vllm.engine.llm_engine

_LOCAL_LOGGING_INTERVAL_SEC module-attribute

_LOCAL_LOGGING_INTERVAL_SEC = 5

_O module-attribute

_O = TypeVar('_O', RequestOutput, PoolingRequestOutput)

_R module-attribute

_R = TypeVar('_R', default=Any)

logger module-attribute

logger = init_logger(__name__)

LLMEngine

An LLM engine that receives requests and generates texts.

This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput.

The LLM class wraps this class for offline batched inference and the AsyncLLMEngine class wraps this class for online serving.

The config arguments are derived from EngineArgs.

Parameters:

Name	Type	Description	Default
vllm_config	VllmConfig	The configuration for initializing and running vLLM.	required
executor_class	Type[ExecutorBase]	The model executor class for managing distributed execution.	required
log_stats	bool	Whether to log statistics.	required
usage_context	UsageContext	Specified entry point, used for usage info collection.	ENGINE_CONTEXT

Source code in vllm/engine/llm_engine.py

class LLMEngine:
    """An LLM engine that receives requests and generates texts.

    This is the main class for the vLLM engine. It receives requests
    from clients and generates texts from the LLM. It includes a tokenizer, a
    language model (possibly distributed across multiple GPUs), and GPU memory
    space allocated for intermediate states (aka KV cache). This class utilizes
    iteration-level scheduling and efficient memory management to maximize the
    serving throughput.

    The [`LLM`][vllm.LLM] class wraps this class for offline batched inference
    and the [`AsyncLLMEngine`][vllm.engine.async_llm_engine.AsyncLLMEngine]
    class wraps this class for online serving.

    The config arguments are derived from [`EngineArgs`][vllm.EngineArgs].

    Args:
        vllm_config: The configuration for initializing and running vLLM.
        executor_class: The model executor class for managing distributed
            execution.
        log_stats: Whether to log statistics.
        usage_context: Specified entry point, used for usage info collection.
    """

    DO_VALIDATE_OUTPUT: ClassVar[bool] = False
    """A flag to toggle whether to validate the type of request output."""

    @classmethod
    @contextmanager
    def enable_output_validation(cls):
        cls.DO_VALIDATE_OUTPUT = True

        yield

        cls.DO_VALIDATE_OUTPUT = False

    @classmethod
    def validate_output(
        cls,
        output: object,
        output_type: Type[_O],
    ) -> _O:
        do_validate = cls.DO_VALIDATE_OUTPUT

        if ((TYPE_CHECKING or do_validate)
                and not isinstance(output, output_type)):
            raise TypeError(f"Expected output of type {output_type}, "
                            f"but found type {type(output)}")

        return cast(_O, output)

    @classmethod
    def validate_outputs(
        cls,
        outputs: GenericSequence[object],
        output_type: Type[_O],
    ) -> List[_O]:
        do_validate = cls.DO_VALIDATE_OUTPUT

        outputs_: List[_O]
        if TYPE_CHECKING or do_validate:
            outputs_ = []
            for output in outputs:
                if not isinstance(output, output_type):
                    raise TypeError(f"Expected output of type {output_type}, "
                                    f"but found type {type(output)}")

                outputs_.append(output)
        else:
            outputs_ = outputs

        return outputs_

    tokenizer: Optional[TokenizerGroup]

    def __init__(
        self,
        vllm_config: VllmConfig,
        executor_class: Type[ExecutorBase],
        log_stats: bool,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        use_cached_outputs: bool = False,
    ) -> None:
        if envs.VLLM_USE_V1:
            raise ValueError(
                "Using V0 LLMEngine, but envs.VLLM_USE_V1=True. "
                "This should not happen. As a workaround, try using "
                "LLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github.")

        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.device_config = vllm_config.device_config
        self.speculative_config = vllm_config.speculative_config  # noqa
        self.load_config = vllm_config.load_config
        self.decoding_config = vllm_config.decoding_config or DecodingConfig(  # noqa
        )
        self.prompt_adapter_config = vllm_config.prompt_adapter_config  # noqa
        self.observability_config = vllm_config.observability_config or ObservabilityConfig(  # noqa
        )

        logger.info(
            "Initializing a V0 LLM engine (v%s) with config: %s, "
            "use_cached_outputs=%s, ",
            VLLM_VERSION,
            vllm_config,
            use_cached_outputs,
        )

        self.log_stats = log_stats
        self.use_cached_outputs = use_cached_outputs

        if not self.model_config.skip_tokenizer_init:
            self.tokenizer = self._init_tokenizer()
            self.detokenizer = Detokenizer(self.tokenizer)
            tokenizer_group = self.get_tokenizer_group()
        else:
            self.tokenizer = None
            self.detokenizer = None
            tokenizer_group = None

        # Ensure that the function doesn't contain a reference to self,
        # to avoid engine GC issues
        def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
            assert tokenizer_group, ("tokenizer_group cannot be None, "
                                     "make sure skip_tokenizer_init is False")
            return tokenizer_group.get_lora_tokenizer(sequence.lora_request)

        self.seq_counter = Counter()
        self.generation_config_fields = (
            self.model_config.try_get_generation_config())

        self.input_preprocessor = InputPreprocessor(self.model_config,
                                                    self.tokenizer,
                                                    mm_registry)

        self.model_executor = executor_class(vllm_config=vllm_config)

        if self.model_config.runner_type != "pooling":
            self._initialize_kv_caches()

        # If usage stat is enabled, collect relevant info.
        if is_usage_stats_enabled():
            from vllm.model_executor.model_loader import (
                get_architecture_class_name)
            usage_message.report_usage(
                get_architecture_class_name(self.model_config),
                usage_context,
                extra_kvs={
                    # Common configuration
                    "dtype":
                    str(self.model_config.dtype),
                    "tensor_parallel_size":
                    self.parallel_config.tensor_parallel_size,
                    "block_size":
                    self.cache_config.block_size,
                    "gpu_memory_utilization":
                    self.cache_config.gpu_memory_utilization,

                    # Quantization
                    "quantization":
                    self.model_config.quantization,
                    "kv_cache_dtype":
                    str(self.cache_config.cache_dtype),

                    # Feature flags
                    "enable_lora":
                    bool(self.lora_config),
                    "enable_prompt_adapter":
                    bool(self.prompt_adapter_config),
                    "enable_prefix_caching":
                    self.cache_config.enable_prefix_caching,
                    "enforce_eager":
                    self.model_config.enforce_eager,
                    "disable_custom_all_reduce":
                    self.parallel_config.disable_custom_all_reduce,
                })

        self.cached_scheduler_outputs = [
            SchedulerOutputState()
            for _ in range(self.parallel_config.pipeline_parallel_size)
        ]

        self.scheduler_contexts = [
            SchedulerContext(multi_step_stream_outputs=self.scheduler_config.
                             multi_step_stream_outputs)
            for _ in range(self.parallel_config.pipeline_parallel_size)
        ]

        if self.model_config.use_async_output_proc:
            process_model_outputs = weak_bind(self._process_model_outputs)

            self.async_callbacks = [
                partial(process_model_outputs,
                        ctx=self.scheduler_contexts[v_id])
                for v_id in range(self.parallel_config.pipeline_parallel_size)
            ]
        else:
            self.async_callbacks = []

        # Currently used by AsyncLLMEngine to ensure quick append
        # of request outputs to asyncio queues
        self.process_request_outputs_callback: Optional[Callable] = None

        # Create the scheduler.
        # NOTE: the cache_config here have been updated with the numbers of
        # GPU and CPU blocks, which are profiled in the distributed executor.
        if isinstance(self.vllm_config.scheduler_config.scheduler_cls, str):
            Scheduler = resolve_obj_by_qualname(
                self.vllm_config.scheduler_config.scheduler_cls)
        else:
            Scheduler = self.vllm_config.scheduler_config.scheduler_cls
        self.scheduler = [
            Scheduler(
                self.scheduler_config, self.cache_config, self.lora_config,
                self.parallel_config.pipeline_parallel_size,
                self.async_callbacks[v_id]
                if self.model_config.use_async_output_proc else None)
            for v_id in range(self.parallel_config.pipeline_parallel_size)
        ]

        # Metric Logging.
        if self.log_stats:
            if stat_loggers is not None:
                self.stat_loggers = stat_loggers
            else:
                # Lazy import for prometheus multiprocessing.
                # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
                # before prometheus_client is imported.
                # See https://prometheus.github.io/client_python/multiprocess/
                from vllm.engine.metrics import (LoggingStatLogger,
                                                 PrometheusStatLogger)

                self.stat_loggers = {
                    "logging":
                    LoggingStatLogger(
                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                        vllm_config=vllm_config),
                    "prometheus":
                    PrometheusStatLogger(
                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                        labels=dict(
                            model_name=self.model_config.served_model_name),
                        vllm_config=vllm_config),
                }
                self.stat_loggers["prometheus"].info("cache_config",
                                                     self.cache_config)

        self.tracer = None
        if self.observability_config.otlp_traces_endpoint:
            self.tracer = init_tracer(
                "vllm.llm_engine",
                self.observability_config.otlp_traces_endpoint)

        # Create sequence output processor, e.g. for beam search or
        # speculative decoding.
        self.output_processor = (
            SequenceGroupOutputProcessor.create_output_processor(
                self.scheduler_config,
                self.detokenizer,
                self.scheduler,
                self.seq_counter,
                get_tokenizer_for_seq,
                stop_checker=StopChecker(self.scheduler_config.max_model_len,
                                         get_tokenizer_for_seq),
            ))

        self.seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {}

        # Flag to set when an input fails to process and the engine should run
        # the next step without re-scheduling.
        self._skip_scheduling_next_step = False

        # Don't keep the dummy data in memory
        self.reset_mm_cache()

    def _initialize_kv_caches(self) -> None:
        """Initialize the KV cache in the worker(s).

        The workers will determine the number of blocks in both the GPU cache
        and the swap CPU cache.
        """
        start = time.time()
        num_gpu_blocks, num_cpu_blocks = (
            self.model_executor.determine_num_available_blocks())

        if self.cache_config.num_gpu_blocks_override is not None:
            num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override
            logger.info(
                "Overriding num_gpu_blocks=%d with "
                "num_gpu_blocks_override=%d", num_gpu_blocks,
                num_gpu_blocks_override)
            num_gpu_blocks = num_gpu_blocks_override

        self.cache_config.num_gpu_blocks = num_gpu_blocks
        self.cache_config.num_cpu_blocks = num_cpu_blocks

        self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
        elapsed = time.time() - start
        logger.info(("init engine (profile, create kv cache, "
                     "warmup model) took %.2f seconds"), elapsed)

    @classmethod
    def _get_executor_cls(cls,
                          engine_config: VllmConfig) -> Type[ExecutorBase]:
        # distributed_executor_backend must be set in VllmConfig.__post_init__
        distributed_executor_backend = (
            engine_config.parallel_config.distributed_executor_backend)
        # Initialize the cluster and specify the executor class.
        if isinstance(distributed_executor_backend, type):
            if not issubclass(distributed_executor_backend, ExecutorBase):
                raise TypeError(
                    "distributed_executor_backend must be a subclass of "
                    f"ExecutorBase. Got {distributed_executor_backend}.")
            executor_class = distributed_executor_backend
        elif distributed_executor_backend == "ray":
            from vllm.executor.ray_distributed_executor import (
                RayDistributedExecutor)
            executor_class = RayDistributedExecutor
        elif distributed_executor_backend == "mp":
            from vllm.executor.mp_distributed_executor import (
                MultiprocessingDistributedExecutor)
            assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
                "multiprocessing distributed executor backend does not "
                "support VLLM_USE_RAY_SPMD_WORKER=1")
            executor_class = MultiprocessingDistributedExecutor
        elif distributed_executor_backend == "uni":
            # JAX-style, single-process, multi-device executor.
            from vllm.executor.uniproc_executor import UniProcExecutor
            executor_class = UniProcExecutor
        elif distributed_executor_backend == "external_launcher":
            # executor with external launcher
            from vllm.executor.uniproc_executor import (  # noqa
                ExecutorWithExternalLauncher)
            executor_class = ExecutorWithExternalLauncher
        else:
            raise ValueError("unrecognized distributed_executor_backend: "
                             f"{distributed_executor_backend}")
        return executor_class

    @classmethod
    def from_vllm_config(
        cls,
        vllm_config: VllmConfig,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
        disable_log_stats: bool = False,
    ) -> "LLMEngine":
        return cls(
            vllm_config=vllm_config,
            executor_class=cls._get_executor_cls(vllm_config),
            log_stats=(not disable_log_stats),
            usage_context=usage_context,
            stat_loggers=stat_loggers,
        )

    @classmethod
    def from_engine_args(
        cls,
        engine_args: EngineArgs,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
    ) -> "LLMEngine":
        """Creates an LLM engine from the engine arguments."""
        # Create the engine configs.
        vllm_config = engine_args.create_engine_config(usage_context)

        engine_cls = cls
        if envs.VLLM_USE_V1:
            from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
            engine_cls = V1LLMEngine

        return engine_cls.from_vllm_config(
            vllm_config=vllm_config,
            usage_context=usage_context,
            stat_loggers=stat_loggers,
            disable_log_stats=engine_args.disable_log_stats,
        )

    def __reduce__(self):
        # This is to ensure that the LLMEngine is not referenced in
        # the closure used to initialize Ray worker actors
        raise RuntimeError("LLMEngine should not be pickled!")

    def __del__(self):
        # Shutdown model executor when engine is garbage collected
        # Use getattr since __init__ can fail before the field is set
        if model_executor := getattr(self, "model_executor", None):
            model_executor.shutdown()

    def get_tokenizer_group(self) -> TokenizerGroup:
        if self.tokenizer is None:
            raise ValueError("Unable to get tokenizer because "
                             "skip_tokenizer_init is True")

        return self.tokenizer

    def get_tokenizer(
        self,
        lora_request: Optional[LoRARequest] = None,
    ) -> AnyTokenizer:
        return self.get_tokenizer_group().get_lora_tokenizer(lora_request)

    def _init_tokenizer(self) -> TokenizerGroup:
        return init_tokenizer_from_configs(
            model_config=self.model_config,
            scheduler_config=self.scheduler_config,
            lora_config=self.lora_config)

    def _verify_args(self) -> None:
        self.model_config.verify_with_parallel_config(self.parallel_config)
        self.cache_config.verify_with_parallel_config(self.parallel_config)
        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)

    def _add_processed_request(
        self,
        request_id: str,
        processed_inputs: ProcessorInputs,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: float,
        lora_request: Optional[LoRARequest],
        prompt_adapter_request: Optional[PromptAdapterRequest],
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
    ) -> Optional[SequenceGroup]:
        """Add a processed request to the engine's request pool.
        return the created sequence group.
        """
        if isinstance(params, SamplingParams) and params.n > 1:
            ParallelSampleSequenceGroup.add_request(
                request_id,
                self,
                params,
                processed_inputs=processed_inputs,
                arrival_time=arrival_time,
                lora_request=lora_request,
                trace_headers=trace_headers,
                prompt_adapter_request=prompt_adapter_request,
                priority=priority,
            )
            return None

        self._validate_model_inputs(processed_inputs, lora_request)
        # Create the sequences.
        block_size = self.cache_config.block_size
        seq_id = next(self.seq_counter)
        eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)

        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)

        seq = Sequence(seq_id, decoder_inputs, block_size, eos_token_id,
                       lora_request, prompt_adapter_request)

        encoder_seq = (None if encoder_inputs is None else Sequence(
            seq_id, encoder_inputs, block_size, eos_token_id, lora_request,
            prompt_adapter_request))

        # Create a SequenceGroup based on SamplingParams or PoolingParams
        if isinstance(params, SamplingParams):
            seq_group = self._create_sequence_group_with_sampling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
                trace_headers=trace_headers,
                prompt_adapter_request=prompt_adapter_request,
                encoder_seq=encoder_seq,
                priority=priority)
        elif isinstance(params, PoolingParams):
            seq_group = self._create_sequence_group_with_pooling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
                prompt_adapter_request=prompt_adapter_request,
                encoder_seq=encoder_seq,
                priority=priority)
        else:
            raise ValueError(
                "Either SamplingParams or PoolingParams must be provided.")

        # Add the sequence group to the scheduler with least unfinished seqs.
        costs = [
            scheduler.get_num_unfinished_seq_groups()
            for scheduler in self.scheduler
        ]
        min_cost_scheduler = self.scheduler[costs.index(min(costs))]
        min_cost_scheduler.add_seq_group(seq_group)

        return seq_group

    def stop_remote_worker_execution_loop(self) -> None:
        self.model_executor.stop_remote_worker_execution_loop()

    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,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        priority: int = 0,
    ) -> None:
        """Add a request to the engine's request pool.

        The request is added to the request pool and will be processed by the
        scheduler as `engine.step()` is called. The exact scheduling policy is
        determined by the scheduler.

        Args:
            request_id: The unique ID of the request.
            prompt: The prompt to the LLM. See
                [PromptType][vllm.inputs.PromptType]
                for more details about the format of each input.
            params: Parameters for sampling or pooling.
                [SamplingParams][vllm.SamplingParams] for text generation.
                [PoolingParams][vllm.PoolingParams] for pooling.
            arrival_time: The arrival time of the request. If None, we use
                the current monotonic time.
            lora_request: The LoRA request to add.
            trace_headers: OpenTelemetry trace headers.
            prompt_adapter_request: The prompt adapter request to add.
            priority: The priority of the request.
                Only applicable with priority scheduling.

        Details:
            - Set arrival_time to the current time if it is None.
            - Set prompt_token_ids to the encoded prompt if it is None.
            - Create `n` number of [Sequence][vllm.Sequence] objects.
            - Create a [SequenceGroup][vllm.SequenceGroup] object
              from the list of [Sequence][vllm.Sequence].
            - Add the [SequenceGroup][vllm.SequenceGroup] object to the
              scheduler.

