2b05b8ce69
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Signed-off-by: Andrew Feldman <afeldman@neuralmagic.com> Signed-off-by: Nick Hill <nhill@redhat.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Russell Bryant <rbryant@redhat.com> Co-authored-by: Andrew Feldman <afeldman@neuralmagic.com> Co-authored-by: afeldman-nm <156691304+afeldman-nm@users.noreply.github.com> Co-authored-by: Nick Hill <nhill@redhat.com>
509 lines
20 KiB
Python
509 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import logging
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from collections.abc import AsyncGenerator, Mapping
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from copy import copy
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from typing import Optional, Union
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import numpy as np
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import vllm.envs as envs
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from vllm.config import ModelConfig, VllmConfig
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.engine.protocol import EngineClient
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from vllm.envs import VLLM_V1_OUTPUT_PROC_CHUNK_SIZE
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from vllm.inputs import PromptType
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from vllm.inputs.preprocess import InputPreprocessor
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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.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
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from vllm.outputs import 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.transformers_utils.tokenizer import AnyTokenizer
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from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import Device, cdiv
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.core_client import AsyncMPClient, DPAsyncMPClient
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from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
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from vllm.v1.engine.output_processor import (OutputProcessor,
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RequestOutputCollector)
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from vllm.v1.engine.parallel_sampling import ParentRequest
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from vllm.v1.engine.processor import Processor
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from vllm.v1.executor.abstract import Executor
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from vllm.v1.metrics.loggers import (LoggingStatLogger, PrometheusStatLogger,
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StatLoggerBase)
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from vllm.v1.metrics.stats import IterationStats, SchedulerStats
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logger = init_logger(__name__)
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class AsyncLLM(EngineClient):
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def __init__(
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self,
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vllm_config: VllmConfig,
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executor_class: type[Executor],
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log_stats: bool,
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usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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use_cached_outputs: bool = False,
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log_requests: bool = True,
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start_engine_loop: bool = True,
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) -> None:
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if not envs.VLLM_USE_V1:
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raise ValueError(
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"Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
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"This should not happen. As a workaround, try using "
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"AsyncLLMEngine.from_vllm_config(...) or explicitly set "
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"VLLM_USE_V1=0 or 1 and report this issue on Github.")
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self.model_config = vllm_config.model_config
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self.vllm_config = vllm_config
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self.log_requests = log_requests
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self.log_stats = log_stats
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# Set up stat loggers; independent set for each DP rank.
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self.stat_loggers: list[list[StatLoggerBase]] = []
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if self.log_stats:
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for i in range(vllm_config.parallel_config.data_parallel_size):
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loggers: list[StatLoggerBase] = []
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if logger.isEnabledFor(logging.INFO):
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loggers.append(LoggingStatLogger(engine_index=i))
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loggers.append(
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PrometheusStatLogger(vllm_config, engine_index=i))
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self.stat_loggers.append(loggers)
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# Tokenizer (+ ensure liveness if running in another process).
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self.tokenizer = init_tokenizer_from_configs(
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model_config=vllm_config.model_config,
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scheduler_config=vllm_config.scheduler_config,
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parallel_config=vllm_config.parallel_config,
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lora_config=vllm_config.lora_config)
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self.tokenizer.ping()
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# Processor (converts Inputs --> EngineCoreRequests).
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self.processor = Processor(
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vllm_config=vllm_config,
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tokenizer=self.tokenizer,
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mm_registry=mm_registry,
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)
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# OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
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self.output_processor = OutputProcessor(self.tokenizer,
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log_stats=self.log_stats)
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# EngineCore (starts the engine in background process).
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core_client_class = AsyncMPClient if (
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vllm_config.parallel_config.data_parallel_size
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== 1) else DPAsyncMPClient
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self.engine_core = core_client_class(
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vllm_config=vllm_config,
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executor_class=executor_class,
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log_stats=self.log_stats,
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)
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self.output_handler: Optional[asyncio.Task] = None
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try:
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# Start output handler eagerly if we are in the asyncio eventloop.
