mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-19 01:24:30 +08:00
e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
1199 lines
47 KiB
Python
1199 lines
47 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import asyncio
|
|
import copy
|
|
import time
|
|
import weakref
|
|
from functools import partial
|
|
from typing import (Any, AsyncGenerator, Callable, Coroutine, Dict, Iterable,
|
|
List, Mapping, Optional, Set, Tuple, Type, Union, overload)
|
|
from weakref import ReferenceType
|
|
|
|
from typing_extensions import deprecated
|
|
|
|
import vllm.envs as envs
|
|
from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
|
|
ParallelConfig, SchedulerConfig, VllmConfig)
|
|
from vllm.core.scheduler import SchedulerOutputs
|
|
from vllm.engine.arg_utils import AsyncEngineArgs
|
|
from vllm.engine.async_timeout import asyncio_timeout
|
|
from vllm.engine.llm_engine import LLMEngine, SchedulerOutputState
|
|
from vllm.engine.metrics_types import StatLoggerBase
|
|
from vllm.engine.protocol import EngineClient
|
|
from vllm.executor.executor_base import ExecutorBase
|
|
from vllm.inputs import PromptType
|
|
from vllm.inputs.preprocess import InputPreprocessor
|
|
from vllm.logger import init_logger
|
|
from vllm.lora.request import LoRARequest
|
|
from vllm.model_executor.guided_decoding import (
|
|
get_guided_decoding_logits_processor)
|
|
from vllm.model_executor.layers.sampler import SamplerOutput
|
|
from vllm.outputs import PoolingRequestOutput, RequestOutput
|
|
from vllm.pooling_params import PoolingParams
|
|
from vllm.prompt_adapter.request import PromptAdapterRequest
|
|
from vllm.sampling_params import SamplingParams
|
|
from vllm.sequence import ExecuteModelRequest
|
|
from vllm.transformers_utils.tokenizer import AnyTokenizer
|
|
from vllm.usage.usage_lib import UsageContext
|
|
from vllm.utils import deprecate_kwargs, weak_bind
|
|
|
|
logger = init_logger(__name__)
|
|
ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
|
|
|
|
|
|
class AsyncEngineDeadError(RuntimeError):
|
|
pass
|
|
|
|
|
|
def _log_task_completion(task: asyncio.Task,
|
|
error_callback: Callable[[Exception], None]) -> None:
|
|
"""This function is only intended for the `engine.run_engine_loop()` task.
|
|
|
|
In particular, that task runs a `while True` loop that can only exit if
|
|
there is an exception.
|
|
"""
|
|
|
|
exception = None
|
|
try:
|
|
return_value = task.result()
|
|
raise AssertionError(
|
|
f"The engine background task should never finish without an "
|
|
f"exception. {return_value}")
|
|
except asyncio.exceptions.CancelledError:
|
|
# We assume that if the task is cancelled, we are gracefully shutting
|
|
# down. This should only happen on program exit.
|
|
logger.info("Engine is gracefully shutting down.")
|
|
except Exception as e:
|
|
exception = e
|
|
logger.error("Engine background task failed", exc_info=e)
|
|
error_callback(exception)
|
|
raise AsyncEngineDeadError(
|
|
"Task finished unexpectedly. This should never happen! "
|
|
"Please open an issue on Github. See stack trace above for the "
|
|
"actual cause.") from e
|
|
|
|
|
|
STOP_ITERATION = Exception() # Sentinel
|
|
|
|
|
|
class AsyncStream:
|
|
"""A stream of RequestOutputs or PoolingRequestOutputs for a request
|
|
that can be iterated over asynchronously via an async generator."""
|
|
|
|
def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
|
|
self.request_id = request_id
|
|
self._cancel = cancel
|
|
self._queue: asyncio.Queue = asyncio.Queue()
|
|
self._finished = False
|
|
|
|
def put(self, item: Union[RequestOutput, PoolingRequestOutput,
|
|
Exception]) -> None:
|
|
if not self._finished:
|
|
self._queue.put_nowait(item)
|
|
|
|
def finish(
|
|
self,
|
|
exception: Optional[Union[BaseException, Type[BaseException]]] = None,
|
|
) -> None:
|
|
if not self._finished:
|
|
self._finished = True
|
|
self._queue.put_nowait(
|
|
exception if self._is_raisable(exception) else STOP_ITERATION)
|
|
|
|
@property
|
|
def finished(self) -> bool:
|
|
return self._finished
|
|
|
|
async def generator(
|
|
self
|
|
) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
|
|
try:
|
|
while True:
|
|
result = await self._queue.get()
|
|
if self._is_raisable(result):
|
|
if result == STOP_ITERATION:
|
|
return
|
|
raise result
|
|
yield result
|
|
except GeneratorExit:
|
|
self._cancel(self.request_id)
|
|
raise asyncio.CancelledError from None
|
|
|
|
@staticmethod
|
|
def _is_raisable(value: Any):
|
|
return isinstance(value, BaseException) or \
|
|
(isinstance(value, type) and \
|
|
issubclass(value, BaseException))
|
|
|
|
|
|
class RequestTracker:
|
|
"""Synchronous abstraction for tracking requests."""
|
|
|
|
def __init__(self) -> None:
|
|
self._request_streams: Dict[str, AsyncStream] = {}
|
|
self._aborted_requests: asyncio.Queue[str] = asyncio.Queue()
|
|
self._new_requests: asyncio.Queue[Tuple[AsyncStream,
|
|
dict]] = asyncio.Queue()
|
|
self.new_requests_event = asyncio.Event()
|
|
|
|
def __contains__(self, item):
|
|
return item in self._request_streams
|
|
|
|
def __len__(self) -> int:
|
|
return len(self._request_streams)
|
|
|
|
def propagate_exception(self,
|
|
exc: Exception,
|
|
request_id: Optional[str] = None) -> None:
|
|
"""Propagate an exception to request streams
|
|
(all if request_id is None)."""
