744 lines
29 KiB
Python
744 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import os
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import socket
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import time
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from collections.abc import AsyncGenerator, Iterable, Mapping
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from copy import copy
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from typing import Any, Optional, Union
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import numpy as np
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import torch
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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.entrypoints.utils import _validate_truncation_size
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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 PoolingRequestOutput, RequestOutput
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingParams
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from vllm.tasks import SupportedTask
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from vllm.transformers_utils.config import (
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maybe_register_config_serialize_by_value)
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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, as_list, cancel_task_threadsafe, cdiv,
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deprecate_kwargs)
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.core_client import EngineCoreClient
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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 StatLoggerFactory, StatLoggerManager
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from vllm.v1.metrics.prometheus import shutdown_prometheus
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from vllm.v1.metrics.stats import IterationStats
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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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stat_loggers: Optional[list[StatLoggerFactory]] = None,
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client_addresses: Optional[dict[str, str]] = None,
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client_count: int = 1,
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client_index: int = 0,
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) -> None:
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"""
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Create an AsyncLLM.
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Args:
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vllm_config: global configuration.
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executor_class: an Executor impl, e.g. MultiprocExecutor.
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log_stats: Whether to log stats.
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usage_context: Usage context of the LLM.
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mm_registry: Multi-modal registry.
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use_cached_outputs: Whether to use cached outputs.
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log_requests: Whether to log requests.
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start_engine_loop: Whether to start the engine loop.
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stat_loggers: customized stat loggers for the engine.
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If not provided, default stat loggers will be used.
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PLEASE BE AWARE THAT STAT LOGGER IS NOT STABLE
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IN V1, AND ITS BASE CLASS INTERFACE MIGHT CHANGE.
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Returns:
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None
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"""
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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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# Ensure we can serialize custom transformer configs
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maybe_register_config_serialize_by_value()
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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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if self.model_config.skip_tokenizer_init:
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self.tokenizer = None
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else:
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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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lora_config=vllm_config.lora_config)
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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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self.engine_core = EngineCoreClient.make_async_mp_client(
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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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client_addresses=client_addresses,
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client_count=client_count,
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client_index=client_index,
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)
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# Loggers.
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self.logger_manager: Optional[StatLoggerManager] = None
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if self.log_stats:
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self.logger_manager = StatLoggerManager(
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vllm_config=vllm_config,
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engine_idxs=self.engine_core.engine_ranks_managed,
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custom_stat_loggers=stat_loggers,
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)
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self.logger_manager.log_engine_initialized()
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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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if envs.VLLM_TORCH_PROFILER_DIR:
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logger.info(
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"Torch profiler enabled. AsyncLLM CPU traces will be collected under %s", # noqa: E501
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envs.VLLM_TORCH_PROFILER_DIR)
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worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
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self.profiler = torch.profiler.profile(
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activities=[
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torch.profiler.ProfilerActivity.CPU,
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],
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with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
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on_trace_ready=torch.profiler.tensorboard_trace_handler(
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envs.VLLM_TORCH_PROFILER_DIR,
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worker_name=worker_name,
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use_gzip=True))
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else:
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logger.info(
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"Torch profiler disabled. AsyncLLM CPU traces will not be collected." # noqa: E501
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)
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self.profiler = None
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@classmethod
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@deprecate_kwargs(
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"disable_log_requests",
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additional_message=("This argument will have no effect. "
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"Use `enable_log_requests` instead."),
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)
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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[list[StatLoggerFactory]] = None,
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enable_log_requests: bool = False,
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disable_log_stats: bool = False,
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client_addresses: Optional[dict[str, str]] = None,
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client_count: int = 1,
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client_index: int = 0,
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disable_log_requests: bool = True, # Deprecated, will be removed
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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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# 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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stat_loggers=stat_loggers,
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log_requests=enable_log_requests,
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log_stats=not disable_log_stats,
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usage_context=usage_context,
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client_addresses=client_addresses,
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client_count=client_count,
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client_index=client_index,
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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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stat_loggers: Optional[list[StatLoggerFactory]] = None,
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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=engine_args.enable_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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stat_loggers=stat_loggers,
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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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shutdown_prometheus()
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if engine_core := getattr(self, "engine_core", None):
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engine_core.shutdown()
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cancel_task_threadsafe(getattr(self, "output_handler", None))
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async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
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return await self.engine_core.get_supported_tasks_async()
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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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tokenization_kwargs: Optional[dict[str, Any]] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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priority: int = 0,
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data_parallel_rank: Optional[int] = None,
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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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is_pooling = isinstance(params, PoolingParams)
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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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prompt_str, request = self.processor.process_inputs(
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request_id, prompt, params, arrival_time, lora_request,
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tokenization_kwargs, trace_headers, priority, data_parallel_rank)
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if is_pooling or params.n == 1:
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await self._add_request(request, prompt_str, 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, prompt_str, parent_request,
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idx, queue)
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return queue
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async def _add_request(self, request: EngineCoreRequest,
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prompt: Optional[str],
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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, prompt, parent_req, index,
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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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priority: int = 0,
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data_parallel_rank: Optional[int] = None,
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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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if (self.vllm_config.cache_config.kv_sharing_fast_prefill
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and sampling_params.prompt_logprobs):
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raise ValueError(
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"--kv-sharing-fast-prefill produces incorrect logprobs for "
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"prompt tokens, please disable it when the requests need "
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"prompt logprobs")
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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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tokenization_kwargs: dict[str, Any] = {}
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truncate_prompt_tokens = sampling_params.truncate_prompt_tokens
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_validate_truncation_size(
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self.model_config.max_model_len,
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truncate_prompt_tokens,
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tokenization_kwargs,
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)
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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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priority=priority,
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tokenization_kwargs=tokenization_kwargs,
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data_parallel_rank=data_parallel_rank,
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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 or the generator is garbage collected. So,
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# we abort the request if we end up here.