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asyncio.get_running_loop()
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self._run_output_handler()
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except RuntimeError:
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pass
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@classmethod
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def from_vllm_config(
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cls,
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vllm_config: VllmConfig,
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start_engine_loop: bool = True,
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usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
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stat_loggers: Optional[dict[str, StatLoggerBase]] = None,
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disable_log_requests: bool = False,
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disable_log_stats: bool = False,
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) -> "AsyncLLM":
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if not envs.VLLM_USE_V1:
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raise ValueError(
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"Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
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"This should not happen. As a workaround, try using "
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"AsyncLLMEngine.from_vllm_config(...) or explicitly set "
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"VLLM_USE_V1=0 or 1 and report this issue on Github.")
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# FIXME(rob): refactor VllmConfig to include the StatLoggers
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# include StatLogger in the Oracle decision.
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if stat_loggers is not None:
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raise ValueError("Custom StatLoggers are not yet supported on V1. "
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"Explicitly set VLLM_USE_V1=0 to disable V1.")
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# Create the LLMEngine.
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return cls(
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vllm_config=vllm_config,
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executor_class=Executor.get_class(vllm_config),
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start_engine_loop=start_engine_loop,
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log_requests=not disable_log_requests,
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log_stats=not disable_log_stats,
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usage_context=usage_context,
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)
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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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) -> "AsyncLLM":
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"""Create an AsyncLLM from the EngineArgs."""
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# Create the engine configs.
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vllm_config = engine_args.create_engine_config(usage_context)
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executor_class = Executor.get_class(vllm_config)
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# Create the AsyncLLM.
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return cls(
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vllm_config=vllm_config,
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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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start_engine_loop=start_engine_loop,
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usage_context=usage_context,
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)
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def __del__(self):
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self.shutdown()
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def shutdown(self):
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"""Shutdown, cleaning up the background proc and IPC."""
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if engine_core := getattr(self, "engine_core", None):
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engine_core.shutdown()
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if handler := getattr(self, "output_handler", None):
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handler.cancel()
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async def add_request(
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self,
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request_id: str,
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prompt: PromptType,
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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[Mapping[str, str]] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None,
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priority: int = 0,
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) -> RequestOutputCollector:
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"""Add new request to the AsyncLLM."""
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if self.errored:
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raise EngineDeadError()
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assert isinstance(params, SamplingParams), \
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"Pooling is not supported in V1"
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# Create a new output collector for the request.
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queue = RequestOutputCollector(output_kind=params.output_kind)
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# Convert Input --> Request.
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request = self.processor.process_inputs(request_id, prompt, params,
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arrival_time, lora_request,
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trace_headers,
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prompt_adapter_request,
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priority)
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if params.n == 1:
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await self._add_request(request, None, 0, queue)
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return queue
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# Fan out child requests (for n>1).
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parent_request = ParentRequest(request_id, params)
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for idx in range(params.n):
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request_id, params = parent_request.get_child_info(idx)
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child_request = request if idx == params.n - 1 else copy(request)
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child_request.request_id = request_id
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child_request.sampling_params = params
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await self._add_request(child_request, parent_request, idx, queue)
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return queue
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async def _add_request(self, request: EngineCoreRequest,
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parent_req: Optional[ParentRequest], index: int,
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queue: RequestOutputCollector):
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# Add the request to OutputProcessor (this process).
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self.output_processor.add_request(request, parent_req, index, queue)
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# Add the EngineCoreRequest to EngineCore (separate process).
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await self.engine_core.add_request_async(request)
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if self.log_requests:
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logger.info("Added request %s.", request.request_id)
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# TODO: we should support multiple prompts in one call, as you
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# can do with LLM.generate. So that for multi-prompt completion
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# requests we don't need to send multiple messages to core proc,
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# and so we don't need multiple streams which then get
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# re-multiplexed in the API server anyhow.
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async def generate(
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self,
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prompt: PromptType,
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sampling_params: SamplingParams,
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request_id: str,
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lora_request: Optional[LoRARequest] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None,
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priority: int = 0,
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) -> AsyncGenerator[RequestOutput, None]:
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"""
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Main function called by the API server to kick off a request
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* 1) Making an AsyncStream corresponding to the Request.
