llm.py 80.8 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import itertools
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import warnings
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from collections.abc import Callable, Sequence
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from typing import TYPE_CHECKING, Any, cast
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import cloudpickle
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import torch.nn as nn
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from pydantic import ValidationError
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from tqdm.auto import tqdm
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from typing_extensions import TypeVar
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from vllm.beam_search import (
    BeamSearchInstance,
    BeamSearchOutput,
    BeamSearchSequence,
    create_sort_beams_key_function,
)
from vllm.config import (
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    AttentionConfig,
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    CompilationConfig,
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    PoolerConfig,
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    ProfilerConfig,
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    StructuredOutputsConfig,
    is_init_field,
)
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from vllm.config.compilation import CompilationMode
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from vllm.config.model import (
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    ConvertOption,
    HfOverrides,
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    ModelDType,
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    RunnerOption,
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    TokenizerMode,
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)
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from vllm.distributed.weight_transfer.base import (
    WeightTransferInitRequest,
    WeightTransferUpdateRequest,
)
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from vllm.engine.arg_utils import EngineArgs
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
)
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from vllm.entrypoints.pooling.score.utils import (
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    ScoreData,
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    ScoreMultiModalParam,
    _cosine_similarity,
    compress_token_type_ids,
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    compute_maxsim_score,
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    get_score_prompt,
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    validate_score_input,
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)
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from vllm.entrypoints.utils import log_non_default_args
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from vllm.inputs.data import (
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    DataPrompt,
    PromptType,
    SingletonPrompt,
    TextPrompt,
    TokensPrompt,
)
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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.layers.quantization import QuantizationMethods
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from vllm.outputs import (
    ClassificationRequestOutput,
    EmbeddingRequestOutput,
    PoolingRequestOutput,
    RequestOutput,
    ScoringRequestOutput,
)
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from vllm.platforms import current_platform
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from vllm.pooling_params import PoolingParams
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from vllm.renderers import ChatParams, TokenizeParams, merge_kwargs
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from vllm.renderers.inputs import DictPrompt, TokPrompt
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from vllm.renderers.inputs.preprocess import (
    conversation_to_seq,
    extract_prompt_components,
    parse_model_prompt,
    prompt_to_seq,
)
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from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams
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from vllm.tasks import PoolingTask
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from vllm.tokenizers import TokenizerLike
from vllm.tokenizers.mistral import MistralTokenizer
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils.collection_utils import as_iter, is_list_of
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from vllm.utils.counter import Counter
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from vllm.v1.engine.llm_engine import LLMEngine
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from vllm.v1.sample.logits_processor import LogitsProcessor
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if TYPE_CHECKING:
    from vllm.v1.metrics.reader import Metric

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logger = init_logger(__name__)

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

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class LLM:
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    """An LLM for generating texts from given prompts and sampling parameters.

    This class includes a tokenizer, a language model (possibly distributed
    across multiple GPUs), and GPU memory space allocated for intermediate
    states (aka KV cache). Given a batch of prompts and sampling parameters,
    this class generates texts from the model, using an intelligent batching
    mechanism and efficient memory management.

