llm_engine.py 35.7 KB
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import time
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from typing import Iterable, List, Optional, Tuple, Type, Union
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from transformers import PreTrainedTokenizer

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import vllm
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from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
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                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
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from vllm.core.scheduler import Scheduler, SchedulerOutputs
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from vllm.engine.arg_utils import EngineArgs
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from vllm.engine.metrics import StatLogger, Stats
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from vllm.engine.ray_utils import initialize_ray_cluster
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from vllm.executor.executor_base import ExecutorBase
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.model_executor.model_loader import get_architecture_class_name
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from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
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from vllm.sequence import (MultiModalData, SamplerOutput, Sequence,
                           SequenceGroup, SequenceGroupOutput, SequenceOutput,
                           SequenceStatus)
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from vllm.transformers_utils.detokenizer import Detokenizer
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from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
                                                     get_tokenizer_group)
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from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                  usage_message)
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from vllm.utils import Counter
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logger = init_logger(__name__)
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_LOCAL_LOGGING_INTERVAL_SEC = 5
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class LLMEngine:
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    """An LLM engine that receives requests and generates texts.
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    This is the main class for the vLLM engine. It receives requests
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    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
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    `AsyncLLMEngine` class wraps this class for online serving.
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    NOTE: The config arguments are derived from the `EngineArgs` class. For the
    comprehensive list of arguments, see `EngineArgs`.
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    Args:
        model_config: The configuration related to the LLM model.
        cache_config: The configuration related to the KV cache memory
            management.
        parallel_config: The configuration related to distributed execution.
        scheduler_config: The configuration related to the request scheduler.
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        device_config: The configuration related to the device.
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        executor_class: The model executor class for managing distributed
            execution.
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        log_stats: Whether to log statistics.
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        usage_context: Specified entry point, used for usage info collection
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    """
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    def __init__(
        self,
        model_config: ModelConfig,
        cache_config: CacheConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
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        device_config: DeviceConfig,
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        lora_config: Optional[LoRAConfig],
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        vision_language_config: Optional["VisionLanguageConfig"],
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        executor_class: Type[ExecutorBase],
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        log_stats: bool,
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        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
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    ) -> None:
        logger.info(
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            f"Initializing an LLM engine (v{vllm.__version__}) with config: "
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            f"model={model_config.model!r}, "
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            f"tokenizer={model_config.tokenizer!r}, "
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            f"tokenizer_mode={model_config.tokenizer_mode}, "
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            f"revision={model_config.revision}, "
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            f"tokenizer_revision={model_config.tokenizer_revision}, "
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            f"trust_remote_code={model_config.trust_remote_code}, "
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            f"dtype={model_config.dtype}, "
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            f"max_seq_len={model_config.max_model_len}, "
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            f"download_dir={model_config.download_dir!r}, "
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            f"load_format={model_config.load_format}, "
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            f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
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            f"disable_custom_all_reduce="
            f"{parallel_config.disable_custom_all_reduce}, "
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            f"quantization={model_config.quantization}, "
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            f"enforce_eager={model_config.enforce_eager}, "
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            f"kv_cache_dtype={cache_config.cache_dtype}, "
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            f"device_config={device_config.device}, "
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            f"seed={model_config.seed})")
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        # TODO(woosuk): Print more configs in debug mode.

        self.model_config = model_config
        self.cache_config = cache_config
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        self.lora_config = lora_config
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        self.vision_language_config = vision_language_config
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        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config
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        self.device_config = device_config
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        self.log_stats = log_stats
        self._verify_args()

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        self._init_tokenizer()
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        self.detokenizer = Detokenizer(self.tokenizer)
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        self.seq_counter = Counter()

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

                    # Quantization
                    "quantization":
                    model_config.quantization,
                    "kv_cache_dtype":
                    cache_config.cache_dtype,

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

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        # Ping the tokenizer to ensure liveness if it runs in a
        # different process.
        self.tokenizer.ping()

