llm_engine.py 39.7 KB
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import time
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from contextlib import contextmanager
from typing import TYPE_CHECKING, ClassVar, Iterable, List, Optional
from typing import Sequence as GenericSequence
from typing import Type, TypeVar, Union
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from transformers import GenerationConfig, PreTrainedTokenizer
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import vllm
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from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
                         LoRAConfig, ModelConfig, ParallelConfig,
                         SchedulerConfig, SpeculativeConfig,
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                         VisionLanguageConfig)
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from vllm.core.scheduler import (ScheduledSequenceGroup, 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.output_processor.interfaces import (
    SequenceGroupOutputProcessor)
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.engine.output_processor.util import create_output_by_sequence_group
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from vllm.executor.executor_base import ExecutorBase
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from vllm.executor.ray_utils import initialize_ray_cluster
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from vllm.inputs import LLMInputs, PromptInputs
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
                          RequestOutputFactory)
from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
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                           PoolerOutput, SamplerOutput, Sequence,
                           SequenceGroup, SequenceGroupMetadata,
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                           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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def _load_generation_config_dict(model_config: ModelConfig):
    try:
        return GenerationConfig.from_pretrained(
            model_config.model,
            revision=model_config.revision,
        ).to_diff_dict()
    except OSError:
        # Not found.
        return {}


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_O = TypeVar("_O", RequestOutput, EmbeddingRequestOutput)


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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.

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    The :class:`~vllm.LLM` class wraps this class for offline batched inference
    and the :class:`AsyncLLMEngine` class wraps this class for online serving.
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    The config arguments are derived from :class:`~vllm.EngineArgs`. (See
    :ref:`engine_args`)
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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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        lora_config (Optional): The configuration related to serving multi-LoRA.
        vision_language_config (Optional): The configuration related to vision
            language models.
        speculative_config (Optional): The configuration related to speculative
            decoding.
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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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    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 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[BaseTokenizerGroup]

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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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        load_config: LoadConfig,
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        lora_config: Optional[LoRAConfig],
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        vision_language_config: Optional[VisionLanguageConfig],
        speculative_config: Optional[SpeculativeConfig],
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        decoding_config: Optional[DecodingConfig],
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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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            "Initializing an LLM engine (v%s) with config: "
            "model=%r, speculative_config=%r, tokenizer=%r, "
            "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
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            "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
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            "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
            "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
            "disable_custom_all_reduce=%s, quantization=%s, "
            "enforce_eager=%s, kv_cache_dtype=%s, "
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            "quantization_param_path=%s, device_config=%s, "
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            "decoding_config=%r, seed=%d, served_model_name=%s)",
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            vllm.__version__,
            model_config.model,
            speculative_config,
            model_config.tokenizer,
            model_config.skip_tokenizer_init,
            model_config.tokenizer_mode,
            model_config.revision,
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            model_config.rope_scaling,
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            model_config.rope_theta,
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            model_config.tokenizer_revision,
            model_config.trust_remote_code,
            model_config.dtype,
            model_config.max_model_len,
            load_config.download_dir,
            load_config.load_format,
            parallel_config.tensor_parallel_size,
            parallel_config.disable_custom_all_reduce,
            model_config.quantization,
            model_config.enforce_eager,
            cache_config.cache_dtype,
            model_config.quantization_param_path,
            device_config.device,
            decoding_config,
            model_config.seed,
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            model_config.served_model_name,
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        )
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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.speculative_config = speculative_config
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        self.load_config = load_config
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        self.decoding_config = decoding_config or DecodingConfig()
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        self.log_stats = log_stats

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        if not self.model_config.skip_tokenizer_init:
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            self.tokenizer = self._init_tokenizer()
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            self.detokenizer = Detokenizer(self.tokenizer)
        else:
            self.tokenizer = None
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            self.detokenizer = None
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        self.seq_counter = Counter()
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        self.generation_config_fields = _load_generation_config_dict(
            model_config)
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        self.model_executor = executor_class(
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            vision_language_config=vision_language_config,
            speculative_config=speculative_config,
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            load_config=load_config,
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        )
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        if not self.model_config.embedding_mode:
            self._initialize_kv_caches()
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        # If usage stat is enabled, collect relevant info.
        if is_usage_stats_enabled():
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            from vllm.model_executor.model_loader import (
                get_architecture_class_name)
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            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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        if self.tokenizer:
            # 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,
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                labels=dict(model_name=model_config.served_model_name),
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                max_model_len=self.model_config.max_model_len)
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            self.stat_logger.info("cache_config", self.cache_config)
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        # 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,
                self.get_tokenizer_for_seq,
                stop_checker=StopChecker(
                    self.scheduler_config.max_model_len,
                    self.get_tokenizer_for_seq,
                ),
            ))