    Args:
        model: The name or path of a HuggingFace Transformers model.
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        tokenizer: The name or path of a HuggingFace Transformers tokenizer.
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        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
            if available, and "slow" will always use the slow tokenizer.
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        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer. Expect valid prompt_token_ids and None for prompt
            from the input.
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        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
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        allowed_local_media_path: Allowing API requests to read local images
            or videos from directories specified by the server file system.
            This is a security risk. Should only be enabled in trusted
            environments.
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        allowed_media_domains: If set, only media URLs that belong to this
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            domain can be used for multi-modal inputs.
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        tensor_parallel_size: The number of GPUs to use for distributed
            execution with tensor parallelism.
        dtype: The data type for the model weights and activations. Currently,
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            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
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            the `dtype` attribute of the Transformers model's config. However,
            if the `dtype` in the config is `float32`, we will use `float16` instead.
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        quantization: The method used to quantize the model weights. Currently,
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            we support "awq", "gptq", and "fp8" (experimental).
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            If None, we first check the `quantization_config` attribute in the
            model config file. If that is None, we assume the model weights are
            not quantized and use `dtype` to determine the data type of
            the weights.
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        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id.
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        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id.
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        seed: The seed to initialize the random number generator for sampling.
        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
            reserve for the model weights, activations, and KV cache. Higher
            values will increase the KV cache size and thus improve the model's
            throughput. However, if the value is too high, it may cause out-of-
            memory (OOM) errors.
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        kv_cache_memory_bytes: Size of KV Cache per GPU in bytes. By default,
            this is set to None and vllm can automatically infer the kv cache
            size based on gpu_memory_utilization. However, users may want to
            manually specify the kv cache memory size. kv_cache_memory_bytes
            allows more fine-grain control of how much memory gets used when
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            compared with using gpu_memory_utilization. Note that
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            kv_cache_memory_bytes (when not-None) ignores
            gpu_memory_utilization
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        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
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            This can be used for temporarily storing the states of the requests
            when their `best_of` sampling parameters are larger than 1. If all
            requests will have `best_of=1`, you can safely set this to 0.
            Noting that `best_of` is only supported in V0. Otherwise, too small
            values may cause out-of-memory (OOM) errors.
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        cpu_offload_gb: The size (GiB) of CPU memory to use for offloading
            the model weights. This virtually increases the GPU memory space
            you can use to hold the model weights, at the cost of CPU-GPU data
            transfer for every forward pass.
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        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
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        enable_return_routed_experts: Whether to return routed experts.
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        disable_custom_all_reduce: See
            [ParallelConfig][vllm.config.ParallelConfig].
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        hf_token: The token to use as HTTP bearer authorization for remote files
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            . If `True`, will use the token generated when running
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            `hf auth login` (stored in `~/.cache/huggingface/token`).
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        hf_overrides: If a dictionary, contains arguments to be forwarded to the
            HuggingFace config. If a callable, it is called to update the
            HuggingFace config.
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        mm_processor_kwargs: Arguments to be forwarded to the model's processor
            for multi-modal data, e.g., image processor. Overrides for the
            multi-modal processor obtained from `AutoProcessor.from_pretrained`.
            The available overrides depend on the model that is being run.
            For example, for Phi-3-Vision: `{"num_crops": 4}`.
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        pooler_config: Initialize non-default pooling config for the pooling model,
            e.g., `PoolerConfig(seq_pooling_type="MEAN", use_activation=False)`.
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        compilation_config: Either an integer or a dictionary. If it is an
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            integer, it is used as the mode of compilation optimization. If it
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            is a dictionary, it can specify the full compilation configuration.
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        attention_config: Configuration for attention mechanisms. Can be a
            dictionary or an AttentionConfig instance. If a dictionary, it will
            be converted to an AttentionConfig. Allows specifying the attention
            backend and other attention-related settings.
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        **kwargs: Arguments for [`EngineArgs`][vllm.EngineArgs].
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    Note:
        This class is intended to be used for offline inference. For online
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        serving, use the [AsyncLLMEngine][vllm.AsyncLLMEngine] class instead.
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    """
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    def __init__(
        self,
        model: str,
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        *,
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        runner: RunnerOption = "auto",
        convert: ConvertOption = "auto",
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        tokenizer: str | None = None,
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        tokenizer_mode: TokenizerMode | str = "auto",
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        skip_tokenizer_init: bool = False,
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        trust_remote_code: bool = False,
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        allowed_local_media_path: str = "",
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        allowed_media_domains: list[str] | None = None,
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        tensor_parallel_size: int = 1,
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        dtype: ModelDType = "auto",
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        quantization: QuantizationMethods | None = None,
        revision: str | None = None,
        tokenizer_revision: str | None = None,
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        seed: int = 0,
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        gpu_memory_utilization: float = 0.9,
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        swap_space: float = 4,
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        cpu_offload_gb: float = 0,
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        enforce_eager: bool = False,
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        enable_return_routed_experts: bool = False,
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        disable_custom_all_reduce: bool = False,
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        hf_token: bool | str | None = None,
        hf_overrides: HfOverrides | None = None,
        mm_processor_kwargs: dict[str, Any] | None = None,
        pooler_config: PoolerConfig | None = None,
        structured_outputs_config: dict[str, Any]
        | StructuredOutputsConfig
        | None = None,
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        profiler_config: dict[str, Any] | ProfilerConfig | None = None,
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        attention_config: dict[str, Any] | AttentionConfig | None = None,
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        kv_cache_memory_bytes: int | None = None,
        compilation_config: int | dict[str, Any] | CompilationConfig | None = None,
        logits_processors: list[str | type[LogitsProcessor]] | None = None,
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        **kwargs: Any,
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    ) -> None:
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        """LLM constructor."""
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        if "disable_log_stats" not in kwargs:
            kwargs["disable_log_stats"] = True
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        if "worker_cls" in kwargs:
            worker_cls = kwargs["worker_cls"]
            # if the worker_cls is not qualified string name,
            # we serialize it using cloudpickle to avoid pickling issues
            if isinstance(worker_cls, type):
                kwargs["worker_cls"] = cloudpickle.dumps(worker_cls)

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        if "kv_transfer_config" in kwargs and isinstance(
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            kwargs["kv_transfer_config"], dict
        ):
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            from vllm.config.kv_transfer import KVTransferConfig
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            raw_config_dict = kwargs["kv_transfer_config"]
            try:
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                kwargs["kv_transfer_config"] = KVTransferConfig(**raw_config_dict)
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            except ValidationError as e:
                logger.error(
                    "Failed to convert 'kv_transfer_config' dict to "
                    "KVTransferConfig object. Dict: %s. Error: %s",
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                    raw_config_dict,
                    e,
                )
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                # Consider re-raising a more specific vLLM error or ValueError
                # to provide better context to the user.
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                raise ValueError(f"Invalid 'kv_transfer_config' provided: {e}") from e
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        if hf_overrides is None:
            hf_overrides = {}