|
|
if request_id is not None:
|
|
self.abort_request(request_id, exception=exc)
|
|
else:
|
|
# NB: tuple() used here because self.abort_request pops the stream
|
|
# out of self._request_streams, so we can't iterate on it directly
|
|
for rid in tuple(self._request_streams.keys()):
|
|
self.abort_request(rid, exception=exc)
|
|
|
|
def process_request_output(self,
|
|
request_output: Union[RequestOutput,
|
|
PoolingRequestOutput],
|
|
*,
|
|
verbose: bool = False) -> None:
|
|
"""Process a request output from the engine."""
|
|
request_id = request_output.request_id
|
|
finished = request_output.finished
|
|
|
|
if finished:
|
|
stream = self._request_streams.pop(request_id, None)
|
|
else:
|
|
stream = self._request_streams.get(request_id)
|
|
# Guard against a KeyError which can occur if the request was aborted
|
|
# while the output was generated
|
|
if stream is not None:
|
|
stream.put(request_output)
|
|
if finished:
|
|
stream.finish()
|
|
|
|
if verbose and finished:
|
|
logger.info("Finished request %s.", request_id)
|
|
|
|
def process_exception(self,
|
|
request_id: str,
|
|
exception: BaseException,
|
|
*,
|
|
verbose: bool = False) -> None:
|
|
"""Propagate an exception from the engine."""
|
|
if verbose:
|
|
logger.info("Finished request %s.", request_id)
|
|
self.abort_request(request_id, exception=exception)
|
|
|
|
def add_request(self,
|
|
request_id: str,
|
|
*,
|
|
verbose: bool = False,
|
|
**engine_add_request_kwargs) -> AsyncStream:
|
|
"""Add a request to be sent to the engine on the next background
|
|
loop iteration."""
|
|
if request_id in self._request_streams:
|
|
raise KeyError(f"Request {request_id} already exists.")
|
|
|
|
abort_request = partial(self.abort_request, verbose=verbose)
|
|
stream = AsyncStream(request_id, abort_request)
|
|
self._new_requests.put_nowait((stream, {
|
|
"request_id": request_id,
|
|
**engine_add_request_kwargs
|
|
}))
|
|
|
|
self.new_requests_event.set()
|
|
|
|
if verbose:
|
|
logger.info("Added request %s.", request_id)
|
|
|
|
return stream
|
|
|
|
def abort_request(self,
|
|
request_id: str,
|
|
*,
|
|
exception: Optional[Union[BaseException,
|
|
Type[BaseException]]] = None,
|
|
verbose: bool = False) -> None:
|
|
"""Abort a request during next background loop iteration."""
|
|
if verbose:
|
|
logger.info("Aborted request %s.", request_id)
|
|
|
|
self._aborted_requests.put_nowait(request_id)
|
|
|
|
stream = self._request_streams.pop(request_id, None)
|
|
if stream is not None:
|
|
stream.finish(exception=exception)
|
|
|
|
def get_new_and_aborted_requests(self) -> Tuple[List[Dict], Set[str]]:
|
|
"""Get the new requests and finished requests to be
|
|
sent to the engine."""
|
|
new_requests: List[Dict] = []
|
|
finished_requests: Set[str] = set()
|
|
|
|
while not self._aborted_requests.empty():
|
|
request_id = self._aborted_requests.get_nowait()
|
|
finished_requests.add(request_id)
|
|
|
|
while not self._new_requests.empty():
|
|
stream, new_request = self._new_requests.get_nowait()
|
|
request_id = stream.request_id
|
|
if request_id in finished_requests:
|
|
# The request has already been aborted.
|
|
stream.finish(asyncio.CancelledError)
|
|
finished_requests.discard(request_id)
|
|
else:
|
|
self._request_streams[request_id] = stream
|
|
new_requests.append(new_request)
|
|
|
|
return new_requests, finished_requests
|
|
|
|
async def wait_for_new_requests(self):
|
|
if not self.has_new_requests():
|
|
await self.new_requests_event.wait()
|
|
self.new_requests_event.clear()
|
|
|
|
def has_new_requests(self):
|
|
return not self._new_requests.empty()
|
|
|
|
|
|
class _AsyncLLMEngine(LLMEngine):
|
|
"""Extension of LLMEngine to add async methods."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
async def step_async(
|
|
self, virtual_engine: int
|
|
) -> List[Union[RequestOutput, PoolingRequestOutput]]:
|
|
"""Performs one decoding iteration and returns newly generated results.
|
|
The workers are ran asynchronously if possible.
|
|
|
|
This function performs one decoding iteration of the engine. It first
|
|
schedules the sequences to be executed in the next iteration and the
|
|
token blocks to be swapped in/out/copy. Then, it executes the model
|
|
and updates the scheduler with the model outputs. Finally, it decodes
|
|
the sequences and returns the newly generated results.