        Example:
            >>> # initialize engine
            >>> engine = LLMEngine.from_engine_args(engine_args)
            >>> # set request arguments
            >>> example_prompt = "Who is the president of the United States?"
            >>> sampling_params = SamplingParams(temperature=0.0)
            >>> request_id = 0
            >>>
            >>> # add the request to the engine
            >>> engine.add_request(
            >>>    str(request_id),
            >>>    example_prompt,
            >>>    SamplingParams(temperature=0.0))
            >>> # continue the request processing
            >>> ...
        """
        if not isinstance(request_id, str):
            raise TypeError(
                f"request_id must be a string, got {type(request_id)}")

        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")

        if priority != 0 and not self.scheduler_config.policy == "priority":
            raise ValueError(f"Got priority {priority} but "
                             "Priority scheduling is not enabled.")

        if isinstance(params, SamplingParams) \
            and (params.guided_decoding or params.logits_processors) \
            and self.scheduler_config.num_scheduler_steps > 1:
            raise ValueError(
                "Guided decoding and logits processors are not supported "
                "in multi-step decoding")

        if arrival_time is None:
            arrival_time = time.time()

        if (isinstance(prompt, dict)
                and prompt.get("prompt_embeds", None) is not None
                and not prompt.get("prompt_token_ids", None)):
            seq_len = prompt["prompt_embeds"].shape[0]
            prompt["prompt_token_ids"] = [0] * seq_len

        processed_inputs = self.input_preprocessor.preprocess(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
        )

        self._add_processed_request(
            request_id=request_id,
            processed_inputs=processed_inputs,
            params=params,
            arrival_time=arrival_time,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
            trace_headers=trace_headers,
            priority=priority,
        )

    def _create_sequence_group_with_sampling(
        self,
        request_id: str,
        seq: Sequence,
        sampling_params: SamplingParams,
        arrival_time: float,
        lora_request: Optional[LoRARequest],
        trace_headers: Optional[Mapping[str, str]] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        encoder_seq: Optional[Sequence] = None,
        priority: int = 0,
    ) -> SequenceGroup:
        """Creates a SequenceGroup with SamplingParams."""
        max_logprobs = self.get_model_config().max_logprobs
        if (sampling_params.logprobs
                and sampling_params.logprobs > max_logprobs) or (
                    sampling_params.prompt_logprobs
                    and sampling_params.prompt_logprobs > max_logprobs):
            raise ValueError(f"Cannot request more than "
                             f"{max_logprobs} logprobs.")

        sampling_params = self._build_logits_processors(
            sampling_params, lora_request)

        # Defensive copy of SamplingParams, which are used by the sampler,
        # this doesn't deep-copy LogitsProcessor objects
        sampling_params = sampling_params.clone()

        sampling_params.update_from_generation_config(
            self.generation_config_fields, seq.eos_token_id)

        # Create the sequence group.
        draft_size = 1
        if self.vllm_config.speculative_config is not None:
            draft_size = \
                self.vllm_config.speculative_config.num_speculative_tokens + 1
        seq_group = SequenceGroup(
            request_id=request_id,
            seqs=[seq],
            arrival_time=arrival_time,
            sampling_params=sampling_params,
            lora_request=lora_request,
            trace_headers=trace_headers,
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq,
            priority=priority,
            draft_size=draft_size)

        return seq_group

    def _create_sequence_group_with_pooling(
        self,
        request_id: str,
        seq: Sequence,
        pooling_params: PoolingParams,
        arrival_time: float,
        lora_request: Optional[LoRARequest],
        prompt_adapter_request: Optional[PromptAdapterRequest],
        encoder_seq: Optional[Sequence] = None,
        priority: int = 0,
    ) -> SequenceGroup:
        """Creates a SequenceGroup with PoolingParams."""
        # Defensive copy of PoolingParams, which are used by the pooler
        pooling_params = pooling_params.clone()
        # Create the sequence group.
        seq_group = SequenceGroup(
            request_id=request_id,
            seqs=[seq],
            arrival_time=arrival_time,
            lora_request=lora_request,
            pooling_params=pooling_params,
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq,
            priority=priority)
        return seq_group

    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
        """Aborts a request(s) with the given ID.

        Args:
            request_id: The ID(s) of the request to abort.

        Details:
            - Refer to [vllm.core.scheduler.Scheduler.abort_seq_group][].

        Example:
            >>> # initialize engine and add a request with request_id
            >>> request_id = str(0)
            >>> # abort the request
            >>> engine.abort_request(request_id)
        """
        for scheduler in self.scheduler:
            scheduler.abort_seq_group(
                request_id, seq_id_to_seq_group=self.seq_id_to_seq_group)

    def get_vllm_config(self) -> VllmConfig:
        """Gets the vllm configuration."""
        return self.vllm_config

    def get_model_config(self) -> ModelConfig:
        """Gets the model configuration."""
        return self.model_config

    def get_parallel_config(self) -> ParallelConfig:
        """Gets the parallel configuration."""
        return self.parallel_config

    def get_decoding_config(self) -> DecodingConfig:
        """Gets the decoding configuration."""
        return self.decoding_config

    def get_scheduler_config(self) -> SchedulerConfig:
        """Gets the scheduler configuration."""
        return self.scheduler_config

    def get_lora_config(self) -> LoRAConfig:
        """Gets the LoRA configuration."""
        return self.lora_config

    def get_num_unfinished_requests(self) -> int:
        """Gets the number of unfinished requests."""
        return sum(scheduler.get_num_unfinished_seq_groups()
                   for scheduler in self.scheduler)

    def has_unfinished_requests(self) -> bool:
        """Returns True if there are unfinished requests."""
        return any(scheduler.has_unfinished_seqs()
                   for scheduler in self.scheduler)

    def has_unfinished_requests_for_virtual_engine(
            self, virtual_engine: int) -> bool:
        """
        Returns True if there are unfinished requests for the virtual engine.
        """
        return self.scheduler[virtual_engine].has_unfinished_seqs()

    def reset_mm_cache(self) -> bool:
        """Reset the multi-modal cache."""
        return self.input_preprocessor.mm_registry.reset_processor_cache()

    def reset_prefix_cache(self, device: Optional[Device] = None) -> bool:
        """Reset prefix cache for all devices."""

        success = True
        for scheduler in self.scheduler:
            success = success and scheduler.reset_prefix_cache(device)
        return success

    @staticmethod
    def _process_sequence_group_outputs(
        seq_group: SequenceGroup,
        outputs: List[PoolingSequenceGroupOutput],
    ) -> None:
        seq_group.pooled_data = outputs[0].data

        for seq in seq_group.get_seqs():
            seq.status = SequenceStatus.FINISHED_STOPPED

        return

    def _update_num_computed_tokens_for_multi_step_prefill(
            self, seq_group: SequenceGroup,
            seq_group_meta: SequenceGroupMetadata,
            is_first_step_output: Optional[bool]):
        """
        This function updates num_computed_tokens for prompt sequences
        when Multi-Step is enabled.

        seq_group: SequenceGroup to update the num_computed_tokens for.
        seq_group_meta: Metadata of the given SequenceGroup.
        is_first_step_output: Optional[bool] -
            When available, is_first_step_output indicates if the appended
            output token is the output of the first-step in multi-step.
            A value of None indicates that outputs from all steps in
            in multi-step are submitted in a single burst.
        """

        assert self.scheduler_config.is_multi_step

        if not seq_group_meta.is_prompt:
            # num_computed_token updates for multi-step decodes happen after
            # the tokens are appended to the sequence.
            return

        do_update: bool = False
        if self.scheduler_config.chunked_prefill_enabled:
            # In multi-step + chunked-prefill case, the prompt sequences
            # that are scheduled are fully processed in the first step.
            do_update = is_first_step_output is None or is_first_step_output
        else:
            # Normal multi-step decoding case. In this case prompt-sequences
            # are actually single-stepped. Always update in this case.
            assert seq_group.state.num_steps == 1
            do_update = True

        if do_update:
            seq_group.update_num_computed_tokens(
                seq_group_meta.token_chunk_size)

    def _process_model_outputs(self,
                               ctx: SchedulerContext,
                               request_id: Optional[str] = None) -> None:
        """Apply the model output to the sequences in the scheduled seq groups
        and return responses.

        ctx: The virtual engine context to work on
        request_id: If provided, then only this request is going to be processed
        """

        now = time.time()

        if len(ctx.output_queue) == 0:
            return None

        # Get pending async postprocessor
        if request_id:
            # When we process only one request, no pop is required
            # (since later we will process all of the rest)
            (outputs, seq_group_metadata_list, scheduler_outputs, is_async,
             is_last_step, is_first_step_output, skip) = ctx.output_queue[0]
        else:
            (outputs, seq_group_metadata_list, scheduler_outputs, is_async,
             is_last_step, is_first_step_output,
             skip) = ctx.output_queue.popleft()

        # Sanity check
        assert len(seq_group_metadata_list) == len(
            scheduler_outputs.scheduled_seq_groups)

        has_multiple_outputs: bool = len(outputs) > 1
        outputs_by_sequence_group: List[List[SequenceGroupOutput]]
        if has_multiple_outputs:
            assert self.scheduler_config.is_multi_step or \
                     self.speculative_config
            # Organize outputs by [step][sequence group] instead of
            # [sequence group][step].
            if self.scheduler_config.is_multi_step:
                outputs_by_sequence_group = create_output_by_sequence_group(
                    outputs, len(seq_group_metadata_list))
            elif self.speculative_config:
                # Decodes are multi-steps while prefills are not, outputting at
                # most 1 token. Separate them so that we can trigger chunk
                # processing without having to pad or copy over prompts K times
                # to match decodes structure (costly with prompt_logprobs).
                num_prefills = sum(sg.is_prompt
                                   for sg in seq_group_metadata_list)
                prefills, decodes = outputs[:num_prefills], outputs[
                    num_prefills:]
                outputs_by_sequence_group = create_output_by_sequence_group(
                    decodes,
                    num_seq_groups=len(seq_group_metadata_list) - num_prefills)
                outputs_by_sequence_group = [p.outputs for p in prefills
                                             ] + outputs_by_sequence_group
            # We have outputs for multiple steps submitted in a single burst,
            # so invalidate is_first_step_output.
            is_first_step_output = None
        else:
            outputs_by_sequence_group = outputs

        # Determine the requests we need to operate on
        if request_id:
            indices = []
            for i, seq_group_meta in enumerate(seq_group_metadata_list):
                if seq_group_meta.request_id == request_id:
                    assert i not in skip  # Cannot be called twice
                    indices.append(i)
                    break

            # If the request_id was not found, then it means that
            # this is a new request that has no pending async
            # postprocessor
            if not indices:
                return
        else:
            indices = range(len(seq_group_metadata_list))  # type: ignore

        finished_before: List[int] = []
        finished_now: List[int] = []
        for i in indices:
            if i in skip:
                continue

            seq_group_meta = seq_group_metadata_list[i]
            scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

            seq_group: SequenceGroup = scheduled_seq_group.seq_group

            if seq_group.is_finished():
                finished_before.append(i)
                continue

            output: List[SequenceGroupOutput]
            if has_multiple_outputs:
                output = outputs_by_sequence_group[i]
            else:
                output = [outputs_by_sequence_group[0][i]]

            if not is_async:
                if self.scheduler_config.is_multi_step:
                    # Updates happen only if the sequence is prefill
                    self._update_num_computed_tokens_for_multi_step_prefill(
                        seq_group, seq_group_meta, is_first_step_output)
                else:
                    seq_group.update_num_computed_tokens(
                        seq_group_meta.token_chunk_size or 0)

            if outputs:
                for o in outputs:
                    if (isinstance(o, SamplerOutput)
                            and seq_group.metrics is not None):
                        if seq_group.metrics.model_forward_time is not None:
                            seq_group.metrics.model_forward_time += (
                                o.model_forward_time or 0)
                        else:
                            seq_group.metrics.model_forward_time = (
                                o.model_forward_time)
                        if seq_group.metrics.model_execute_time is not None:
                            seq_group.metrics.model_execute_time += (
                                o.model_execute_time or 0)
                        else:
                            seq_group.metrics.model_execute_time = (
                                o.model_execute_time)

            if self.model_config.runner_type == "pooling":
                self._process_sequence_group_outputs(seq_group, output)
            else:
                self.output_processor.process_prompt_logprob(seq_group, output)
                if seq_group_meta.do_sample:
                    self.output_processor.process_outputs(
                        seq_group, output, is_async)

            if seq_group.is_finished():
                finished_now.append(i)

        # Generate outputs for the requests that finished this iteration
        for i in finished_now:
            scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

            seq_group = scheduled_seq_group.seq_group
            seq_group.maybe_set_first_token_time(now)
            if not seq_group.is_prefill():
                seq_group.set_last_token_time(now)
            request_output = RequestOutputFactory.create(
                seq_group,
                self.seq_id_to_seq_group,
                use_cache=self.use_cached_outputs)
            if request_output:
                ctx.request_outputs.append(request_output)

        # When we process a single request, we skip it for the next time,
        # and invoke the request output callback (if there was final output)
        if request_id:
            assert len(indices) == 1
            skip.append(indices[0])

            if (finished_now
                    and self.process_request_outputs_callback is not None):
                self.process_request_outputs_callback(ctx.request_outputs)
                ctx.request_outputs.clear()
            return

        # Free currently finished requests
        if finished_now:
            for scheduler in self.scheduler:
                scheduler.free_finished_seq_groups()

        # For multi-step without streaming, don't create outputs each iteration
        if not is_last_step and not ctx.multi_step_stream_outputs:
            # Immediately process request outputs here (if callback is given)
            if (finished_now
                    and self.process_request_outputs_callback is not None):
                self.process_request_outputs_callback(ctx.request_outputs)
                ctx.request_outputs.clear()
            return

        # Create the outputs
        for i in indices:
            if i in skip or i in finished_before or i in finished_now:
                continue  # Avoids double processing

            scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

            seq_group = scheduled_seq_group.seq_group
            seq_group.maybe_set_first_token_time(now)
            if not seq_group.is_prefill():
                seq_group.set_last_token_time(now)
            request_output = RequestOutputFactory.create(
                seq_group,
                self.seq_id_to_seq_group,
                use_cache=self.use_cached_outputs)
            if request_output:
                ctx.request_outputs.append(request_output)

        # For multi-step with streaming, create outputs each iteration
        if not is_last_step and ctx.multi_step_stream_outputs:
            # Immediately process request outputs here (if callback is given)
            if self.process_request_outputs_callback is not None:
                self.process_request_outputs_callback(ctx.request_outputs)
                ctx.request_outputs.clear()
            return

        for seq_group in scheduler_outputs.ignored_seq_groups:
            params = seq_group.sampling_params
            if params is not None and params.output_kind == (
                    RequestOutputKind.DELTA) and not seq_group.is_finished():
                continue

            request_output = RequestOutputFactory.create(
                seq_group,
                self.seq_id_to_seq_group,
                use_cache=self.use_cached_outputs,
            )
            if request_output:
                ctx.request_outputs.append(request_output)

        # Immediately process request outputs here (if callback is given)
        if (ctx.request_outputs
                and self.process_request_outputs_callback is not None):
            self.process_request_outputs_callback(ctx.request_outputs)
            ctx.request_outputs.clear()

        # For async case, we need to record the stats here.
        # For non-async case, the stats are done in the
        # LLMEngine/AsyncLLMEngine directly
        if is_async:
            # Log stats.
            self.do_log_stats(scheduler_outputs, outputs, finished_before,
                              skip)

            # Tracing
            self.do_tracing(scheduler_outputs, finished_before)

        return None

    def _advance_to_next_step(
            self, output: SamplerOutput,
            seq_group_metadata_list: List[SequenceGroupMetadata],
            scheduled_seq_groups: List[ScheduledSequenceGroup]) -> None:
        """Given model output from a single run, append the tokens to the
        sequences. This is normally done inside output processor, but it is
        required if the worker is to perform async forward pass to next step.
        """
        for seq_group_metadata, sequence_group_outputs, scheduled_seq_group in \
            zip(seq_group_metadata_list, output, scheduled_seq_groups):
            seq_group = scheduled_seq_group.seq_group

            if seq_group.is_finished():
                continue

            if self.scheduler_config.is_multi_step:
                # Updates happen only if the sequence is prefill
                self._update_num_computed_tokens_for_multi_step_prefill(
                    seq_group, seq_group_metadata,
                    seq_group.state.num_steps == 1)
            else:
                token_chunk_size = (seq_group_metadata.token_chunk_size
                                    if seq_group_metadata.token_chunk_size
                                    is not None else 0)
                seq_group.update_num_computed_tokens(token_chunk_size)

            if seq_group_metadata.do_sample:
                assert len(sequence_group_outputs.samples) == 1, (
                    "Async output processor expects a single sample"
                    " (i.e sampling_params.n == 1)")
                sample = sequence_group_outputs.samples[0]

                assert len(seq_group.seqs) == 1
                seq = seq_group.seqs[0]

                if self.scheduler_config.is_multi_step:
                    is_prefill_append = seq.data.get_num_uncomputed_tokens(
                    ) == 0
                    seq.append_token_id(sample.output_token, sample.logprobs,
                                        sample.output_embed)
                    if not is_prefill_append:
                        seq_group.update_num_computed_tokens(1)
                else:
                    seq.append_token_id(sample.output_token, sample.logprobs,
                                        sample.output_embed)

    def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
        """Performs one decoding iteration and returns newly generated results.

        <figure markdown="span">
        ![Overview of the step function](https://i.imgur.com/sv2HssD.png)
        <figcaption>Overview of the step function</figcaption>
        </figure>

        Details:
        - Step 1: Schedules the sequences to be executed in the next
            iteration and the token blocks to be swapped in/out/copy.

            - Depending on the scheduling policy,
                sequences may be `preempted/reordered`.
            - A Sequence Group (SG) refer to a group of sequences
                that are generated from the same prompt.

        - Step 2: Calls the distributed executor to execute the model.
        - Step 3: Processes the model output. This mainly includes:

            - Decodes the relevant outputs.
            - Updates the scheduled sequence groups with model outputs
                based on its `sampling parameters` (`use_beam_search` or not).
            - Frees the finished sequence groups.

        - Finally, it creates and returns the newly generated results.

        Example:
        ```
        # Please see the example/ folder for more detailed examples.

        # initialize engine and request arguments
        engine = LLMEngine.from_engine_args(engine_args)
        example_inputs = [(0, "What is LLM?",
        SamplingParams(temperature=0.0))]

        # Start the engine with an event loop
        while True:
            if example_inputs:
                req_id, prompt, sampling_params = example_inputs.pop(0)
                engine.add_request(str(req_id),prompt,sampling_params)

            # continue the request processing
            request_outputs = engine.step()
            for request_output in request_outputs:
                if request_output.finished:
                    # return or show the request output

            if not (engine.has_unfinished_requests() or example_inputs):
                break
        ```
        """
        if self.parallel_config.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is only supported through AsyncLLMEngine "
                "as performance will be severely degraded otherwise.")