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        def _make_config(value: Any, cls: type[_R]) -> _R:
            """Convert dict/None/instance to a config instance."""
            if value is None:
                return cls()
            if isinstance(value, dict):
                return cls(**{k: v for k, v in value.items() if is_init_field(cls, k)})  # type: ignore[arg-type]
            return value
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        if isinstance(compilation_config, int):
            compilation_config_instance = CompilationConfig(
                mode=CompilationMode(compilation_config)
            )
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        else:
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            compilation_config_instance = _make_config(
                compilation_config, CompilationConfig
            )
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        structured_outputs_instance = _make_config(
            structured_outputs_config, StructuredOutputsConfig
        )
        profiler_config_instance = _make_config(profiler_config, ProfilerConfig)
        attention_config_instance = _make_config(attention_config, AttentionConfig)
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        # warn about single-process data parallel usage.
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        _dp_size = int(kwargs.get("data_parallel_size", 1))
        _distributed_executor_backend = kwargs.get("distributed_executor_backend")
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        if (
            _dp_size > 1
            and not _distributed_executor_backend == "external_launcher"
            and not current_platform.is_tpu()
        ):
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            raise ValueError(
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                f"LLM(data_parallel_size={_dp_size}) is not supported for single-"
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                "process usage and may hang. Please use "
                "the explicit multi-process data-parallel example at "
                "'examples/offline_inference/data_parallel.py'."
            )

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        engine_args = EngineArgs(
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            model=model,
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            runner=runner,
            convert=convert,
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            tokenizer=tokenizer,
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            tokenizer_mode=tokenizer_mode,
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            skip_tokenizer_init=skip_tokenizer_init,
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            trust_remote_code=trust_remote_code,
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            allowed_local_media_path=allowed_local_media_path,
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            allowed_media_domains=allowed_media_domains,
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            tensor_parallel_size=tensor_parallel_size,
            dtype=dtype,
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            quantization=quantization,
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            revision=revision,
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            tokenizer_revision=tokenizer_revision,
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            seed=seed,
            gpu_memory_utilization=gpu_memory_utilization,
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            kv_cache_memory_bytes=kv_cache_memory_bytes,
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            swap_space=swap_space,
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            cpu_offload_gb=cpu_offload_gb,
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            enforce_eager=enforce_eager,
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            enable_return_routed_experts=enable_return_routed_experts,
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            disable_custom_all_reduce=disable_custom_all_reduce,
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            hf_token=hf_token,
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            hf_overrides=hf_overrides,
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            mm_processor_kwargs=mm_processor_kwargs,
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            pooler_config=pooler_config,
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            structured_outputs_config=structured_outputs_instance,
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            profiler_config=profiler_config_instance,
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            attention_config=attention_config_instance,
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            compilation_config=compilation_config_instance,
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            logits_processors=logits_processors,
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            **kwargs,
        )
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        log_non_default_args(engine_args)

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        self.llm_engine = LLMEngine.from_engine_args(
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            engine_args=engine_args, usage_context=UsageContext.LLM_CLASS
        )
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        self.engine_class = type(self.llm_engine)
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        self.request_counter = Counter()
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        self.default_sampling_params: dict[str, Any] | None = None
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        supported_tasks = self.llm_engine.get_supported_tasks()
        logger.info("Supported tasks: %s", supported_tasks)
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        self.supported_tasks = supported_tasks

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        self.model_config = self.llm_engine.model_config
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        self.input_processor = self.llm_engine.input_processor
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        self.io_processor = self.llm_engine.io_processor
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        # Cache for __repr__ to avoid repeated collective_rpc calls
        self._cached_repr: str | None = None

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    def get_tokenizer(self) -> TokenizerLike:
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        return self.llm_engine.get_tokenizer()
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    def get_world_size(self, include_dp: bool = True) -> int:
        """Get the world size from the parallel config.

        Args:
            include_dp: If True (default), returns the world size including
                data parallelism (TP * PP * DP). If False, returns the world
                size without data parallelism (TP * PP).

        Returns:
            The world size (tensor_parallel_size * pipeline_parallel_size),
            optionally multiplied by data_parallel_size if include_dp is True.
        """
        parallel_config = self.llm_engine.vllm_config.parallel_config
        if include_dp:
            return parallel_config.world_size_across_dp
        return parallel_config.world_size

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    def reset_mm_cache(self) -> None:
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        self.input_processor.clear_mm_cache()
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        self.llm_engine.reset_mm_cache()

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    def get_default_sampling_params(self) -> SamplingParams:
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        if self.default_sampling_params is None:
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            self.default_sampling_params = self.model_config.get_diff_sampling_param()
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        if self.default_sampling_params:
            return SamplingParams.from_optional(**self.default_sampling_params)
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        return SamplingParams()

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    def generate(
        self,
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        prompts: PromptType | Sequence[PromptType],
        sampling_params: SamplingParams | Sequence[SamplingParams] | None = None,
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        *,
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        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
        priority: list[int] | None = None,
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        tokenization_kwargs: dict[str, Any] | None = None,
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    ) -> list[RequestOutput]:
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        """Generates the completions for the input prompts.

