vllm.v1.engine.async_llm

class AsyncLLM(EngineClient):

    def __init__(
        self,
        vllm_config: VllmConfig,
        executor_class: type[Executor],
        log_stats: bool,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        use_cached_outputs: bool = False,
        log_requests: bool = True,
        start_engine_loop: bool = True,
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
        client_addresses: Optional[dict[str, str]] = None,
        client_count: int = 1,
        client_index: int = 0,
    ) -> None:
        """
        Create an AsyncLLM.

        Args:
            vllm_config: global configuration.
            executor_class: an Executor impl, e.g. MultiprocExecutor.
            log_stats: Whether to log stats.
            usage_context: Usage context of the LLM.
            mm_registry: Multi-modal registry.
            use_cached_outputs: Whether to use cached outputs.
            log_requests: Whether to log requests.
            start_engine_loop: Whether to start the engine loop.
            stat_loggers: customized stat loggers for the engine.
                If not provided, default stat loggers will be used.
                PLEASE BE AWARE THAT STAT LOGGER IS NOT STABLE
                IN V1, AND ITS BASE CLASS INTERFACE MIGHT CHANGE.

        Returns:
            None
        """
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github.")

        # Ensure we can serialize custom transformer configs
        maybe_register_config_serialize_by_value()

        self.model_config = vllm_config.model_config
        self.vllm_config = vllm_config
        self.log_requests = log_requests
        self.log_stats = log_stats

        if self.model_config.skip_tokenizer_init:
            self.tokenizer = None
        else:
            # Tokenizer (+ ensure liveness if running in another process).
            self.tokenizer = init_tokenizer_from_configs(
                model_config=vllm_config.model_config,
                scheduler_config=vllm_config.scheduler_config,
                lora_config=vllm_config.lora_config)

        # Processor (converts Inputs --> EngineCoreRequests).
        self.processor = Processor(
            vllm_config=vllm_config,
            tokenizer=self.tokenizer,
            mm_registry=mm_registry,
        )

        # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
        self.output_processor = OutputProcessor(self.tokenizer,
                                                log_stats=self.log_stats)

        # EngineCore (starts the engine in background process).
        self.engine_core = EngineCoreClient.make_async_mp_client(
            vllm_config=vllm_config,
            executor_class=executor_class,
            log_stats=self.log_stats,
            client_addresses=client_addresses,
            client_count=client_count,
            client_index=client_index,
        )

        # Loggers.
        self.logger_manager: Optional[StatLoggerManager] = None
        if self.log_stats:
            self.logger_manager = StatLoggerManager(
                vllm_config=vllm_config,
                engine_idxs=self.engine_core.engine_ranks_managed,
                custom_stat_loggers=stat_loggers,
            )
            self.logger_manager.log_engine_initialized()

        self.output_handler: Optional[asyncio.Task] = None
        try:
            # Start output handler eagerly if we are in the asyncio eventloop.
            asyncio.get_running_loop()
            self._run_output_handler()
        except RuntimeError:
            pass

        if envs.VLLM_TORCH_PROFILER_DIR:
            logger.info(
                "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s",  # noqa: E501
                envs.VLLM_TORCH_PROFILER_DIR)
            worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                ],
                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    envs.VLLM_TORCH_PROFILER_DIR,
                    worker_name=worker_name,
                    use_gzip=True))
        else:
            logger.info(
                "Torch profiler disabled. AsyncLLM CPU traces will not be collected."  # noqa: E501
            )
            self.profiler = None

    @classmethod
    @deprecate_kwargs(
        "disable_log_requests",
        additional_message=("This argument will have no effect. "
                            "Use `enable_log_requests` instead."),
    )
    def from_vllm_config(
            cls,
            vllm_config: VllmConfig,
            start_engine_loop: bool = True,
            usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
            stat_loggers: Optional[list[StatLoggerFactory]] = None,
            enable_log_requests: bool = False,
            disable_log_stats: bool = False,
            client_addresses: Optional[dict[str, str]] = None,
            client_count: int = 1,
            client_index: int = 0,
            disable_log_requests: bool = True,  # Deprecated, will be removed
    ) -> "AsyncLLM":
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github.")

        # Create the LLMEngine.
        return cls(
            vllm_config=vllm_config,
            executor_class=Executor.get_class(vllm_config),
            start_engine_loop=start_engine_loop,
            stat_loggers=stat_loggers,
            log_requests=enable_log_requests,
            log_stats=not disable_log_stats,
            usage_context=usage_context,
            client_addresses=client_addresses,
            client_count=client_count,
            client_index=client_index,
        )

    @classmethod
    def from_engine_args(
        cls,
        engine_args: AsyncEngineArgs,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
    ) -> "AsyncLLM":
        """Create an AsyncLLM from the EngineArgs."""