        # For llm_engine, there is no pipeline parallel support, so the engine
        # used is always 0.
        virtual_engine = 0

        # These are cached outputs from previous iterations. None if on first
        # iteration
        cached_outputs = self.cached_scheduler_outputs[virtual_engine]
        seq_group_metadata_list = cached_outputs.seq_group_metadata_list
        scheduler_outputs = cached_outputs.scheduler_outputs
        allow_async_output_proc = cached_outputs.allow_async_output_proc

        ctx = self.scheduler_contexts[virtual_engine]

        # Clear outputs for each new scheduler iteration
        ctx.request_outputs.clear()

        # Skip the scheduler if there are any remaining steps in the seq groups.
        # This ensures that the scheduler is only called again when the current
        # batch has completed.
        # The scheduler is also skipped if a single request caused the last
        # engine step to fail, and the previous schedule needs to be rerun.
        if not self._has_remaining_steps(
                seq_group_metadata_list
        ) and not self._skip_scheduling_next_step:
            # Schedule iteration
            (seq_group_metadata_list, scheduler_outputs,
             allow_async_output_proc
             ) = self.scheduler[virtual_engine].schedule()

            ctx.seq_group_metadata_list = seq_group_metadata_list
            ctx.scheduler_outputs = scheduler_outputs

            finished_requests_ids = self.scheduler[
                virtual_engine].get_and_reset_finished_requests_ids()
            # When n>1, elements in self.seq_id_to_seq_group should be deleted
            # here, otherwise memory leaks.
            for finished_request_id in finished_requests_ids:
                if finished_request_id in self.seq_id_to_seq_group:
                    del self.seq_id_to_seq_group[finished_request_id]

            # Maybe switch from async mode to sync mode
            if not allow_async_output_proc and len(ctx.output_queue) > 0:
                self._process_model_outputs(ctx=ctx)

            if (self.scheduler_config.is_multi_step
                    and scheduler_outputs.num_lookahead_slots > 0):
                # cache the scheduler outputs for the next iteration if we have
                # lookahead slots
                self._cache_scheduler_outputs_for_multi_step(
                    virtual_engine, seq_group_metadata_list, scheduler_outputs,
                    allow_async_output_proc)
        else:
            finished_requests_ids = list()

        assert seq_group_metadata_list is not None
        assert scheduler_outputs is not None

        if not scheduler_outputs.is_empty():

            # Check if we have a cached last_output from the previous iteration.
            # For supporting PP this is probably the best way to pass the
            # sampled_token_ids, as a separate broadcast over all the PP stages
            # will cause one virtual engine's microbatch to block the pipeline.
            last_sampled_token_ids = \
                self._get_last_sampled_token_ids(virtual_engine)

            execute_model_req = ExecuteModelRequest(
                seq_group_metadata_list=seq_group_metadata_list,
                blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
                blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
                blocks_to_copy=scheduler_outputs.blocks_to_copy,
                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                running_queue_size=scheduler_outputs.running_queue_size,
                finished_requests_ids=finished_requests_ids,
                # We use ExecuteModelRequest to pass the last sampled_token_ids
                # to each of the non-last PP stages for in-place prepare_input.
                last_sampled_token_ids=last_sampled_token_ids)

            if allow_async_output_proc:
                execute_model_req.async_callback = self.async_callbacks[
                    virtual_engine]

            try:
                outputs = self.model_executor.execute_model(
                    execute_model_req=execute_model_req)
                self._skip_scheduling_next_step = False
            except InputProcessingError as e:
                # The input for this request cannot be processed, so we must
                # abort it. If there are remaining requests in the batch that
                # have been scheduled, they will be retried on the next step.
                invalid_request_id = e.request_id
                self._abort_and_cache_schedule(
                    request_id=invalid_request_id,
                    virtual_engine=virtual_engine,
                    seq_group_metadata_list=seq_group_metadata_list,
                    scheduler_outputs=scheduler_outputs,
                    allow_async_output_proc=allow_async_output_proc)
                # Raise so the caller is notified that this request failed
                raise

            # We need to do this here so that last step's sampled_token_ids can
            # be passed to the next iteration for PP.
            if self.scheduler_config.is_multi_step:
                self._update_cached_scheduler_output(virtual_engine, outputs)
        else:
            # Nothing scheduled => If there is pending async postprocessor,
            # then finish it here.
            if len(ctx.output_queue) > 0:
                self._process_model_outputs(ctx=ctx)
            # No outputs in this case
            outputs = []

        # Finish the current step for all the sequence groups.
        if self.scheduler_config.is_multi_step:
            for seq_group in seq_group_metadata_list:
                seq_group.finish_step()

        if not self._has_remaining_steps(seq_group_metadata_list):
            # clear the cache if we have finished all the steps.
            if self.scheduler_config.is_multi_step:
                self.cached_scheduler_outputs[0] = SchedulerOutputState()

            # is_first_step_output is True only when the num_steps of all
            # the sequences are 1. When the num_steps > 1,
            # multi_step_model_runner does the first-step output append.
            is_first_step_output: bool = False if not seq_group_metadata_list \
                else seq_group_metadata_list[0].state.num_steps == 1

            # Add results to the output_queue
            ctx.append_output(outputs=outputs,
                              seq_group_metadata_list=seq_group_metadata_list,
                              scheduler_outputs=scheduler_outputs,
                              is_async=allow_async_output_proc,
                              is_last_step=True,
                              is_first_step_output=is_first_step_output)

            if outputs and allow_async_output_proc:
                assert len(outputs) == 1, (
                    "Async postprocessor expects only a single output set")

                self._advance_to_next_step(
                    outputs[0], seq_group_metadata_list,
                    scheduler_outputs.scheduled_seq_groups)

            # Check if need to run the usual non-async path
            if not allow_async_output_proc:
                self._process_model_outputs(ctx=ctx)

                # Log stats.
                self.do_log_stats(scheduler_outputs, outputs)

                # Tracing
                self.do_tracing(scheduler_outputs)
        else:
            # Multi-step case
            return ctx.request_outputs

        if not self.has_unfinished_requests():
            # Drain async postprocessor (if exists)
            if len(ctx.output_queue) > 0:
                self._process_model_outputs(ctx=ctx)
            assert len(ctx.output_queue) == 0

            # Stop the execute model loop in parallel workers until there are
            # more requests to process. This avoids waiting indefinitely in
            # torch.distributed ops which may otherwise timeout, and unblocks
            # the RPC thread in the workers so that they can process any other
            # queued control plane messages, such as add/remove lora adapters.
            logger.debug("Stopping remote worker execution loop.")
            self.model_executor.stop_remote_worker_execution_loop()

        return ctx.request_outputs

    def _abort_and_cache_schedule(
            self, request_id: str, virtual_engine: int,
            seq_group_metadata_list: List[SequenceGroupMetadata],
            scheduler_outputs: SchedulerOutputs,
            allow_async_output_proc: bool) -> None:
        """Aborts a single request, and caches the scheduler outputs minus that
        request. This allows the next step to continue processing the remaining
        requests without having to re-run the scheduler."""

        # Abort the request and remove its sequence group from the current
        # schedule
        self.abort_request(request_id)
        for i, metadata in enumerate(seq_group_metadata_list):
            if metadata.request_id == request_id:
                del seq_group_metadata_list[i]
                break
        for i, group in enumerate(scheduler_outputs.scheduled_seq_groups):
            if group.seq_group.request_id == request_id:
                del scheduler_outputs.scheduled_seq_groups[i]
                break

        # If there are still other sequence groups left in the schedule, cache
        # them and flag the engine to reuse the schedule.
        if len(seq_group_metadata_list) > 0:
            self._skip_scheduling_next_step = True
            # Reuse multi-step caching logic
            self._cache_scheduler_outputs_for_multi_step(
                virtual_engine=virtual_engine,
                scheduler_outputs=scheduler_outputs,
                seq_group_metadata_list=seq_group_metadata_list,
                allow_async_output_proc=allow_async_output_proc)

    def _has_remaining_steps(
        self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
    ) -> bool:
        if (not self.scheduler_config.is_multi_step
                or not seq_group_metadata_list):
            return False

        # TODO(will) this is a sanity check for nowto make sure that all the
        # seqs are on the same steps. Eventually we will want to do some sort of
        # dynamic scheduling when doing multi-step decoding.
        ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps
        if any([
                seq_group.state.remaining_steps != ref_remaining_steps
                for seq_group in seq_group_metadata_list[1:]
        ]):
            raise AssertionError("All running sequence groups should "
                                 "have the same remaining steps.")

        return ref_remaining_steps > 0

    def _cache_scheduler_outputs_for_multi_step(
            self, virtual_engine: int,
            seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
            scheduler_outputs: SchedulerOutputs,
            allow_async_output_proc: bool) -> None:
        co = self.cached_scheduler_outputs[virtual_engine]

        co.seq_group_metadata_list = seq_group_metadata_list
        co.scheduler_outputs = scheduler_outputs
        co.allow_async_output_proc = allow_async_output_proc
        co.last_output = None

    def _update_cached_scheduler_output(
            self, virtual_engine: int,
            output: List[Optional[SamplerOutput]]) -> None:
        if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0
                and output[0] is not None):
            last_output = output[-1]
            assert last_output is not None
            assert last_output.sampled_token_ids_cpu is not None
            assert last_output.sampled_token_ids is None
            assert last_output.sampled_token_probs is None
            self.cached_scheduler_outputs[
                virtual_engine].last_output = last_output

    def _get_last_sampled_token_ids(
            self, virtual_engine: int) -> Optional[torch.Tensor]:
        cached_last_output = self.cached_scheduler_outputs[
            virtual_engine].last_output
        if (self.scheduler_config.is_multi_step
                and self.parallel_config.pipeline_parallel_size > 1
                and cached_last_output is not None
                and cached_last_output.sampled_token_ids_cpu is not None):
            return cached_last_output.sampled_token_ids_cpu
        return None

    def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
        if not self.log_stats:
            raise RuntimeError(
                "Stat logging is disabled. Set `disable_log_stats=False` "
                "argument to enable.")
        if logger_name in self.stat_loggers:
            raise KeyError(f"Logger with name {logger_name} already exists.")
        self.stat_loggers[logger_name] = logger

    def remove_logger(self, logger_name: str) -> None:
        if not self.log_stats:
            raise RuntimeError(
                "Stat logging is disabled. Set `disable_log_stats=False` "
                "argument to enable.")
        if logger_name not in self.stat_loggers:
            raise KeyError(f"Logger with name {logger_name} does not exist.")
        del self.stat_loggers[logger_name]

    def do_log_stats(self,
                     scheduler_outputs: Optional[SchedulerOutputs] = None,
                     model_output: Optional[List[SamplerOutput]] = None,
                     finished_before: Optional[List[int]] = None,
                     skip: Optional[List[int]] = None) -> None:
        """Forced log when no requests active."""
        if self.log_stats:
            stats = self._get_stats(scheduler_outputs, model_output,
                                    finished_before, skip)
            for logger in self.stat_loggers.values():
                logger.log(stats)

    def _get_stats(self,
                   scheduler_outputs: Optional[SchedulerOutputs],
                   model_output: Optional[List[SamplerOutput]] = None,
                   finished_before: Optional[List[int]] = None,
                   skip: Optional[List[int]] = None) -> Stats:
        """Get Stats to be Logged to Prometheus.

        Args:
            scheduler_outputs: Optional, used to populate metrics related to
                the scheduled batch,
            model_output: Optional, used to emit speculative decoding metrics
                which are created by the workers.
            finished_before: Optional, indices of sequences that were finished
                before. These sequences will be ignored.
            skip: Optional, indices of sequences that were preempted. These
                sequences will be ignored.
        """
        now = time.time()

        # System State
        #   Scheduler State
        num_running_sys = sum(
            len(scheduler.running) for scheduler in self.scheduler)
        num_swapped_sys = sum(
            len(scheduler.swapped) for scheduler in self.scheduler)
        num_waiting_sys = sum(
            len(scheduler.waiting) for scheduler in self.scheduler)

        # KV Cache Usage in %
        num_total_gpu = self.cache_config.num_gpu_blocks
        gpu_cache_usage_sys = 0.
        if num_total_gpu:  # Guard against both None and 0
            num_free_gpu = sum(
                scheduler.block_manager.get_num_free_gpu_blocks()
                for scheduler in self.scheduler)
            gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)

        num_total_cpu = self.cache_config.num_cpu_blocks
        cpu_cache_usage_sys = 0.
        if num_total_cpu:  # Guard against both None and 0
            num_free_cpu = sum(
                scheduler.block_manager.get_num_free_cpu_blocks()
                for scheduler in self.scheduler)
            cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu)

        # Prefix Cache Hit Rate. Note that we always use
        # the cache hit rate of the first virtual engine.
        cpu_prefix_cache_hit_rate = self.scheduler[
            0].get_prefix_cache_hit_rate(Device.CPU)
        gpu_prefix_cache_hit_rate = self.scheduler[
            0].get_prefix_cache_hit_rate(Device.GPU)

        # Exchange the uasge and cache hit stats between gpu and cpu when
        # running on cpu because the cpu_worker.py intentionally reports the
        # number of cpu blocks as gpu blocks in favor of cache management.
        if self.device_config.device_type == "cpu":
            num_total_gpu, num_total_cpu = num_total_cpu, num_total_gpu
            gpu_cache_usage_sys, cpu_cache_usage_sys = (
                cpu_cache_usage_sys,
                gpu_cache_usage_sys,
            )
            gpu_prefix_cache_hit_rate, cpu_prefix_cache_hit_rate = (
                cpu_prefix_cache_hit_rate,
                gpu_prefix_cache_hit_rate,
            )

        # Iteration stats
        num_prompt_tokens_iter = 0
        num_generation_tokens_iter = 0
        num_tokens_iter = 0
        time_to_first_tokens_iter: List[float] = []
        time_per_output_tokens_iter: List[float] = []
        num_preemption_iter = (0 if scheduler_outputs is None else
                               scheduler_outputs.preempted)

        # Request stats
        #   Latency
        time_e2e_requests: List[float] = []
        time_queue_requests: List[float] = []
        time_inference_requests: List[float] = []
        time_prefill_requests: List[float] = []
        time_decode_requests: List[float] = []
        #   Metadata
        num_prompt_tokens_requests: List[int] = []
        num_generation_tokens_requests: List[int] = []
        n_requests: List[int] = []
        max_num_generation_tokens_requests: List[int] = []
        max_tokens_requests: List[int] = []
        finished_reason_requests: List[str] = []

        # LoRA requests
        running_lora_adapters = dict(
            collectionsCounter([
                running_request.lora_request.lora_name
                for scheduler in self.scheduler
                for running_request in scheduler.running
                if running_request.lora_request
            ]))
        waiting_lora_adapters = dict(
            collectionsCounter([
                waiting_request.lora_request.lora_name
                for scheduler in self.scheduler
                for waiting_request in scheduler.waiting
                if waiting_request.lora_request
            ]))
        max_lora_stat = "0"
        if self.lora_config:
            max_lora_stat = str(self.lora_config.max_loras)

        # NOTE: This loop assumes prefill seq_groups are before
        # decode seq_groups in scheduled_seq_groups.
        if scheduler_outputs is not None:
            # For async postprocessor, already finished sequences need to be
            # not counted (to avoid double counting)
            actual_num_batched_tokens = scheduler_outputs.num_batched_tokens  # type: ignore

            num_generation_tokens_from_prefill_groups = 0
            # NOTE: if scheduler_outputs.num_prefill_groups > 0 and
            # the len of scheduler_outputs.scheduled_seq_groups is !=
            # scheduler_outputs.num_prefill_groups, this means that
            # chunked prefills have been detected.

            for idx, scheduled_seq_group in enumerate(
                    scheduler_outputs.scheduled_seq_groups):
                # Skip double logging when using async output proc
                if finished_before and idx in finished_before:
                    actual_num_batched_tokens -= 1
                    continue

                # Currently, skip == preempted sequences, so we need to skip
                # their log stats
                if skip and idx in skip:
                    continue

                group_was_prefill = idx < scheduler_outputs.num_prefill_groups
                seq_group = scheduled_seq_group.seq_group

                # NOTE: a seq_group that completed all of its prefill tokens
                # in the last iteration will have seq_group.is_prefill() = False
                # with group_was_prefill = True
                if group_was_prefill:
                    # Number of prompt tokens.
                    num_prompt_tokens_iter += (
                        scheduled_seq_group.token_chunk_size)

                    # If the seq_group just finished the prefill state
                    # get TTFT.
                    if not seq_group.is_prefill():
                        latency = seq_group.get_last_token_latency()
                        time_to_first_tokens_iter.append(latency)

                        # One generation token per finished prefill.
                        num_generation_tokens_from_prefill_groups += (
                            seq_group.num_seqs())
                else:
                    # TPOTs.
                    latency = seq_group.get_last_token_latency()
                    time_per_output_tokens_iter.append(latency)
                    if seq_group.state.current_step == 0:
                        # For async_output_proc, the do_log_stats()
                        # is called following init_multi_step(), which
                        # sets the current_step to zero.
                        actual_num_batched_tokens +=\
                            seq_group.state.num_steps - 1
                    else:
                        actual_num_batched_tokens +=\
                            seq_group.state.current_step - 1

                # Because of chunked prefill, we can have a single sequence
                # group that does multiple prompt_runs. To prevent logging
                # the same metadata more than once per request, we standardize
                # on logging request level information for finished requests,
                # which can only happen once.
                if seq_group.is_finished():
                    # Latency timings
                    time_e2e_requests.append(now -
                                             seq_group.metrics.arrival_time)
                    if (seq_group.metrics.first_scheduled_time is not None and
                            seq_group.metrics.first_token_time is not None):
                        time_queue_requests.append(
                            seq_group.metrics.first_scheduled_time -
                            seq_group.metrics.arrival_time)
                        time_prefill_requests.append(
                            seq_group.metrics.first_token_time -
                            seq_group.metrics.first_scheduled_time)
                        time_decode_requests.append(
                            now - seq_group.metrics.first_token_time)
                        time_inference_requests.append(
                            now - seq_group.metrics.first_scheduled_time)
                    # Metadata
                    num_prompt_tokens_requests.append(
                        len(seq_group.prompt_token_ids))
                    num_generation_tokens_requests.extend([
                        seq.get_output_len()
                        for seq in seq_group.get_finished_seqs()
                    ])
                    max_num_generation_tokens_requests.append(
                        max(seq.get_output_len()
                            for seq in seq_group.get_seqs()))
                    if seq_group.sampling_params is not None:
                        n_requests.append(seq_group.sampling_params.n)
                        max_tokens_requests.append(
                            seq_group.sampling_params.max_tokens)
                    finished_reason_requests.extend([
                        SequenceStatus.get_finished_reason(seq.status)
                        for seq in seq_group.get_finished_seqs()
                    ])

            # Number of generation tokens.
            #   num_batched_tokens equals the number of prompt_tokens plus the
            #   number of decode_tokens in a single iteration. So,
            #   num_generation_tokens = num_batched_tokens - num_prompt_tokens
            #   + num_generation_tokens_from_prefill_groups (since we generate
            #   one token on prefills on iters where the prefill finishes).
            num_generation_tokens_iter = (
                actual_num_batched_tokens - num_prompt_tokens_iter +
                num_generation_tokens_from_prefill_groups)
            num_tokens_iter = (num_generation_tokens_iter +
                               num_prompt_tokens_iter)
        # Spec decode, if enabled, emits specialized metrics from the worker in
        # sampler output.
        if model_output and isinstance(model_output[0], SamplerOutput) and (
                model_output[0].spec_decode_worker_metrics is not None):
            spec_decode_metrics = model_output[0].spec_decode_worker_metrics
        else:
            spec_decode_metrics = None

        return Stats(
            now=now,
            # System stats
            #   Scheduler State
            num_running_sys=num_running_sys,
            num_swapped_sys=num_swapped_sys,
            num_waiting_sys=num_waiting_sys,
            #   KV Cache Usage in %
            gpu_cache_usage_sys=gpu_cache_usage_sys,
            cpu_cache_usage_sys=cpu_cache_usage_sys,
            #   Prefix Cache Hit Rate
            cpu_prefix_cache_hit_rate=cpu_prefix_cache_hit_rate,
            gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate,

            # Iteration stats
            num_prompt_tokens_iter=num_prompt_tokens_iter,
            num_generation_tokens_iter=num_generation_tokens_iter,
            num_tokens_iter=num_tokens_iter,
            time_to_first_tokens_iter=time_to_first_tokens_iter,
            time_per_output_tokens_iter=time_per_output_tokens_iter,
            spec_decode_metrics=spec_decode_metrics,
            num_preemption_iter=num_preemption_iter,

            # Request stats
            #   Latency
            time_e2e_requests=time_e2e_requests,
            time_queue_requests=time_queue_requests,
            time_inference_requests=time_inference_requests,
            time_prefill_requests=time_prefill_requests,
            time_decode_requests=time_decode_requests,
            #   Metadata
            num_prompt_tokens_requests=num_prompt_tokens_requests,
            num_generation_tokens_requests=num_generation_tokens_requests,
            max_num_generation_tokens_requests=
            max_num_generation_tokens_requests,
            n_requests=n_requests,
            max_tokens_requests=max_tokens_requests,
            finished_reason_requests=finished_reason_requests,
            max_lora=str(max_lora_stat),
            waiting_lora_adapters=list(waiting_lora_adapters.keys()),
            running_lora_adapters=list(running_lora_adapters.keys()))

    def add_lora(self, lora_request: LoRARequest) -> bool:
        return self.model_executor.add_lora(lora_request)

    def remove_lora(self, lora_id: int) -> bool:
        return self.model_executor.remove_lora(lora_id)

    def list_loras(self) -> Set[int]:
        return self.model_executor.list_loras()

    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)

    def add_prompt_adapter(
            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
        return self.model_executor.add_prompt_adapter(prompt_adapter_request)