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        This class automatically batches the given prompts, considering
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        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
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            prompts: The prompts to the LLM. You may pass a sequence of prompts
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                for batch inference. See [PromptType][vllm.inputs.PromptType]
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                for more details about the format of each prompt.
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            sampling_params: The sampling parameters for text generation. If
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                None, we use the default sampling parameters.
                When it is a single value, it is applied to every prompt.
                When it is a list, the list must have the same length as the
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                prompts and it is paired one by one with the prompt.
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            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
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            lora_request: LoRA request to use for generation, if any.
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            priority: The priority of the requests, if any.
                Only applicable when priority scheduling policy is enabled.
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                If provided, must be a list of integers matching the length
                of `prompts`, where each priority value corresponds to the prompt
                at the same index.
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            tokenization_kwargs: Overrides for `tokenizer.encode`.
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        Returns:
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            A list of `RequestOutput` objects containing the
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            generated completions in the same order as the input prompts.
        """
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        model_config = self.model_config
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        runner_type = model_config.runner_type
        if runner_type != "generate":
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            raise ValueError(
                "LLM.generate() is only supported for generative models. "
                "Try passing `--runner generate` to use the model as a "
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                "generative model."
            )
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        if sampling_params is None:
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            sampling_params = self.get_default_sampling_params()
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        outputs = self._run_completion(
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            prompts=prompts,
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            params=sampling_params,
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            use_tqdm=use_tqdm,
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            lora_request=lora_request,
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            tokenization_kwargs=tokenization_kwargs,
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            priority=priority,
        )
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        return self.engine_class.validate_outputs(outputs, RequestOutput)
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    def _get_modality_specific_lora_reqs(
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        self,
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        prompts: Sequence[DictPrompt | TokPrompt],
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        lora_request: list[LoRARequest] | LoRARequest | None,
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    ):
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        # Grab the lora config off the vllm config on the engine,
        # since this is the same for both v0 & v1.
        lora_config = self.llm_engine.vllm_config.lora_config

        # If there's no lora config / default_mm_loras, or the model
        # isn't multimodal, leave the lora as is.
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        if (
            lora_config is None
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            or not self.model_config.is_multimodal_model
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            or (lora_config and lora_config.default_mm_loras is None)
        ):
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            return lora_request

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        optional_loras = (
            [lora_request] * len(prompts)
            if not isinstance(lora_request, Sequence)
            else lora_request
        )
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        return [
            self._resolve_single_prompt_mm_lora(
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                prompt,
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                opt_lora_req,
                lora_config.default_mm_loras,
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            )
            for prompt, opt_lora_req in zip(prompts, optional_loras)
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        ]

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    def _resolve_single_prompt_mm_lora(
        self,
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        prompt: DictPrompt | TokPrompt,
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        lora_request: LoRARequest | None,
        default_mm_loras: dict[str, str] | None,
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    ):
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        if not default_mm_loras or not (
            mm_data := prompt.get("multi_modal_data") or {}
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        ):
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            return lora_request

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        intersection = set(
            mm_data.keys()  # type: ignore
        ).intersection(default_mm_loras.keys())
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        if not intersection:
            return lora_request
        if len(intersection) > 1:
            # TODO: Would be nice to be able to have multiple loras per prompt
            logger.warning(
                "Multiple modality specific loras were registered and would be"
                " used by a single prompt consuming several modalities; "
                " currently we only support one lora per request; as such,"
                " lora(s) registered with modalities: %s"
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                " will be skipped",
                intersection,
            )
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            return lora_request

        # Build the LoRA request; the ID of the default mm lora is the
        # index of the modality name sorted alphabetically + 1.
        modality_name = intersection.pop()
        modality_lora_path = default_mm_loras[modality_name]
        modality_lora_id = sorted(default_mm_loras).index(modality_name) + 1

        # If we have a collision, warn if there is a collision,
        # but always send the explicitly provided request.
        if lora_request:
            if lora_request.lora_int_id != modality_lora_id:
                logger.warning(
                    "A modality with a registered lora and a lora_request "
                    "with a different ID were provided; falling back to the "
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                    "lora_request as we only apply one LoRARequest per prompt"
                )
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            return lora_request

        return LoRARequest(
            modality_name,
            modality_lora_id,
            modality_lora_path,
        )

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    def collective_rpc(
        self,
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        method: str | Callable[..., _R],
        timeout: float | None = None,
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        args: tuple = (),
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        kwargs: dict[str, Any] | None = None,
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    ) -> list[_R]:
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        """
        Execute an RPC call on all workers.

        Args:
            method: Name of the worker method to execute, or a callable that
                is serialized and sent to all workers to execute.

                If the method is a callable, it should accept an additional
                `self` argument, in addition to the arguments passed in `args`
                and `kwargs`. The `self` argument will be the worker object.
            timeout: Maximum time in seconds to wait for execution. Raises a
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                [`TimeoutError`][] on timeout. `None` means wait indefinitely.
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            args: Positional arguments to pass to the worker method.
            kwargs: Keyword arguments to pass to the worker method.

