model_config.get_diff_sampling_param())
|
|
if request.use_beam_search:
|
|
sampling_params = request.to_beam_search_params(
|
|
default_max_tokens, default_sampling_params)
|
|
else:
|
|
sampling_params = request.to_sampling_params(
|
|
default_max_tokens,
|
|
self.model_config.logits_processor_pattern,
|
|
default_sampling_params)
|
|
|
|
request_id_item = f"{request_id}-{i}"
|
|
|
|
self._log_inputs(request_id_item,
|
|
request_prompts[i],
|
|
params=sampling_params,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request)
|
|
|
|
trace_headers = (await
|
|
self._get_trace_headers(raw_request.headers))
|
|
|
|
if isinstance(sampling_params, BeamSearchParams):
|
|
generator = self.engine_client.beam_search(
|
|
prompt=engine_prompt,
|
|
request_id=request_id,
|
|
params=sampling_params,
|
|
)
|
|
else:
|
|
generator = self.engine_client.generate(
|
|
engine_prompt,
|
|
sampling_params,
|
|
request_id_item,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
trace_headers=trace_headers,
|
|
priority=request.priority,
|
|
)
|
|
|
|
generators.append(generator)
|
|
except ValueError as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
return self.create_error_response(str(e))
|
|
|
|
result_generator = merge_async_iterators(*generators)
|
|
|
|
model_name = self.models.model_name(lora_request)
|
|
num_prompts = len(engine_prompts)
|
|
|
|
# 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 self.completion_stream_generator(
|
|
request,
|
|
result_generator,
|
|
request_id,
|
|
created_time,
|
|
model_name,
|
|
num_prompts=num_prompts,
|
|
tokenizer=tokenizer,
|
|
request_metadata=request_metadata)
|
|
|
|
# Non-streaming response
|
|
final_res_batch: List[Optional[RequestOutput]] = [None] * num_prompts
|
|
try:
|
|
async for i, res in result_generator:
|
|
final_res_batch[i] = res
|
|
|
|
for i, final_res in enumerate(final_res_batch):
|
|
assert final_res is not None
|
|
|
|
# The output should contain the input text
|
|
# We did not pass it into vLLM engine to avoid being redundant
|
|
# with the inputs token IDs
|
|
if final_res.prompt is None:
|
|
final_res.prompt = request_prompts[i]["prompt"]
|
|
|
|
final_res_batch_checked = cast(List[RequestOutput],
|
|
final_res_batch)
|
|
|
|
response = self.request_output_to_completion_response(
|
|
final_res_batch_checked,
|
|
request,
|
|
request_id,
|
|
created_time,
|
|
model_name,
|
|
tokenizer,
|
|
request_metadata,
|
|
)
|
|
except asyncio.CancelledError:
|
|
return self.create_error_response("Client disconnected")
|
|
except ValueError as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
return self.create_error_response(str(e))
|
|
|
|
# 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
|
|
|
|
async def completion_stream_generator(
|
|
self,
|
|
request: CompletionRequest,
|
|
result_generator: AsyncIterator[Tuple[int, RequestOutput]],
|
|
request_id: str,
|
|
created_time: int,
|
|
model_name: str,
|
|
num_prompts: int,
|
|
tokenizer: AnyTokenizer,
|
|
request_metadata: RequestResponseMetadata,
|
|
) -> AsyncGenerator[str, None]:
|
|
num_choices = 1 if request.n is None else request.n
|
|
previous_text_lens = [0] * num_choices * num_prompts
|
|
previous_num_tokens = [0] * num_choices * num_prompts
|
|
has_echoed = [False] * num_choices * num_prompts
|
|
num_prompt_tokens = [0] * num_prompts
|
|
|
|
stream_options = request.stream_options
|
|
if stream_options:
|
|
include_usage = stream_options.include_usage
|
|
include_continuous_usage = include_usage and \
|
|
stream_options.continuous_usage_stats
|
|
else:
|
|
include_usage, include_continuous_usage = False, False
|
|
|
|
try:
|
|
async for prompt_idx, res in result_generator:
|
|
prompt_token_ids = res.prompt_token_ids
|
|
prompt_logprobs = res.prompt_logprobs
|
|
prompt_text = res.prompt
|
|
|
|
# Prompt details are excluded from later streamed outputs
|
|
if res.prompt_token_ids is not None:
|
|
num_prompt_tokens[prompt_idx] = len(res.prompt_token_ids)
|
|
|
|
delta_token_ids: GenericSequence[int]
|
|