    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        return self.model_executor.remove_prompt_adapter(prompt_adapter_id)

    def list_prompt_adapters(self) -> List[int]:
        return self.model_executor.list_prompt_adapters()

    def start_profile(self) -> None:
        self.model_executor.start_profile()

    def stop_profile(self) -> None:
        self.model_executor.stop_profile()

    def sleep(self, level: int = 1) -> None:
        assert self.vllm_config.model_config.enable_sleep_mode, (
            "Sleep mode is not enabled in the model config")
        self.model_executor.sleep(level=level)

    def wake_up(self, tags: Optional[list[str]] = None) -> None:
        assert self.vllm_config.model_config.enable_sleep_mode, (
            "Sleep mode is not enabled in the model config")
        self.model_executor.wake_up(tags)

    def is_sleeping(self) -> bool:
        return self.model_executor.is_sleeping

    def check_health(self) -> None:
        self.model_executor.check_health()

    def is_tracing_enabled(self) -> bool:
        return self.tracer is not None

    def do_tracing(self,
                   scheduler_outputs: SchedulerOutputs,
                   finished_before: Optional[List[int]] = None) -> None:
        if self.tracer is None:
            return

        for idx, scheduled_seq_group in enumerate(
                scheduler_outputs.scheduled_seq_groups):
            # Skip double tracing when using async output proc
            if finished_before and idx in finished_before:
                continue

            seq_group = scheduled_seq_group.seq_group
            if seq_group.is_finished():
                self.create_trace_span(seq_group)

    def create_trace_span(self, seq_group: SequenceGroup) -> None:
        if self.tracer is None or seq_group.sampling_params is None:
            return
        arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9)

        trace_context = extract_trace_context(seq_group.trace_headers)

        with self.tracer.start_as_current_span(
                "llm_request",
                kind=SpanKind.SERVER,
                context=trace_context,
                start_time=arrival_time_nano_seconds) as seq_span:
            metrics = seq_group.metrics
            ttft = metrics.first_token_time - metrics.arrival_time
            e2e_time = metrics.finished_time - metrics.arrival_time
            seq_span.set_attribute(SpanAttributes.GEN_AI_RESPONSE_MODEL,
                                   self.model_config.model)
            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID,
                                   seq_group.request_id)
            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE,
                                   seq_group.sampling_params.temperature)
            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P,
                                   seq_group.sampling_params.top_p)
            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS,
                                   seq_group.sampling_params.max_tokens)
            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N,
                                   seq_group.sampling_params.n)
            seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_NUM_SEQUENCES,
                                   seq_group.num_seqs())
            seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS,
                                   len(seq_group.prompt_token_ids))
            seq_span.set_attribute(
                SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS,
                sum([
                    seq.get_output_len()
                    for seq in seq_group.get_finished_seqs()
                ]))
            seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE,
                                   metrics.time_in_queue)
            seq_span.set_attribute(
                SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
            seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E, e2e_time)
            if metrics.scheduler_time is not None:
                seq_span.set_attribute(
                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER,
                    metrics.scheduler_time)
            if metrics.model_forward_time is not None:
                seq_span.set_attribute(
                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD,
                    metrics.model_forward_time / 1000.0)
            if metrics.model_execute_time is not None:
                seq_span.set_attribute(
                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE,
                    metrics.model_execute_time)

    def _validate_model_inputs(self, inputs: ProcessorInputs,
                               lora_request: Optional[LoRARequest]):
        encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)

        if encoder_inputs is not None:
            self._validate_model_input(encoder_inputs,
                                       lora_request,
                                       prompt_type="encoder")

        self._validate_model_input(decoder_inputs,
                                   lora_request,
                                   prompt_type="decoder")

    def _validate_model_input(
        self,
        prompt_inputs: SingletonInputs,
        lora_request: Optional[LoRARequest],
        *,
        prompt_type: Literal["encoder", "decoder"],
    ):
        model_config = self.model_config
        tokenizer = (None if self.tokenizer is None else
                     self.tokenizer.get_lora_tokenizer(lora_request))

        prompt_ids = prompt_inputs.get("prompt_token_ids", [])
        if not prompt_ids:
            if prompt_type == "encoder" and model_config.is_multimodal_model:
                pass  # Mllama may have empty encoder inputs for text-only data
            elif prompt_inputs["type"] == "embeds":
                pass
            else:
                raise ValueError(f"The {prompt_type} prompt cannot be empty")

        if tokenizer is not None:
            max_input_id = max(prompt_ids, default=0)
            if max_input_id > tokenizer.max_token_id:
                raise ValueError(
                    f"Token id {max_input_id} is out of vocabulary")

        max_prompt_len = self.model_config.max_model_len
        if len(prompt_ids) > max_prompt_len:
            if prompt_type == "encoder" and model_config.is_multimodal_model:
                mm_registry = self.input_preprocessor.mm_registry
                mm_processor = mm_registry.create_processor(
                    model_config,
                    tokenizer=tokenizer or object(),  # Dummy if no tokenizer
                )
                assert isinstance(mm_processor, EncDecMultiModalProcessor)

                if mm_processor.pad_dummy_encoder_prompt:
                    return  # Skip encoder length check for Whisper

            if model_config.is_multimodal_model:
                suggestion = (
                    "Make sure that `max_model_len` is no smaller than the "
                    "number of text tokens plus multimodal tokens. For image "
                    "inputs, the number of image tokens depends on the number "
                    "of images, and possibly their aspect ratios as well.")
            else:
                suggestion = (
                    "Make sure that `max_model_len` is no smaller than the "
                    "number of text tokens.")

            raise ValueError(
                f"The {prompt_type} prompt (length {len(prompt_ids)}) is "
                f"longer than the maximum model length of {max_prompt_len}. "
                f"{suggestion}")

            # TODO: Find out how many placeholder tokens are there so we can
            # check that chunked prefill does not truncate them
            # max_batch_len = self.scheduler_config.max_num_batched_tokens

    def _build_logits_processors(
            self, sampling_params: SamplingParams,
            lora_request: Optional[LoRARequest]) -> SamplingParams:
        """Constructs logits processors based on the guided_decoding,
        logits_bias, and allowed_token_ids fields in sampling_params. Deletes
        those fields and adds the constructed logits processors to the
        logits_processors field. Returns the modified sampling params."""

        logits_processors = []

        if sampling_params.guided_decoding is not None:
            # Defensively copy sampling params since guided decoding logits
            # processors can have different state for each request
            sampling_params = copy.copy(sampling_params)
            guided_decoding = sampling_params.guided_decoding

            logger.debug(
                "Building guided decoding logits processor in "
                "LLMEngine. Params: %s", guided_decoding)

            tokenizer = self.get_tokenizer(lora_request=lora_request)
            guided_decoding.backend = guided_decoding.backend or \
                self.decoding_config.backend

            if self.decoding_config.reasoning_backend:
                logger.debug("Building with reasoning backend %s",
                             self.decoding_config.reasoning_backend)

            processor = get_local_guided_decoding_logits_processor(
                guided_params=guided_decoding,
                tokenizer=tokenizer,
                model_config=self.model_config,
                reasoning_backend=self.decoding_config.reasoning_backend,
            )
            if processor:
                logits_processors.append(processor)

            # Unset so this doesn't get passed down to the model
            sampling_params.guided_decoding = None

        if (sampling_params.logit_bias or sampling_params.allowed_token_ids):
            tokenizer = self.get_tokenizer(lora_request=lora_request)

            processors = get_openai_logits_processors(
                logit_bias=sampling_params.logit_bias,
                allowed_token_ids=sampling_params.allowed_token_ids,
                tokenizer=tokenizer)
            logits_processors.extend(processors)

            # Unset so these don't get passed down to the model
            sampling_params.logit_bias = None
            sampling_params.allowed_token_ids = None

        if len(sampling_params.bad_words) > 0:
            tokenizer = self.get_tokenizer(lora_request)
            processors = get_bad_words_logits_processors(
                bad_words=sampling_params.bad_words, tokenizer=tokenizer)
            logits_processors.extend(processors)

        if logits_processors:
            if sampling_params.logits_processors is None:
                sampling_params.logits_processors = logits_processors
            else:
                sampling_params.logits_processors.extend(logits_processors)

        return sampling_params

    def collective_rpc(self,
                       method: Union[str, Callable[..., _R]],
                       timeout: Optional[float] = None,
                       args: tuple = (),
                       kwargs: Optional[dict[str, Any]] = None) -> list[_R]:
        return self.model_executor.collective_rpc(method, timeout, args,
                                                  kwargs)

DO_VALIDATE_OUTPUT class-attribute

DO_VALIDATE_OUTPUT: bool = False

A flag to toggle whether to validate the type of request output.

_skip_scheduling_next_step instance-attribute

_skip_scheduling_next_step = False

async_callbacks instance-attribute

async_callbacks = [
    partial(
        process_model_outputs, ctx=scheduler_contexts[v_id]
    )
    for v_id in range(pipeline_parallel_size)
]

cache_config instance-attribute

cache_config = cache_config

cached_scheduler_outputs instance-attribute

cached_scheduler_outputs = [
    SchedulerOutputState()
    for _ in range(pipeline_parallel_size)
]

decoding_config instance-attribute

decoding_config = decoding_config or DecodingConfig()

detokenizer instance-attribute

detokenizer = Detokenizer(tokenizer)

device_config instance-attribute

device_config = device_config

generation_config_fields instance-attribute

generation_config_fields = try_get_generation_config()

input_preprocessor instance-attribute

input_preprocessor = InputPreprocessor(
    model_config, tokenizer, mm_registry
)

load_config instance-attribute

load_config = load_config

log_stats instance-attribute

log_stats = log_stats

lora_config instance-attribute

lora_config = lora_config

model_config instance-attribute

model_config = model_config

model_executor instance-attribute

model_executor = executor_class(vllm_config=vllm_config)

observability_config instance-attribute

observability_config = (
    observability_config or ObservabilityConfig()
)

output_processor instance-attribute

output_processor = create_output_processor(
    scheduler_config,
    detokenizer,
    scheduler,
    seq_counter,
    get_tokenizer_for_seq,
    stop_checker=StopChecker(
        max_model_len, get_tokenizer_for_seq
    ),
)

parallel_config instance-attribute

parallel_config = parallel_config

process_request_outputs_callback instance-attribute

process_request_outputs_callback: Optional[Callable] = None

prompt_adapter_config instance-attribute

prompt_adapter_config = prompt_adapter_config

scheduler instance-attribute

scheduler = [
    Scheduler(
        scheduler_config,
        cache_config,
        lora_config,
        pipeline_parallel_size,
        async_callbacks[v_id]
        if use_async_output_proc
        else None,
    )
    for v_id in range(pipeline_parallel_size)
]

scheduler_config instance-attribute

scheduler_config = scheduler_config

scheduler_contexts instance-attribute

scheduler_contexts = [
    SchedulerContext(
        multi_step_stream_outputs=multi_step_stream_outputs
    )
    for _ in range(pipeline_parallel_size)
]

seq_counter instance-attribute

seq_counter = Counter()

seq_id_to_seq_group instance-attribute

seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {}

speculative_config instance-attribute

speculative_config = speculative_config

stat_loggers instance-attribute

stat_loggers = stat_loggers

tokenizer instance-attribute

tokenizer: Optional[TokenizerGroup]

tracer instance-attribute

tracer = None

use_cached_outputs instance-attribute

use_cached_outputs = use_cached_outputs

vllm_config instance-attribute

vllm_config = vllm_config

__del__

__del__()

Source code in vllm/engine/llm_engine.py

def __del__(self):
    # Shutdown model executor when engine is garbage collected
    # Use getattr since __init__ can fail before the field is set
    if model_executor := getattr(self, "model_executor", None):
        model_executor.shutdown()

__init__

__init__(
    vllm_config: VllmConfig,
    executor_class: Type[ExecutorBase],
    log_stats: bool,
    usage_context: UsageContext = ENGINE_CONTEXT,
    stat_loggers: Optional[
        Dict[str, StatLoggerBase]
    ] = None,
    mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
    use_cached_outputs: bool = False,
) -> None

Source code in vllm/engine/llm_engine.py

def __init__(
    self,
    vllm_config: VllmConfig,
    executor_class: Type[ExecutorBase],
    log_stats: bool,
    usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
    stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
    mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
    use_cached_outputs: bool = False,
) -> None:
    if envs.VLLM_USE_V1:
        raise ValueError(
            "Using V0 LLMEngine, but envs.VLLM_USE_V1=True. "
            "This should not happen. As a workaround, try using "
            "LLMEngine.from_vllm_config(...) or explicitly set "
            "VLLM_USE_V1=0 or 1 and report this issue on Github.")

    self.vllm_config = vllm_config
    self.model_config = vllm_config.model_config
    self.cache_config = vllm_config.cache_config
    self.lora_config = vllm_config.lora_config
    self.parallel_config = vllm_config.parallel_config
    self.scheduler_config = vllm_config.scheduler_config
    self.device_config = vllm_config.device_config
    self.speculative_config = vllm_config.speculative_config  # noqa
    self.load_config = vllm_config.load_config
    self.decoding_config = vllm_config.decoding_config or DecodingConfig(  # noqa
    )
    self.prompt_adapter_config = vllm_config.prompt_adapter_config  # noqa
    self.observability_config = vllm_config.observability_config or ObservabilityConfig(  # noqa
    )

    logger.info(
        "Initializing a V0 LLM engine (v%s) with config: %s, "
        "use_cached_outputs=%s, ",
        VLLM_VERSION,
        vllm_config,
        use_cached_outputs,
    )

    self.log_stats = log_stats
    self.use_cached_outputs = use_cached_outputs

    if not self.model_config.skip_tokenizer_init:
        self.tokenizer = self._init_tokenizer()
        self.detokenizer = Detokenizer(self.tokenizer)
        tokenizer_group = self.get_tokenizer_group()
    else:
        self.tokenizer = None
        self.detokenizer = None
        tokenizer_group = None

    # Ensure that the function doesn't contain a reference to self,
    # to avoid engine GC issues
    def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
        assert tokenizer_group, ("tokenizer_group cannot be None, "
                                 "make sure skip_tokenizer_init is False")
        return tokenizer_group.get_lora_tokenizer(sequence.lora_request)

    self.seq_counter = Counter()
    self.generation_config_fields = (
        self.model_config.try_get_generation_config())

    self.input_preprocessor = InputPreprocessor(self.model_config,
                                                self.tokenizer,
                                                mm_registry)

    self.model_executor = executor_class(vllm_config=vllm_config)

    if self.model_config.runner_type != "pooling":
        self._initialize_kv_caches()

    # If usage stat is enabled, collect relevant info.
    if is_usage_stats_enabled():
        from vllm.model_executor.model_loader import (
            get_architecture_class_name)
        usage_message.report_usage(
            get_architecture_class_name(self.model_config),
            usage_context,
            extra_kvs={
                # Common configuration
                "dtype":
                str(self.model_config.dtype),
                "tensor_parallel_size":
                self.parallel_config.tensor_parallel_size,
                "block_size":
                self.cache_config.block_size,
                "gpu_memory_utilization":
                self.cache_config.gpu_memory_utilization,

                # Quantization
                "quantization":
                self.model_config.quantization,
                "kv_cache_dtype":
                str(self.cache_config.cache_dtype),

                # Feature flags
                "enable_lora":
                bool(self.lora_config),
                "enable_prompt_adapter":
                bool(self.prompt_adapter_config),
                "enable_prefix_caching":
                self.cache_config.enable_prefix_caching,
                "enforce_eager":
                self.model_config.enforce_eager,
                "disable_custom_all_reduce":
                self.parallel_config.disable_custom_all_reduce,
            })

    self.cached_scheduler_outputs = [
        SchedulerOutputState()
        for _ in range(self.parallel_config.pipeline_parallel_size)
    ]

    self.scheduler_contexts = [
        SchedulerContext(multi_step_stream_outputs=self.scheduler_config.
                         multi_step_stream_outputs)
        for _ in range(self.parallel_config.pipeline_parallel_size)
    ]

    if self.model_config.use_async_output_proc:
        process_model_outputs = weak_bind(self._process_model_outputs)

        self.async_callbacks = [
            partial(process_model_outputs,
                    ctx=self.scheduler_contexts[v_id])
            for v_id in range(self.parallel_config.pipeline_parallel_size)
        ]
    else:
        self.async_callbacks = []

    # Currently used by AsyncLLMEngine to ensure quick append
    # of request outputs to asyncio queues
    self.process_request_outputs_callback: Optional[Callable] = None

    # Create the scheduler.
    # NOTE: the cache_config here have been updated with the numbers of
    # GPU and CPU blocks, which are profiled in the distributed executor.
    if isinstance(self.vllm_config.scheduler_config.scheduler_cls, str):
        Scheduler = resolve_obj_by_qualname(
            self.vllm_config.scheduler_config.scheduler_cls)
    else:
        Scheduler = self.vllm_config.scheduler_config.scheduler_cls
    self.scheduler = [
        Scheduler(
            self.scheduler_config, self.cache_config, self.lora_config,
            self.parallel_config.pipeline_parallel_size,
            self.async_callbacks[v_id]
            if self.model_config.use_async_output_proc else None)
        for v_id in range(self.parallel_config.pipeline_parallel_size)
    ]

    # Metric Logging.
    if self.log_stats:
        if stat_loggers is not None:
            self.stat_loggers = stat_loggers
        else:
            # Lazy import for prometheus multiprocessing.
            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
            # before prometheus_client is imported.
            # See https://prometheus.github.io/client_python/multiprocess/
            from vllm.engine.metrics import (LoggingStatLogger,
                                             PrometheusStatLogger)

            self.stat_loggers = {
                "logging":
                LoggingStatLogger(
                    local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                    vllm_config=vllm_config),
                "prometheus":
                PrometheusStatLogger(
                    local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                    labels=dict(
                        model_name=self.model_config.served_model_name),
                    vllm_config=vllm_config),
            }
            self.stat_loggers["prometheus"].info("cache_config",
                                                 self.cache_config)

    self.tracer = None
    if self.observability_config.otlp_traces_endpoint:
        self.tracer = init_tracer(
            "vllm.llm_engine",
            self.observability_config.otlp_traces_endpoint)

    # Create sequence output processor, e.g. for beam search or
    # speculative decoding.
    self.output_processor = (
        SequenceGroupOutputProcessor.create_output_processor(
            self.scheduler_config,
            self.detokenizer,
            self.scheduler,
            self.seq_counter,
            get_tokenizer_for_seq,
            stop_checker=StopChecker(self.scheduler_config.max_model_len,
                                     get_tokenizer_for_seq),
        ))

    self.seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {}

    # Flag to set when an input fails to process and the engine should run
    # the next step without re-scheduling.
    self._skip_scheduling_next_step = False

    # Don't keep the dummy data in memory
    self.reset_mm_cache()

__reduce__

__reduce__()

Source code in vllm/engine/llm_engine.py

def __reduce__(self):
    # This is to ensure that the LLMEngine is not referenced in
    # the closure used to initialize Ray worker actors
    raise RuntimeError("LLMEngine should not be pickled!")

_abort_and_cache_schedule

_abort_and_cache_schedule(
    request_id: str,
    virtual_engine: int,
    seq_group_metadata_list: List[SequenceGroupMetadata],
    scheduler_outputs: SchedulerOutputs,
    allow_async_output_proc: bool,
) -> None

Aborts a single request, and caches the scheduler outputs minus that request. This allows the next step to continue processing the remaining requests without having to re-run the scheduler.

Source code in vllm/engine/llm_engine.py

def _abort_and_cache_schedule(
        self, request_id: str, virtual_engine: int,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        scheduler_outputs: SchedulerOutputs,
        allow_async_output_proc: bool) -> None:
    """Aborts a single request, and caches the scheduler outputs minus that
    request. This allows the next step to continue processing the remaining
    requests without having to re-run the scheduler."""