        Returns:
            A list containing the results from each worker.
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        Note:
            It is recommended to use this API to only pass control messages,
            and set up data-plane communication to pass data.
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        """
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        return self.llm_engine.collective_rpc(method, timeout, args, kwargs)
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    def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:
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        """
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        Run a function directly on the model inside each worker,
        returning the result for each of them.
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        !!! warning
            To reduce the overhead of data transfer, avoid returning large
            arrays or tensors from this method. If you must return them,
            make sure you move them to CPU first to avoid taking up additional
            VRAM!
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        """
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        return self.llm_engine.apply_model(func)
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    def _get_beam_search_lora_requests(
        self,
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        lora_request: list[LoRARequest] | LoRARequest | None,
        prompts: list[TokensPrompt | TextPrompt],
    ) -> list[LoRARequest | None]:
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        """Get the optional lora request corresponding to each prompt."""
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        if isinstance(lora_request, Sequence) and len(lora_request) != len(prompts):
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            raise ValueError(
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                "Lora request list should be the same length as the prompts"
            )
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        if lora_request is None or isinstance(lora_request, LoRARequest):
            return [lora_request] * len(prompts)

        raise TypeError(f"Invalid lora_request type {type(lora_request)}")

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    def beam_search(
        self,
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        prompts: list[TokensPrompt | TextPrompt],
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        params: BeamSearchParams,
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        lora_request: list[LoRARequest] | LoRARequest | None = None,
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        use_tqdm: bool = False,
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        concurrency_limit: int | None = None,
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    ) -> list[BeamSearchOutput]:
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        """
        Generate sequences using beam search.

        Args:
            prompts: A list of prompts. Each prompt can be a string or a list
                of token IDs.
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            params: The beam search parameters.
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            lora_request: LoRA request to use for generation, if any.
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            use_tqdm: Whether to use tqdm to display the progress bar.
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            concurrency_limit: The maximum number of concurrent requests.
                If None, the number of concurrent requests is unlimited.
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        """
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        # TODO: how does beam search work together with length penalty,
        # frequency, penalty, and stopping criteria, etc.?
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        beam_width = params.beam_width
        max_tokens = params.max_tokens
        temperature = params.temperature
        ignore_eos = params.ignore_eos
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        length_penalty = params.length_penalty

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        lora_requests = self._get_beam_search_lora_requests(lora_request, prompts)
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        tokenizer = self.get_tokenizer()
        sort_beams_key = create_sort_beams_key_function(
            tokenizer.eos_token_id,
            length_penalty,
        )
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        if use_tqdm and concurrency_limit is not None:
            logger.warning(
                "Progress bar is not supported when using concurrency_limit. "
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                "Disabling progress bar."
            )
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            use_tqdm = False

        if concurrency_limit is None:
            concurrency_limit = len(prompts)

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        def create_tokens_prompt_from_beam(beam: BeamSearchSequence) -> TokensPrompt:
            token_prompt_kwargs: TokensPrompt = {"prompt_token_ids": beam.tokens}
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            if beam.multi_modal_data is not None:
                token_prompt_kwargs["multi_modal_data"] = beam.multi_modal_data

            if beam.mm_processor_kwargs is not None:
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                token_prompt_kwargs["mm_processor_kwargs"] = beam.mm_processor_kwargs
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            return TokensPrompt(**token_prompt_kwargs)
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        # generate 2 * beam_width candidates at each step
        # following the huggingface transformers implementation
        # at https://github.com/huggingface/transformers/blob/e15687fffe5c9d20598a19aeab721ae0a7580f8a/src/transformers/generation/beam_search.py#L534 # noqa
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        beam_search_params = SamplingParams(
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            logprobs=2 * beam_width,
            max_tokens=1,
            temperature=temperature,
            skip_clone=True,  # Internal beam search, safe to skip clone
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        )
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        instances: list[BeamSearchInstance] = []
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        for lora_req, prompt in zip(lora_requests, prompts):
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            # Add multimodal processor kwargs & data
            mm_kwargs = {}
            if "multi_modal_data" in prompt:
                mm_kwargs["multi_modal_data"] = prompt["multi_modal_data"]
            if "mm_processor_kwargs" in prompt:
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                mm_kwargs["mm_processor_kwargs"] = prompt["mm_processor_kwargs"]
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            if "prompt_token_ids" in prompt:
                prompt = cast(TokensPrompt, prompt)  # Needed for mypy
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                prompt_tokens = prompt["prompt_token_ids"]
            else:
                prompt_tokens = tokenizer.encode(prompt["prompt"])
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            instances.append(
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                BeamSearchInstance(
                    prompt_tokens,
                    lora_request=lora_req,
                    logprobs=None,
                    **mm_kwargs,
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                ),
            )
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        for prompt_start in range(0, len(prompts), concurrency_limit):
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            instances_batch = instances[prompt_start : prompt_start + concurrency_limit]
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            token_iter = range(max_tokens)
            if use_tqdm:
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                token_iter = tqdm(
                    token_iter, desc="Beam search", unit="token", unit_scale=False
                )
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                logger.warning(
                    "The progress bar shows the upper bound on token steps and "
                    "may finish early due to stopping conditions. It does not "
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                    "reflect instance-level progress."
                )
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            for _ in token_iter:
                all_beams: list[BeamSearchSequence] = list(
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                    sum((instance.beams for instance in instances_batch), [])
                )
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                pos = [0] + list(
                    itertools.accumulate(
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                        len(instance.beams) for instance in instances_batch
                    )
                )
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                instance_start_and_end: list[tuple[int, int]] = list(
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                    zip(pos[:-1], pos[1:])
                )
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                if len(all_beams) == 0:
                    break

