80e9afb5bc
Signed-off-by: Aaron Pham <contact@aarnphm.xyz> Signed-off-by: Russell Bryant <rbryant@redhat.com> Co-authored-by: Russell Bryant <rbryant@redhat.com> Co-authored-by: Michael Goin <mgoin64@gmail.com> Co-authored-by: Nick Hill <nhill@redhat.com>
407 lines
15 KiB
Python
407 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import logging
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import os
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from collections.abc import AsyncGenerator, Mapping
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from typing import Optional, Union
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import numpy as np
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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 INPUT_REGISTRY, InputRegistry, 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.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 RequestOutputKind, 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 cdiv, kill_process_tree
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from vllm.v1.engine.core_client import EngineCoreClient
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from vllm.v1.engine.output_processor import OutputProcessor
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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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input_registry: InputRegistry = INPUT_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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assert start_engine_loop
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self.model_config = vllm_config.model_config
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self.log_requests = log_requests
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self.log_stats = log_stats
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self.stat_loggers: list[StatLoggerBase] = []
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if self.log_stats:
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if logger.isEnabledFor(logging.INFO):
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self.stat_loggers.append(LoggingStatLogger())
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self.stat_loggers.append(PrometheusStatLogger(vllm_config))
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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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input_registry=input_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_client(
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multiprocess_mode=True,
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asyncio_mode=True,
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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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@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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engine_config: Optional[VllmConfig] = None,
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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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if engine_config is None:
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vllm_config = engine_args.create_engine_config(usage_context)
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else:
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vllm_config = engine_config
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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 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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) -> asyncio.Queue[RequestOutput]:
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"""Add new request to the AsyncLLM."""
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# 1) Create a new output queue for the request.
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queue: asyncio.Queue[RequestOutput] = asyncio.Queue()
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# 2) Fan out child requests (for n>1)
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parent_req = ParentRequest.from_params(request_id, params)
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n = params.n if isinstance(params, SamplingParams) else 1
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for idx in range(n):
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if parent_req is not None:
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request_id, params = parent_req.get_child_info(idx)
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# 3) 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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# 4) Add the request to OutputProcessor (this process).
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self.output_processor.add_request(request, parent_req, idx, queue)
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# 5) 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_id)
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return queue
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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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if self.output_handler is None:
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self.output_handler = asyncio.create_task(
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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() if not q.empty() else await q.get()
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# Coalesce any additional queued outputs
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while not q.empty():
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next_out = q.get_nowait()
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if sampling_params.output_kind == RequestOutputKind.DELTA:
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out.add(next_out)
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else:
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out = next_out
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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, the
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# generate() task will be canceled. So, we abort the
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# 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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raise
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async def _run_output_handler(self):
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"""Background loop: pulls from EngineCore and pushes to AsyncStreams."""
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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 self.engine_core.get_output_async()
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iteration_stats = IterationStats() if self.log_stats 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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num_outputs = len(outputs.outputs)
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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 = self.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 self.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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self._record_stats(
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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("EngineCore output handler hit an error: %s", e)
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kill_process_tree(os.getpid())
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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 = [request_id]
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await self.engine_core.abort_requests_async(request_ids)
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self.output_processor.abort_requests(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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def _record_stats(
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self,
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scheduler_stats: Optional[SchedulerStats],
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iteration_stats: Optional[IterationStats],
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):
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if not self.log_stats:
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return
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assert scheduler_stats is not None
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assert iteration_stats is not None
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for stat_logger in self.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_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 stat_logger in self.stat_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) -> None:
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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) -> None:
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await self.engine_core.wake_up_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:
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"""Remove an already loaded LoRA adapter."""
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return await self.engine_core.remove_lora_async(lora_id)
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async def list_loras(self) -> set[int]:
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"""List all registered adapters."""
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return await self.engine_core.list_loras_async()
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async def pin_lora(self, lora_id: int) -> bool:
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"""Prevent an adapter from being evicted."""
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return await self.engine_core.pin_lora_async(lora_id)
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@property
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def is_running(self) -> bool:
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return True
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@property
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def is_stopped(self) -> bool:
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return False
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@property
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def errored(self) -> bool:
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return False
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@property
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def dead_error(self) -> BaseException:
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return Exception() # TODO: implement
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