out_logprobs: Optional[GenericSequence[Optional[Dict[
|
|
int, Logprob]]]]
|
|
|
|
for output in res.outputs:
|
|
i = output.index + prompt_idx * num_choices
|
|
|
|
assert request.max_tokens is not None
|
|
if request.echo and not has_echoed[i]:
|
|
assert prompt_token_ids is not None
|
|
assert prompt_text is not None
|
|
if request.max_tokens == 0:
|
|
# only return the prompt
|
|
delta_text = prompt_text
|
|
delta_token_ids = prompt_token_ids
|
|
out_logprobs = prompt_logprobs
|
|
else:
|
|
assert prompt_logprobs is not None
|
|
# echo the prompt and first token
|
|
delta_text = prompt_text + output.text
|
|
delta_token_ids = [
|
|
*prompt_token_ids, *output.token_ids
|
|
]
|
|
out_logprobs = [
|
|
*prompt_logprobs,
|
|
*(output.logprobs or []),
|
|
]
|
|
has_echoed[i] = True
|
|
else:
|
|
# return just the delta
|
|
delta_text = output.text
|
|
delta_token_ids = output.token_ids
|
|
out_logprobs = output.logprobs
|
|
|
|
if not delta_text and not delta_token_ids \
|
|
and not previous_num_tokens[i]:
|
|
# Chunked prefill case, don't return empty chunks
|
|
continue
|
|
|
|
if request.logprobs is not None:
|
|
assert out_logprobs is not None, (
|
|
"Did not output logprobs")
|
|
logprobs = self._create_completion_logprobs(
|
|
token_ids=delta_token_ids,
|
|
top_logprobs=out_logprobs,
|
|
num_output_top_logprobs=request.logprobs,
|
|
tokenizer=tokenizer,
|
|
initial_text_offset=previous_text_lens[i],
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
previous_text_lens[i] += len(output.text)
|
|
previous_num_tokens[i] += len(output.token_ids)
|
|
finish_reason = output.finish_reason
|
|
stop_reason = output.stop_reason
|
|
|
|
chunk = CompletionStreamResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=[
|
|
CompletionResponseStreamChoice(
|
|
index=i,
|
|
text=delta_text,
|
|
logprobs=logprobs,
|
|
finish_reason=finish_reason,
|
|
stop_reason=stop_reason,
|
|
)
|
|
])
|
|
if include_continuous_usage:
|
|
prompt_tokens = num_prompt_tokens[prompt_idx]
|
|
completion_tokens = previous_num_tokens[i]
|
|
chunk.usage = UsageInfo(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=prompt_tokens + completion_tokens,
|
|
)
|
|
|
|
response_json = chunk.model_dump_json(exclude_unset=False)
|
|
yield f"data: {response_json}\n\n"
|
|
|
|
total_prompt_tokens = sum(num_prompt_tokens)
|
|
total_completion_tokens = sum(previous_num_tokens)
|
|
final_usage_info = UsageInfo(
|
|
prompt_tokens=total_prompt_tokens,
|
|
completion_tokens=total_completion_tokens,
|
|
total_tokens=total_prompt_tokens + total_completion_tokens)
|
|
|
|
if include_usage:
|
|
final_usage_chunk = CompletionStreamResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=[],
|
|
usage=final_usage_info,
|
|
)
|
|
final_usage_data = (final_usage_chunk.model_dump_json(
|
|
exclude_unset=False, exclude_none=True))
|
|
yield f"data: {final_usage_data}\n\n"
|
|
|
|
# report to FastAPI middleware aggregate usage across all choices
|
|
request_metadata.final_usage_info = final_usage_info
|
|
|
|
except Exception as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
data = self.create_streaming_error_response(str(e))
|
|
yield f"data: {data}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
|
|
def request_output_to_completion_response(
|
|
self,
|
|
final_res_batch: List[RequestOutput],
|
|
request: CompletionRequest,
|
|
request_id: str,
|
|
created_time: int,
|
|
model_name: str,
|
|
tokenizer: AnyTokenizer,
|
|
request_metadata: RequestResponseMetadata,
|
|
) -> CompletionResponse:
|
|
choices: List[CompletionResponseChoice] = []
|
|
num_prompt_tokens = 0
|
|
num_generated_tokens = 0
|
|
|
|
for final_res in final_res_batch:
|
|
prompt_token_ids = final_res.prompt_token_ids
|
|
assert prompt_token_ids is not None
|
|
prompt_logprobs = final_res.prompt_logprobs
|
|
if prompt_logprobs:
|
|
for logprob_dict in prompt_logprobs:
|
|
if logprob_dict:
|
|
for logprob_values in logprob_dict.values():
|
|
if logprob_values.logprob == float('-inf'):
|
|
logprob_values.logprob = -9999.0