                # create corresponding batch entries for prompt & optional lora
                prompts_batch, lora_req_batch = zip(
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                    *[
                        (create_tokens_prompt_from_beam(beam), beam.lora_request)
                        for beam in all_beams
                    ]
                )
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                # only runs for one step
                # we don't need to use tqdm here
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                output = self.generate(
                    prompts_batch,
                    sampling_params=beam_search_params,
                    use_tqdm=False,
                    lora_request=lora_req_batch,
                )
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                for (start, end), instance in zip(
                    instance_start_and_end, instances_batch
                ):
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                    instance_new_beams = []
                    for i in range(start, end):
                        current_beam = all_beams[i]
                        result = output[i]

                        if result.outputs[0].logprobs is not None:
                            # if `result.outputs[0].logprobs` is None, it means
                            # the sequence is completed because of the
                            # max-model-len or abortion. we don't need to add
                            # it to the new beams.
                            logprobs = result.outputs[0].logprobs[0]
                            for token_id, logprob_obj in logprobs.items():
                                new_beam = BeamSearchSequence(
                                    tokens=current_beam.tokens + [token_id],
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                                    logprobs=current_beam.logprobs + [logprobs],
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                                    lora_request=current_beam.lora_request,
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                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                    multi_modal_data=current_beam.multi_modal_data,
                                    mm_processor_kwargs=current_beam.mm_processor_kwargs,
                                )

                                if (
                                    token_id == tokenizer.eos_token_id
                                    and not ignore_eos
                                ):
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                                    instance.completed.append(new_beam)
                                else:
                                    instance_new_beams.append(new_beam)
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                    sorted_beams = sorted(
                        instance_new_beams, key=sort_beams_key, reverse=True
                    )
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                    instance.beams = sorted_beams[:beam_width]
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        outputs = []
        for instance in instances:
            instance.completed.extend(instance.beams)
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            sorted_completed = sorted(
                instance.completed, key=sort_beams_key, reverse=True
            )
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            best_beams = sorted_completed[:beam_width]

            for beam in best_beams:
                beam.text = tokenizer.decode(beam.tokens)
            outputs.append(BeamSearchOutput(sequences=best_beams))

        return outputs

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    def _get_cmpl_tok_params(self, tokenization_kwargs: dict[str, Any] | None):
        model_config = self.model_config
        encoder_config = model_config.encoder_config or {}

        return TokenizeParams(
            max_total_tokens=model_config.max_model_len,
            do_lower_case=encoder_config.get("do_lower_case", False),
            # For Whisper, special tokens should be provided by the user based
            # on the task and language of their request. Also needed to avoid
            # appending an EOS token to the prompt which disrupts generation.
            add_special_tokens=not model_config.is_encoder_decoder,
        ).with_kwargs(tokenization_kwargs)

    def _preprocess_completion(
        self,
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        prompts: Sequence[PromptType],
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        tokenization_kwargs: dict[str, Any] | None = None,
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    ) -> Sequence[DictPrompt | TokPrompt]:
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        """
        Convert prompt inputs from LLM APIs (other than [LLM.chat][]) into
        a format that can be passed to `_add_request`.

        Refer to [LLM.generate][] for a complete description of the arguments.

        Returns:
            A list of `TokensPrompts` objects containing the tokenized prompt
            after chat template interpolation, and the raw multi-modal inputs.
        """
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        renderer = self.llm_engine.renderer
        model_config = self.model_config

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        parsed_prompts = [
            parse_model_prompt(model_config, prompt) for prompt in prompts
        ]
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        tok_params = self._get_cmpl_tok_params(tokenization_kwargs)

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        return renderer.render_cmpl(parsed_prompts, tok_params)
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    def _get_chat_tok_params(self, tokenization_kwargs: dict[str, Any] | None):
        model_config = self.model_config
        encoder_config = model_config.encoder_config or {}

        return TokenizeParams(
            max_total_tokens=model_config.max_model_len,
            do_lower_case=encoder_config.get("do_lower_case", False),
            add_special_tokens=False,
        ).with_kwargs(tokenization_kwargs)

    def _preprocess_chat(
        self,
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        conversations: Sequence[list[ChatCompletionMessageParam]],
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        chat_template: str | None = None,
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        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
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        chat_template_kwargs: dict[str, Any] | None = None,
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        add_generation_prompt: bool = True,
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        continue_final_message: bool = False,
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        tools: list[dict[str, Any]] | None = None,
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        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> Sequence[TokPrompt]:
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        """
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        Convert a list of conversations into prompts so that they can then
        be used as input for other LLM APIs.