    # Abort the request and remove its sequence group from the current
    # schedule
    self.abort_request(request_id)
    for i, metadata in enumerate(seq_group_metadata_list):
        if metadata.request_id == request_id:
            del seq_group_metadata_list[i]
            break
    for i, group in enumerate(scheduler_outputs.scheduled_seq_groups):
        if group.seq_group.request_id == request_id:
            del scheduler_outputs.scheduled_seq_groups[i]
            break

    # If there are still other sequence groups left in the schedule, cache
    # them and flag the engine to reuse the schedule.
    if len(seq_group_metadata_list) > 0:
        self._skip_scheduling_next_step = True
        # Reuse multi-step caching logic
        self._cache_scheduler_outputs_for_multi_step(
            virtual_engine=virtual_engine,
            scheduler_outputs=scheduler_outputs,
            seq_group_metadata_list=seq_group_metadata_list,
            allow_async_output_proc=allow_async_output_proc)

_add_processed_request

_add_processed_request(
    request_id: str,
    processed_inputs: ProcessorInputs,
    params: Union[SamplingParams, PoolingParams],
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    prompt_adapter_request: Optional[PromptAdapterRequest],
    trace_headers: Optional[Mapping[str, str]] = None,
    priority: int = 0,
) -> Optional[SequenceGroup]

Add a processed request to the engine's request pool. return the created sequence group.

Source code in vllm/engine/llm_engine.py

def _add_processed_request(
    self,
    request_id: str,
    processed_inputs: ProcessorInputs,
    params: Union[SamplingParams, PoolingParams],
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    prompt_adapter_request: Optional[PromptAdapterRequest],
    trace_headers: Optional[Mapping[str, str]] = None,
    priority: int = 0,
) -> Optional[SequenceGroup]:
    """Add a processed request to the engine's request pool.
    return the created sequence group.
    """
    if isinstance(params, SamplingParams) and params.n > 1:
        ParallelSampleSequenceGroup.add_request(
            request_id,
            self,
            params,
            processed_inputs=processed_inputs,
            arrival_time=arrival_time,
            lora_request=lora_request,
            trace_headers=trace_headers,
            prompt_adapter_request=prompt_adapter_request,
            priority=priority,
        )
        return None

    self._validate_model_inputs(processed_inputs, lora_request)
    # Create the sequences.
    block_size = self.cache_config.block_size
    seq_id = next(self.seq_counter)
    eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)

    encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)

    seq = Sequence(seq_id, decoder_inputs, block_size, eos_token_id,
                   lora_request, prompt_adapter_request)

    encoder_seq = (None if encoder_inputs is None else Sequence(
        seq_id, encoder_inputs, block_size, eos_token_id, lora_request,
        prompt_adapter_request))

    # Create a SequenceGroup based on SamplingParams or PoolingParams
    if isinstance(params, SamplingParams):
        seq_group = self._create_sequence_group_with_sampling(
            request_id,
            seq,
            params,
            arrival_time=arrival_time,
            lora_request=lora_request,
            trace_headers=trace_headers,
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq,
            priority=priority)
    elif isinstance(params, PoolingParams):
        seq_group = self._create_sequence_group_with_pooling(
            request_id,
            seq,
            params,
            arrival_time=arrival_time,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq,
            priority=priority)
    else:
        raise ValueError(
            "Either SamplingParams or PoolingParams must be provided.")

    # Add the sequence group to the scheduler with least unfinished seqs.
    costs = [
        scheduler.get_num_unfinished_seq_groups()
        for scheduler in self.scheduler
    ]
    min_cost_scheduler = self.scheduler[costs.index(min(costs))]
    min_cost_scheduler.add_seq_group(seq_group)

    return seq_group

_advance_to_next_step

_advance_to_next_step(
    output: SamplerOutput,
    seq_group_metadata_list: List[SequenceGroupMetadata],
    scheduled_seq_groups: List[ScheduledSequenceGroup],
) -> None

Given model output from a single run, append the tokens to the sequences. This is normally done inside output processor, but it is required if the worker is to perform async forward pass to next step.

Source code in vllm/engine/llm_engine.py

def _advance_to_next_step(
        self, output: SamplerOutput,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        scheduled_seq_groups: List[ScheduledSequenceGroup]) -> None:
    """Given model output from a single run, append the tokens to the
    sequences. This is normally done inside output processor, but it is
    required if the worker is to perform async forward pass to next step.
    """
    for seq_group_metadata, sequence_group_outputs, scheduled_seq_group in \
        zip(seq_group_metadata_list, output, scheduled_seq_groups):
        seq_group = scheduled_seq_group.seq_group

        if seq_group.is_finished():
            continue

        if self.scheduler_config.is_multi_step:
            # Updates happen only if the sequence is prefill
            self._update_num_computed_tokens_for_multi_step_prefill(
                seq_group, seq_group_metadata,
                seq_group.state.num_steps == 1)
        else:
            token_chunk_size = (seq_group_metadata.token_chunk_size
                                if seq_group_metadata.token_chunk_size
                                is not None else 0)
            seq_group.update_num_computed_tokens(token_chunk_size)

        if seq_group_metadata.do_sample:
            assert len(sequence_group_outputs.samples) == 1, (
                "Async output processor expects a single sample"
                " (i.e sampling_params.n == 1)")
            sample = sequence_group_outputs.samples[0]

            assert len(seq_group.seqs) == 1
            seq = seq_group.seqs[0]

            if self.scheduler_config.is_multi_step:
                is_prefill_append = seq.data.get_num_uncomputed_tokens(
                ) == 0
                seq.append_token_id(sample.output_token, sample.logprobs,
                                    sample.output_embed)
                if not is_prefill_append:
                    seq_group.update_num_computed_tokens(1)
            else:
                seq.append_token_id(sample.output_token, sample.logprobs,
                                    sample.output_embed)

_build_logits_processors

_build_logits_processors(
    sampling_params: SamplingParams,
    lora_request: Optional[LoRARequest],
) -> SamplingParams

Constructs logits processors based on the guided_decoding, logits_bias, and allowed_token_ids fields in sampling_params. Deletes those fields and adds the constructed logits processors to the logits_processors field. Returns the modified sampling params.

Source code in vllm/engine/llm_engine.py

def _build_logits_processors(
        self, sampling_params: SamplingParams,
        lora_request: Optional[LoRARequest]) -> SamplingParams:
    """Constructs logits processors based on the guided_decoding,
    logits_bias, and allowed_token_ids fields in sampling_params. Deletes
    those fields and adds the constructed logits processors to the
    logits_processors field. Returns the modified sampling params."""

    logits_processors = []

    if sampling_params.guided_decoding is not None:
        # Defensively copy sampling params since guided decoding logits
        # processors can have different state for each request
        sampling_params = copy.copy(sampling_params)
        guided_decoding = sampling_params.guided_decoding

        logger.debug(
            "Building guided decoding logits processor in "
            "LLMEngine. Params: %s", guided_decoding)

        tokenizer = self.get_tokenizer(lora_request=lora_request)
        guided_decoding.backend = guided_decoding.backend or \
            self.decoding_config.backend

        if self.decoding_config.reasoning_backend:
            logger.debug("Building with reasoning backend %s",
                         self.decoding_config.reasoning_backend)

        processor = get_local_guided_decoding_logits_processor(
            guided_params=guided_decoding,
            tokenizer=tokenizer,
            model_config=self.model_config,
            reasoning_backend=self.decoding_config.reasoning_backend,
        )
        if processor:
            logits_processors.append(processor)

        # Unset so this doesn't get passed down to the model
        sampling_params.guided_decoding = None

    if (sampling_params.logit_bias or sampling_params.allowed_token_ids):
        tokenizer = self.get_tokenizer(lora_request=lora_request)

        processors = get_openai_logits_processors(
            logit_bias=sampling_params.logit_bias,
            allowed_token_ids=sampling_params.allowed_token_ids,
            tokenizer=tokenizer)
        logits_processors.extend(processors)

        # Unset so these don't get passed down to the model
        sampling_params.logit_bias = None
        sampling_params.allowed_token_ids = None

    if len(sampling_params.bad_words) > 0:
        tokenizer = self.get_tokenizer(lora_request)
        processors = get_bad_words_logits_processors(
            bad_words=sampling_params.bad_words, tokenizer=tokenizer)
        logits_processors.extend(processors)

    if logits_processors:
        if sampling_params.logits_processors is None:
            sampling_params.logits_processors = logits_processors
        else:
            sampling_params.logits_processors.extend(logits_processors)

    return sampling_params

_cache_scheduler_outputs_for_multi_step

_cache_scheduler_outputs_for_multi_step(
    virtual_engine: int,
    seq_group_metadata_list: Optional[
        List[SequenceGroupMetadata]
    ],
    scheduler_outputs: SchedulerOutputs,
    allow_async_output_proc: bool,
) -> None

Source code in vllm/engine/llm_engine.py

def _cache_scheduler_outputs_for_multi_step(
        self, virtual_engine: int,
        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
        scheduler_outputs: SchedulerOutputs,
        allow_async_output_proc: bool) -> None:
    co = self.cached_scheduler_outputs[virtual_engine]

    co.seq_group_metadata_list = seq_group_metadata_list
    co.scheduler_outputs = scheduler_outputs
    co.allow_async_output_proc = allow_async_output_proc
    co.last_output = None

_create_sequence_group_with_pooling

_create_sequence_group_with_pooling(
    request_id: str,
    seq: Sequence,
    pooling_params: PoolingParams,
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    prompt_adapter_request: Optional[PromptAdapterRequest],
    encoder_seq: Optional[Sequence] = None,
    priority: int = 0,
) -> SequenceGroup

Creates a SequenceGroup with PoolingParams.

Source code in vllm/engine/llm_engine.py

def _create_sequence_group_with_pooling(
    self,
    request_id: str,
    seq: Sequence,
    pooling_params: PoolingParams,
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    prompt_adapter_request: Optional[PromptAdapterRequest],
    encoder_seq: Optional[Sequence] = None,
    priority: int = 0,
) -> SequenceGroup:
    """Creates a SequenceGroup with PoolingParams."""
    # Defensive copy of PoolingParams, which are used by the pooler
    pooling_params = pooling_params.clone()
    # Create the sequence group.
    seq_group = SequenceGroup(
        request_id=request_id,
        seqs=[seq],
        arrival_time=arrival_time,
        lora_request=lora_request,
        pooling_params=pooling_params,
        prompt_adapter_request=prompt_adapter_request,
        encoder_seq=encoder_seq,
        priority=priority)
    return seq_group

_create_sequence_group_with_sampling

_create_sequence_group_with_sampling(
    request_id: str,
    seq: Sequence,
    sampling_params: SamplingParams,
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    trace_headers: Optional[Mapping[str, str]] = None,
    prompt_adapter_request: Optional[
        PromptAdapterRequest
    ] = None,
    encoder_seq: Optional[Sequence] = None,
    priority: int = 0,
) -> SequenceGroup

Creates a SequenceGroup with SamplingParams.

Source code in vllm/engine/llm_engine.py

def _create_sequence_group_with_sampling(
    self,
    request_id: str,
    seq: Sequence,
    sampling_params: SamplingParams,
    arrival_time: float,
    lora_request: Optional[LoRARequest],
    trace_headers: Optional[Mapping[str, str]] = None,
    prompt_adapter_request: Optional[PromptAdapterRequest] = None,
    encoder_seq: Optional[Sequence] = None,
    priority: int = 0,
) -> SequenceGroup:
    """Creates a SequenceGroup with SamplingParams."""
    max_logprobs = self.get_model_config().max_logprobs
    if (sampling_params.logprobs
            and sampling_params.logprobs > max_logprobs) or (
                sampling_params.prompt_logprobs
                and sampling_params.prompt_logprobs > max_logprobs):
        raise ValueError(f"Cannot request more than "
                         f"{max_logprobs} logprobs.")

    sampling_params = self._build_logits_processors(
        sampling_params, lora_request)

    # Defensive copy of SamplingParams, which are used by the sampler,
    # this doesn't deep-copy LogitsProcessor objects
    sampling_params = sampling_params.clone()

    sampling_params.update_from_generation_config(
        self.generation_config_fields, seq.eos_token_id)

    # Create the sequence group.
    draft_size = 1
    if self.vllm_config.speculative_config is not None:
        draft_size = \
            self.vllm_config.speculative_config.num_speculative_tokens + 1
    seq_group = SequenceGroup(
        request_id=request_id,
        seqs=[seq],
        arrival_time=arrival_time,
        sampling_params=sampling_params,
        lora_request=lora_request,
        trace_headers=trace_headers,
        prompt_adapter_request=prompt_adapter_request,
        encoder_seq=encoder_seq,
        priority=priority,
        draft_size=draft_size)

    return seq_group

_get_executor_cls classmethod

_get_executor_cls(
    engine_config: VllmConfig,
) -> Type[ExecutorBase]

Source code in vllm/engine/llm_engine.py

@classmethod
def _get_executor_cls(cls,
                      engine_config: VllmConfig) -> Type[ExecutorBase]:
    # distributed_executor_backend must be set in VllmConfig.__post_init__
    distributed_executor_backend = (
        engine_config.parallel_config.distributed_executor_backend)
    # Initialize the cluster and specify the executor class.
    if isinstance(distributed_executor_backend, type):
        if not issubclass(distributed_executor_backend, ExecutorBase):
            raise TypeError(
                "distributed_executor_backend must be a subclass of "
                f"ExecutorBase. Got {distributed_executor_backend}.")
        executor_class = distributed_executor_backend
    elif distributed_executor_backend == "ray":
        from vllm.executor.ray_distributed_executor import (
            RayDistributedExecutor)
        executor_class = RayDistributedExecutor
    elif distributed_executor_backend == "mp":
        from vllm.executor.mp_distributed_executor import (
            MultiprocessingDistributedExecutor)
        assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
            "multiprocessing distributed executor backend does not "
            "support VLLM_USE_RAY_SPMD_WORKER=1")
        executor_class = MultiprocessingDistributedExecutor
    elif distributed_executor_backend == "uni":
        # JAX-style, single-process, multi-device executor.
        from vllm.executor.uniproc_executor import UniProcExecutor
        executor_class = UniProcExecutor
    elif distributed_executor_backend == "external_launcher":
        # executor with external launcher
        from vllm.executor.uniproc_executor import (  # noqa
            ExecutorWithExternalLauncher)
        executor_class = ExecutorWithExternalLauncher
    else:
        raise ValueError("unrecognized distributed_executor_backend: "
                         f"{distributed_executor_backend}")
    return executor_class

_get_last_sampled_token_ids

_get_last_sampled_token_ids(
    virtual_engine: int,
) -> Optional[Tensor]

Source code in vllm/engine/llm_engine.py

def _get_last_sampled_token_ids(
        self, virtual_engine: int) -> Optional[torch.Tensor]:
    cached_last_output = self.cached_scheduler_outputs[
        virtual_engine].last_output
    if (self.scheduler_config.is_multi_step
            and self.parallel_config.pipeline_parallel_size > 1
            and cached_last_output is not None
            and cached_last_output.sampled_token_ids_cpu is not None):
        return cached_last_output.sampled_token_ids_cpu
    return None

_get_stats

_get_stats(
    scheduler_outputs: Optional[SchedulerOutputs],
    model_output: Optional[List[SamplerOutput]] = None,
    finished_before: Optional[List[int]] = None,
    skip: Optional[List[int]] = None,
) -> Stats

Get Stats to be Logged to Prometheus.

Parameters:

Name	Type	Description	Default
scheduler_outputs	Optional[SchedulerOutputs]	Optional, used to populate metrics related to the scheduled batch,	required
model_output	Optional[List[SamplerOutput]]	Optional, used to emit speculative decoding metrics which are created by the workers.	None
finished_before	Optional[List[int]]	Optional, indices of sequences that were finished before. These sequences will be ignored.	None
skip	Optional[List[int]]	Optional, indices of sequences that were preempted. These sequences will be ignored.	None

Source code in vllm/engine/llm_engine.py

def _get_stats(self,
               scheduler_outputs: Optional[SchedulerOutputs],
               model_output: Optional[List[SamplerOutput]] = None,
               finished_before: Optional[List[int]] = None,
               skip: Optional[List[int]] = None) -> Stats:
    """Get Stats to be Logged to Prometheus.

    Args:
        scheduler_outputs: Optional, used to populate metrics related to
            the scheduled batch,
        model_output: Optional, used to emit speculative decoding metrics
            which are created by the workers.
        finished_before: Optional, indices of sequences that were finished
            before. These sequences will be ignored.
        skip: Optional, indices of sequences that were preempted. These
            sequences will be ignored.
    """
    now = time.time()

    # System State
    #   Scheduler State
    num_running_sys = sum(
        len(scheduler.running) for scheduler in self.scheduler)
    num_swapped_sys = sum(
        len(scheduler.swapped) for scheduler in self.scheduler)
    num_waiting_sys = sum(
        len(scheduler.waiting) for scheduler in self.scheduler)

    # KV Cache Usage in %
    num_total_gpu = self.cache_config.num_gpu_blocks
    gpu_cache_usage_sys = 0.
    if num_total_gpu:  # Guard against both None and 0
        num_free_gpu = sum(
            scheduler.block_manager.get_num_free_gpu_blocks()
            for scheduler in self.scheduler)
        gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)

    num_total_cpu = self.cache_config.num_cpu_blocks
    cpu_cache_usage_sys = 0.
    if num_total_cpu:  # Guard against both None and 0
        num_free_cpu = sum(
            scheduler.block_manager.get_num_free_cpu_blocks()
            for scheduler in self.scheduler)
        cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu)

    # Prefix Cache Hit Rate. Note that we always use
    # the cache hit rate of the first virtual engine.
    cpu_prefix_cache_hit_rate = self.scheduler[
        0].get_prefix_cache_hit_rate(Device.CPU)
    gpu_prefix_cache_hit_rate = self.scheduler[
        0].get_prefix_cache_hit_rate(Device.GPU)

    # Exchange the uasge and cache hit stats between gpu and cpu when
    # running on cpu because the cpu_worker.py intentionally reports the
    # number of cpu blocks as gpu blocks in favor of cache management.
    if self.device_config.device_type == "cpu":
        num_total_gpu, num_total_cpu = num_total_cpu, num_total_gpu
        gpu_cache_usage_sys, cpu_cache_usage_sys = (
            cpu_cache_usage_sys,
            gpu_cache_usage_sys,
        )
        gpu_prefix_cache_hit_rate, cpu_prefix_cache_hit_rate = (
            cpu_prefix_cache_hit_rate,
            gpu_prefix_cache_hit_rate,
        )

    # Iteration stats
    num_prompt_tokens_iter = 0
    num_generation_tokens_iter = 0
    num_tokens_iter = 0
    time_to_first_tokens_iter: List[float] = []
    time_per_output_tokens_iter: List[float] = []
    num_preemption_iter = (0 if scheduler_outputs is None else
                           scheduler_outputs.preempted)

    # Request stats
    #   Latency
    time_e2e_requests: List[float] = []
    time_queue_requests: List[float] = []
    time_inference_requests: List[float] = []
    time_prefill_requests: List[float] = []
    time_decode_requests: List[float] = []
    #   Metadata
    num_prompt_tokens_requests: List[int] = []
    num_generation_tokens_requests: List[int] = []
    n_requests: List[int] = []
    max_num_generation_tokens_requests: List[int] = []
    max_tokens_requests: List[int] = []
    finished_reason_requests: List[str] = []

    # LoRA requests
    running_lora_adapters = dict(
        collectionsCounter([
            running_request.lora_request.lora_name
            for scheduler in self.scheduler
            for running_request in scheduler.running
            if running_request.lora_request
        ]))
    waiting_lora_adapters = dict(
        collectionsCounter([
            waiting_request.lora_request.lora_name
            for scheduler in self.scheduler
            for waiting_request in scheduler.waiting
            if waiting_request.lora_request
        ]))
    max_lora_stat = "0"
    if self.lora_config:
        max_lora_stat = str(self.lora_config.max_loras)