|
|
"""
|
|
# these are cached outputs from previous iterations. None if on first
|
|
# iteration
|
|
cached_outputs = self.cached_scheduler_outputs[virtual_engine]
|
|
seq_group_metadata_list = cached_outputs.seq_group_metadata_list
|
|
scheduler_outputs = cached_outputs.scheduler_outputs
|
|
allow_async_output_proc = cached_outputs.allow_async_output_proc
|
|
|
|
ctx = self.scheduler_contexts[virtual_engine]
|
|
|
|
# Clear outputs for each new scheduler iteration
|
|
ctx.request_outputs.clear()
|
|
|
|
# skip the scheduler if there are any remaining steps in the seq groups.
|
|
# This ensures that the scheduler is only called again when the current
|
|
# batch has completed.
|
|
if not self._has_remaining_steps(seq_group_metadata_list):
|
|
|
|
# Schedule iteration
|
|
(seq_group_metadata_list, scheduler_outputs,
|
|
allow_async_output_proc
|
|
) = self.scheduler[virtual_engine].schedule()
|
|
|
|
ctx.seq_group_metadata_list = seq_group_metadata_list
|
|
ctx.scheduler_outputs = scheduler_outputs
|
|
|
|
finished_requests_ids = self.scheduler[
|
|
virtual_engine].get_and_reset_finished_requests_ids()
|
|
|
|
# Maybe switch from async mode to sync mode
|
|
if not allow_async_output_proc and len(ctx.output_queue) > 0:
|
|
self._process_model_outputs(ctx=ctx)
|
|
|
|
if (self.scheduler_config.is_multi_step
|
|
and scheduler_outputs.num_lookahead_slots > 0):
|
|
# cache the scheduler outputs for the next iteration if we have
|
|
# lookahead slots
|
|
self._cache_scheduler_outputs_for_multi_step(
|
|
virtual_engine, seq_group_metadata_list, scheduler_outputs,
|
|
allow_async_output_proc)
|
|
else:
|
|
finished_requests_ids = list()
|
|
|
|
assert seq_group_metadata_list is not None
|
|
assert scheduler_outputs is not None
|
|
|
|
if not scheduler_outputs.is_empty():
|
|
|
|
# Check if we have a cached last_output from the previous iteration.
|
|
# For supporting PP this is probably the best way to pass the
|
|
# sampled_token_ids, as a separate broadcast over all the PP stages
|
|
# will cause one virtual engine's microbatch to block the pipeline.
|
|
last_sampled_token_ids = \
|
|
self._get_last_sampled_token_ids(virtual_engine)
|
|
|
|
execute_model_req = ExecuteModelRequest(
|
|
seq_group_metadata_list=seq_group_metadata_list,
|
|
blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
|
|
blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
|
|
blocks_to_copy=scheduler_outputs.blocks_to_copy,
|
|
virtual_engine=virtual_engine,
|
|
num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
|
|
running_queue_size=scheduler_outputs.running_queue_size,
|
|
finished_requests_ids=finished_requests_ids,
|
|
# We use ExecuteModelRequest to pass the last sampled_token_ids
|
|
# to each of the non-last PP stages for in-place prepare_input.
|
|
last_sampled_token_ids=last_sampled_token_ids)
|
|
|
|
if allow_async_output_proc:
|
|
execute_model_req.async_callback = self.async_callbacks[
|
|
virtual_engine]
|
|
|
|
# Execute the model.
|
|
outputs = await self.model_executor.execute_model_async(
|
|
execute_model_req)
|
|
|
|
# we need to do this here so that last step's sampled_token_ids can
|
|
# be passed to the next iteration for PP.
|
|
if self.scheduler_config.is_multi_step:
|
|
self._update_cached_scheduler_output(virtual_engine, outputs)
|
|
else:
|
|
if len(ctx.output_queue) > 0:
|
|
self._process_model_outputs(ctx=ctx)
|
|
outputs = []
|
|
|
|
# Finish the current step for all the sequence groups.
|
|
if self.scheduler_config.is_multi_step:
|
|
for seq_group in seq_group_metadata_list:
|
|
seq_group.finish_step()
|
|
|
|
if not self._has_remaining_steps(seq_group_metadata_list):
|
|
# Clear the cache if we have finished all the steps
|
|
if self.scheduler_config.is_multi_step:
|
|
self.cached_scheduler_outputs[
|
|
virtual_engine] = SchedulerOutputState()
|
|
|
|
# is_first_step_output is True only when the num_steps of all
|
|
# the sequences are 1. When the num_steps > 1,
|
|
# multi_step_model_runner does the first-step output append.
|
|
is_first_step_output: bool = False if not seq_group_metadata_list \
|
|
else seq_group_metadata_list[0].state.num_steps == 1
|
|
|
|
ctx.append_output(outputs=outputs,
|
|
seq_group_metadata_list=seq_group_metadata_list,
|
|
scheduler_outputs=scheduler_outputs,
|
|
is_async=allow_async_output_proc,
|
|
is_last_step=True,
|
|
is_first_step_output=is_first_step_output)
|
|
|
|
if outputs and allow_async_output_proc:
|
|
assert len(
|
|
outputs
|
|
) == 1, "Async postprocessor expects only a single output set"
|
|
self._advance_to_next_step(
|
|
outputs[0], seq_group_metadata_list,
|
|
scheduler_outputs.scheduled_seq_groups)
|
|
|
|
if not allow_async_output_proc:
|
|
self._process_model_outputs(ctx=ctx)
|
|
|
|
# Log stats.
|
|
self.do_log_stats(scheduler_outputs, outputs)
|
|
|
|
# Tracing
|
|
self.do_tracing(scheduler_outputs)
|
|
|
|
else:
|
|
# Multi-step case
|
|
return ctx.request_outputs
|
|
|
|
if not self.has_unfinished_requests():
|
|
# Drain async postprocessor (if exists)
|
|
if len(ctx.output_queue) > 0:
|
|
self._process_model_outputs(ctx=ctx)
|
|
assert len(ctx.output_queue) == 0
|
|
|
|
return ctx.request_outputs
|
|
|
|
async def stop_remote_worker_execution_loop_async(self) -> None:
|
|
"""Stop the remote worker execution loop."""