        Refer to [LLM.chat][] for a complete description of the arguments.
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        Returns:
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            A list of `TokensPrompts` objects containing the tokenized prompt
            after chat template interpolation, and the raw multi-modal inputs.
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        """
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        renderer = self.llm_engine.renderer
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        chat_params = ChatParams(
            chat_template=chat_template,
            chat_template_content_format=chat_template_content_format,
            chat_template_kwargs=merge_kwargs(
                chat_template_kwargs,
                dict(
                    add_generation_prompt=add_generation_prompt,
                    continue_final_message=continue_final_message,
                    tools=tools,
                    tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
                ),
            ),
        )
        tok_params = self._get_chat_tok_params(tokenization_kwargs)

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        _, engine_prompts = renderer.render_chat(
            conversations,
            chat_params,
            tok_params,
            prompt_extras={"mm_processor_kwargs": mm_processor_kwargs},
        )
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        return engine_prompts
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    def chat(
        self,
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        messages: list[ChatCompletionMessageParam]
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        | Sequence[list[ChatCompletionMessageParam]],
        sampling_params: SamplingParams | Sequence[SamplingParams] | None = None,
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        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: LoRARequest | None = None,
        chat_template: str | None = None,
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        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
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        tools: list[dict[str, Any]] | None = None,
        chat_template_kwargs: dict[str, Any] | None = None,
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        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> list[RequestOutput]:
        """
        Generate responses for a chat conversation.

        The chat conversation is converted into a text prompt using the
        tokenizer and calls the [generate][vllm.LLM.generate] method to generate
        the responses.

        Multi-modal inputs can be passed in the same way you would pass them
        to the OpenAI API.

        Args:
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            messages: A sequence of conversations or a single conversation.
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                - Each conversation is represented as a list of messages.
                - Each message is a dictionary with 'role' and 'content' keys.

            sampling_params: The sampling parameters for text generation.
                If None, we use the default sampling parameters. When it
                is a single value, it is applied to every prompt. When it
                is a list, the list must have the same length as the
                prompts and it is paired one by one with the prompt.
            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
            lora_request: LoRA request to use for generation, if any.
            chat_template: The template to use for structuring the chat.
                If not provided, the model's default chat template will be used.
            chat_template_content_format: The format to render message content.

                - "string" will render the content as a string.
                  Example: `"Who are you?"`
                - "openai" will render the content as a list of dictionaries,
                  similar to OpenAI schema.
                  Example: `[{"type": "text", "text": "Who are you?"}]`

            add_generation_prompt: If True, adds a generation template
                to each message.
            continue_final_message: If True, continues the final message in
                the conversation instead of starting a new one. Cannot be
                `True` if `add_generation_prompt` is also `True`.
            chat_template_kwargs: Additional kwargs to pass to the chat
                template.
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            tokenization_kwargs: Overrides for `tokenizer.encode`.
            mm_processor_kwargs: Overrides for `processor.__call__`.
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        Returns:
            A list of `RequestOutput` objects containing the generated
            responses in the same order as the input messages.
        """
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        model_config = self.model_config
        runner_type = model_config.runner_type
        if runner_type != "generate":
            raise ValueError(
                "LLM.chat() is only supported for generative models. "
                "Try passing `--runner generate` to use the model as a "
                "generative model."
            )

        if sampling_params is None:
            sampling_params = self.get_default_sampling_params()

        outputs = self._run_chat(
            messages=messages,
            params=sampling_params,
            use_tqdm=use_tqdm,
            lora_request=lora_request,
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            chat_template=chat_template,
            chat_template_content_format=chat_template_content_format,
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            chat_template_kwargs=chat_template_kwargs,
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            add_generation_prompt=add_generation_prompt,
            continue_final_message=continue_final_message,
            tools=tools,
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            tokenization_kwargs=tokenization_kwargs,
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            mm_processor_kwargs=mm_processor_kwargs,
        )

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        return self.engine_class.validate_outputs(outputs, RequestOutput)
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    def encode(
        self,
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        prompts: PromptType | Sequence[PromptType] | DataPrompt,
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
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        *,
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        truncate_prompt_tokens: int | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
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        pooling_task: PoolingTask | None = None,
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        tokenization_kwargs: dict[str, Any] | None = None,
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    ) -> list[PoolingRequestOutput]:
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        """Apply pooling to the hidden states corresponding to the input
        prompts.
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        This class automatically batches the given prompts, considering
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        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
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            prompts: The prompts to the LLM. You may pass a sequence of prompts
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                for batch inference. See [PromptType][vllm.inputs.PromptType]
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                for more details about the format of each prompt.
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            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
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            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
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            lora_request: LoRA request to use for generation, if any.
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            pooling_task: Override the pooling task to use.
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            tokenization_kwargs: Overrides for `tokenizer.encode`.
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        Returns:
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            A list of `PoolingRequestOutput` objects containing the
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            pooled hidden states in the same order as the input prompts.
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        """
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        if pooling_task is None:
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            raise ValueError(
                "pooling_task required for `LLM.encode`\n"
                "Please use one of the more specific methods or set the "
                "pooling_task when using `LLM.encode`:\n"
                "  - For embeddings, use `LLM.embed(...)` "
                'or `pooling_task="embed"`.\n'
                "  - For classification logits, use `LLM.classify(...)` "
                'or `pooling_task="classify"`.\n'
                "  - For similarity scores, use `LLM.score(...)`.\n"
                "  - For rewards, use `LLM.reward(...)` "
                'or `pooling_task="token_classify"`\n'
                "  - For token classification, "
                'use `pooling_task="token_classify"`\n'
                '  - For multi-vector retrieval, use `pooling_task="token_embed"`'
            )
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        model_config = self.model_config
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        runner_type = model_config.runner_type
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        if runner_type != "pooling":
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            raise ValueError(
                "LLM.encode() is only supported for pooling models. "
                "Try passing `--runner pooling` to use the model as a "
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                "pooling model."
            )
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        if truncate_prompt_tokens is not None:
            warnings.warn(
                "The `truncate_prompt_tokens` parameter in `LLM.encode()` "
                "is deprecated and will be removed in v0.16. "
                "Please pass it via `tokenization_kwargs` instead.",
                DeprecationWarning,
                stacklevel=2,
            )

            tokenization_kwargs = merge_kwargs(
                tokenization_kwargs,
                dict(truncate_prompt_tokens=truncate_prompt_tokens),
            )