    # NOTE: This loop assumes prefill seq_groups are before
    # decode seq_groups in scheduled_seq_groups.
    if scheduler_outputs is not None:
        # For async postprocessor, already finished sequences need to be
        # not counted (to avoid double counting)
        actual_num_batched_tokens = scheduler_outputs.num_batched_tokens  # type: ignore

        num_generation_tokens_from_prefill_groups = 0
        # NOTE: if scheduler_outputs.num_prefill_groups > 0 and
        # the len of scheduler_outputs.scheduled_seq_groups is !=
        # scheduler_outputs.num_prefill_groups, this means that
        # chunked prefills have been detected.

        for idx, scheduled_seq_group in enumerate(
                scheduler_outputs.scheduled_seq_groups):
            # Skip double logging when using async output proc
            if finished_before and idx in finished_before:
                actual_num_batched_tokens -= 1
                continue

            # Currently, skip == preempted sequences, so we need to skip
            # their log stats
            if skip and idx in skip:
                continue

            group_was_prefill = idx < scheduler_outputs.num_prefill_groups
            seq_group = scheduled_seq_group.seq_group

            # NOTE: a seq_group that completed all of its prefill tokens
            # in the last iteration will have seq_group.is_prefill() = False
            # with group_was_prefill = True
            if group_was_prefill:
                # Number of prompt tokens.
                num_prompt_tokens_iter += (
                    scheduled_seq_group.token_chunk_size)

                # If the seq_group just finished the prefill state
                # get TTFT.
                if not seq_group.is_prefill():
                    latency = seq_group.get_last_token_latency()
                    time_to_first_tokens_iter.append(latency)

                    # One generation token per finished prefill.
                    num_generation_tokens_from_prefill_groups += (
                        seq_group.num_seqs())
            else:
                # TPOTs.
                latency = seq_group.get_last_token_latency()
                time_per_output_tokens_iter.append(latency)
                if seq_group.state.current_step == 0:
                    # For async_output_proc, the do_log_stats()
                    # is called following init_multi_step(), which
                    # sets the current_step to zero.
                    actual_num_batched_tokens +=\
                        seq_group.state.num_steps - 1
                else:
                    actual_num_batched_tokens +=\
                        seq_group.state.current_step - 1

            # Because of chunked prefill, we can have a single sequence
            # group that does multiple prompt_runs. To prevent logging
            # the same metadata more than once per request, we standardize
            # on logging request level information for finished requests,
            # which can only happen once.
            if seq_group.is_finished():
                # Latency timings
                time_e2e_requests.append(now -
                                         seq_group.metrics.arrival_time)
                if (seq_group.metrics.first_scheduled_time is not None and
                        seq_group.metrics.first_token_time is not None):
                    time_queue_requests.append(
                        seq_group.metrics.first_scheduled_time -
                        seq_group.metrics.arrival_time)
                    time_prefill_requests.append(
                        seq_group.metrics.first_token_time -
                        seq_group.metrics.first_scheduled_time)
                    time_decode_requests.append(
                        now - seq_group.metrics.first_token_time)
                    time_inference_requests.append(
                        now - seq_group.metrics.first_scheduled_time)
                # Metadata
                num_prompt_tokens_requests.append(
                    len(seq_group.prompt_token_ids))
                num_generation_tokens_requests.extend([
                    seq.get_output_len()
                    for seq in seq_group.get_finished_seqs()
                ])
                max_num_generation_tokens_requests.append(
                    max(seq.get_output_len()
                        for seq in seq_group.get_seqs()))
                if seq_group.sampling_params is not None:
                    n_requests.append(seq_group.sampling_params.n)
                    max_tokens_requests.append(
                        seq_group.sampling_params.max_tokens)
                finished_reason_requests.extend([
                    SequenceStatus.get_finished_reason(seq.status)
                    for seq in seq_group.get_finished_seqs()
                ])

        # Number of generation tokens.
        #   num_batched_tokens equals the number of prompt_tokens plus the
        #   number of decode_tokens in a single iteration. So,
        #   num_generation_tokens = num_batched_tokens - num_prompt_tokens
        #   + num_generation_tokens_from_prefill_groups (since we generate
        #   one token on prefills on iters where the prefill finishes).
        num_generation_tokens_iter = (
            actual_num_batched_tokens - num_prompt_tokens_iter +
            num_generation_tokens_from_prefill_groups)
        num_tokens_iter = (num_generation_tokens_iter +
                           num_prompt_tokens_iter)
    # Spec decode, if enabled, emits specialized metrics from the worker in
    # sampler output.
    if model_output and isinstance(model_output[0], SamplerOutput) and (
            model_output[0].spec_decode_worker_metrics is not None):
        spec_decode_metrics = model_output[0].spec_decode_worker_metrics
    else:
        spec_decode_metrics = None

    return Stats(
        now=now,
        # System stats
        #   Scheduler State
        num_running_sys=num_running_sys,
        num_swapped_sys=num_swapped_sys,
        num_waiting_sys=num_waiting_sys,
        #   KV Cache Usage in %
        gpu_cache_usage_sys=gpu_cache_usage_sys,
        cpu_cache_usage_sys=cpu_cache_usage_sys,
        #   Prefix Cache Hit Rate
        cpu_prefix_cache_hit_rate=cpu_prefix_cache_hit_rate,
        gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate,

        # Iteration stats
        num_prompt_tokens_iter=num_prompt_tokens_iter,
        num_generation_tokens_iter=num_generation_tokens_iter,
        num_tokens_iter=num_tokens_iter,
        time_to_first_tokens_iter=time_to_first_tokens_iter,
        time_per_output_tokens_iter=time_per_output_tokens_iter,
        spec_decode_metrics=spec_decode_metrics,
        num_preemption_iter=num_preemption_iter,

        # Request stats
        #   Latency
        time_e2e_requests=time_e2e_requests,
        time_queue_requests=time_queue_requests,
        time_inference_requests=time_inference_requests,
        time_prefill_requests=time_prefill_requests,
        time_decode_requests=time_decode_requests,
        #   Metadata
        num_prompt_tokens_requests=num_prompt_tokens_requests,
        num_generation_tokens_requests=num_generation_tokens_requests,
        max_num_generation_tokens_requests=
        max_num_generation_tokens_requests,
        n_requests=n_requests,
        max_tokens_requests=max_tokens_requests,
        finished_reason_requests=finished_reason_requests,
        max_lora=str(max_lora_stat),
        waiting_lora_adapters=list(waiting_lora_adapters.keys()),
        running_lora_adapters=list(running_lora_adapters.keys()))

_has_remaining_steps

_has_remaining_steps(
    seq_group_metadata_list: Optional[
        List[SequenceGroupMetadata]
    ],
) -> bool

Source code in vllm/engine/llm_engine.py

def _has_remaining_steps(
    self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
) -> bool:
    if (not self.scheduler_config.is_multi_step
            or not seq_group_metadata_list):
        return False

    # TODO(will) this is a sanity check for nowto make sure that all the
    # seqs are on the same steps. Eventually we will want to do some sort of
    # dynamic scheduling when doing multi-step decoding.
    ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps
    if any([
            seq_group.state.remaining_steps != ref_remaining_steps
            for seq_group in seq_group_metadata_list[1:]
    ]):
        raise AssertionError("All running sequence groups should "
                             "have the same remaining steps.")

    return ref_remaining_steps > 0

_init_tokenizer

_init_tokenizer() -> TokenizerGroup

Source code in vllm/engine/llm_engine.py

def _init_tokenizer(self) -> TokenizerGroup:
    return init_tokenizer_from_configs(
        model_config=self.model_config,
        scheduler_config=self.scheduler_config,
        lora_config=self.lora_config)

_initialize_kv_caches

_initialize_kv_caches() -> None

Initialize the KV cache in the worker(s).

The workers will determine the number of blocks in both the GPU cache and the swap CPU cache.

Source code in vllm/engine/llm_engine.py

def _initialize_kv_caches(self) -> None:
    """Initialize the KV cache in the worker(s).

    The workers will determine the number of blocks in both the GPU cache
    and the swap CPU cache.
    """
    start = time.time()
    num_gpu_blocks, num_cpu_blocks = (
        self.model_executor.determine_num_available_blocks())

    if self.cache_config.num_gpu_blocks_override is not None:
        num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override
        logger.info(
            "Overriding num_gpu_blocks=%d with "
            "num_gpu_blocks_override=%d", num_gpu_blocks,
            num_gpu_blocks_override)
        num_gpu_blocks = num_gpu_blocks_override

    self.cache_config.num_gpu_blocks = num_gpu_blocks
    self.cache_config.num_cpu_blocks = num_cpu_blocks

    self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
    elapsed = time.time() - start
    logger.info(("init engine (profile, create kv cache, "
                 "warmup model) took %.2f seconds"), elapsed)

_process_model_outputs

_process_model_outputs(
    ctx: SchedulerContext, request_id: Optional[str] = None
) -> None

Apply the model output to the sequences in the scheduled seq groups and return responses.

ctx: The virtual engine context to work on request_id: If provided, then only this request is going to be processed

Source code in vllm/engine/llm_engine.py

def _process_model_outputs(self,
                           ctx: SchedulerContext,
                           request_id: Optional[str] = None) -> None:
    """Apply the model output to the sequences in the scheduled seq groups
    and return responses.

    ctx: The virtual engine context to work on
    request_id: If provided, then only this request is going to be processed
    """

    now = time.time()

    if len(ctx.output_queue) == 0:
        return None

    # Get pending async postprocessor
    if request_id:
        # When we process only one request, no pop is required
        # (since later we will process all of the rest)
        (outputs, seq_group_metadata_list, scheduler_outputs, is_async,
         is_last_step, is_first_step_output, skip) = ctx.output_queue[0]
    else:
        (outputs, seq_group_metadata_list, scheduler_outputs, is_async,
         is_last_step, is_first_step_output,
         skip) = ctx.output_queue.popleft()

    # Sanity check
    assert len(seq_group_metadata_list) == len(
        scheduler_outputs.scheduled_seq_groups)

    has_multiple_outputs: bool = len(outputs) > 1
    outputs_by_sequence_group: List[List[SequenceGroupOutput]]
    if has_multiple_outputs:
        assert self.scheduler_config.is_multi_step or \
                 self.speculative_config
        # Organize outputs by [step][sequence group] instead of
        # [sequence group][step].
        if self.scheduler_config.is_multi_step:
            outputs_by_sequence_group = create_output_by_sequence_group(
                outputs, len(seq_group_metadata_list))
        elif self.speculative_config:
            # Decodes are multi-steps while prefills are not, outputting at
            # most 1 token. Separate them so that we can trigger chunk
            # processing without having to pad or copy over prompts K times
            # to match decodes structure (costly with prompt_logprobs).
            num_prefills = sum(sg.is_prompt
                               for sg in seq_group_metadata_list)
            prefills, decodes = outputs[:num_prefills], outputs[
                num_prefills:]
            outputs_by_sequence_group = create_output_by_sequence_group(
                decodes,
                num_seq_groups=len(seq_group_metadata_list) - num_prefills)
            outputs_by_sequence_group = [p.outputs for p in prefills
                                         ] + outputs_by_sequence_group
        # We have outputs for multiple steps submitted in a single burst,
        # so invalidate is_first_step_output.
        is_first_step_output = None
    else:
        outputs_by_sequence_group = outputs

    # Determine the requests we need to operate on
    if request_id:
        indices = []
        for i, seq_group_meta in enumerate(seq_group_metadata_list):
            if seq_group_meta.request_id == request_id:
                assert i not in skip  # Cannot be called twice
                indices.append(i)
                break

        # If the request_id was not found, then it means that
        # this is a new request that has no pending async
        # postprocessor
        if not indices:
            return
    else:
        indices = range(len(seq_group_metadata_list))  # type: ignore

    finished_before: List[int] = []
    finished_now: List[int] = []
    for i in indices:
        if i in skip:
            continue

        seq_group_meta = seq_group_metadata_list[i]
        scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

        seq_group: SequenceGroup = scheduled_seq_group.seq_group

        if seq_group.is_finished():
            finished_before.append(i)
            continue

        output: List[SequenceGroupOutput]
        if has_multiple_outputs:
            output = outputs_by_sequence_group[i]
        else:
            output = [outputs_by_sequence_group[0][i]]

        if not is_async:
            if self.scheduler_config.is_multi_step:
                # Updates happen only if the sequence is prefill
                self._update_num_computed_tokens_for_multi_step_prefill(
                    seq_group, seq_group_meta, is_first_step_output)
            else:
                seq_group.update_num_computed_tokens(
                    seq_group_meta.token_chunk_size or 0)

        if outputs:
            for o in outputs:
                if (isinstance(o, SamplerOutput)
                        and seq_group.metrics is not None):
                    if seq_group.metrics.model_forward_time is not None:
                        seq_group.metrics.model_forward_time += (
                            o.model_forward_time or 0)
                    else:
                        seq_group.metrics.model_forward_time = (
                            o.model_forward_time)
                    if seq_group.metrics.model_execute_time is not None:
                        seq_group.metrics.model_execute_time += (
                            o.model_execute_time or 0)
                    else:
                        seq_group.metrics.model_execute_time = (
                            o.model_execute_time)

        if self.model_config.runner_type == "pooling":
            self._process_sequence_group_outputs(seq_group, output)
        else:
            self.output_processor.process_prompt_logprob(seq_group, output)
            if seq_group_meta.do_sample:
                self.output_processor.process_outputs(
                    seq_group, output, is_async)

        if seq_group.is_finished():
            finished_now.append(i)

    # Generate outputs for the requests that finished this iteration
    for i in finished_now:
        scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

        seq_group = scheduled_seq_group.seq_group
        seq_group.maybe_set_first_token_time(now)
        if not seq_group.is_prefill():
            seq_group.set_last_token_time(now)
        request_output = RequestOutputFactory.create(
            seq_group,
            self.seq_id_to_seq_group,
            use_cache=self.use_cached_outputs)
        if request_output:
            ctx.request_outputs.append(request_output)

    # When we process a single request, we skip it for the next time,
    # and invoke the request output callback (if there was final output)
    if request_id:
        assert len(indices) == 1
        skip.append(indices[0])

        if (finished_now
                and self.process_request_outputs_callback is not None):
            self.process_request_outputs_callback(ctx.request_outputs)
            ctx.request_outputs.clear()
        return

    # Free currently finished requests
    if finished_now:
        for scheduler in self.scheduler:
            scheduler.free_finished_seq_groups()

    # For multi-step without streaming, don't create outputs each iteration
    if not is_last_step and not ctx.multi_step_stream_outputs:
        # Immediately process request outputs here (if callback is given)
        if (finished_now
                and self.process_request_outputs_callback is not None):
            self.process_request_outputs_callback(ctx.request_outputs)
            ctx.request_outputs.clear()
        return

    # Create the outputs
    for i in indices:
        if i in skip or i in finished_before or i in finished_now:
            continue  # Avoids double processing

        scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

        seq_group = scheduled_seq_group.seq_group
        seq_group.maybe_set_first_token_time(now)
        if not seq_group.is_prefill():
            seq_group.set_last_token_time(now)
        request_output = RequestOutputFactory.create(
            seq_group,
            self.seq_id_to_seq_group,
            use_cache=self.use_cached_outputs)
        if request_output:
            ctx.request_outputs.append(request_output)

    # For multi-step with streaming, create outputs each iteration
    if not is_last_step and ctx.multi_step_stream_outputs:
        # Immediately process request outputs here (if callback is given)
        if self.process_request_outputs_callback is not None:
            self.process_request_outputs_callback(ctx.request_outputs)
            ctx.request_outputs.clear()
        return

    for seq_group in scheduler_outputs.ignored_seq_groups:
        params = seq_group.sampling_params
        if params is not None and params.output_kind == (
                RequestOutputKind.DELTA) and not seq_group.is_finished():
            continue

        request_output = RequestOutputFactory.create(
            seq_group,
            self.seq_id_to_seq_group,
            use_cache=self.use_cached_outputs,
        )
        if request_output:
            ctx.request_outputs.append(request_output)

    # Immediately process request outputs here (if callback is given)
    if (ctx.request_outputs
            and self.process_request_outputs_callback is not None):
        self.process_request_outputs_callback(ctx.request_outputs)
        ctx.request_outputs.clear()

    # For async case, we need to record the stats here.
    # For non-async case, the stats are done in the
    # LLMEngine/AsyncLLMEngine directly
    if is_async:
        # Log stats.
        self.do_log_stats(scheduler_outputs, outputs, finished_before,
                          skip)

        # Tracing
        self.do_tracing(scheduler_outputs, finished_before)

    return None

_process_sequence_group_outputs staticmethod

_process_sequence_group_outputs(
    seq_group: SequenceGroup,
    outputs: List[PoolingSequenceGroupOutput],
) -> None

Source code in vllm/engine/llm_engine.py

@staticmethod
def _process_sequence_group_outputs(
    seq_group: SequenceGroup,
    outputs: List[PoolingSequenceGroupOutput],
) -> None:
    seq_group.pooled_data = outputs[0].data

    for seq in seq_group.get_seqs():
        seq.status = SequenceStatus.FINISHED_STOPPED

    return

_update_cached_scheduler_output

_update_cached_scheduler_output(
    virtual_engine: int,
    output: List[Optional[SamplerOutput]],
) -> None

Source code in vllm/engine/llm_engine.py

def _update_cached_scheduler_output(
        self, virtual_engine: int,
        output: List[Optional[SamplerOutput]]) -> None:
    if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0
            and output[0] is not None):
        last_output = output[-1]
        assert last_output is not None
        assert last_output.sampled_token_ids_cpu is not None
        assert last_output.sampled_token_ids is None
        assert last_output.sampled_token_probs is None
        self.cached_scheduler_outputs[
            virtual_engine].last_output = last_output

_update_num_computed_tokens_for_multi_step_prefill

_update_num_computed_tokens_for_multi_step_prefill(
    seq_group: SequenceGroup,
    seq_group_meta: SequenceGroupMetadata,
    is_first_step_output: Optional[bool],
)

This function updates num_computed_tokens for prompt sequences when Multi-Step is enabled.

seq_group: SequenceGroup to update the num_computed_tokens for. seq_group_meta: Metadata of the given SequenceGroup. is_first_step_output: Optional[bool] - When available, is_first_step_output indicates if the appended output token is the output of the first-step in multi-step. A value of None indicates that outputs from all steps in in multi-step are submitted in a single burst.