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        # Create the scheduler.
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        # NOTE: the cache_config here have been updated with the numbers of
        # GPU and CPU blocks, which are profiled in the distributed executor.
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        self.scheduler = Scheduler(scheduler_config, cache_config, lora_config)
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        # Metric Logging.
        if self.log_stats:
            self.stat_logger = StatLogger(
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                local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                labels=dict(model_name=model_config.model))
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            self.stat_logger.info("cache_config", self.cache_config)
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    @classmethod
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    def from_engine_args(
        cls,
        engine_args: EngineArgs,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
    ) -> "LLMEngine":
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        """Creates an LLM engine from the engine arguments."""
        # Create the engine configs.
        engine_configs = engine_args.create_engine_configs()
        parallel_config = engine_configs[2]
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        device_config = engine_configs[4]
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        # Initialize the cluster and specify the executor class.
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        if device_config.device_type == "neuron":
            from vllm.executor.neuron_executor import NeuronExecutor
            executor_class = NeuronExecutor
        elif parallel_config.worker_use_ray:
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            initialize_ray_cluster(parallel_config)
            from vllm.executor.ray_gpu_executor import RayGPUExecutor
            executor_class = RayGPUExecutor
        else:
            assert parallel_config.world_size == 1, (
                "Ray is required if parallel_config.world_size > 1.")
            from vllm.executor.gpu_executor import GPUExecutor
            executor_class = GPUExecutor

        # Create the LLM engine.
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        engine = cls(
            *engine_configs,
            executor_class=executor_class,
            log_stats=not engine_args.disable_log_stats,
            usage_context=usage_context,
        )
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        return engine
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    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!")

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    def get_tokenizer(self) -> "PreTrainedTokenizer":
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        return self.tokenizer.get_lora_tokenizer(None)
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    def get_tokenizer_for_seq(self,
                              sequence: Sequence) -> "PreTrainedTokenizer":
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        return self.tokenizer.get_lora_tokenizer(sequence.lora_request)

    def _init_tokenizer(self, **tokenizer_init_kwargs):
        init_kwargs = dict(
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            tokenizer_id=self.model_config.tokenizer,
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            enable_lora=bool(self.lora_config),
            max_num_seqs=self.scheduler_config.max_num_seqs,
            max_input_length=None,
            tokenizer_mode=self.model_config.tokenizer_mode,
            trust_remote_code=self.model_config.trust_remote_code,
            revision=self.model_config.tokenizer_revision)
        init_kwargs.update(tokenizer_init_kwargs)
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        self.tokenizer: BaseTokenizerGroup = get_tokenizer_group(
            self.parallel_config.tokenizer_pool_config, **init_kwargs)
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    def _verify_args(self) -> None:
        self.model_config.verify_with_parallel_config(self.parallel_config)
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        self.cache_config.verify_with_parallel_config(self.parallel_config)
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        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
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    def encode_request(
        self,
        request_id: str,  # pylint: disable=unused-argument
        prompt: Optional[str],
        prompt_token_ids: Optional[List[int]] = None,
        lora_request: Optional[LoRARequest] = None,
    ):
        if prompt_token_ids is None:
            assert prompt is not None
            prompt_token_ids = self.tokenizer.encode(request_id=request_id,
                                                     prompt=prompt,
                                                     lora_request=lora_request)
        return prompt_token_ids

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    def add_request(
        self,
        request_id: str,
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        prompt: Optional[str],
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        sampling_params: SamplingParams,
        prompt_token_ids: Optional[List[int]] = None,
        arrival_time: Optional[float] = None,
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        lora_request: Optional[LoRARequest] = None,
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        multi_modal_data: Optional[MultiModalData] = None,
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    ) -> None:
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        """Add a request to the engine's request pool.
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        The request is added to the request pool and will be processed by the
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        scheduler as `engine.step()` is called. The exact scheduling policy is
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        determined by the scheduler.