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    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.
        """
        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
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            logger.info(
                "Overriding num_gpu_blocks=%d with "
                "num_gpu_blocks_override=%d", num_gpu_blocks,
                num_gpu_blocks_override)
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            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)

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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.
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        engine_config = engine_args.create_engine_config()
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        distributed_executor_backend = (
            engine_config.parallel_config.distributed_executor_backend)
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        # Initialize the cluster and specify the executor class.
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        if engine_config.device_config.device_type == "neuron":
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            from vllm.executor.neuron_executor import NeuronExecutor
            executor_class = NeuronExecutor
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        elif engine_config.device_config.device_type == "tpu":
            from vllm.executor.tpu_executor import TPUExecutor
            executor_class = TPUExecutor
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        elif engine_config.device_config.device_type == "cpu":
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            from vllm.executor.cpu_executor import CPUExecutor
            executor_class = CPUExecutor
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        elif distributed_executor_backend == "ray":
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            initialize_ray_cluster(engine_config.parallel_config)
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            from vllm.executor.ray_gpu_executor import RayGPUExecutor
            executor_class = RayGPUExecutor
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        elif distributed_executor_backend == "mp":
            from vllm.executor.multiproc_gpu_executor import (
                MultiprocessingGPUExecutor)
            executor_class = MultiprocessingGPUExecutor
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        else:
            from vllm.executor.gpu_executor import GPUExecutor
            executor_class = GPUExecutor

        # Create the LLM engine.
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        engine = cls(
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            **engine_config.to_dict(),
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            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 __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()

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    MISSING_TOKENIZER_GROUP_MSG = ("Unable to get tokenizer because "
                                   "skip_tokenizer_init is True")

    def get_tokenizer_group(
            self,
            fail_msg: str = MISSING_TOKENIZER_GROUP_MSG) -> BaseTokenizerGroup:
        if self.tokenizer is None:
            raise ValueError(fail_msg)

        return self.tokenizer

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    def get_tokenizer(self) -> "PreTrainedTokenizer":
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        return self.get_tokenizer_group().get_lora_tokenizer(None)
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    def get_tokenizer_for_seq(self,
                              sequence: Sequence) -> "PreTrainedTokenizer":
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        return self.get_tokenizer_group().get_lora_tokenizer(
            sequence.lora_request)
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    def _init_tokenizer(self, **tokenizer_init_kwargs) -> BaseTokenizerGroup:
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        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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        return 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 _get_eos_token_id(
            self, lora_request: Optional[LoRARequest]) -> Optional[int]:
        if self.tokenizer is None:
            logger.warning("Using None for EOS token id because tokenizer "
                           "is not initialized")
            return None

        return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id

    def _add_processed_request(
        self,
        request_id: str,
        processed_inputs: LLMInputs,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: float,
        lora_request: Optional[LoRARequest],
    ) -> None:
        # Create the sequences.
        block_size = self.cache_config.block_size
        seq_id = next(self.seq_counter)
        eos_token_id = self._get_eos_token_id(lora_request)

        seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id,
                       lora_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,
            )
        elif isinstance(params, PoolingParams):
            seq_group = self._create_sequence_group_with_pooling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
            )
        else:
            raise ValueError(
                "Either SamplingParams or PoolingParams must be provided.")

        # Add the sequence group to the scheduler.
        self.scheduler.add_seq_group(seq_group)

    def process_model_inputs(
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        self,
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        request_id: str,
        inputs: PromptInputs,
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        lora_request: Optional[LoRARequest] = None,
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    ) -> LLMInputs:
        if isinstance(inputs, str):
            inputs = {"prompt": inputs}

        if "prompt_token_ids" not in inputs:
            tokenizer = self.get_tokenizer_group("prompts must be None if "
                                                 "skip_tokenizer_init is True")

            prompt_token_ids = tokenizer.encode(request_id=request_id,
                                                prompt=inputs["prompt"],
                                                lora_request=lora_request)
        else:
            prompt_token_ids = inputs["prompt_token_ids"]

