4d6ada947c
Co-authored-by: Swapnil Parekh <swapnilp@ibm.com> Co-authored-by: Joe G <joseph.granados@h2o.ai> Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
958 lines
38 KiB
Python
958 lines
38 KiB
Python
import asyncio
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import time
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from functools import partial
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from typing import (AsyncIterator, Callable, Dict, Iterable, List, Optional,
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Set, Tuple, Type, Union)
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from transformers import PreTrainedTokenizer
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import vllm.envs as envs
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from vllm.config import DecodingConfig, ModelConfig
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from vllm.core.scheduler import SchedulerOutputs
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.engine.async_timeout import asyncio_timeout
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from vllm.engine.llm_engine import LLMEngine
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from vllm.executor.ray_utils import initialize_ray_cluster, ray
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from vllm.inputs import LLMInputs, PromptInputs
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.outputs import EmbeddingRequestOutput, RequestOutput
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from vllm.pooling_params import PoolingParams
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import ExecuteModelRequest, SamplerOutput
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from vllm.usage.usage_lib import UsageContext
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logger = init_logger(__name__)
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ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
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class AsyncEngineDeadError(RuntimeError):
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pass
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def _log_task_completion(task: asyncio.Task,
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error_callback: Callable[[Exception], None]) -> None:
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"""This function is only intended for the `engine.run_engine_loop()` task.
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In particular, that task runs a `while True` loop that can only exit if
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there is an exception.
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"""
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exception = None
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try:
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return_value = task.result()
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raise AssertionError(
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f"The engine background task should never finish without an "
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f"exception. {return_value}")
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except asyncio.exceptions.CancelledError:
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# We assume that if the task is cancelled, we are gracefully shutting
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# down. This should only happen on program exit.
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logger.info("Engine is gracefully shutting down.")
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except Exception as e:
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exception = e
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logger.error("Engine background task failed", exc_info=e)
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error_callback(exception)
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raise AsyncEngineDeadError(
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"Task finished unexpectedly. This should never happen! "
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"Please open an issue on Github. See stack trace above for the"
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"actual cause.") from e
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class AsyncStream:
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"""A stream of RequestOutputs or EmbeddingRequestOutputs for a request
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that can be iterated over asynchronously."""
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def __init__(self, request_id: str) -> None:
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self.request_id = request_id
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self._queue: asyncio.Queue = asyncio.Queue()
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self._finished = False
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def put(self, item: Union[RequestOutput, EmbeddingRequestOutput,
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Exception]) -> None:
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if self._finished:
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return
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self._queue.put_nowait(item)
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def finish(self) -> None:
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self._queue.put_nowait(StopAsyncIteration())
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self._finished = True
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@property
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def finished(self) -> bool:
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return self._finished
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def __aiter__(self):
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return self
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async def __anext__(self) -> Union[RequestOutput, EmbeddingRequestOutput]:
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result = await self._queue.get()
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if isinstance(result, Exception):
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raise result
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return result
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class RequestTracker:
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"""Synchronous abstraction for tracking requests."""
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def __init__(self) -> None:
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self._request_streams: Dict[str, AsyncStream] = {}
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self._finished_requests: asyncio.Queue[str] = asyncio.Queue()
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self._new_requests: asyncio.Queue[Tuple[AsyncStream,
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dict]] = asyncio.Queue()
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self.new_requests_event = asyncio.Event()
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def __contains__(self, item):
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return item in self._request_streams
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def __len__(self) -> int:
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return len(self._request_streams)
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def propagate_exception(self,
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exc: Exception,
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request_id: Optional[str] = None) -> None:
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"""Propagate an exception to request streams
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(all if request_id is None)."""
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if request_id is not None:
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self._request_streams[request_id].put(exc)
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self.abort_request(request_id)
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else:
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for rid, stream in self._request_streams.items():
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stream.put(exc)
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self.abort_request(rid)
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def process_request_output(self,
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request_output: Union[RequestOutput,
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EmbeddingRequestOutput],
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*,
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verbose: bool = False) -> None:
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"""Process a request output from the engine."""