        Args:
            request_id: The unique ID of the request.
            prompt: The prompt string. Can be None if prompt_token_ids is
                provided.
            sampling_params: The sampling parameters for text generation.
            prompt_token_ids: The token IDs of the prompt. If None, we
                use the tokenizer to convert the prompts to token IDs.
            arrival_time: The arrival time of the request. If None, we use
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                the current monotonic time.
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            multi_modal_data: Multi modal data per request.
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        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 `best_of` number of :class:`~vllm.Sequence` objects.
            - Create a :class:`~vllm.SequenceGroup` object
              from the list of :class:`~vllm.Sequence`.
            - Add the :class:`~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
            >>> ...
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        """
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        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")
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        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.")
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        if arrival_time is None:
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            arrival_time = time.time()
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        prompt_token_ids = self.encode_request(
            request_id=request_id,
            prompt=prompt,
            prompt_token_ids=prompt_token_ids,
            lora_request=lora_request)
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        # Create the sequences.
        block_size = self.cache_config.block_size
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        seq_id = next(self.seq_counter)
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        eos_token_id = self.tokenizer.get_lora_tokenizer(
            lora_request).eos_token_id
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        seq = Sequence(seq_id, prompt, prompt_token_ids, block_size,
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                       eos_token_id, lora_request)
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        # Defensive copy of SamplingParams, which are used by the sampler,
        # this doesn't deep-copy LogitsProcessor objects
        sampling_params = sampling_params.clone()
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        # inject the eos token id into the sampling_params to support min_tokens
        # processing
        sampling_params.eos_token_id = seq.eos_token_id
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        # Create the sequence group.
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        seq_group = SequenceGroup(request_id, [seq], sampling_params,
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                                  arrival_time, lora_request, multi_modal_data)
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        # Add the sequence group to the scheduler.
        self.scheduler.add_seq_group(seq_group)

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    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
        """Aborts a request(s) with the given ID.
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        Args:
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            request_id: The ID(s) of the request to abort.
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        Details:
            - Refer to the
              :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group`
              from class :class:`~vllm.core.scheduler.Scheduler`.

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

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

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

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

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    def _check_beam_search_early_stopping(
        self,
        early_stopping: Union[bool, str],
        sampling_params: SamplingParams,
        best_running_seq: Sequence,
        current_worst_seq: Sequence,
    ) -> bool:
        assert sampling_params.use_beam_search
        length_penalty = sampling_params.length_penalty
        if early_stopping is True:
            return True

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        current_worst_score = current_worst_seq.get_beam_search_score(
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            length_penalty=length_penalty,
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            eos_token_id=current_worst_seq.eos_token_id)
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        if early_stopping is False:
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            highest_attainable_score = best_running_seq.get_beam_search_score(
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                length_penalty=length_penalty,
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                eos_token_id=best_running_seq.eos_token_id)
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        else:
            assert early_stopping == "never"
            if length_penalty > 0.0:
                # If length_penalty > 0.0, beam search will prefer longer
                # sequences. The highest attainable score calculation is
                # based on the longest possible sequence length in this case.
                max_possible_length = max(
                    best_running_seq.get_prompt_len() +
                    sampling_params.max_tokens,
                    self.scheduler_config.max_model_len)
                highest_attainable_score = (
                    best_running_seq.get_beam_search_score(
                        length_penalty=length_penalty,
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                        eos_token_id=best_running_seq.eos_token_id,
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                        seq_len=max_possible_length))
            else:
                # Otherwise, beam search will prefer shorter sequences. The
                # highest attainable score calculation is based on the current
                # sequence length.
                highest_attainable_score = (
                    best_running_seq.get_beam_search_score(
                        length_penalty=length_penalty,
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                        eos_token_id=best_running_seq.eos_token_id))
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        return current_worst_score >= highest_attainable_score

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    def _process_sequence_group_outputs(self, seq_group: SequenceGroup,
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                                        outputs: SequenceGroupOutput) -> None:
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        # Process prompt logprobs
        prompt_logprobs = outputs.prompt_logprobs
        if prompt_logprobs is not None:
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            self.detokenizer.decode_prompt_logprobs_inplace(
                seq_group, prompt_logprobs)
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            seq_group.prompt_logprobs = prompt_logprobs

        # Process samples
        samples = outputs.samples
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        parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
        existing_finished_seqs = seq_group.get_finished_seqs()
        parent_child_dict = {
            parent_seq.seq_id: []
            for parent_seq in parent_seqs
        }
        for sample in samples:
            parent_child_dict[sample.parent_seq_id].append(sample)
        # List of (child, parent)
        child_seqs: List[Tuple[Sequence, Sequence]] = []