        return LLMInputs(prompt_token_ids=prompt_token_ids,
                         prompt=inputs.get("prompt"),
                         multi_modal_data=inputs.get("multi_modal_data"))
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    def add_request(
        self,
        request_id: str,
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        inputs: PromptInputs,
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        params: Union[SamplingParams, PoolingParams],
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        arrival_time: Optional[float] = None,
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        lora_request: Optional[LoRARequest] = None,
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    ) -> 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.
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            inputs: The inputs to the LLM. See
                :class:`~vllm.inputs.PromptInputs`
                for more details about the format of each input.
            params: Parameters for sampling or pooling.
                :class:`~vllm.SamplingParams` for text generation.
                :class:`~vllm.PoolingParams` for pooling.
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            arrival_time: The arrival time of the request. If None, we use
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                the current monotonic time.
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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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        if arrival_time is None:
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            arrival_time = time.time()
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        processed_inputs = self.process_model_inputs(request_id=request_id,
                                                     inputs=inputs,
                                                     lora_request=lora_request)
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        self._add_processed_request(
            request_id=request_id,
            processed_inputs=processed_inputs,
            params=params,
            arrival_time=arrival_time,
            lora_request=lora_request,
        )
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    def _create_sequence_group_with_sampling(
        self,
        request_id: str,
        seq: Sequence,
        sampling_params: SamplingParams,
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        arrival_time: float,
        lora_request: Optional[LoRARequest],
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    ) -> 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.")

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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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        # Add the eos token id into the sampling_params to support min_tokens
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        # processing
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        if seq.eos_token_id is not None:
            sampling_params.all_stop_token_ids.add(seq.eos_token_id)
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        sampling_params.update_from_generation_config(
            self.generation_config_fields)
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        # Create the sequence group.
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        seq_group = SequenceGroup(request_id=request_id,
                                  seqs=[seq],
                                  arrival_time=arrival_time,
                                  sampling_params=sampling_params,
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                                  lora_request=lora_request)
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        return seq_group

    def _create_sequence_group_with_pooling(
        self,
        request_id: str,
        seq: Sequence,
        pooling_params: PoolingParams,
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        arrival_time: float,
        lora_request: Optional[LoRARequest],
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    ) -> 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)
        return 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_decoding_config(self) -> DecodingConfig:
        """Gets the decoding configuration."""
        return self.decoding_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 _process_sequence_group_outputs(
        self,
        seq_group: SequenceGroup,
        outputs: List[EmbeddingSequenceGroupOutput],
    ) -> None:
        seq_group.embeddings = outputs[0].embeddings

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

        return

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    def _process_model_outputs(
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        self,
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        output: GenericSequence[Union[SamplerOutput, PoolerOutput]],
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        scheduled_seq_groups: List[ScheduledSequenceGroup],
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        ignored_seq_groups: List[SequenceGroup],
        seq_group_metadata_list: List[SequenceGroupMetadata],
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    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
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        """Apply the model output to the sequences in the scheduled seq groups.
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        Returns RequestOutputs that can be returned to the client.
        """

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        now = time.time()
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        # Organize outputs by [sequence group][step] instead of
        # [step][sequence group].
        output_by_sequence_group = create_output_by_sequence_group(
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            output, num_seq_groups=len(scheduled_seq_groups))
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        # Update the scheduled sequence groups with the model outputs.
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        for scheduled_seq_group, outputs, seq_group_meta in zip(
                scheduled_seq_groups, output_by_sequence_group,
                seq_group_metadata_list):
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            seq_group = scheduled_seq_group.seq_group
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            seq_group.update_num_computed_tokens(
                scheduled_seq_group.token_chunk_size)
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            if self.model_config.embedding_mode:
                self._process_sequence_group_outputs(seq_group, outputs)
                continue
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            self.output_processor.process_prompt_logprob(seq_group, outputs)
            if seq_group_meta.do_sample:
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                self.output_processor.process_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.
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        request_outputs: List[Union[RequestOutput,
                                    EmbeddingRequestOutput]] = []
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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 = RequestOutputFactory.create(seq_group)
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            request_outputs.append(request_output)
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        for seq_group in ignored_seq_groups:
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            request_output = RequestOutputFactory.create(seq_group)
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            request_outputs.append(request_output)
        return request_outputs