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except (asyncio.CancelledError, GeneratorExit):
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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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logger_manager = self.logger_manager
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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 logger_manager:
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logger_manager.record(
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engine_idx=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: Union[str, Iterable[str]]) -> None:
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"""Abort RequestId in OutputProcessor and EngineCore."""
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request_ids = (request_id, ) if isinstance(
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request_id, str) else as_list(request_id)
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all_request_ids = self.output_processor.abort_requests(request_ids)
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await self.engine_core.abort_requests_async(all_request_ids)
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if self.log_requests:
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|
logger.info("Aborted request(s) %s.", ",".join(request_ids))
|
|
|
|
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,
|
|
truncate_prompt_tokens: Optional[int] = None,
|
|
tokenization_kwargs: Optional[dict[str, Any]] = None,
|
|
) -> AsyncGenerator[PoolingRequestOutput, None]:
|
|
"""
|
|
Main function called by the API server to kick off a request
|
|
* 1) Making an AsyncStream corresponding to the Request.
|
|
* 2) Processing the Input.
|
|
* 3) Adding the Request to the EngineCore (separate process).
|
|
|
|
A separate output_handler loop runs in a background AsyncIO task,
|
|
pulling outputs from EngineCore and putting them into the
|
|
per-request AsyncStream.
|
|
|
|
The caller of generate() iterates the returned AsyncGenerator,
|
|
returning the RequestOutput back to the caller.
|
|
"""
|
|
|
|
try:
|
|
# We start the output_handler on the first call to generate() so
|
|
# we can call __init__ before the event loop, which enables us
|
|
# to handle startup failure gracefully in the OpenAI server.
|
|
self._run_output_handler()
|
|
|
|
if tokenization_kwargs is None:
|
|
tokenization_kwargs = dict[str, Any]()
|
|
_validate_truncation_size(
|
|
self.model_config.max_model_len,
|
|
truncate_prompt_tokens,
|
|
tokenization_kwargs,
|
|
)
|
|
|
|
q = await self.add_request(
|
|
request_id,
|
|
prompt,
|
|
pooling_params,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
)
|
|
|
|
# The output_handler task pushes items into the queue.
|
|
# This task pulls from the queue and yields to caller.
|
|
finished = False
|
|
while not finished:
|
|
# Note: drain queue without await if possible (avoids
|
|
# task switching under load which helps performance).
|
|
out = q.get_nowait() or await q.get()
|
|
assert isinstance(out, PoolingRequestOutput)
|
|
# Note: both OutputProcessor and EngineCore handle their
|
|
# own request cleanup based on finished.
|
|
finished = out.finished
|
|
yield out
|
|
|
|
# If the request is disconnected by the client, generate()
|
|
# is cancelled. So, we abort the request if we end up here.
|
|
except asyncio.CancelledError:
|
|
await self.abort(request_id)
|
|
if self.log_requests:
|
|
logger.info("Request %s aborted.", request_id)
|
|
raise
|
|
|
|
# Engine is dead. Do not abort since we shut down.
|
|
except EngineDeadError:
|
|
if self.log_requests:
|
|
logger.info("Request %s failed (engine dead).", request_id)
|
|
raise
|
|
|
|
# Request validation error.
|
|
except ValueError:
|
|
if self.log_requests:
|
|
logger.info("Request %s failed (bad request).", request_id)
|
|
raise
|
|
|
|
# Unexpected error in the generate() task (possibly recoverable).
|
|
except Exception as e:
|
|
await self.abort(request_id)
|
|
if self.log_requests:
|
|
logger.info("Request %s failed.", request_id)
|
|
raise EngineGenerateError() from e
|
|
|
|
async def get_vllm_config(self) -> VllmConfig:
|
|
return self.vllm_config
|
|
|
|
async def get_model_config(self) -> ModelConfig:
|
|
return self.model_config
|
|
|
|
async def get_decoding_config(self):
|
|
raise ValueError("Not Supported on V1 yet.")