|
|
await self.model_executor.stop_remote_worker_execution_loop_async()
|
|
|
|
async def get_tokenizer_async(self,
|
|
lora_request: Optional[LoRARequest] = None
|
|
) -> AnyTokenizer:
|
|
return await (
|
|
self.get_tokenizer_group().get_lora_tokenizer_async(lora_request))
|
|
|
|
@overload
|
|
@deprecated("'inputs' will be renamed to 'prompt")
|
|
async def add_request_async(
|
|
self,
|
|
request_id: str,
|
|
*,
|
|
inputs: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> None:
|
|
...
|
|
|
|
@overload
|
|
async def add_request_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> None:
|
|
...
|
|
|
|
@deprecate_kwargs(
|
|
"inputs",
|
|
additional_message="Please use the 'prompt' parameter instead.",
|
|
)
|
|
async def add_request_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: Optional[PromptType] = None,
|
|
params: Optional[Union[SamplingParams, PoolingParams]] = None,
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
*,
|
|
inputs: Optional[PromptType] = None, # DEPRECATED
|
|
) -> None:
|
|
"""Async version of :meth:`add_request`."""
|
|
if inputs is not None:
|
|
prompt = inputs
|
|
assert prompt is not None and params is not None
|
|
|
|
if lora_request is not None and not self.lora_config:
|
|
raise ValueError(f"Got lora_request {lora_request} but LoRA is "
|
|
"not enabled!")
|
|
if priority != 0 and not self.scheduler_config.policy == "priority":
|
|
raise ValueError(f"Got priority {priority} but "
|
|
"Priority scheduling is not enabled.")
|
|
if arrival_time is None:
|
|
arrival_time = time.time()
|
|
|
|
if self.tokenizer is not None:
|
|
tokenizer = await self.get_tokenizer_async(lora_request)
|
|
self._validate_token_prompt(prompt, tokenizer=tokenizer)
|
|
|
|
preprocessed_inputs = await self.input_preprocessor.preprocess_async(
|
|
prompt,
|
|
request_id=request_id,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
processed_inputs = self.input_processor(preprocessed_inputs)
|
|
|
|
if isinstance(params, SamplingParams) and \
|
|
params.guided_decoding is not None:
|
|
# Guided decoding has an async implementation for building logits
|
|
# processors in a separate threadpool.
|
|
# We want to invoke that here instead of using the blocking
|
|
# implementation in the LLMEngine
|
|
params = await build_guided_decoding_logits_processor_async(
|
|
sampling_params=params,
|
|
tokenizer=await self.get_tokenizer_async(lora_request),
|
|
default_guided_backend=self.decoding_config.
|
|
guided_decoding_backend,
|
|
model_config=self.model_config)
|
|
|
|
self._add_processed_request(
|
|
request_id=request_id,
|
|
processed_inputs=processed_inputs,
|
|
params=params,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
)
|
|
|
|
async def check_health_async(self) -> None:
|
|
if self.tokenizer:
|
|
self.tokenizer.check_health()
|
|
self.model_executor.check_health()
|
|
|
|
|
|
async def build_guided_decoding_logits_processor_async(
|
|
sampling_params: SamplingParams, tokenizer: AnyTokenizer,
|
|
default_guided_backend: str,
|
|
model_config: ModelConfig) -> SamplingParams:
|
|
"""Constructs logits processors based on the guided_decoding,
|
|
logits_bias, and allowed_token_ids fields in sampling_params. Deletes
|
|
those fields and adds the constructed logits processors to the
|
|
logits_processors field. Modifies sampling params in-place and returns
|
|
the modified sampling params."""
|
|
if sampling_params.guided_decoding is None:
|
|
return sampling_params
|
|
|
|
# Defensively copy sampling params since guided decoding logits
|
|
# processors can have different state for each request
|
|
sampling_params = copy.copy(sampling_params)
|
|
guided_decoding = sampling_params.guided_decoding
|
|
|
|
logger.debug("Building guided decoding logits processor. "
|
|
"Params: %s", guided_decoding)
|
|
|
|
guided_decoding.backend = guided_decoding.backend or default_guided_backend
|
|
|
|
processor = await get_guided_decoding_logits_processor(
|
|
guided_params=guided_decoding,
|
|
tokenizer=tokenizer,
|
|
model_config=model_config)
|
|
|
|
if processor:
|
|
if sampling_params.logits_processors is None:
|
|
sampling_params.logits_processors = []
|
|
sampling_params.logits_processors.append(processor)
|
|
|
|
# Unset guided decoding params after constructing the lp from them
|
|
sampling_params.guided_decoding = None
|
|
|
|
return sampling_params
|
|
|
|
|
|
class AsyncLLMEngine(EngineClient):
|
|
"""An asynchronous wrapper for :class:`LLMEngine`.
|
|
|
|
This class is used to wrap the :class:`LLMEngine` class to make it
|
|
asynchronous. It uses asyncio to create a background loop that keeps
|
|
processing incoming requests. The :class:`LLMEngine` is kicked by the
|
|
generate method when there are requests in the waiting queue. The generate
|
|
method yields the outputs from the :class:`LLMEngine` to the caller.
|
|
|
|
Args:
|
|
log_requests: Whether to log the requests.
|
|
start_engine_loop: If True, the background task to run the engine
|
|
will be automatically started in the generate call.
|
|
*args: Arguments for :class:`LLMEngine`.