        # Process the child samples for each parent sequence
        for parent in parent_seqs:
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            child_samples: List[SequenceOutput] = parent_child_dict[
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                parent.seq_id]
            if len(child_samples) == 0:
                # This parent sequence has no children samples. Remove
                # the parent sequence from the sequence group since it will
                # not be used in the future iterations.
                parent.status = SequenceStatus.FINISHED_ABORTED
                seq_group.remove(parent.seq_id)
                self.scheduler.free_seq(parent)
                continue
            # Fork the parent sequence if there are multiple child samples.
            for child_sample in child_samples[:-1]:
                new_child_seq_id = next(self.seq_counter)
                child = parent.fork(new_child_seq_id)
                child.append_token_id(child_sample.output_token,
                                      child_sample.logprobs)
                child_seqs.append((child, parent))
            # Continue the parent sequence for the last child sample.
            # We reuse the parent sequence here to reduce redundant memory
            # copies, especially when using non-beam search sampling methods.
            last_child_sample = child_samples[-1]
            parent.append_token_id(last_child_sample.output_token,
                                   last_child_sample.logprobs)
            child_seqs.append((parent, parent))

        for seq, _ in child_seqs:
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            self.detokenizer.decode_sequence_inplace(seq,
                                                     seq_group.sampling_params)
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            self._check_stop(seq, seq_group.sampling_params)

        # Non-beam search case
        if not seq_group.sampling_params.use_beam_search:
            # For newly created child sequences, add them to the sequence group
            # and fork them in block manager if they are not finished.
            for seq, parent in child_seqs:
                if seq is not parent:
                    seq_group.add(seq)
                    if not seq.is_finished():
                        self.scheduler.fork_seq(parent, seq)

            # Free the finished and selected parent sequences' memory in block
            # manager. Keep them in the sequence group as candidate output.
            # NOTE: we need to fork the new sequences before freeing the
            # old sequences.
            for seq, parent in child_seqs:
                if seq is parent and seq.is_finished():
                    self.scheduler.free_seq(seq)
            return

        # Beam search case
        # Select the child sequences to keep in the sequence group.
        selected_child_seqs = []
        unselected_child_seqs = []
        beam_width = seq_group.sampling_params.best_of
        length_penalty = seq_group.sampling_params.length_penalty

        # Select the newly finished sequences with the highest scores
        # to replace existing finished sequences.
        # Tuple of (seq, parent, is_new)
        existing_finished_seqs = [(seq, None, False)
                                  for seq in existing_finished_seqs]
        new_finished_seqs = [(seq, parent, True) for seq, parent in child_seqs
                             if seq.is_finished()]
        all_finished_seqs = existing_finished_seqs + new_finished_seqs
        # Sort the finished sequences by their scores.
        all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score(
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            length_penalty=length_penalty, eos_token_id=x[0].eos_token_id),
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                               reverse=True)
        for seq, parent, is_new in all_finished_seqs[:beam_width]:
            if is_new:
                # A newly generated child sequence finishes and has a high
                # score, so we will add it into the sequence group.
                selected_child_seqs.append((seq, parent))
        for seq, parent, is_new in all_finished_seqs[beam_width:]:
            if is_new:
                # A newly generated child sequence finishes but has a low
                # score, so we will not add it into the sequence group.
                # Additionally, if this sequence is a continuation of a
                # parent sequence, we will need remove the parent sequence
                # from the sequence group.
                unselected_child_seqs.append((seq, parent))
            else:
                # An existing finished sequence has a low score, so we will
                # remove it from the sequence group.
                seq_group.remove(seq.seq_id)

        # select the top beam_width sequences from the running
        # sequences for the next iteration to continue the beam
        # search.
        running_child_seqs = [(seq, parent) for seq, parent in child_seqs
                              if not seq.is_finished()]
        # Sort the running sequences by their scores.
        running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score(
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            length_penalty=length_penalty, eos_token_id=x[0].eos_token_id),
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                                reverse=True)

        # Check if we can stop the beam search.
        if len(running_child_seqs) == 0:
            # No running sequences, stop the beam search.
            stop_beam_search = True
        elif len(all_finished_seqs) < beam_width:
            # Not enough finished sequences, continue the beam search.
            stop_beam_search = False
        else:
            # Check the early stopping criteria
            best_running_seq = running_child_seqs[0][0]
            current_worst_seq = all_finished_seqs[beam_width - 1][0]
            stop_beam_search = self._check_beam_search_early_stopping(
                seq_group.sampling_params.early_stopping,
                seq_group.sampling_params, best_running_seq, current_worst_seq)

        if stop_beam_search:
            # Stop the beam search and remove all the running sequences from
            # the sequence group.
            unselected_child_seqs.extend(running_child_seqs)
        else:
            # Continue the beam search and select the top beam_width sequences
            # to continue the beam search.
            selected_child_seqs.extend(running_child_seqs[:beam_width])
            # The remaining running sequences will not be used in the next
            # iteration. Again, if these sequences are continuations of
            # parent sequences, we will need to remove the parent sequences
            # from the sequence group.
            unselected_child_seqs.extend(running_child_seqs[beam_width:])