|
|
|
|
async def get_input_preprocessor(self) -> InputPreprocessor:
|
|
return self.processor.input_preprocessor
|
|
|
|
async def get_tokenizer(
|
|
self,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
) -> AnyTokenizer:
|
|
if self.tokenizer is None:
|
|
raise ValueError("Unable to get tokenizer because "
|
|
"skip_tokenizer_init is True")
|
|
|
|
return self.tokenizer.get_lora_tokenizer(lora_request)
|
|
|
|
async def is_tracing_enabled(self) -> bool:
|
|
return False
|
|
|
|
async def do_log_stats(
|
|
self,
|
|
scheduler_outputs=None,
|
|
model_output=None,
|
|
) -> None:
|
|
if self.logger_manager:
|
|
self.logger_manager.log()
|
|
|
|
async def check_health(self) -> None:
|
|
logger.debug("Called check_health.")
|
|
if self.errored:
|
|
raise self.dead_error
|
|
|
|
async def start_profile(self) -> None:
|
|
coros = [self.engine_core.profile_async(True)]
|
|
if self.profiler is not None:
|
|
coros.append(asyncio.to_thread(self.profiler.start))
|
|
await asyncio.gather(*coros)
|
|
|
|
async def stop_profile(self) -> None:
|
|
coros = [self.engine_core.profile_async(False)]
|
|
if self.profiler is not None:
|
|
coros.append(asyncio.to_thread(self.profiler.stop))
|
|
await asyncio.gather(*coros)
|
|
|
|
async def reset_mm_cache(self) -> None:
|
|
self.processor.clear_cache()
|
|
await self.engine_core.reset_mm_cache_async()
|
|
|
|
async def reset_prefix_cache(self,
|
|
device: Optional[Device] = None) -> None:
|
|
if device == Device.CPU:
|
|
raise ValueError("Not supported on CPU.")
|
|
await self.engine_core.reset_prefix_cache_async()
|
|
|
|
async def sleep(self, level: int = 1) -> None:
|
|
await self.reset_prefix_cache()
|
|
await self.engine_core.sleep_async(level)
|
|
|
|
async def wake_up(self, tags: Optional[list[str]] = None) -> None:
|
|
await self.engine_core.wake_up_async(tags)
|
|
|
|
async def is_sleeping(self) -> bool:
|
|
return await self.engine_core.is_sleeping_async()
|
|
|
|
async def add_lora(self, lora_request: LoRARequest) -> bool:
|
|
"""Load a new LoRA adapter into the engine for future requests."""
|
|
return await self.engine_core.add_lora_async(lora_request)
|
|
|
|
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)
|
|
|
|
async def collective_rpc(self,
|
|
method: str,
|
|
timeout: Optional[float] = None,
|
|
args: tuple = (),
|
|
kwargs: Optional[dict] = None):
|
|
"""
|
|
Perform a collective RPC call to the given path.
|
|
"""
|
|
return await self.engine_core.collective_rpc_async(
|
|
method, timeout, args, kwargs)
|
|
|
|
async def wait_for_requests_to_drain(self, drain_timeout: int = 300):
|
|
"""Wait for all requests to be drained."""
|
|
start_time = time.time()
|
|
while time.time() - start_time < drain_timeout:
|
|
if not self.engine_core.dp_engines_running():
|
|
logger.info("Engines are idle, requests have been drained")
|
|
return
|
|
|
|
logger.info(
|
|
"Engines are still running, waiting for requests to drain...")
|
|
await asyncio.sleep(1) # Wait 1 second before checking again
|
|
|
|
raise TimeoutError(f"Timeout reached after {drain_timeout} seconds "
|
|
"waiting for requests to drain.")
|
|
|
|
async def scale_elastic_ep(self,
|
|
new_data_parallel_size: int,
|
|
drain_timeout: int = 300):
|
|
"""
|
|
Scale up or down the data parallel size by adding or removing
|
|
engine cores.
|
|
Args:
|
|
new_data_parallel_size: The new number of data parallel workers
|
|
drain_timeout:
|
|
Maximum time to wait for requests to drain (seconds)
|
|
"""
|
|
old_data_parallel_size = \
|
|
self.vllm_config.parallel_config.data_parallel_size
|
|
if old_data_parallel_size == new_data_parallel_size:
|
|
logger.info("Data parallel size is already %s, skipping scale",
|
|
new_data_parallel_size)
|
|
return
|
|
logger.info(
|
|
"Waiting for requests to drain before "
|
|
"scaling up to %s engines...", new_data_parallel_size)
|
|
await self.wait_for_requests_to_drain(drain_timeout)
|
|
logger.info(
|
|
"Requests have been drained, proceeding with scale "
|
|
"to %s engines", new_data_parallel_size)
|
|
await self.engine_core.scale_elastic_ep(new_data_parallel_size)
|
|
self.vllm_config.parallel_config.data_parallel_size = \
|
|
new_data_parallel_size
|
|
|
|
# recreate stat loggers
|
|
if new_data_parallel_size > old_data_parallel_size and self.log_stats:
|
|
# TODO(rob): fix this after talking with Ray team.
|
|
# This resets all the prometheus metrics since we
|
|
# unregister during initialization. Need to understand
|
|
# the intended behavior here better.
|
|
self.logger_manager = StatLoggerManager(
|
|
vllm_config=self.vllm_config,
|
|
engine_idxs=list(range(new_data_parallel_size)),
|
|
custom_stat_loggers=None,
|
|
)
|
|
|
|
@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()
|