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* 2) Processing the Input.
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* 3) Adding the Request to the Detokenizer.
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* 4) Adding the Request to the EngineCore (separate process).
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A separate output_handler loop runs in a background AsyncIO task,
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pulling outputs from EngineCore and putting them into the
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per-request AsyncStream.
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The caller of generate() iterates the returned AsyncGenerator,
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returning the RequestOutput back to the caller.
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"""
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try:
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# We start the output_handler on the first call to generate() so
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# we can call __init__ before the event loop, which enables us
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# to handle startup failure gracefully in the OpenAI server.
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self._run_output_handler()
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q = await self.add_request(
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request_id,
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prompt,
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sampling_params,
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lora_request=lora_request,
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trace_headers=trace_headers,
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prompt_adapter_request=prompt_adapter_request,
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priority=priority,
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)
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# The output_handler task pushes items into the queue.
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# This task pulls from the queue and yields to caller.
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finished = False
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while not finished:
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# Note: drain queue without await if possible (avoids
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# task switching under load which helps performance).
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out = q.get_nowait() or await q.get()
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# Note: both OutputProcessor and EngineCore handle their
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# own request cleanup based on finished.
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finished = out.finished
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yield out
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# If the request is disconnected by the client, generate()
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# is cancelled. So, we abort the request if we end up here.
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except asyncio.CancelledError:
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await self.abort(request_id)
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if self.log_requests:
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logger.info("Request %s aborted.", request_id)
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raise
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# Engine is dead. Do not abort since we shut down.
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except EngineDeadError:
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if self.log_requests:
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logger.info("Request %s failed (engine dead).", request_id)
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raise
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# Request validation error.
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except ValueError:
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if self.log_requests:
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logger.info("Request %s failed (bad request).", request_id)
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raise
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# Unexpected error in the generate() task (possibly recoverable).
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except Exception as e:
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await self.abort(request_id)
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if self.log_requests:
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logger.info("Request %s failed.", request_id)
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raise EngineGenerateError() from e
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def _run_output_handler(self):
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"""Background loop: pulls from EngineCore and pushes to AsyncStreams."""
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if self.output_handler is not None:
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return
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# Ensure that the task doesn't have a circular ref back to the AsyncLLM
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# object, or else it won't be garbage collected and cleaned up properly.
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engine_core = self.engine_core
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output_processor = self.output_processor
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log_stats = self.log_stats
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stat_loggers = self.stat_loggers if log_stats else None
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async def output_handler():
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try:
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while True:
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# 1) Pull EngineCoreOutputs from the EngineCore.
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outputs = await engine_core.get_output_async()
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num_outputs = len(outputs.outputs)
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iteration_stats = IterationStats() if (
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log_stats and num_outputs) else None
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# Split outputs into chunks of at most
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# VLLM_V1_OUTPUT_PROC_CHUNK_SIZE, so that we don't block the
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# event loop for too long.
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if num_outputs <= VLLM_V1_OUTPUT_PROC_CHUNK_SIZE:
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slices = (outputs.outputs, )
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else:
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slices = np.array_split(
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outputs.outputs,
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cdiv(num_outputs, VLLM_V1_OUTPUT_PROC_CHUNK_SIZE))
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for i, outputs_slice in enumerate(slices):
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# 2) Process EngineCoreOutputs.
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processed_outputs = output_processor.process_outputs(
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outputs_slice, outputs.timestamp, iteration_stats)
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# NOTE: RequestOutputs are pushed to their queues.
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assert not processed_outputs.request_outputs
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# Allow other asyncio tasks to run between chunks
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if i + 1 < len(slices):
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await asyncio.sleep(0)
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# 3) Abort any reqs that finished due to stop strings.
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await engine_core.abort_requests_async(
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processed_outputs.reqs_to_abort)
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# 4) Logging.
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# TODO(rob): make into a coroutine and launch it in
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# background thread once Prometheus overhead is non-trivial.
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if stat_loggers:
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assert outputs.scheduler_stats is not None
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AsyncLLM._record_stats(
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stat_loggers[outputs.engine_index],
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scheduler_stats=outputs.scheduler_stats,
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iteration_stats=iteration_stats,
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)
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except Exception as e:
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logger.exception("AsyncLLM output_handler failed.")