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request_id = request_output.request_id
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self._request_streams[request_id].put(request_output)
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if request_output.finished:
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if verbose:
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logger.info("Finished request %s.", request_id)
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self.abort_request(request_id)
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def process_exception(self,
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request_id: str,
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exception: Exception,
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*,
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verbose: bool = False) -> None:
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"""Propagate an exception from the engine."""
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self._request_streams[request_id].put(exception)
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if verbose:
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logger.info("Finished request %s.", request_id)
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self.abort_request(request_id)
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def add_request(self, request_id: str,
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**engine_add_request_kwargs) -> AsyncStream:
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"""Add a request to be sent to the engine on the next background
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loop iteration."""
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if request_id in self._request_streams:
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raise KeyError(f"Request {request_id} already exists.")
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stream = AsyncStream(request_id)
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self._new_requests.put_nowait((stream, {
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"request_id": request_id,
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**engine_add_request_kwargs
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}))
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self.new_requests_event.set()
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return stream
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def abort_request(self, request_id: str, *, verbose: bool = False) -> None:
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"""Abort a request during next background loop iteration."""
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if verbose:
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logger.info("Aborted request %s.", request_id)
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self._finished_requests.put_nowait(request_id)
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if request_id not in self._request_streams or self._request_streams[
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request_id].finished:
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# The request has already finished or been aborted.
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return
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self._request_streams[request_id].finish()
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def get_new_and_finished_requests(self) -> Tuple[List[Dict], Set[str]]:
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"""Get the new requests and finished requests to be
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sent to the engine."""
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new_requests: List[Dict] = []
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finished_requests: Set[str] = set()
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while not self._finished_requests.empty():
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request_id = self._finished_requests.get_nowait()
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finished_requests.add(request_id)
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self._request_streams.pop(request_id, None)
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while not self._new_requests.empty():
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stream, new_request = self._new_requests.get_nowait()
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if stream.request_id in finished_requests:
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# The request has already been aborted.
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stream.finish()
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continue
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self._request_streams[stream.request_id] = stream
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new_requests.append(new_request)
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return new_requests, finished_requests
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async def wait_for_new_requests(self):
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if not self.has_new_requests():
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await self.new_requests_event.wait()
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self.new_requests_event.clear()
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def has_new_requests(self):
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return not self._new_requests.empty()
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class _AsyncLLMEngine(LLMEngine):
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"""Extension of LLMEngine to add async methods."""
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async def step_async(
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self, virtual_engine: int
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) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
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"""Performs one decoding iteration and returns newly generated results.
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The workers are ran asynchronously if possible.
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This function performs one decoding iteration of the engine. It first
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schedules the sequences to be executed in the next iteration and the
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token blocks to be swapped in/out/copy. Then, it executes the model
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and updates the scheduler with the model outputs. Finally, it decodes
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the sequences and returns the newly generated results.
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"""
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seq_group_metadata_list, scheduler_outputs = self.scheduler[
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virtual_engine].schedule()
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finished_requests_ids = self.scheduler[
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virtual_engine].get_and_reset_finished_requests_ids()
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if not scheduler_outputs.is_empty():
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# Execute the model.
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execute_model_req = ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
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blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
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blocks_to_copy=scheduler_outputs.blocks_to_copy,
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virtual_engine=virtual_engine,
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num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
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running_queue_size=scheduler_outputs.running_queue_size,
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finished_requests_ids=finished_requests_ids)
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output = await self.model_executor.execute_model_async(
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execute_model_req)
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else:
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output = []
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request_outputs = self._process_model_outputs(
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output, scheduler_outputs.scheduled_seq_groups,
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scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
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# Log stats.
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self.do_log_stats(scheduler_outputs, output)
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# Tracing
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self.do_tracing(scheduler_outputs)
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return request_outputs
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async def stop_remote_worker_execution_loop_async(self) -> None:
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"""Stop the remote worker execution loop."""