|
|
prompt_text = final_res.prompt
|
|
|
|
token_ids: GenericSequence[int]
|
|
out_logprobs: Optional[GenericSequence[Optional[Dict[int,
|
|
Logprob]]]]
|
|
|
|
for output in final_res.outputs:
|
|
assert request.max_tokens is not None
|
|
if request.echo:
|
|
assert prompt_text is not None
|
|
if request.max_tokens == 0:
|
|
token_ids = prompt_token_ids
|
|
out_logprobs = prompt_logprobs
|
|
output_text = prompt_text
|
|
else:
|
|
token_ids = [*prompt_token_ids, *output.token_ids]
|
|
|
|
if request.logprobs is None:
|
|
out_logprobs = None
|
|
else:
|
|
assert prompt_logprobs is not None
|
|
assert output.logprobs is not None
|
|
out_logprobs = [
|
|
*prompt_logprobs,
|
|
*output.logprobs,
|
|
]
|
|
|
|
output_text = prompt_text + output.text
|
|
else:
|
|
token_ids = output.token_ids
|
|
out_logprobs = output.logprobs
|
|
output_text = output.text
|
|
|
|
if request.logprobs is not None:
|
|
assert out_logprobs is not None, "Did not output logprobs"
|
|
logprobs = self._create_completion_logprobs(
|
|
token_ids=token_ids,
|
|
top_logprobs=out_logprobs,
|
|
tokenizer=tokenizer,
|
|
num_output_top_logprobs=request.logprobs,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
choice_data = CompletionResponseChoice(
|
|
index=len(choices),
|
|
text=output_text,
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason,
|
|
stop_reason=output.stop_reason,
|
|
prompt_logprobs=final_res.prompt_logprobs,
|
|
)
|
|
choices.append(choice_data)
|
|
|
|
num_generated_tokens += len(output.token_ids)
|
|
|
|
num_prompt_tokens += len(prompt_token_ids)
|
|
|
|
usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_generated_tokens,
|
|
total_tokens=num_prompt_tokens + num_generated_tokens,
|
|
)
|
|
|
|
request_metadata.final_usage_info = usage
|
|
|
|
return CompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
)
|
|
|
|
def _create_completion_logprobs(
|
|
self,
|
|
token_ids: GenericSequence[int],
|
|
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
|
|
num_output_top_logprobs: int,
|
|
tokenizer: AnyTokenizer,
|
|
initial_text_offset: int = 0,
|
|
) -> CompletionLogProbs:
|
|
"""Create logprobs for OpenAI Completion API."""
|
|
out_text_offset: List[int] = []
|
|
out_token_logprobs: List[Optional[float]] = []
|
|
out_tokens: List[str] = []
|
|
out_top_logprobs: List[Optional[Dict[str, float]]] = []
|
|
|
|
last_token_len = 0
|
|
|
|
for i, token_id in enumerate(token_ids):
|
|
step_top_logprobs = top_logprobs[i]
|
|
if step_top_logprobs is None:
|
|
token = tokenizer.decode(token_id)
|
|
if self.return_tokens_as_token_ids:
|
|
token = f"token_id:{token_id}"
|
|
|
|
out_tokens.append(token)
|
|
out_token_logprobs.append(None)
|
|
out_top_logprobs.append(None)
|
|
else:
|
|
step_token = step_top_logprobs[token_id]
|
|
|
|
token = self._get_decoded_token(
|
|
step_token,
|
|
token_id,
|
|
tokenizer,
|
|
return_as_token_id=self.return_tokens_as_token_ids,
|
|
)
|
|
token_logprob = max(step_token.logprob, -9999.0)
|
|
|
|
out_tokens.append(token)
|
|
out_token_logprobs.append(token_logprob)
|
|
|
|
# makes sure to add the top num_output_top_logprobs + 1
|
|
# logprobs, as defined in the openai API
|
|
# (cf. https://github.com/openai/openai-openapi/blob/
|
|
# 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
|
|
out_top_logprobs.append({
|
|
# Convert float("-inf") to the
|
|
# JSON-serializable float that OpenAI uses
|
|
self._get_decoded_token(top_lp[1],
|
|
top_lp[0],
|
|
tokenizer,
|
|
return_as_token_id=self.return_tokens_as_token_ids):
|
|
max(top_lp[1].logprob, -9999.0)
|
|
for i, top_lp in enumerate(step_top_logprobs.items())
|
|
if num_output_top_logprobs >= i
|
|
})
|
|
|
|
if len(out_text_offset) == 0:
|
|
out_text_offset.append(initial_text_offset)
|
|
else:
|
|
out_text_offset.append(out_text_offset[-1] + last_token_len)
|
|
last_token_len = len(token)
|
|
|
|
return CompletionLogProbs(
|
|
text_offset=out_text_offset,
|
|
token_logprobs=out_token_logprobs,
|
|
tokens=out_tokens,
|
|
top_logprobs=out_top_logprobs,
|
|
)
|