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output_processor.propagate_error(e)
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self.output_handler = asyncio.create_task(output_handler())
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async def abort(self, request_id: str) -> None:
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"""Abort RequestId in OutputProcessor and EngineCore."""
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request_ids = self.output_processor.abort_requests((request_id, ))
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await self.engine_core.abort_requests_async(request_ids)
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if self.log_requests:
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logger.info("Aborted request %s.", request_id)
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@staticmethod
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def _record_stats(
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stat_loggers: list[StatLoggerBase],
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scheduler_stats: SchedulerStats,
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iteration_stats: Optional[IterationStats],
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):
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"""static so that it can be used from the output_handler task
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without a circular ref to AsyncLLM."""
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for stat_logger in stat_loggers:
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stat_logger.record(scheduler_stats=scheduler_stats,
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iteration_stats=iteration_stats)
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def encode(
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self,
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prompt: PromptType,
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pooling_params: PoolingParams,
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request_id: str,
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lora_request: Optional[LoRARequest] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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priority: int = 0,
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):
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raise ValueError("Not Supported on V1 yet.")
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async def get_vllm_config(self) -> VllmConfig:
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return self.vllm_config
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async def get_model_config(self) -> ModelConfig:
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return self.model_config
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async def get_decoding_config(self):
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raise ValueError("Not Supported on V1 yet.")
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async def get_input_preprocessor(self) -> InputPreprocessor:
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return self.processor.input_preprocessor
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async def get_tokenizer(
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self,
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lora_request: Optional[LoRARequest] = None,
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) -> AnyTokenizer:
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return self.tokenizer.get_lora_tokenizer(lora_request)
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async def is_tracing_enabled(self) -> bool:
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return False
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async def do_log_stats(
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self,
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scheduler_outputs=None,
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model_output=None,
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) -> None:
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for loggers in self.stat_loggers:
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for stat_logger in loggers:
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stat_logger.log()
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async def check_health(self) -> None:
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logger.debug("Called check_health.")
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async def start_profile(self) -> None:
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await self.engine_core.profile_async(True)
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async def stop_profile(self) -> None:
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await self.engine_core.profile_async(False)
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async def reset_prefix_cache(self,
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device: Optional[Device] = None) -> None:
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if device == Device.CPU:
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raise ValueError("Not supported on CPU.")
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await self.engine_core.reset_prefix_cache_async()
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async def sleep(self, level: int = 1) -> None:
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await self.engine_core.sleep_async(level)
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async def wake_up(self, tags: Optional[list[str]] = None) -> None:
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await self.engine_core.wake_up_async(tags)
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async def is_sleeping(self) -> bool:
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return await self.engine_core.is_sleeping_async()
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async def add_lora(self, lora_request: LoRARequest) -> bool:
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"""Load a new LoRA adapter into the engine for future requests."""
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return await self.engine_core.add_lora_async(lora_request)
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async def remove_lora(self, lora_id: int) -> bool:
|
|
"""Remove an already loaded LoRA adapter."""
|
|
return await self.engine_core.remove_lora_async(lora_id)
|
|
|
|
async def list_loras(self) -> set[int]:
|
|
"""List all registered adapters."""
|
|
return await self.engine_core.list_loras_async()
|
|
|
|
async def pin_lora(self, lora_id: int) -> bool:
|
|
"""Prevent an adapter from being evicted."""
|
|
return await self.engine_core.pin_lora_async(lora_id)
|
|
|
|
@property
|
|
def is_running(self) -> bool:
|
|
# Is None before the loop is started.
|
|
return self.output_handler is None or not self.output_handler.done()
|
|
|
|
@property
|
|
def is_stopped(self) -> bool:
|
|
return self.errored
|
|
|
|
@property
|
|
def errored(self) -> bool:
|
|
return self.engine_core.resources.engine_dead or not self.is_running
|
|
|
|
@property
|
|
def dead_error(self) -> BaseException:
|
|
return EngineDeadError()
|