        # Create the engine configs.
        vllm_config = engine_args.create_engine_config(usage_context)
        executor_class = Executor.get_class(vllm_config)

        # Create the AsyncLLM.
        return cls(
            vllm_config=vllm_config,
            executor_class=executor_class,
            log_requests=engine_args.enable_log_requests,
            log_stats=not engine_args.disable_log_stats,
            start_engine_loop=start_engine_loop,
            usage_context=usage_context,
            stat_loggers=stat_loggers,
        )

    def __del__(self):
        self.shutdown()

    def shutdown(self):
        """Shutdown, cleaning up the background proc and IPC."""

        shutdown_prometheus()

        if engine_core := getattr(self, "engine_core", None):
            engine_core.shutdown()

        cancel_task_threadsafe(getattr(self, "output_handler", None))

    async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return await self.engine_core.get_supported_tasks_async()

    async def add_request(
        self,
        request_id: str,
        prompt: PromptType,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: Optional[float] = None,
        lora_request: Optional[LoRARequest] = None,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        data_parallel_rank: Optional[int] = None,
    ) -> RequestOutputCollector:
        """Add new request to the AsyncLLM."""

        if self.errored:
            raise EngineDeadError()

        is_pooling = isinstance(params, PoolingParams)

        # Create a new output collector for the request.
        queue = RequestOutputCollector(output_kind=params.output_kind)

        # Convert Input --> Request.
        prompt_str, request = self.processor.process_inputs(
            request_id, prompt, params, arrival_time, lora_request,
            tokenization_kwargs, trace_headers, priority, data_parallel_rank)

        if is_pooling or params.n == 1:
            await self._add_request(request, prompt_str, None, 0, queue)
            return queue

        # Fan out child requests (for n>1).
        parent_request = ParentRequest(request_id, params)
        for idx in range(params.n):
            request_id, params = parent_request.get_child_info(idx)
            child_request = request if idx == params.n - 1 else copy(request)
            child_request.request_id = request_id
            child_request.sampling_params = params
            await self._add_request(child_request, prompt_str, parent_request,
                                    idx, queue)
        return queue

    async def _add_request(self, request: EngineCoreRequest,
                           prompt: Optional[str],
                           parent_req: Optional[ParentRequest], index: int,
                           queue: RequestOutputCollector):

        # Add the request to OutputProcessor (this process).
        self.output_processor.add_request(request, prompt, parent_req, index,
                                          queue)

        # Add the EngineCoreRequest to EngineCore (separate process).
        await self.engine_core.add_request_async(request)

        if self.log_requests:
            logger.info("Added request %s.", request.request_id)

    # TODO: we should support multiple prompts in one call, as you
    # can do with LLM.generate. So that for multi-prompt completion
    # requests we don't need to send multiple messages to core proc,
    # and so we don't need multiple streams which then get
    # re-multiplexed in the API server anyhow.
    async def generate(
        self,
        prompt: PromptType,
        sampling_params: SamplingParams,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        data_parallel_rank: Optional[int] = None,
    ) -> AsyncGenerator[RequestOutput, 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 Detokenizer.
            * 4) 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()

            q = await self.add_request(
                request_id,
                prompt,
                sampling_params,
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
                data_parallel_rank=data_parallel_rank,
            )

            # 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()

                # 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 or the generator is garbage collected. So,
        # we abort the request if we end up here.
        except (asyncio.CancelledError, GeneratorExit):
            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

    def _run_output_handler(self):
        """Background loop: pulls from EngineCore and pushes to AsyncStreams."""

        if self.output_handler is not None:
            return

        # Ensure that the task doesn't have a circular ref back to the AsyncLLM
        # object, or else it won't be garbage collected and cleaned up properly.
        engine_core = self.engine_core
        output_processor = self.output_processor
        log_stats = self.log_stats
        logger_manager = self.logger_manager

        async def output_handler():
            try:
                while True:
                    # 1) Pull EngineCoreOutputs from the EngineCore.
                    outputs = await engine_core.get_output_async()
                    num_outputs = len(outputs.outputs)

                    iteration_stats = IterationStats() if (
                        log_stats and num_outputs) else None

                    # Split outputs into chunks of at most
                    # VLLM_V1_OUTPUT_PROC_CHUNK_SIZE, so that we don't block the
                    # event loop for too long.
                    if num_outputs <= VLLM_V1_OUTPUT_PROC_CHUNK_SIZE:
                        slices = (outputs.outputs, )
                    else:
                        slices = np.array_split(
                            outputs.outputs,
                            cdiv(num_outputs, VLLM_V1_OUTPUT_PROC_CHUNK_SIZE))

                    for i, outputs_slice in enumerate(slices):
                        # 2) Process EngineCoreOutputs.
                        processed_outputs = output_processor.process_outputs(
                            outputs_slice, outputs.timestamp, iteration_stats)
                        # NOTE: RequestOutputs are pushed to their queues.
                        assert not processed_outputs.request_outputs

                        # Allow other asyncio tasks to run between chunks
                        if i + 1 < len(slices):
                            await asyncio.sleep(0)

                        # 3) Abort any reqs that finished due to stop strings.
                        await engine_core.abort_requests_async(
                            processed_outputs.reqs_to_abort)

                    # 4) Logging.
                    # TODO(rob): make into a coroutine and launch it in
                    # background thread once Prometheus overhead is non-trivial.
                    if logger_manager:
                        logger_manager.record(
                            engine_idx=outputs.engine_index,
                            scheduler_stats=outputs.scheduler_stats,
                            iteration_stats=iteration_stats,
                        )
            except Exception as e:
                logger.exception("AsyncLLM output_handler failed.")
                output_processor.propagate_error(e)

        self.output_handler = asyncio.create_task(output_handler())

    async def abort(self, request_id: Union[str, Iterable[str]]) -> None:
        """Abort RequestId in OutputProcessor and EngineCore."""

        request_ids = (request_id, ) if isinstance(
            request_id, str) else as_list(request_id)
        all_request_ids = self.output_processor.abort_requests(request_ids)
        await self.engine_core.abort_requests_async(all_request_ids)

        if self.log_requests:
            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,
        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()

            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.mm_registry.reset_processor_cache(self.model_config)
        self.processor.mm_input_cache_client.reset()
        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()