Source code in vllm/engine/llm_engine.py

def _update_num_computed_tokens_for_multi_step_prefill(
        self, seq_group: SequenceGroup,
        seq_group_meta: SequenceGroupMetadata,
        is_first_step_output: Optional[bool]):
    """
    This function updates num_computed_tokens for prompt sequences
    when Multi-Step is enabled.

    seq_group: SequenceGroup to update the num_computed_tokens for.
    seq_group_meta: Metadata of the given SequenceGroup.
    is_first_step_output: Optional[bool] -
        When available, is_first_step_output indicates if the appended
        output token is the output of the first-step in multi-step.
        A value of None indicates that outputs from all steps in
        in multi-step are submitted in a single burst.
    """

    assert self.scheduler_config.is_multi_step

    if not seq_group_meta.is_prompt:
        # num_computed_token updates for multi-step decodes happen after
        # the tokens are appended to the sequence.
        return

    do_update: bool = False
    if self.scheduler_config.chunked_prefill_enabled:
        # In multi-step + chunked-prefill case, the prompt sequences
        # that are scheduled are fully processed in the first step.
        do_update = is_first_step_output is None or is_first_step_output
    else:
        # Normal multi-step decoding case. In this case prompt-sequences
        # are actually single-stepped. Always update in this case.
        assert seq_group.state.num_steps == 1
        do_update = True

    if do_update:
        seq_group.update_num_computed_tokens(
            seq_group_meta.token_chunk_size)

_validate_model_input

_validate_model_input(
    prompt_inputs: SingletonInputs,
    lora_request: Optional[LoRARequest],
    *,
    prompt_type: Literal["encoder", "decoder"],
)

Source code in vllm/engine/llm_engine.py

def _validate_model_input(
    self,
    prompt_inputs: SingletonInputs,
    lora_request: Optional[LoRARequest],
    *,
    prompt_type: Literal["encoder", "decoder"],
):
    model_config = self.model_config
    tokenizer = (None if self.tokenizer is None else
                 self.tokenizer.get_lora_tokenizer(lora_request))

    prompt_ids = prompt_inputs.get("prompt_token_ids", [])
    if not prompt_ids:
        if prompt_type == "encoder" and model_config.is_multimodal_model:
            pass  # Mllama may have empty encoder inputs for text-only data
        elif prompt_inputs["type"] == "embeds":
            pass
        else:
            raise ValueError(f"The {prompt_type} prompt cannot be empty")

    if tokenizer is not None:
        max_input_id = max(prompt_ids, default=0)
        if max_input_id > tokenizer.max_token_id:
            raise ValueError(
                f"Token id {max_input_id} is out of vocabulary")

    max_prompt_len = self.model_config.max_model_len
    if len(prompt_ids) > max_prompt_len:
        if prompt_type == "encoder" and model_config.is_multimodal_model:
            mm_registry = self.input_preprocessor.mm_registry
            mm_processor = mm_registry.create_processor(
                model_config,
                tokenizer=tokenizer or object(),  # Dummy if no tokenizer
            )
            assert isinstance(mm_processor, EncDecMultiModalProcessor)

            if mm_processor.pad_dummy_encoder_prompt:
                return  # Skip encoder length check for Whisper

        if model_config.is_multimodal_model:
            suggestion = (
                "Make sure that `max_model_len` is no smaller than the "
                "number of text tokens plus multimodal tokens. For image "
                "inputs, the number of image tokens depends on the number "
                "of images, and possibly their aspect ratios as well.")
        else:
            suggestion = (
                "Make sure that `max_model_len` is no smaller than the "
                "number of text tokens.")

        raise ValueError(
            f"The {prompt_type} prompt (length {len(prompt_ids)}) is "
            f"longer than the maximum model length of {max_prompt_len}. "
            f"{suggestion}")

_validate_model_inputs

_validate_model_inputs(
    inputs: ProcessorInputs,
    lora_request: Optional[LoRARequest],
)

Source code in vllm/engine/llm_engine.py

def _validate_model_inputs(self, inputs: ProcessorInputs,
                           lora_request: Optional[LoRARequest]):
    encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)

    if encoder_inputs is not None:
        self._validate_model_input(encoder_inputs,
                                   lora_request,
                                   prompt_type="encoder")

    self._validate_model_input(decoder_inputs,
                               lora_request,
                               prompt_type="decoder")

_verify_args

_verify_args() -> None

Source code in vllm/engine/llm_engine.py

def _verify_args(self) -> None:
    self.model_config.verify_with_parallel_config(self.parallel_config)
    self.cache_config.verify_with_parallel_config(self.parallel_config)
    if self.lora_config:
        self.lora_config.verify_with_model_config(self.model_config)
        self.lora_config.verify_with_scheduler_config(
            self.scheduler_config)
    if self.prompt_adapter_config:
        self.prompt_adapter_config.verify_with_model_config(
            self.model_config)

abort_request

abort_request(
    request_id: Union[str, Iterable[str]],
) -> None

Aborts a request(s) with the given ID.

Parameters:

Name	Type	Description	Default
request_id	Union[str, Iterable[str]]	The ID(s) of the request to abort.	required

Details

• Refer to vllm.core.scheduler.Scheduler.abort_seq_group.

Example

initialize engine and add a request with request_id

request_id = str(0)

abort the request

engine.abort_request(request_id)

Source code in vllm/engine/llm_engine.py

def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
    """Aborts a request(s) with the given ID.

    Args:
        request_id: The ID(s) of the request to abort.

    Details:
        - Refer to [vllm.core.scheduler.Scheduler.abort_seq_group][].

    Example:
        >>> # initialize engine and add a request with request_id
        >>> request_id = str(0)
        >>> # abort the request
        >>> engine.abort_request(request_id)
    """
    for scheduler in self.scheduler:
        scheduler.abort_seq_group(
            request_id, seq_id_to_seq_group=self.seq_id_to_seq_group)

add_logger

add_logger(
    logger_name: str, logger: StatLoggerBase
) -> None

Source code in vllm/engine/llm_engine.py

def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
    if not self.log_stats:
        raise RuntimeError(
            "Stat logging is disabled. Set `disable_log_stats=False` "
            "argument to enable.")
    if logger_name in self.stat_loggers:
        raise KeyError(f"Logger with name {logger_name} already exists.")
    self.stat_loggers[logger_name] = logger

add_lora

add_lora(lora_request: LoRARequest) -> bool

Source code in vllm/engine/llm_engine.py

def add_lora(self, lora_request: LoRARequest) -> bool:
    return self.model_executor.add_lora(lora_request)

add_prompt_adapter

add_prompt_adapter(
    prompt_adapter_request: PromptAdapterRequest,
) -> bool

Source code in vllm/engine/llm_engine.py

def add_prompt_adapter(
        self, prompt_adapter_request: PromptAdapterRequest) -> bool:
    return self.model_executor.add_prompt_adapter(prompt_adapter_request)

add_request

add_request(
    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,
    prompt_adapter_request: Optional[
        PromptAdapterRequest
    ] = None,
    priority: int = 0,
) -> None

Add a request to the engine's request pool.

The request is added to the request pool and will be processed by the scheduler as engine.step() is called. The exact scheduling policy is determined by the scheduler.

Parameters:

Name	Type	Description	Default
request_id	str	The unique ID of the request.	required
prompt	PromptType	The prompt to the LLM. See PromptType for more details about the format of each input.	required
params	Union[SamplingParams, PoolingParams]	Parameters for sampling or pooling. SamplingParams for text generation. PoolingParams for pooling.	required
arrival_time	Optional[float]	The arrival time of the request. If None, we use the current monotonic time.	None
lora_request	Optional[LoRARequest]	The LoRA request to add.	None
trace_headers	Optional[Mapping[str, str]]	OpenTelemetry trace headers.	None
prompt_adapter_request	Optional[PromptAdapterRequest]	The prompt adapter request to add.	None
priority	int	The priority of the request. Only applicable with priority scheduling.	0

Details

• Set arrival_time to the current time if it is None.
• Set prompt_token_ids to the encoded prompt if it is None.
• Create n number of [Sequence][vllm.Sequence] objects.
• Create a [SequenceGroup][vllm.SequenceGroup] object from the list of [Sequence][vllm.Sequence].
• Add the [SequenceGroup][vllm.SequenceGroup] object to the scheduler.

Example

initialize engine

engine = LLMEngine.from_engine_args(engine_args)

set request arguments

example_prompt = "Who is the president of the United States?" sampling_params = SamplingParams(temperature=0.0) request_id = 0

add the request to the engine

engine.add_request( str(request_id), example_prompt, SamplingParams(temperature=0.0))

continue the request processing

...

Source code in vllm/engine/llm_engine.py

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,
    prompt_adapter_request: Optional[PromptAdapterRequest] = None,
    priority: int = 0,
) -> None:
    """Add a request to the engine's request pool.

    The request is added to the request pool and will be processed by the
    scheduler as `engine.step()` is called. The exact scheduling policy is
    determined by the scheduler.

    Args:
        request_id: The unique ID of the request.
        prompt: The prompt to the LLM. See
            [PromptType][vllm.inputs.PromptType]
            for more details about the format of each input.
        params: Parameters for sampling or pooling.
            [SamplingParams][vllm.SamplingParams] for text generation.
            [PoolingParams][vllm.PoolingParams] for pooling.
        arrival_time: The arrival time of the request. If None, we use
            the current monotonic time.
        lora_request: The LoRA request to add.
        trace_headers: OpenTelemetry trace headers.
        prompt_adapter_request: The prompt adapter request to add.
        priority: The priority of the request.
            Only applicable with priority scheduling.

    Details:
        - Set arrival_time to the current time if it is None.
        - Set prompt_token_ids to the encoded prompt if it is None.
        - Create `n` number of [Sequence][vllm.Sequence] objects.
        - Create a [SequenceGroup][vllm.SequenceGroup] object
          from the list of [Sequence][vllm.Sequence].
        - Add the [SequenceGroup][vllm.SequenceGroup] object to the
          scheduler.

    Example:
        >>> # initialize engine
        >>> engine = LLMEngine.from_engine_args(engine_args)
        >>> # set request arguments
        >>> example_prompt = "Who is the president of the United States?"
        >>> sampling_params = SamplingParams(temperature=0.0)
        >>> request_id = 0
        >>>
        >>> # add the request to the engine
        >>> engine.add_request(
        >>>    str(request_id),
        >>>    example_prompt,
        >>>    SamplingParams(temperature=0.0))
        >>> # continue the request processing
        >>> ...
    """
    if not isinstance(request_id, str):
        raise TypeError(
            f"request_id must be a string, got {type(request_id)}")

    if lora_request is not None and not self.lora_config:
        raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                         "not enabled!")

    if priority != 0 and not self.scheduler_config.policy == "priority":
        raise ValueError(f"Got priority {priority} but "
                         "Priority scheduling is not enabled.")

    if isinstance(params, SamplingParams) \
        and (params.guided_decoding or params.logits_processors) \
        and self.scheduler_config.num_scheduler_steps > 1:
        raise ValueError(
            "Guided decoding and logits processors are not supported "
            "in multi-step decoding")

    if arrival_time is None:
        arrival_time = time.time()

    if (isinstance(prompt, dict)
            and prompt.get("prompt_embeds", None) is not None
            and not prompt.get("prompt_token_ids", None)):
        seq_len = prompt["prompt_embeds"].shape[0]
        prompt["prompt_token_ids"] = [0] * seq_len

    processed_inputs = self.input_preprocessor.preprocess(
        prompt,
        tokenization_kwargs=tokenization_kwargs,
        lora_request=lora_request,
        prompt_adapter_request=prompt_adapter_request,
    )

    self._add_processed_request(
        request_id=request_id,
        processed_inputs=processed_inputs,
        params=params,
        arrival_time=arrival_time,
        lora_request=lora_request,
        prompt_adapter_request=prompt_adapter_request,
        trace_headers=trace_headers,
        priority=priority,
    )

check_health

check_health() -> None

Source code in vllm/engine/llm_engine.py

def check_health(self) -> None:
    self.model_executor.check_health()

collective_rpc

collective_rpc(
    method: Union[str, Callable[..., _R]],
    timeout: Optional[float] = None,
    args: tuple = (),
    kwargs: Optional[dict[str, Any]] = None,
) -> list[_R]

Source code in vllm/engine/llm_engine.py

def collective_rpc(self,
                   method: Union[str, Callable[..., _R]],
                   timeout: Optional[float] = None,
                   args: tuple = (),
                   kwargs: Optional[dict[str, Any]] = None) -> list[_R]:
    return self.model_executor.collective_rpc(method, timeout, args,
                                              kwargs)

create_trace_span

create_trace_span(seq_group: SequenceGroup) -> None

Source code in vllm/engine/llm_engine.py

def create_trace_span(self, seq_group: SequenceGroup) -> None:
    if self.tracer is None or seq_group.sampling_params is None:
        return
    arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9)

    trace_context = extract_trace_context(seq_group.trace_headers)

    with self.tracer.start_as_current_span(
            "llm_request",
            kind=SpanKind.SERVER,
            context=trace_context,
            start_time=arrival_time_nano_seconds) as seq_span:
        metrics = seq_group.metrics
        ttft = metrics.first_token_time - metrics.arrival_time
        e2e_time = metrics.finished_time - metrics.arrival_time
        seq_span.set_attribute(SpanAttributes.GEN_AI_RESPONSE_MODEL,
                               self.model_config.model)
        seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID,
                               seq_group.request_id)
        seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE,
                               seq_group.sampling_params.temperature)
        seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P,
                               seq_group.sampling_params.top_p)
        seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS,
                               seq_group.sampling_params.max_tokens)
        seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N,
                               seq_group.sampling_params.n)
        seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_NUM_SEQUENCES,
                               seq_group.num_seqs())
        seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS,
                               len(seq_group.prompt_token_ids))
        seq_span.set_attribute(
            SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS,
            sum([
                seq.get_output_len()
                for seq in seq_group.get_finished_seqs()
            ]))
        seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE,
                               metrics.time_in_queue)
        seq_span.set_attribute(
            SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
        seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E, e2e_time)
        if metrics.scheduler_time is not None:
            seq_span.set_attribute(
                SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER,
                metrics.scheduler_time)
        if metrics.model_forward_time is not None:
            seq_span.set_attribute(
                SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD,
                metrics.model_forward_time / 1000.0)
        if metrics.model_execute_time is not None:
            seq_span.set_attribute(
                SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE,
                metrics.model_execute_time)

do_log_stats

do_log_stats(
    scheduler_outputs: Optional[SchedulerOutputs] = None,
    model_output: Optional[List[SamplerOutput]] = None,
    finished_before: Optional[List[int]] = None,
    skip: Optional[List[int]] = None,
) -> None

Forced log when no requests active.

Source code in vllm/engine/llm_engine.py

def do_log_stats(self,
                 scheduler_outputs: Optional[SchedulerOutputs] = None,
                 model_output: Optional[List[SamplerOutput]] = None,
                 finished_before: Optional[List[int]] = None,
                 skip: Optional[List[int]] = None) -> None:
    """Forced log when no requests active."""
    if self.log_stats:
        stats = self._get_stats(scheduler_outputs, model_output,
                                finished_before, skip)
        for logger in self.stat_loggers.values():
            logger.log(stats)

do_tracing

do_tracing(
    scheduler_outputs: SchedulerOutputs,
    finished_before: Optional[List[int]] = None,
) -> None

Source code in vllm/engine/llm_engine.py

def do_tracing(self,
               scheduler_outputs: SchedulerOutputs,
               finished_before: Optional[List[int]] = None) -> None:
    if self.tracer is None:
        return

    for idx, scheduled_seq_group in enumerate(
            scheduler_outputs.scheduled_seq_groups):
        # Skip double tracing when using async output proc
        if finished_before and idx in finished_before:
            continue

        seq_group = scheduled_seq_group.seq_group
        if seq_group.is_finished():
            self.create_trace_span(seq_group)

enable_output_validation classmethod

enable_output_validation()

Source code in vllm/engine/llm_engine.py

@classmethod
@contextmanager
def enable_output_validation(cls):
    cls.DO_VALIDATE_OUTPUT = True

    yield

    cls.DO_VALIDATE_OUTPUT = False

from_engine_args classmethod

from_engine_args(
    engine_args: EngineArgs,
    usage_context: UsageContext = ENGINE_CONTEXT,
    stat_loggers: Optional[
        Dict[str, StatLoggerBase]
    ] = None,
) -> LLMEngine

Creates an LLM engine from the engine arguments.

Source code in vllm/engine/llm_engine.py

@classmethod
def from_engine_args(
    cls,
    engine_args: EngineArgs,
    usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
    stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
) -> "LLMEngine":
    """Creates an LLM engine from the engine arguments."""
    # Create the engine configs.
    vllm_config = engine_args.create_engine_config(usage_context)

    engine_cls = cls
    if envs.VLLM_USE_V1:
        from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
        engine_cls = V1LLMEngine

    return engine_cls.from_vllm_config(
        vllm_config=vllm_config,
        usage_context=usage_context,
        stat_loggers=stat_loggers,
        disable_log_stats=engine_args.disable_log_stats,
    )

from_vllm_config classmethod

from_vllm_config(
    vllm_config: VllmConfig,
    usage_context: UsageContext = ENGINE_CONTEXT,
    stat_loggers: Optional[
        Dict[str, StatLoggerBase]
    ] = None,
    disable_log_stats: bool = False,
) -> LLMEngine

Source code in vllm/engine/llm_engine.py

@classmethod
def from_vllm_config(
    cls,
    vllm_config: VllmConfig,
    usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
    stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
    disable_log_stats: bool = False,
) -> "LLMEngine":
    return cls(
        vllm_config=vllm_config,
        executor_class=cls._get_executor_cls(vllm_config),
        log_stats=(not disable_log_stats),
        usage_context=usage_context,
        stat_loggers=stat_loggers,
    )

get_decoding_config

get_decoding_config() -> DecodingConfig

Gets the decoding configuration.

Source code in vllm/engine/llm_engine.py

def get_decoding_config(self) -> DecodingConfig:
    """Gets the decoding configuration."""
    return self.decoding_config

get_lora_config

get_lora_config() -> LoRAConfig

Gets the LoRA configuration.

Source code in vllm/engine/llm_engine.py

def get_lora_config(self) -> LoRAConfig:
    """Gets the LoRA configuration."""
    return self.lora_config

get_model_config

get_model_config() -> ModelConfig

Gets the model configuration.

Source code in vllm/engine/llm_engine.py

def get_model_config(self) -> ModelConfig:
    """Gets the model configuration."""
    return self.model_config

get_num_unfinished_requests

get_num_unfinished_requests() -> int

Gets the number of unfinished requests.

Source code in vllm/engine/llm_engine.py

def get_num_unfinished_requests(self) -> int:
    """Gets the number of unfinished requests."""
    return sum(scheduler.get_num_unfinished_seq_groups()
               for scheduler in self.scheduler)

get_parallel_config

get_parallel_config() -> ParallelConfig

Gets the parallel configuration.

Source code in vllm/engine/llm_engine.py

def get_parallel_config(self) -> ParallelConfig:
    """Gets the parallel configuration."""
    return self.parallel_config

get_scheduler_config

get_scheduler_config() -> SchedulerConfig

Gets the scheduler configuration.

Source code in vllm/engine/llm_engine.py

def get_scheduler_config(self) -> SchedulerConfig:
    """Gets the scheduler configuration."""
    return self.scheduler_config

get_tokenizer

get_tokenizer(
    lora_request: Optional[LoRARequest] = None,
) -> AnyTokenizer

Source code in vllm/engine/llm_engine.py

def get_tokenizer(
    self,
    lora_request: Optional[LoRARequest] = None,
) -> AnyTokenizer:
    return self.get_tokenizer_group().get_lora_tokenizer(lora_request)

get_tokenizer_group

get_tokenizer_group() -> TokenizerGroup

Source code in vllm/engine/llm_engine.py

def get_tokenizer_group(self) -> TokenizerGroup:
    if self.tokenizer is None:
        raise ValueError("Unable to get tokenizer because "
                         "skip_tokenizer_init is True")

    return self.tokenizer

get_vllm_config

get_vllm_config() -> VllmConfig

Gets the vllm configuration.

Source code in vllm/engine/llm_engine.py

def get_vllm_config(self) -> VllmConfig:
    """Gets the vllm configuration."""
    return self.vllm_config

has_unfinished_requests

has_unfinished_requests() -> bool

Returns True if there are unfinished requests.

Source code in vllm/engine/llm_engine.py

def has_unfinished_requests(self) -> bool:
    """Returns True if there are unfinished requests."""
    return any(scheduler.has_unfinished_seqs()
               for scheduler in self.scheduler)

has_unfinished_requests_for_virtual_engine

has_unfinished_requests_for_virtual_engine(
    virtual_engine: int,
) -> bool

Returns True if there are unfinished requests for the virtual engine.