|
|
**kwargs: Arguments for :class:`LLMEngine`.
|
|
"""
|
|
|
|
_engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine
|
|
|
|
def __init__(self,
|
|
*args,
|
|
log_requests: bool = True,
|
|
start_engine_loop: bool = True,
|
|
**kwargs) -> None:
|
|
self.log_requests = log_requests
|
|
self.engine = self._engine_class(*args, **kwargs)
|
|
|
|
# This ensures quick processing of request outputs
|
|
# so the append to asyncio queues is not delayed,
|
|
# especially for multi-step.
|
|
self.use_process_request_outputs_callback = (
|
|
self.engine.model_config.use_async_output_proc)
|
|
|
|
if self.use_process_request_outputs_callback:
|
|
self.engine.process_request_outputs_callback = \
|
|
weak_bind(self.process_request_outputs)
|
|
|
|
self.background_loop: Optional[asyncio.Future] = None
|
|
# We need to keep a reference to unshielded
|
|
# task as well to prevent it from being garbage
|
|
# collected
|
|
self._background_loop_unshielded: Optional[asyncio.Task] = None
|
|
self.start_engine_loop = start_engine_loop
|
|
self._errored_with: Optional[BaseException] = None
|
|
|
|
# Lazy initialized fields
|
|
self._request_tracker: RequestTracker
|
|
|
|
def __del__(self):
|
|
if rt := getattr(self, "request_tracker", None):
|
|
# Wake up engine loop so that it will exit cleanly
|
|
rt.new_requests_event.set()
|
|
|
|
@classmethod
|
|
def _get_executor_cls(cls,
|
|
engine_config: VllmConfig) -> Type[ExecutorBase]:
|
|
return LLMEngine._get_executor_cls(engine_config)
|
|
|
|
@classmethod
|
|
def from_engine_args(
|
|
cls,
|
|
engine_args: AsyncEngineArgs,
|
|
engine_config: Optional[VllmConfig] = None,
|
|
start_engine_loop: bool = True,
|
|
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
|
|
stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
|
|
) -> "AsyncLLMEngine":
|
|
"""Creates an async LLM engine from the engine arguments."""
|
|
# Create the engine configs.
|
|
if engine_config is None:
|
|
engine_config = engine_args.create_engine_config(usage_context)
|
|
|
|
executor_class = cls._get_executor_cls(engine_config)
|
|
|
|
# Create the async LLM engine.
|
|
engine = cls(
|
|
vllm_config=engine_config,
|
|
executor_class=executor_class,
|
|
log_requests=not engine_args.disable_log_requests,
|
|
log_stats=not engine_args.disable_log_stats,
|
|
start_engine_loop=start_engine_loop,
|
|
usage_context=usage_context,
|
|
stat_loggers=stat_loggers,
|
|
)
|
|
return engine
|
|
|
|
@property
|
|
def is_running(self) -> bool:
|
|
return (self.background_loop is not None
|
|
and self._background_loop_unshielded is not None
|
|
and not self._background_loop_unshielded.done())
|
|
|
|
@property
|
|
def is_stopped(self) -> bool:
|
|
return self.errored or (self.background_loop is not None and
|
|
self._background_loop_unshielded is not None
|
|
and self._background_loop_unshielded.done())
|
|
|
|
@property
|
|
def errored(self) -> bool:
|
|
return self._errored_with is not None
|
|
|
|
@property
|
|
def dead_error(self) -> BaseException:
|
|
return AsyncEngineDeadError(
|
|
"Background loop is not running. If it was running, "
|
|
"inspect the output to find the stacktrace of the "
|
|
"error that caused the background loop to stop "
|
|
"(AsyncEngineDeadError).")
|
|
|
|
def set_errored(self, exc: Exception) -> None:
|
|
self._errored_with = exc
|
|
|
|
def _error_callback(self, exc: Exception) -> None:
|
|
self.set_errored(exc)
|
|
self._request_tracker.propagate_exception(exc)
|
|
|
|
async def get_input_preprocessor(self) -> InputPreprocessor:
|
|
return self.engine.input_preprocessor
|
|
|
|
async def get_tokenizer(
|
|
self,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
) -> AnyTokenizer:
|
|
return await self.engine.get_tokenizer_async(lora_request)
|
|
|
|
def start_background_loop(self) -> None:
|
|
"""Start the background loop."""
|
|
if self.errored:
|
|
raise AsyncEngineDeadError(
|
|
"Background loop has errored already.") from self._errored_with
|
|
if self.is_running:
|
|
raise RuntimeError("Background loop is already running.")
|
|
# Initialize the RequestTracker here so it uses the right event loop.
|
|
self._request_tracker = RequestTracker()
|
|
|
|
self._background_loop_unshielded = asyncio.get_event_loop(
|
|
).create_task(self.run_engine_loop(weakref.ref(self)))
|
|
self._background_loop_unshielded.add_done_callback(
|
|
partial(_log_task_completion, error_callback=self._error_callback))
|
|
self.background_loop = asyncio.shield(self._background_loop_unshielded)
|
|
|
|
def shutdown_background_loop(self) -> None:
|
|
"""
|
|
Shut down the background loop.
|
|
|
|
This method needs to be called during cleanup to remove
|
|
references to `self` and properly GC the resources held
|
|
by the async LLM engine (e.g., the executors as well as
|
|
their resources).
|
|
"""
|
|
if self._background_loop_unshielded is not None:
|
|
self._background_loop_unshielded.cancel()
|
|
self._background_loop_unshielded = None
|
|
self.background_loop = None
|
|
|
|
async def engine_step(self, virtual_engine: int) -> bool:
|
|
"""Kick the engine to process the waiting requests.
|
|
|
|
Returns True if there are in-progress requests."""