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await self.model_executor.stop_remote_worker_execution_loop_async()
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async def process_model_inputs_async(
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self,
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request_id: str,
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inputs: PromptInputs,
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lora_request: Optional[LoRARequest] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None,
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) -> LLMInputs:
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if isinstance(inputs, str):
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inputs = {"prompt": inputs}
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if "prompt_token_ids" not in inputs:
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tokenizer = self.get_tokenizer_group("prompts must be None if "
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"skip_tokenizer_init is True")
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prompt_token_ids = await tokenizer.encode_async(
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request_id=request_id,
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prompt=inputs["prompt"],
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lora_request=lora_request)
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else:
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prompt_token_ids = inputs["prompt_token_ids"]
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if prompt_adapter_request:
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prompt_token_ids = [
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0
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] * prompt_adapter_request.prompt_adapter_num_virtual_tokens + \
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prompt_token_ids
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llm_inputs = LLMInputs(prompt_token_ids=prompt_token_ids,
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prompt=inputs.get("prompt"),
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multi_modal_data=inputs.get("multi_modal_data"))
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return self.input_processor(llm_inputs)
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async def add_request_async(
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self,
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request_id: str,
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inputs: PromptInputs,
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params: Union[SamplingParams, PoolingParams],
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arrival_time: Optional[float] = None,
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lora_request: Optional[LoRARequest] = None,
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trace_headers: Optional[Dict[str, str]] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None
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) -> None:
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if lora_request is not None and not self.lora_config:
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raise ValueError(f"Got lora_request {lora_request} but LoRA is "
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"not enabled!")
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if arrival_time is None:
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arrival_time = time.time()
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processed_inputs = await self.process_model_inputs_async(
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request_id=request_id,
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inputs=inputs,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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self._add_processed_request(
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request_id=request_id,
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processed_inputs=processed_inputs,
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params=params,
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arrival_time=arrival_time,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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trace_headers=trace_headers,
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)
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async def check_health_async(self) -> None:
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if self.tokenizer:
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self.tokenizer.check_health()
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self.model_executor.check_health()
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class AsyncLLMEngine:
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"""An asynchronous wrapper for :class:`LLMEngine`.
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This class is used to wrap the :class:`LLMEngine` class to make it
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asynchronous. It uses asyncio to create a background loop that keeps
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processing incoming requests. The :class:`LLMEngine` is kicked by the
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generate method when there are requests in the waiting queue. The generate
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method yields the outputs from the :class:`LLMEngine` to the caller.
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Args:
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worker_use_ray: Whether to use Ray for model workers. Required for
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distributed execution. Should be the same as
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`parallel_config.worker_use_ray`.
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engine_use_ray: Whether to make LLMEngine a Ray actor. If so, the
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async frontend will be executed in a separate process as the
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model workers.
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log_requests: Whether to log the requests.
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max_log_len: Maximum number of prompt characters or prompt ID numbers
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being printed in log.
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start_engine_loop: If True, the background task to run the engine
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will be automatically started in the generate call.
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*args: Arguments for :class:`LLMEngine`.
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**kwargs: Arguments for :class:`LLMEngine`.
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"""
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_engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine
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def __init__(self,
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worker_use_ray: bool,
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engine_use_ray: bool,
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*args,
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log_requests: bool = True,
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max_log_len: Optional[int] = None,
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start_engine_loop: bool = True,
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**kwargs) -> None:
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self.worker_use_ray = worker_use_ray
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self.engine_use_ray = engine_use_ray
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self.log_requests = log_requests
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self.max_log_len = max_log_len
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self.engine = self._init_engine(*args, **kwargs)
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self.background_loop: Optional[asyncio.Future] = None
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# We need to keep a reference to unshielded
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# task as well to prevent it from being garbage
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# collected
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self._background_loop_unshielded: Optional[asyncio.Task] = None
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self.start_engine_loop = start_engine_loop
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self._errored_with: Optional[BaseException] = None
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# Lazy initialized fields
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self._request_tracker: RequestTracker
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@classmethod
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def from_engine_args(
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cls,
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engine_args: AsyncEngineArgs,
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start_engine_loop: bool = True,
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usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
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) -> "AsyncLLMEngine":
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"""Creates an async LLM engine from the engine arguments."""
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# Create the engine configs.