        # For newly created child sequences, add them to the sequence group
        # and fork them in block manager if they are not finished.
        for seq, parent in selected_child_seqs:
            if seq is not parent:
                seq_group.add(seq)
                if not seq.is_finished():
                    self.scheduler.fork_seq(parent, seq)

        # Free the finished and selected parent sequences' memory in block
        # manager. Keep them in the sequence group as candidate output.
        for seq, parent in selected_child_seqs:
            if seq is parent and seq.is_finished():
                self.scheduler.free_seq(seq)

        # Remove the unselected parent sequences from the sequence group and
        # free their memory in block manager.
        for seq, parent in unselected_child_seqs:
            if seq is parent:
                # Remove the parent sequence if it is not selected for next
                # iteration
                seq_group.remove(seq.seq_id)
                self.scheduler.free_seq(seq)

    def _process_model_outputs(
            self, output: SamplerOutput,
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            scheduler_outputs: SchedulerOutputs) -> List[RequestOutput]:
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        now = time.time()
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        # Update the scheduled sequence groups with the model outputs.
        scheduled_seq_groups = scheduler_outputs.scheduled_seq_groups
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        for scheduled_seq_group, outputs in zip(scheduled_seq_groups, output):
            seq_group = scheduled_seq_group.seq_group
            token_chunk_size = scheduled_seq_group.token_chunk_size
            seq_group.update_num_computed_tokens(token_chunk_size)
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            self._process_sequence_group_outputs(seq_group, outputs)
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        # Free the finished sequence groups.
        self.scheduler.free_finished_seq_groups()
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        # Create the outputs.
        request_outputs: List[RequestOutput] = []
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        for scheduled_seq_group in scheduled_seq_groups:
            seq_group = scheduled_seq_group.seq_group
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            seq_group.maybe_set_first_token_time(now)
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            request_output = RequestOutput.from_seq_group(seq_group)
            request_outputs.append(request_output)
        for seq_group in scheduler_outputs.ignored_seq_groups:
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            request_output = RequestOutput.from_seq_group(seq_group)
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            request_outputs.append(request_output)
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        # Log stats.
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        if self.log_stats:
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            self.stat_logger.log(self._get_stats(scheduler_outputs))
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        return request_outputs

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

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        .. figure:: https://i.imgur.com/sv2HssD.png
            :alt: Overview of the step function
            :align: center

            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.

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            - Step 2: Calls the distributed executor to execute the model.
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            - 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
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        """
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        seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
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        if not scheduler_outputs.is_empty():
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            output = self.model_executor.execute_model(
                seq_group_metadata_list, scheduler_outputs.blocks_to_swap_in,
                scheduler_outputs.blocks_to_swap_out,
                scheduler_outputs.blocks_to_copy)
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        else:
            output = []
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        return self._process_model_outputs(output, scheduler_outputs)
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    def do_log_stats(self) -> None:
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        """Forced log when no requests active."""
        if self.log_stats:
            self.stat_logger.log(self._get_stats(scheduler_outputs=None))
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    def _get_stats(self,
                   scheduler_outputs: Optional[SchedulerOutputs]) -> Stats:
        """Get Stats to be Logged to Prometheus."""
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        now = time.time()
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        # KV Cache Usage in %.
        num_total_gpu = self.cache_config.num_gpu_blocks
        num_free_gpu = self.scheduler.block_manager.get_num_free_gpu_blocks()
        gpu_cache_usage = 1.0 - (num_free_gpu / num_total_gpu)
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        num_total_cpu = self.cache_config.num_cpu_blocks
        cpu_cache_usage = 0.
        if num_total_cpu > 0:
            num_free_cpu = self.scheduler.block_manager.get_num_free_cpu_blocks(
            )
            cpu_cache_usage = 1.0 - (num_free_cpu / num_total_cpu)