|
|
|
|
new_requests, aborted_requests = (
|
|
self._request_tracker.get_new_and_aborted_requests())
|
|
|
|
for new_request in new_requests:
|
|
# Add the request into the vLLM engine's waiting queue.
|
|
try:
|
|
await self.engine.add_request_async(**new_request)
|
|
except ValueError as e:
|
|
# TODO: use a vLLM specific error for failed validation
|
|
self._request_tracker.process_exception(
|
|
new_request["request_id"],
|
|
e,
|
|
verbose=self.log_requests,
|
|
)
|
|
|
|
if aborted_requests:
|
|
await self._engine_abort(aborted_requests)
|
|
|
|
request_outputs = await self.engine.step_async(virtual_engine)
|
|
|
|
# Put the outputs into the corresponding streams.
|
|
# If used as a callback, then already invoked inside
|
|
# LLMEngine's _process_model_outputs
|
|
if not self.use_process_request_outputs_callback:
|
|
all_finished = self.process_request_outputs(request_outputs)
|
|
else:
|
|
# For callback case, we only need to detect when all
|
|
# requests are finished
|
|
all_finished = all(request_output.finished
|
|
for request_output in request_outputs)
|
|
|
|
return not all_finished
|
|
|
|
def process_request_outputs(self, request_outputs) -> bool:
|
|
# Put the outputs into the corresponding streams.
|
|
all_finished = True
|
|
for request_output in request_outputs:
|
|
self._request_tracker.process_request_output(
|
|
request_output, verbose=self.log_requests)
|
|
all_finished = all_finished and request_output.finished
|
|
|
|
return all_finished
|
|
|
|
async def _engine_abort(self, request_ids: Iterable[str]):
|
|
self.engine.abort_request(request_ids)
|
|
|
|
@staticmethod
|
|
async def run_engine_loop(engine_ref: ReferenceType):
|
|
"""We use a weakref to the engine so that the running loop
|
|
doesn't prevent the engine being garbage collected."""
|
|
engine: Optional[AsyncLLMEngine] = engine_ref()
|
|
if not engine:
|
|
return
|
|
|
|
pipeline_parallel_size = \
|
|
engine.engine.parallel_config.pipeline_parallel_size
|
|
has_requests_in_progress = [False] * pipeline_parallel_size
|
|
while True:
|
|
if not any(has_requests_in_progress):
|
|
logger.debug("Waiting for new requests...")
|
|
# Stop the execute model loop in parallel workers until there
|
|
# are more requests to process. This avoids waiting
|
|
# indefinitely in torch.distributed ops which may otherwise
|
|
# timeout, and unblocks the RPC thread in the workers so that
|
|
# they can process any other queued control plane messages,
|
|
# such as add/remove lora adapters.
|
|
await engine.engine.stop_remote_worker_execution_loop_async()
|
|
request_tracker = engine._request_tracker
|
|
# Allow engine to be garbage collected while
|
|
# waiting for new requests
|
|
del engine
|
|
await asyncio.sleep(0)
|
|
if engine_ref() is None:
|
|
return
|
|
await request_tracker.wait_for_new_requests()
|
|
engine = engine_ref()
|
|
if not engine:
|
|
return
|
|
logger.debug("Got new requests!")
|
|
requests_in_progress = [
|
|
asyncio.create_task(engine.engine_step(ve))
|
|
for ve in range(pipeline_parallel_size)
|
|
]
|
|
has_requests_in_progress = [True] * pipeline_parallel_size
|
|
|
|
# Abort if iteration takes too long due to unrecoverable errors
|
|
# (eg. NCCL timeouts).
|
|
try:
|
|
async with asyncio_timeout(ENGINE_ITERATION_TIMEOUT_S):
|
|
done, _ = await asyncio.wait(
|
|
requests_in_progress,
|
|
return_when=asyncio.FIRST_COMPLETED)
|
|
for _ in range(pipeline_parallel_size):
|
|
await asyncio.sleep(0)
|
|
for task in done:
|
|
result = task.result()
|
|
virtual_engine = requests_in_progress.index(task)
|
|
has_unfinished_requests = (
|
|
engine.engine.
|
|
has_unfinished_requests_for_virtual_engine(
|
|
virtual_engine))
|
|
if result or has_unfinished_requests:
|
|
requests_in_progress[virtual_engine] = (
|
|
asyncio.create_task(
|
|
engine.engine_step(virtual_engine)))
|
|
has_requests_in_progress[virtual_engine] = True
|
|
else:
|
|
has_requests_in_progress[virtual_engine] = False
|
|
except asyncio.TimeoutError as exc:
|
|
logger.error(
|
|
"Engine iteration timed out. This should never happen!")
|
|
engine.set_errored(exc)
|
|
raise
|
|
await asyncio.sleep(0)
|
|
|
|
# This method does not need to be async, but kept that way
|
|
# for backwards compatibility.
|
|
@overload
|
|
@deprecated("'inputs' will be renamed to 'prompt")
|
|
def add_request(
|
|
self,
|
|
request_id: str,
|
|
*,
|
|
inputs: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> Coroutine[None, None, AsyncGenerator[Union[
|
|
RequestOutput, PoolingRequestOutput], None]]:
|
|
...
|
|
|
|
@overload
|
|
def add_request(
|
|
self,
|
|
request_id: str,
|
|
prompt: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> Coroutine[None, None, AsyncGenerator[Union[
|
|
RequestOutput, PoolingRequestOutput], None]]:
|
|
...