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engine_config = engine_args.create_engine_config()
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if engine_args.engine_use_ray:
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from vllm.executor import ray_utils
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ray_utils.assert_ray_available()
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distributed_executor_backend = (
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engine_config.parallel_config.distributed_executor_backend)
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if engine_config.device_config.device_type == "neuron":
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from vllm.executor.neuron_executor import NeuronExecutorAsync
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executor_class = NeuronExecutorAsync
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elif engine_config.device_config.device_type == "tpu":
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from vllm.executor.tpu_executor import TPUExecutorAsync
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executor_class = TPUExecutorAsync
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elif engine_config.device_config.device_type == "cpu":
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assert distributed_executor_backend is None, (
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"Distributed execution is not supported with the CPU backend.")
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from vllm.executor.cpu_executor import CPUExecutorAsync
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executor_class = CPUExecutorAsync
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elif engine_config.device_config.device_type == "openvino":
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assert distributed_executor_backend is None, (
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"Distributed execution is not supported with "
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"the OpenVINO backend.")
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from vllm.executor.openvino_executor import OpenVINOExecutorAsync
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executor_class = OpenVINOExecutorAsync
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elif engine_config.device_config.device_type == "xpu":
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if distributed_executor_backend is None:
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from vllm.executor.xpu_executor import XPUExecutorAsync
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executor_class = XPUExecutorAsync
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elif distributed_executor_backend == "ray":
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initialize_ray_cluster(engine_config.parallel_config)
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from vllm.executor.ray_xpu_executor import RayXPUExecutorAsync
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executor_class = RayXPUExecutorAsync
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else:
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raise RuntimeError(
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"Not supported distributed execution model on XPU device.")
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elif distributed_executor_backend == "ray":
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initialize_ray_cluster(engine_config.parallel_config)
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from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync
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executor_class = RayGPUExecutorAsync
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elif distributed_executor_backend == "mp":
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from vllm.executor.multiproc_gpu_executor import (
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MultiprocessingGPUExecutorAsync)
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executor_class = MultiprocessingGPUExecutorAsync
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else:
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from vllm.executor.gpu_executor import GPUExecutorAsync
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executor_class = GPUExecutorAsync
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# Create the async LLM engine.
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engine = cls(
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distributed_executor_backend == "ray",
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engine_args.engine_use_ray,
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**engine_config.to_dict(),
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executor_class=executor_class,
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log_requests=not engine_args.disable_log_requests,
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log_stats=not engine_args.disable_log_stats,
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max_log_len=engine_args.max_log_len,
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start_engine_loop=start_engine_loop,
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usage_context=usage_context,
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)
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return engine
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@property
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def is_running(self) -> bool:
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return (self.background_loop is not None
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and self._background_loop_unshielded is not None
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and not self._background_loop_unshielded.done())
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@property
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def is_stopped(self) -> bool:
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return self.errored or (self.background_loop is not None and
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self._background_loop_unshielded is not None
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and self._background_loop_unshielded.done())
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@property
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def errored(self) -> bool:
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return self._errored_with is not None
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def set_errored(self, exc: Exception) -> None:
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self._errored_with = exc
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def _error_callback(self, exc: Exception) -> None:
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self.set_errored(exc)
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self._request_tracker.propagate_exception(exc)
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async def get_tokenizer(self) -> "PreTrainedTokenizer":
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if self.engine_use_ray:
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return await self.engine.get_tokenizer.remote() # type: ignore
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else:
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return self.engine.get_tokenizer()
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def start_background_loop(self) -> None:
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"""Start the background loop."""
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if self.errored:
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raise AsyncEngineDeadError(
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"Background loop has errored already.") from self._errored_with
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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())
|
|
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 _init_engine(self, *args,
|
|
**kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]:
|
|
if not self.engine_use_ray:
|
|
engine_class = self._engine_class
|
|
elif self.worker_use_ray:
|
|
engine_class = ray.remote(num_cpus=0)(self._engine_class).remote
|
|
else:
|
|
# FIXME(woosuk): This is a bit hacky. Be careful when changing the
|
|
# order of the arguments.
|
|
cache_config = kwargs["cache_config"]
|
|
parallel_config = kwargs["parallel_config"]
|
|
if (parallel_config.tensor_parallel_size == 1
|
|
and parallel_config.pipeline_parallel_size == 1):
|
|
num_gpus = cache_config.gpu_memory_utilization
|
|
else:
|
|
num_gpus = 1
|
|
engine_class = ray.remote(num_gpus=num_gpus)(
|
|
self._engine_class).remote
|
|
return engine_class(*args, **kwargs)
|
|
|
|
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, finished_requests = (
|
|
self._request_tracker.get_new_and_finished_requests())
|
|
|
|
for new_request in new_requests:
|
|
# Add the request into the vLLM engine's waiting queue.
|
|
# TODO: Maybe add add_request_batch to reduce Ray overhead
|
|
try:
|
|
if self.engine_use_ray:
|
|
await self.engine.add_request.remote( # type: ignore
|
|
**new_request)
|
|
else:
|
|
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 finished_requests:
|
|
await self._engine_abort(finished_requests)
|
|
|
|
if self.engine_use_ray:
|
|
request_outputs = await self.engine.step.remote() # type: ignore
|
|
else:
|
|
request_outputs = await self.engine.step_async(virtual_engine)
|
|
|
|
# Put the outputs into the corresponding streams.
|
|
for request_output in request_outputs:
|
|
self._request_tracker.process_request_output(
|
|
request_output, verbose=self.log_requests)
|
|
|
|
return len(request_outputs) > 0
|
|
|
|
async def _engine_abort(self, request_ids: Iterable[str]):
|
|
if self.engine_use_ray:
|
|
await self.engine.abort_request.remote(request_ids) # type: ignore
|
|
else:
|
|
self.engine.abort_request(request_ids)
|
|
|
|
async def run_engine_loop(self):
|
|
if self.engine_use_ray:
|
|
pipeline_parallel_size = 1 # type: ignore
|
|
else:
|
|
pipeline_parallel_size = \
|
|
self.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.
|
|
if self.engine_use_ray:
|
|
await (self.engine.stop_remote_worker_execution_loop.
|
|
remote() # type: ignore
|
|
)
|
|
else:
|
|
await self.engine.stop_remote_worker_execution_loop_async()
|
|
await self._request_tracker.wait_for_new_requests()
|
|
logger.debug("Got new requests!")
|
|
requests_in_progress = [
|
|
asyncio.create_task(self.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)
|
|
if self.engine_use_ray:
|
|
has_unfinished_requests = (
|
|
await (self.engine.
|
|
has_unfinished_requests_for_virtual_engine.
|
|
remote( # type: ignore
|
|
virtual_engine)))
|
|
else:
|
|
has_unfinished_requests = (
|
|
self.engine.
|
|
has_unfinished_requests_for_virtual_engine(
|
|
virtual_engine))
|
|
if result or has_unfinished_requests:
|
|
requests_in_progress[virtual_engine] = (
|
|
asyncio.create_task(
|
|
self.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!")
|
|
self.set_errored(exc)
|
|
raise
|
|
await asyncio.sleep(0)
|
|
|
|
async def add_request(
|
|
self,
|
|
request_id: str,
|
|
inputs: PromptInputs,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Dict[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None
|
|
) -> AsyncStream:
|
|
if self.log_requests:
|
|
if isinstance(inputs, str):
|
|
shortened_prompt = inputs
|
|
shortened_token_ids = None
|
|
else:
|
|
shortened_prompt = inputs.get("prompt")
|
|
shortened_token_ids = inputs.get("prompt_token_ids")
|
|
|
|
max_log_len = self.max_log_len
|
|
if max_log_len is not None:
|
|
if shortened_prompt is not None:
|
|
shortened_prompt = shortened_prompt[:max_log_len]
|
|
if shortened_token_ids is not None:
|
|
shortened_token_ids = shortened_token_ids[:max_log_len]
|
|
|
|
logger.info(
|
|
"Received request %s: prompt: %r, "
|
|
"params: %s, prompt_token_ids: %s, "
|
|
"lora_request: %s.", request_id, shortened_prompt, params,
|
|
shortened_token_ids, lora_request)
|
|
|
|
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 arrival_time is None:
|
|
arrival_time = time.time()
|
|
|
|
stream = self._request_tracker.add_request(
|
|
request_id,
|
|
inputs=inputs,
|
|
params=params,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
prompt_adapter_request=prompt_adapter_request)
|
|
|
|
return stream
|
|
|
|
async def generate(
|
|
self,
|
|
inputs: PromptInputs,
|
|
sampling_params: SamplingParams,
|
|
request_id: str,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Dict[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None
|
|
) -> AsyncIterator[RequestOutput]:
|
|
"""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:
|
|
inputs: The inputs to the LLM. See
|
|
:class:`~vllm.inputs.PromptInputs`
|
|
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.