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    def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
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        """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)
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            >>>         engine.add_request(str(req_id),prompt,sampling_params)
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            >>>
            >>>     # 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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            execute_model_req = ExecuteModelRequest(
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                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,
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                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                running_queue_size=scheduler_outputs.running_queue_size,
            )
            output = self.model_executor.execute_model(
                execute_model_req=execute_model_req)
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        else:
            output = []
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        request_outputs = self._process_model_outputs(
            output, scheduler_outputs.scheduled_seq_groups,
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            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
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        # Log stats.
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        self.do_log_stats(scheduler_outputs, output)
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        if not request_outputs:
            # 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.
            self.model_executor.stop_remote_worker_execution_loop()

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        return request_outputs
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    def do_log_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs] = None,
            model_output: Optional[List[SamplerOutput]] = None) -> None:
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        """Forced log when no requests active."""
        if self.log_stats:
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            self.stat_logger.log(
                self._get_stats(scheduler_outputs, model_output))
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    def _get_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs],
            model_output: Optional[List[SamplerOutput]] = 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.
        """
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        now = time.time()
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        # System State
        #   Scheduler State
        num_running_sys = len(self.scheduler.running)
        num_swapped_sys = len(self.scheduler.swapped)
        num_waiting_sys = len(self.scheduler.waiting)

        # KV Cache Usage in %
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        num_total_gpu = self.cache_config.num_gpu_blocks
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        gpu_cache_usage_sys = 0.
        if num_total_gpu is not None:
            num_free_gpu = self.scheduler.block_manager.get_num_free_gpu_blocks(
            )
            gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)
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        num_total_cpu = self.cache_config.num_cpu_blocks
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        cpu_cache_usage_sys = 0.
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        if num_total_cpu is not None and num_total_cpu > 0:
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            num_free_cpu = self.scheduler.block_manager.get_num_free_cpu_blocks(
            )
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            cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu)

        # Iteration stats
        num_prompt_tokens_iter = 0
        num_generation_tokens_iter = 0
        time_to_first_tokens_iter: List[float] = []
        time_per_output_tokens_iter: List[float] = []
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        num_preemption_iter = (0 if scheduler_outputs is None else
                               scheduler_outputs.preempted)
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        # Request stats
        #   Latency
        time_e2e_requests: List[float] = []
        #   Metadata
        num_prompt_tokens_requests: List[int] = []
        num_generation_tokens_requests: List[int] = []
        best_of_requests: List[int] = []
        n_requests: List[int] = []
        finished_reason_requests: List[str] = []

        # NOTE: This loop assumes prefill seq_groups are before
        # decode seq_groups in scheduled_seq_groups.
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        if scheduler_outputs is not None:
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            num_generation_tokens_from_prefill_groups = 0.
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            # 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.
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            for idx, scheduled_seq_group in enumerate(
                    scheduler_outputs.scheduled_seq_groups):
                group_was_prefill = idx < scheduler_outputs.num_prefill_groups
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                seq_group = scheduled_seq_group.seq_group
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                # 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_latency(now)
                        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_latency(now)
                    time_per_output_tokens_iter.append(latency)

                # 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.
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                if seq_group.is_finished():
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                    # Latency timings
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                    time_e2e_requests.append(now -
                                             seq_group.metrics.arrival_time)
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                    # 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()
                    ])
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                    if seq_group.sampling_params is not None:
                        best_of_requests.append(
                            seq_group.sampling_params.best_of)
                        n_requests.append(seq_group.sampling_params.n)
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                    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 = (
                scheduler_outputs.num_batched_tokens - num_prompt_tokens_iter +
                num_generation_tokens_from_prefill_groups)
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        # Spec decode, if enabled, emits specialized metrics from the worker in
        # sampler output.
        if model_output 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

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        return Stats(
            now=now,
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            # 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,

            # Iteration stats
            num_prompt_tokens_iter=num_prompt_tokens_iter,
            num_generation_tokens_iter=num_generation_tokens_iter,
            time_to_first_tokens_iter=time_to_first_tokens_iter,
            time_per_output_tokens_iter=time_per_output_tokens_iter,
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            spec_decode_metrics=spec_decode_metrics,
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            num_preemption_iter=num_preemption_iter,
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            # Request stats
            #   Latency
            time_e2e_requests=time_e2e_requests,
            #   Metadata
            num_prompt_tokens_requests=num_prompt_tokens_requests,
            num_generation_tokens_requests=num_generation_tokens_requests,
            best_of_requests=best_of_requests,
            n_requests=n_requests,
            finished_reason_requests=finished_reason_requests,
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        )

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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()