|
|
|
|
@deprecate_kwargs(
|
|
"inputs",
|
|
additional_message="Please use the 'prompt' parameter instead.",
|
|
)
|
|
async def add_request(
|
|
self,
|
|
request_id: str,
|
|
prompt: Optional[PromptType] = None,
|
|
params: Optional[Union[SamplingParams, PoolingParams]] = None,
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
*,
|
|
inputs: Optional[PromptType] = None, # DEPRECATED
|
|
) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
|
|
if inputs is not None:
|
|
prompt = inputs
|
|
assert prompt is not None and params is not None
|
|
|
|
if not self.is_running:
|
|
if self.start_engine_loop:
|
|
self.start_background_loop()
|
|
else:
|
|
raise AsyncEngineDeadError(
|
|
"Background loop is not running. If it was running, "
|
|
"inspect the output to find the stacktrace of the "
|
|
"error that caused the background loop to stop "
|
|
"(AsyncEngineDeadError).")
|
|
|
|
if (priority != 0
|
|
and not self.engine.scheduler_config.policy == "priority"):
|
|
raise ValueError(f"Got priority {priority} but "
|
|
"Priority scheduling is not enabled.")
|
|
|
|
stream = self._request_tracker.add_request(
|
|
request_id,
|
|
verbose=self.log_requests,
|
|
prompt=prompt,
|
|
params=params,
|
|
arrival_time=arrival_time or time.time(),
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
priority=priority,
|
|
)
|
|
|
|
return stream.generator()
|
|
|
|
async def generate(
|
|
self,
|
|
prompt: PromptType,
|
|
sampling_params: SamplingParams,
|
|
request_id: str,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> AsyncGenerator[RequestOutput, None]:
|
|
"""Generate outputs for a request.
|
|
|
|
Generate outputs for a request. This method is a coroutine. It adds the
|
|
request into the waiting queue of the LLMEngine and streams the outputs
|
|
from the LLMEngine to the caller.
|
|
|
|
Args:
|
|
prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
|
|
for more details about the format of each input.
|
|
sampling_params: The sampling parameters of the request.
|
|
request_id: The unique id of the request.
|
|
lora_request: LoRA request to use for generation, if any.
|
|
trace_headers: OpenTelemetry trace headers.
|
|
prompt_adapter_request: Prompt Adapter request to use
|
|
for generation, if any.
|
|
priority: The priority of the request.
|
|
Only applicable with priority scheduling.
|
|
|
|
Yields:
|
|
The output `RequestOutput` objects from the LLMEngine
|
|
for the request.
|
|
|
|
Details:
|
|
- If the engine is not running, start the background loop,
|
|
which iteratively invokes
|
|
:meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`
|
|
to process the waiting requests.
|
|
- Add the request to the engine's `RequestTracker`.
|
|
On the next background loop, this request will be sent to
|
|
the underlying engine.
|
|
Also, a corresponding `AsyncStream` will be created.
|
|
- Wait for the request outputs from `AsyncStream` and yield them.
|
|
|
|
Example:
|
|
>>> # Please refer to entrypoints/api_server.py for
|
|
>>> # the complete example.
|
|
>>>
|
|
>>> # initialize the engine and the example input
|
|
>>> # note that engine_args here is AsyncEngineArgs instance
|
|
>>> engine = AsyncLLMEngine.from_engine_args(engine_args)
|
|
>>> example_input = {
|
|
>>> "prompt": "What is LLM?",
|
|
>>> "stream": False, # assume the non-streaming case
|
|
>>> "temperature": 0.0,
|
|
>>> "request_id": 0,
|
|
>>> }
|
|
>>>
|
|
>>> # start the generation
|
|
>>> results_generator = engine.generate(
|
|
>>> example_input["prompt"],
|
|
>>> SamplingParams(temperature=example_input["temperature"]),
|
|
>>> example_input["request_id"])
|
|
>>>
|
|
>>> # get the results
|
|
>>> final_output = None
|
|
>>> async for request_output in results_generator:
|
|
>>> if await request.is_disconnected():
|
|
>>> # Abort the request if the client disconnects.
|
|
>>> await engine.abort(request_id)
|
|
>>> # Return or raise an error
|
|
>>> ...
|
|
>>> final_output = request_output
|
|
>>>
|
|
>>> # Process and return the final output
|
|
>>> ...
|
|
"""
|
|
try:
|
|
async for output in await self.add_request(
|
|
request_id,
|
|
prompt,
|
|
sampling_params,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
priority=priority,
|
|
):
|
|
yield LLMEngine.validate_output(output, RequestOutput)
|
|
except asyncio.CancelledError:
|
|
await self.abort(request_id)
|
|
raise
|
|
|
|
async def encode(
|
|
self,
|
|
prompt: PromptType,
|
|
pooling_params: PoolingParams,
|
|
request_id: str,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
priority: int = 0,
|
|
) -> AsyncGenerator[PoolingRequestOutput, None]:
|
|
"""Generate outputs for a request from a pooling model.
|
|
|
|
Generate outputs for a request. This method is a coroutine. It adds the
|
|
request into the waiting queue of the LLMEngine and streams the outputs
|
|
from the LLMEngine to the caller.
|
|
|
|
Args:
|
|
prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
|
|
for more details about the format of each input.
|
|
pooling_params: The pooling parameters of the request.
|
|
request_id: The unique id of the request.
|
|
lora_request: LoRA request to use for generation, if any.
|
|
trace_headers: OpenTelemetry trace headers.
|
|
priority: The priority of the request.
|
|
Only applicable with priority scheduling.
|
|
|
|
Yields:
|
|
The output `PoolingRequestOutput` objects from the LLMEngine
|
|
for the request.
|
|
|
|
Details:
|
|
- If the engine is not running, start the background loop,
|
|
which iteratively invokes
|
|
:meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`
|
|
to process the waiting requests.