|
|
|
|
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
|
|
>>> 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
|
|
>>> ...
|
|
"""
|
|
async for output in self._process_request(
|
|
request_id,
|
|
inputs,
|
|
sampling_params,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
):
|
|
yield LLMEngine.validate_output(output, RequestOutput)
|
|
|
|
async def encode(
|
|
self,
|
|
inputs: PromptInputs,
|
|
pooling_params: PoolingParams,
|
|
request_id: str,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Dict[str, str]] = None,
|
|
) -> AsyncIterator[EmbeddingRequestOutput]:
|
|
"""Generate outputs for a request from an embedding 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:
|
|
inputs: The inputs to the LLM. See
|
|
:class:`~vllm.inputs.PromptInputs`
|
|
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.
|
|
|
|
Yields:
|
|
The output `EmbeddingRequestOutput` 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
|
|
>>> 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
|
|
>>> ...
|
|
"""
|
|
async for output in self._process_request(
|
|
request_id,
|
|
inputs,
|
|
pooling_params,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
):
|
|
yield LLMEngine.validate_output(output, EmbeddingRequestOutput)
|
|
|
|
async def _process_request(
|
|
self,
|
|
request_id: str,
|
|
inputs: PromptInputs,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
*,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Dict[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
) -> AsyncIterator[Union[RequestOutput, EmbeddingRequestOutput]]:
|
|
"""Common logic to process requests with SamplingParams or
|
|
PoolingParams."""
|
|
arrival_time = time.time()
|
|
|
|
stream = await self.add_request(
|
|
request_id,
|
|
inputs,
|
|
params,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
|
|
try:
|
|
async for request_output in stream:
|
|
yield request_output
|
|
except (Exception, asyncio.CancelledError) as e:
|
|
self._abort(request_id)
|
|
raise e
|
|
|
|
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,
|
|
verbose=self.log_requests)
|
|
|
|
async def get_model_config(self) -> ModelConfig:
|
|
"""Get the model configuration of the vLLM engine."""
|
|
if self.engine_use_ray:
|
|
return await self.engine.get_model_config.remote() # type: ignore
|
|
else:
|
|
return self.engine.get_model_config()
|
|
|
|
async def get_decoding_config(self) -> DecodingConfig:
|
|
"""Get the decoding configuration of the vLLM engine."""
|
|
if self.engine_use_ray:
|
|
return await self.engine.get_decoding_config.remote( # type: ignore
|
|
)
|
|
else:
|
|
return self.engine.get_decoding_config()
|
|
|
|
async def do_log_stats(
|
|
self,
|
|
scheduler_outputs: Optional[SchedulerOutputs] = None,
|
|
model_output: Optional[List[SamplerOutput]] = None) -> None:
|
|
if self.engine_use_ray:
|
|
await self.engine.do_log_stats.remote( # type: ignore
|
|
scheduler_outputs, model_output)
|
|
else:
|
|
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.")
|
|
|
|
if self.engine_use_ray:
|
|
try:
|
|
await self.engine.check_health.remote() # type: ignore
|
|
except ray.exceptions.RayActorError as e:
|
|
raise RuntimeError("Engine is dead.") from e
|
|
else:
|
|
await self.engine.check_health_async()
|
|
logger.debug("Health check took %fs", time.perf_counter() - t)
|
|
|
|
async def is_tracing_enabled(self) -> bool:
|
|
if self.engine_use_ray:
|
|
return await self.engine.is_tracing_enabled.remote( # type: ignore
|
|
)
|
|
else:
|
|
return self.engine.is_tracing_enabled()
|