        # Scheduler State
        num_running = len(self.scheduler.running)
        num_swapped = len(self.scheduler.swapped)
        num_waiting = len(self.scheduler.waiting)

        # Iteration stats if we have scheduler output.
        num_prompt_tokens = 0
        num_generation_tokens = 0
        time_to_first_tokens = []
        time_per_output_tokens = []
        time_e2e_requests = []
        if scheduler_outputs is not None:
            prompt_run = scheduler_outputs.prompt_run

            # Number of Tokens.
            if prompt_run:
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                num_prompt_tokens = sum(
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                    len(scheduled_seq_group.seq_group.prompt_token_ids)
                    for scheduled_seq_group in
                    scheduler_outputs.scheduled_seq_groups)
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                num_generation_tokens = sum(
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                    scheduled_seq_group.seq_group.num_seqs()
                    for scheduled_seq_group in
                    scheduler_outputs.scheduled_seq_groups)
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            else:
                num_generation_tokens = scheduler_outputs.num_batched_tokens

            # Latency Timings.
            time_last_iters = []
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            for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups:
                seq_group = scheduled_seq_group.seq_group
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                # Time since last token.
                # (n.b. updates seq_group.metrics.last_token_time)
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                time_last_iters.append(seq_group.get_last_latency(now))
                # Time since arrival for all finished requests.
                if seq_group.is_finished():
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                    time_e2e_requests.append(now -
                                             seq_group.metrics.arrival_time)
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            time_to_first_tokens = time_last_iters if prompt_run else []
            time_per_output_tokens = [] if prompt_run else time_last_iters

        return Stats(
            now=now,
            num_running=num_running,
            num_swapped=num_swapped,
            num_waiting=num_waiting,
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            gpu_cache_usage=gpu_cache_usage,
            cpu_cache_usage=cpu_cache_usage,
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            num_prompt_tokens=num_prompt_tokens,
            num_generation_tokens=num_generation_tokens,
            time_to_first_tokens=time_to_first_tokens,
            time_per_output_tokens=time_per_output_tokens,
            time_e2e_requests=time_e2e_requests,
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        )

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    def _check_stop(self, seq: Sequence,
                    sampling_params: SamplingParams) -> None:
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        """Stop the finished sequences."""
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        # Check if the sequence has reached max_model_len.
        if seq.get_len() > self.scheduler_config.max_model_len:
            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
            return

        # Check if the sequence has reached max_tokens.
        if seq.get_output_len() == sampling_params.max_tokens:
            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
            return

        # Check if the minimum number of tokens has been generated yet;
        # skip the stop string/token checks if not
        if seq.get_output_len() < sampling_params.min_tokens:
            return

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        for stop_str in sampling_params.stop:
            if seq.output_text.endswith(stop_str):
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                self._finalize_sequence(seq, sampling_params, stop_str)
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                seq.status = SequenceStatus.FINISHED_STOPPED
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                seq.stop_reason = stop_str
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                return
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        last_token_id = seq.get_last_token_id()
        if last_token_id in sampling_params.stop_token_ids:
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            stop_str = self.get_tokenizer_for_seq(seq).convert_ids_to_tokens(
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                last_token_id)
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            self._finalize_sequence(seq, sampling_params, stop_str)
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            seq.status = SequenceStatus.FINISHED_STOPPED
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            seq.stop_reason = last_token_id
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            return
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        # Check if the sequence has generated the EOS token.
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        if ((not sampling_params.ignore_eos)
                and seq.get_last_token_id() == seq.eos_token_id):
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            seq.status = SequenceStatus.FINISHED_STOPPED
            return
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    def _finalize_sequence(self, seq: Sequence,
                           sampling_params: SamplingParams,
                           stop_string: str) -> None:
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        if sampling_params.include_stop_str_in_output:
            return

        if stop_string and seq.output_text.endswith(stop_string):
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            # Truncate the output text so that the stop string is
            # not included in the output.
            seq.output_text = seq.output_text[:-len(stop_string)]

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    def add_lora(self, lora_request: LoRARequest) -> bool:
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        return self.model_executor.add_lora(lora_request)
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    def remove_lora(self, lora_id: int) -> bool:
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        return self.model_executor.remove_lora(lora_id)
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    def list_loras(self) -> List[int]:
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        return self.model_executor.list_loras()
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    def check_health(self) -> None:
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        self.model_executor.check_health()