|
|
- Add the request to the engine's `RequestTracker`.
|
|
On the next background loop, this request will be sent to
|
|
the underlying engine.
|
|
Also, a corresponding `AsyncStream` will be created.
|
|
- Wait for the request outputs from `AsyncStream` and yield them.
|
|
|
|
Example:
|
|
>>> # Please refer to entrypoints/api_server.py for
|
|
>>> # the complete example.
|
|
>>>
|
|
>>> # initialize the engine and the example input
|
|
>>> # note that engine_args here is AsyncEngineArgs instance
|
|
>>> engine = AsyncLLMEngine.from_engine_args(engine_args)
|
|
>>> example_input = {
|
|
>>> "input": "What is LLM?",
|
|
>>> "request_id": 0,
|
|
>>> }
|
|
>>>
|
|
>>> # start the generation
|
|
>>> results_generator = engine.encode(
|
|
>>> example_input["input"],
|
|
>>> PoolingParams(),
|
|
>>> example_input["request_id"])
|
|
>>>
|
|
>>> # get the results
|
|
>>> final_output = None
|
|
>>> async for request_output in results_generator:
|
|
>>> if await request.is_disconnected():
|
|
>>> # Abort the request if the client disconnects.
|
|
>>> await engine.abort(request_id)
|
|
>>> # Return or raise an error
|
|
>>> ...
|
|
>>> final_output = request_output
|
|
>>>
|
|
>>> # Process and return the final output
|
|
>>> ...
|
|
"""
|
|
try:
|
|
async for output in await self.add_request(
|
|
request_id,
|
|
prompt,
|
|
pooling_params,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
):
|
|
yield LLMEngine.validate_output(output, PoolingRequestOutput)
|
|
except asyncio.CancelledError:
|
|
await self.abort(request_id)
|
|
raise
|
|
|
|
async def abort(self, request_id: str) -> None:
|
|
"""Abort a request.
|
|
|
|
Abort a submitted request. If the request is finished or not found,
|
|
this method will be a no-op.
|
|
|
|
Args:
|
|
request_id: The unique id of the request.
|
|
"""
|
|
if not self.is_running:
|
|
raise AsyncEngineDeadError(
|
|
"Background loop is not running. If it was running, "
|
|
"inspect the output to find the stacktrace of the "
|
|
"error that caused the background loop to stop "
|
|
"(AsyncEngineDeadError).")
|
|
|
|
return self._abort(request_id)
|
|
|
|
def _abort(self, request_id: str) -> None:
|
|
"""Abort a request.
|
|
|
|
Abort a submitted request. If the request is finished or not found,
|
|
this method will be a no-op.
|
|
|
|
Args:
|
|
request_id: The unique id of the request.
|
|
"""
|
|
self._request_tracker.abort_request(request_id,
|
|
exception=asyncio.CancelledError,
|
|
verbose=self.log_requests)
|
|
|
|
async def get_model_config(self) -> ModelConfig:
|
|
"""Get the model configuration of the vLLM engine."""
|
|
return self.engine.get_model_config()
|
|
|
|
async def get_parallel_config(self) -> ParallelConfig:
|
|
"""Get the parallel configuration of the vLLM engine."""
|
|
return self.engine.get_parallel_config()
|
|
|
|
async def get_decoding_config(self) -> DecodingConfig:
|
|
"""Get the decoding configuration of the vLLM engine."""
|
|
return self.engine.get_decoding_config()
|
|
|
|
async def get_scheduler_config(self) -> SchedulerConfig:
|
|
"""Get the scheduling configuration of the vLLM engine."""
|
|
return self.engine.get_scheduler_config()
|
|
|
|
async def get_lora_config(self) -> LoRAConfig:
|
|
"""Get the lora configuration of the vLLM engine."""
|
|
return self.engine.get_lora_config()
|
|
|
|
async def do_log_stats(
|
|
self,
|
|
scheduler_outputs: Optional[SchedulerOutputs] = None,
|
|
model_output: Optional[List[SamplerOutput]] = None) -> None:
|
|
self.engine.do_log_stats()
|
|
|
|
async def check_health(self) -> None:
|
|
"""Raises an error if engine is unhealthy."""
|
|
t = time.perf_counter()
|
|
logger.debug("Starting health check...")
|
|
if self.is_stopped:
|
|
raise AsyncEngineDeadError("Background loop is stopped.")
|
|
|
|
await self.engine.check_health_async()
|
|
logger.debug("Health check took %fs", time.perf_counter() - t)
|
|
|
|
async def is_tracing_enabled(self) -> bool:
|
|
return self.engine.is_tracing_enabled()
|
|
|
|
def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
|
|
self.engine.add_logger(logger_name=logger_name, logger=logger)
|
|
|
|
def remove_logger(self, logger_name: str) -> None:
|
|
self.engine.remove_logger(logger_name=logger_name)
|
|
|
|
async def start_profile(self) -> None:
|
|
self.engine.start_profile()
|
|
|
|
async def stop_profile(self) -> None:
|
|
self.engine.stop_profile()
|
|
|
|
async def reset_prefix_cache(self) -> None:
|
|
self.engine.reset_prefix_cache()
|
|
|
|
async def add_lora(self, lora_request: LoRARequest) -> None:
|
|
self.engine.add_lora(lora_request)
|
|
|
|
|
|
# TODO(v1): Remove this class proxy when V1 goes default.
|
|
if envs.VLLM_USE_V1:
|
|
from vllm.v1.engine.async_llm import AsyncLLM
|
|
|
|
AsyncLLMEngine = AsyncLLM # type: ignore
|