Source code in vllm/engine/llm_engine.py

def has_unfinished_requests_for_virtual_engine(
        self, virtual_engine: int) -> bool:
    """
    Returns True if there are unfinished requests for the virtual engine.
    """
    return self.scheduler[virtual_engine].has_unfinished_seqs()

is_sleeping

is_sleeping() -> bool

Source code in vllm/engine/llm_engine.py

def is_sleeping(self) -> bool:
    return self.model_executor.is_sleeping

is_tracing_enabled

is_tracing_enabled() -> bool

Source code in vllm/engine/llm_engine.py

def is_tracing_enabled(self) -> bool:
    return self.tracer is not None

list_loras

list_loras() -> Set[int]

Source code in vllm/engine/llm_engine.py

def list_loras(self) -> Set[int]:
    return self.model_executor.list_loras()

list_prompt_adapters

list_prompt_adapters() -> List[int]

Source code in vllm/engine/llm_engine.py

def list_prompt_adapters(self) -> List[int]:
    return self.model_executor.list_prompt_adapters()

pin_lora

pin_lora(lora_id: int) -> bool

Source code in vllm/engine/llm_engine.py

def pin_lora(self, lora_id: int) -> bool:
    return self.model_executor.pin_lora(lora_id)

remove_logger

remove_logger(logger_name: str) -> None

Source code in vllm/engine/llm_engine.py

def remove_logger(self, logger_name: str) -> None:
    if not self.log_stats:
        raise RuntimeError(
            "Stat logging is disabled. Set `disable_log_stats=False` "
            "argument to enable.")
    if logger_name not in self.stat_loggers:
        raise KeyError(f"Logger with name {logger_name} does not exist.")
    del self.stat_loggers[logger_name]

remove_lora

remove_lora(lora_id: int) -> bool

Source code in vllm/engine/llm_engine.py

def remove_lora(self, lora_id: int) -> bool:
    return self.model_executor.remove_lora(lora_id)

remove_prompt_adapter

remove_prompt_adapter(prompt_adapter_id: int) -> bool

Source code in vllm/engine/llm_engine.py

def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
    return self.model_executor.remove_prompt_adapter(prompt_adapter_id)

reset_mm_cache

reset_mm_cache() -> bool

Reset the multi-modal cache.

Source code in vllm/engine/llm_engine.py

def reset_mm_cache(self) -> bool:
    """Reset the multi-modal cache."""
    return self.input_preprocessor.mm_registry.reset_processor_cache()

reset_prefix_cache

reset_prefix_cache(device: Optional[Device] = None) -> bool

Reset prefix cache for all devices.

Source code in vllm/engine/llm_engine.py

def reset_prefix_cache(self, device: Optional[Device] = None) -> bool:
    """Reset prefix cache for all devices."""

    success = True
    for scheduler in self.scheduler:
        success = success and scheduler.reset_prefix_cache(device)
    return success

sleep

sleep(level: int = 1) -> None

Source code in vllm/engine/llm_engine.py

def sleep(self, level: int = 1) -> None:
    assert self.vllm_config.model_config.enable_sleep_mode, (
        "Sleep mode is not enabled in the model config")
    self.model_executor.sleep(level=level)

start_profile

start_profile() -> None

Source code in vllm/engine/llm_engine.py

def start_profile(self) -> None:
    self.model_executor.start_profile()

step

step() -> List[Union[RequestOutput, PoolingRequestOutput]]

Performs one decoding iteration and returns newly generated results.

Overview of the step function

Details: - Step 1: Schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy.

- Depending on the scheduling policy,
    sequences may be `preempted/reordered`.
- A Sequence Group (SG) refer to a group of sequences
    that are generated from the same prompt.

• Step 2: Calls the distributed executor to execute the model.

• Step 3: Processes the model output. This mainly includes:

  • Decodes the relevant outputs.
  • Updates the scheduled sequence groups with model outputs based on its sampling parameters (use_beam_search or not).
  • Frees the finished sequence groups.

• Finally, it creates and returns the newly generated results.

Example:

# Please see the example/ folder for more detailed examples.

# initialize engine and request arguments
engine = LLMEngine.from_engine_args(engine_args)
example_inputs = [(0, "What is LLM?",
SamplingParams(temperature=0.0))]

# Start the engine with an event loop
while True:
    if example_inputs:
        req_id, prompt, sampling_params = example_inputs.pop(0)
        engine.add_request(str(req_id),prompt,sampling_params)

    # continue the request processing
    request_outputs = engine.step()
    for request_output in request_outputs:
        if request_output.finished:
            # return or show the request output

    if not (engine.has_unfinished_requests() or example_inputs):
        break

Source code in vllm/engine/llm_engine.py

def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
    """Performs one decoding iteration and returns newly generated results.

    <figure markdown="span">
    ![Overview of the step function](https://i.imgur.com/sv2HssD.png)
    <figcaption>Overview of the step function</figcaption>
    </figure>

    Details:
    - Step 1: Schedules the sequences to be executed in the next
        iteration and the token blocks to be swapped in/out/copy.

        - Depending on the scheduling policy,
            sequences may be `preempted/reordered`.
        - A Sequence Group (SG) refer to a group of sequences
            that are generated from the same prompt.

    - Step 2: Calls the distributed executor to execute the model.
    - Step 3: Processes the model output. This mainly includes:

        - Decodes the relevant outputs.
        - Updates the scheduled sequence groups with model outputs
            based on its `sampling parameters` (`use_beam_search` or not).
        - Frees the finished sequence groups.

    - Finally, it creates and returns the newly generated results.

    Example:
    ```
    # Please see the example/ folder for more detailed examples.

    # initialize engine and request arguments
    engine = LLMEngine.from_engine_args(engine_args)
    example_inputs = [(0, "What is LLM?",
    SamplingParams(temperature=0.0))]

    # Start the engine with an event loop
    while True:
        if example_inputs:
            req_id, prompt, sampling_params = example_inputs.pop(0)
            engine.add_request(str(req_id),prompt,sampling_params)

        # continue the request processing
        request_outputs = engine.step()
        for request_output in request_outputs:
            if request_output.finished:
                # return or show the request output

        if not (engine.has_unfinished_requests() or example_inputs):
            break
    ```
    """
    if self.parallel_config.pipeline_parallel_size > 1:
        raise NotImplementedError(
            "Pipeline parallelism is only supported through AsyncLLMEngine "
            "as performance will be severely degraded otherwise.")

    # For llm_engine, there is no pipeline parallel support, so the engine
    # used is always 0.
    virtual_engine = 0

    # These are cached outputs from previous iterations. None if on first
    # iteration
    cached_outputs = self.cached_scheduler_outputs[virtual_engine]
    seq_group_metadata_list = cached_outputs.seq_group_metadata_list
    scheduler_outputs = cached_outputs.scheduler_outputs
    allow_async_output_proc = cached_outputs.allow_async_output_proc

    ctx = self.scheduler_contexts[virtual_engine]

    # Clear outputs for each new scheduler iteration
    ctx.request_outputs.clear()

    # Skip the scheduler if there are any remaining steps in the seq groups.
    # This ensures that the scheduler is only called again when the current
    # batch has completed.
    # The scheduler is also skipped if a single request caused the last
    # engine step to fail, and the previous schedule needs to be rerun.
    if not self._has_remaining_steps(
            seq_group_metadata_list
    ) and not self._skip_scheduling_next_step:
        # Schedule iteration
        (seq_group_metadata_list, scheduler_outputs,
         allow_async_output_proc
         ) = self.scheduler[virtual_engine].schedule()

        ctx.seq_group_metadata_list = seq_group_metadata_list
        ctx.scheduler_outputs = scheduler_outputs

        finished_requests_ids = self.scheduler[
            virtual_engine].get_and_reset_finished_requests_ids()
        # When n>1, elements in self.seq_id_to_seq_group should be deleted
        # here, otherwise memory leaks.
        for finished_request_id in finished_requests_ids:
            if finished_request_id in self.seq_id_to_seq_group:
                del self.seq_id_to_seq_group[finished_request_id]

        # Maybe switch from async mode to sync mode
        if not allow_async_output_proc and len(ctx.output_queue) > 0:
            self._process_model_outputs(ctx=ctx)

        if (self.scheduler_config.is_multi_step
                and scheduler_outputs.num_lookahead_slots > 0):
            # cache the scheduler outputs for the next iteration if we have
            # lookahead slots
            self._cache_scheduler_outputs_for_multi_step(
                virtual_engine, seq_group_metadata_list, scheduler_outputs,
                allow_async_output_proc)
    else:
        finished_requests_ids = list()

    assert seq_group_metadata_list is not None
    assert scheduler_outputs is not None

    if not scheduler_outputs.is_empty():

        # Check if we have a cached last_output from the previous iteration.
        # For supporting PP this is probably the best way to pass the
        # sampled_token_ids, as a separate broadcast over all the PP stages
        # will cause one virtual engine's microbatch to block the pipeline.
        last_sampled_token_ids = \
            self._get_last_sampled_token_ids(virtual_engine)

        execute_model_req = ExecuteModelRequest(
            seq_group_metadata_list=seq_group_metadata_list,
            blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
            blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
            blocks_to_copy=scheduler_outputs.blocks_to_copy,
            num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
            running_queue_size=scheduler_outputs.running_queue_size,
            finished_requests_ids=finished_requests_ids,
            # We use ExecuteModelRequest to pass the last sampled_token_ids
            # to each of the non-last PP stages for in-place prepare_input.
            last_sampled_token_ids=last_sampled_token_ids)

        if allow_async_output_proc:
            execute_model_req.async_callback = self.async_callbacks[
                virtual_engine]

        try:
            outputs = self.model_executor.execute_model(
                execute_model_req=execute_model_req)
            self._skip_scheduling_next_step = False
        except InputProcessingError as e:
            # The input for this request cannot be processed, so we must
            # abort it. If there are remaining requests in the batch that
            # have been scheduled, they will be retried on the next step.
            invalid_request_id = e.request_id
            self._abort_and_cache_schedule(
                request_id=invalid_request_id,
                virtual_engine=virtual_engine,
                seq_group_metadata_list=seq_group_metadata_list,
                scheduler_outputs=scheduler_outputs,
                allow_async_output_proc=allow_async_output_proc)
            # Raise so the caller is notified that this request failed
            raise

        # We need to do this here so that last step's sampled_token_ids can
        # be passed to the next iteration for PP.
        if self.scheduler_config.is_multi_step:
            self._update_cached_scheduler_output(virtual_engine, outputs)
    else:
        # Nothing scheduled => If there is pending async postprocessor,
        # then finish it here.
        if len(ctx.output_queue) > 0:
            self._process_model_outputs(ctx=ctx)
        # No outputs in this case
        outputs = []

    # Finish the current step for all the sequence groups.
    if self.scheduler_config.is_multi_step:
        for seq_group in seq_group_metadata_list:
            seq_group.finish_step()

    if not self._has_remaining_steps(seq_group_metadata_list):
        # clear the cache if we have finished all the steps.
        if self.scheduler_config.is_multi_step:
            self.cached_scheduler_outputs[0] = SchedulerOutputState()

        # is_first_step_output is True only when the num_steps of all
        # the sequences are 1. When the num_steps > 1,
        # multi_step_model_runner does the first-step output append.
        is_first_step_output: bool = False if not seq_group_metadata_list \
            else seq_group_metadata_list[0].state.num_steps == 1

        # Add results to the output_queue
        ctx.append_output(outputs=outputs,
                          seq_group_metadata_list=seq_group_metadata_list,
                          scheduler_outputs=scheduler_outputs,
                          is_async=allow_async_output_proc,
                          is_last_step=True,
                          is_first_step_output=is_first_step_output)

        if outputs and allow_async_output_proc:
            assert len(outputs) == 1, (
                "Async postprocessor expects only a single output set")

            self._advance_to_next_step(
                outputs[0], seq_group_metadata_list,
                scheduler_outputs.scheduled_seq_groups)

        # Check if need to run the usual non-async path
        if not allow_async_output_proc:
            self._process_model_outputs(ctx=ctx)

            # Log stats.
            self.do_log_stats(scheduler_outputs, outputs)

            # Tracing
            self.do_tracing(scheduler_outputs)
    else:
        # Multi-step case
        return ctx.request_outputs

    if not self.has_unfinished_requests():
        # Drain async postprocessor (if exists)
        if len(ctx.output_queue) > 0:
            self._process_model_outputs(ctx=ctx)
        assert len(ctx.output_queue) == 0

        # Stop the execute model loop in parallel workers until there are
        # more requests to process. This avoids waiting indefinitely in
        # torch.distributed ops which may otherwise timeout, and unblocks
        # the RPC thread in the workers so that they can process any other
        # queued control plane messages, such as add/remove lora adapters.
        logger.debug("Stopping remote worker execution loop.")
        self.model_executor.stop_remote_worker_execution_loop()

    return ctx.request_outputs

stop_profile

stop_profile() -> None

Source code in vllm/engine/llm_engine.py

def stop_profile(self) -> None:
    self.model_executor.stop_profile()

stop_remote_worker_execution_loop

stop_remote_worker_execution_loop() -> None

Source code in vllm/engine/llm_engine.py

def stop_remote_worker_execution_loop(self) -> None:
    self.model_executor.stop_remote_worker_execution_loop()

validate_output classmethod

validate_output(
    output: object, output_type: Type[_O]
) -> _O

Source code in vllm/engine/llm_engine.py

@classmethod
def validate_output(
    cls,
    output: object,
    output_type: Type[_O],
) -> _O:
    do_validate = cls.DO_VALIDATE_OUTPUT

    if ((TYPE_CHECKING or do_validate)
            and not isinstance(output, output_type)):
        raise TypeError(f"Expected output of type {output_type}, "
                        f"but found type {type(output)}")

    return cast(_O, output)

validate_outputs classmethod

validate_outputs(
    outputs: Sequence[object], output_type: Type[_O]
) -> List[_O]

Source code in vllm/engine/llm_engine.py

@classmethod
def validate_outputs(
    cls,
    outputs: GenericSequence[object],
    output_type: Type[_O],
) -> List[_O]:
    do_validate = cls.DO_VALIDATE_OUTPUT

    outputs_: List[_O]
    if TYPE_CHECKING or do_validate:
        outputs_ = []
        for output in outputs:
            if not isinstance(output, output_type):
                raise TypeError(f"Expected output of type {output_type}, "
                                f"but found type {type(output)}")

            outputs_.append(output)
    else:
        outputs_ = outputs

    return outputs_

wake_up

wake_up(tags: Optional[list[str]] = None) -> None

Source code in vllm/engine/llm_engine.py

def wake_up(self, tags: Optional[list[str]] = None) -> None:
    assert self.vllm_config.model_config.enable_sleep_mode, (
        "Sleep mode is not enabled in the model config")
    self.model_executor.wake_up(tags)

OutputData

Bases: NamedTuple

Source code in vllm/engine/llm_engine.py

class OutputData(NamedTuple):
    outputs: List[SamplerOutput]
    seq_group_metadata_list: List[SequenceGroupMetadata]
    scheduler_outputs: SchedulerOutputs
    is_async: bool
    is_last_step: bool
    # Indicates if this output is from the first step of the
    # multi-step. When multi-step is disabled, this is always
    # set to True.
    # is_first_step_output is invalid when `outputs` has
    # outputs from multiple steps.
    is_first_step_output: Optional[bool]
    skip: List[int]

is_async instance-attribute

is_async: bool

is_first_step_output instance-attribute

is_first_step_output: Optional[bool]

is_last_step instance-attribute

is_last_step: bool

outputs instance-attribute

outputs: List[SamplerOutput]

scheduler_outputs instance-attribute

scheduler_outputs: SchedulerOutputs

seq_group_metadata_list instance-attribute

seq_group_metadata_list: List[SequenceGroupMetadata]

skip instance-attribute

skip: List[int]

SchedulerContext

Source code in vllm/engine/llm_engine.py

class SchedulerContext:

    def __init__(self, multi_step_stream_outputs: bool = False):
        self.output_queue: Deque[OutputData] = deque()
        self.request_outputs: List[Union[RequestOutput,
                                         PoolingRequestOutput]] = []
        self.seq_group_metadata_list: Optional[
            List[SequenceGroupMetadata]] = None
        self.scheduler_outputs: Optional[SchedulerOutputs] = None

        self.multi_step_stream_outputs: bool = multi_step_stream_outputs

    def append_output(self, outputs: List[SamplerOutput],
                      seq_group_metadata_list: List[SequenceGroupMetadata],
                      scheduler_outputs: SchedulerOutputs, is_async: bool,
                      is_last_step: bool,
                      is_first_step_output: Optional[bool]):
        self.output_queue.append(
            OutputData(outputs=outputs,
                       seq_group_metadata_list=seq_group_metadata_list,
                       scheduler_outputs=scheduler_outputs,
                       is_async=is_async,
                       is_last_step=is_last_step,
                       is_first_step_output=is_first_step_output,
                       skip=[]))

multi_step_stream_outputs instance-attribute

multi_step_stream_outputs: bool = multi_step_stream_outputs

output_queue instance-attribute

output_queue: Deque[OutputData] = deque()

request_outputs instance-attribute

request_outputs: List[
    Union[RequestOutput, PoolingRequestOutput]
] = []

scheduler_outputs instance-attribute

scheduler_outputs: Optional[SchedulerOutputs] = None

seq_group_metadata_list instance-attribute

seq_group_metadata_list: Optional[
    List[SequenceGroupMetadata]
] = None

__init__

__init__(multi_step_stream_outputs: bool = False)

Source code in vllm/engine/llm_engine.py

def __init__(self, multi_step_stream_outputs: bool = False):
    self.output_queue: Deque[OutputData] = deque()
    self.request_outputs: List[Union[RequestOutput,
                                     PoolingRequestOutput]] = []
    self.seq_group_metadata_list: Optional[
        List[SequenceGroupMetadata]] = None
    self.scheduler_outputs: Optional[SchedulerOutputs] = None

    self.multi_step_stream_outputs: bool = multi_step_stream_outputs

append_output

append_output(
    outputs: List[SamplerOutput],
    seq_group_metadata_list: List[SequenceGroupMetadata],
    scheduler_outputs: SchedulerOutputs,
    is_async: bool,
    is_last_step: bool,
    is_first_step_output: Optional[bool],
)

Source code in vllm/engine/llm_engine.py

def append_output(self, outputs: List[SamplerOutput],
                  seq_group_metadata_list: List[SequenceGroupMetadata],
                  scheduler_outputs: SchedulerOutputs, is_async: bool,
                  is_last_step: bool,
                  is_first_step_output: Optional[bool]):
    self.output_queue.append(
        OutputData(outputs=outputs,
                   seq_group_metadata_list=seq_group_metadata_list,
                   scheduler_outputs=scheduler_outputs,
                   is_async=is_async,
                   is_last_step=is_last_step,
                   is_first_step_output=is_first_step_output,
                   skip=[]))

SchedulerOutputState dataclass

Caches the scheduler outputs for a virtual engine. Used for Multi-Step

Source code in vllm/engine/llm_engine.py

@dataclass
class SchedulerOutputState:
    """Caches the scheduler outputs for a virtual engine. Used for Multi-Step"""
    seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
    scheduler_outputs: Optional[SchedulerOutputs] = None
    allow_async_output_proc: bool = False
    last_output: Optional[SamplerOutput] = None

allow_async_output_proc class-attribute instance-attribute

allow_async_output_proc: bool = False

last_output class-attribute instance-attribute

last_output: Optional[SamplerOutput] = None

scheduler_outputs class-attribute instance-attribute

scheduler_outputs: Optional[SchedulerOutputs] = None

seq_group_metadata_list class-attribute instance-attribute

seq_group_metadata_list: Optional[
    List[SequenceGroupMetadata]
] = None

__init__

__init__(
    seq_group_metadata_list: Optional[
        List[SequenceGroupMetadata]
    ] = None,
    scheduler_outputs: Optional[SchedulerOutputs] = None,
    allow_async_output_proc: bool = False,
    last_output: Optional[SamplerOutput] = None,
) -> None