1059
1060
1061
1062
1063
1064
1065
1066
        io_processor_prompt = False
        if isinstance(prompts, dict) and "data" in prompts:
            io_processor_prompt = True
            if self.io_processor is None:
                raise ValueError(
                    "No IOProcessor plugin installed. Please refer "
                    "to the documentation and to the "
                    "'prithvi_geospatial_mae_io_processor' "
1067
1068
                    "offline inference example for more details."
                )
1069
1070
1071
1072
1073
1074
1075

            # Validate the request data is valid for the loaded plugin
            validated_prompt = self.io_processor.parse_request(prompts)

            # obtain the actual model prompts from the pre-processor
            prompts = self.io_processor.pre_process(prompt=validated_prompt)

1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
        if io_processor_prompt:
            assert self.io_processor is not None
            if is_list_of(pooling_params, PoolingParams):
                validated_pooling_params: list[PoolingParams] = []
                for param in as_iter(pooling_params):
                    validated_pooling_params.append(
                        self.io_processor.validate_or_generate_params(param)
                    )
                pooling_params = validated_pooling_params
            else:
                assert not isinstance(pooling_params, Sequence)
                pooling_params = self.io_processor.validate_or_generate_params(
                    pooling_params
                )
1090
1091
1092
1093

        if pooling_params is None:
            # Use default pooling params.
            pooling_params = PoolingParams()
1094
1095

        for param in as_iter(pooling_params):
1096
1097
1098
1099
1100
            if param.task is None:
                param.task = pooling_task
            elif param.task != pooling_task:
                msg = f"You cannot overwrite {param.task=!r} with {pooling_task=!r}!"
                raise ValueError(msg)
1101

1102
        outputs = self._run_completion(
1103
            prompts=prompts,
1104
            params=pooling_params,
1105
            use_tqdm=use_tqdm,
1106
            lora_request=lora_request,
1107
            tokenization_kwargs=tokenization_kwargs,
1108
1109
        )

1110
        model_outputs = self.engine_class.validate_outputs(
1111
1112
            outputs, PoolingRequestOutput
        )
1113
1114
1115
1116
1117

        if io_processor_prompt:
            # get the post-processed model outputs
            assert self.io_processor is not None
            processed_outputs = self.io_processor.post_process(
1118
1119
                model_output=model_outputs
            )
1120
1121

            return [
1122
1123
1124
                PoolingRequestOutput[Any](
                    request_id="",
                    outputs=processed_outputs,
1125
1126
1127
                    num_cached_tokens=getattr(
                        processed_outputs, "num_cached_tokens", 0
                    ),
1128
1129
1130
                    prompt_token_ids=[],
                    finished=True,
                )
1131
1132
1133
            ]
        else:
            return model_outputs
1134

1135
1136
    def embed(
        self,
1137
        prompts: PromptType | Sequence[PromptType],
1138
        *,
1139
1140
1141
1142
        truncate_prompt_tokens: int | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1143
        tokenization_kwargs: dict[str, Any] | None = None,
1144
    ) -> list[EmbeddingRequestOutput]:
1145
1146
1147
1148
1149
1150
1151
1152
1153
        """
        Generate an embedding vector for each prompt.

        This class automatically batches the given prompts, considering
        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
            prompts: The prompts to the LLM. You may pass a sequence of prompts
1154
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1155
                for more details about the format of each prompt.
1156
1157
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1158
1159
1160
1161
            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
1162
            lora_request: LoRA request to use for generation, if any.
1163
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1164
1165

        Returns:
1166
            A list of `EmbeddingRequestOutput` objects containing the
1167
1168
            embedding vectors in the same order as the input prompts.
        """
1169
        if "embed" not in self.supported_tasks:
1170
1171
            raise ValueError(
                "Embedding API is not supported by this model. "
1172
1173
                "Try converting the model using `--convert embed`."
            )
1174

1175
1176
1177
1178
1179
1180
        if truncate_prompt_tokens is not None:
            tokenization_kwargs = merge_kwargs(
                tokenization_kwargs,
                dict(truncate_prompt_tokens=truncate_prompt_tokens),
            )

1181
1182
1183
1184
1185
1186
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
            pooling_params=pooling_params,
            lora_request=lora_request,
            pooling_task="embed",
1187
            tokenization_kwargs=tokenization_kwargs,
1188
        )
1189
1190
1191
1192
1193

        return [EmbeddingRequestOutput.from_base(item) for item in items]

    def classify(
        self,
1194
        prompts: PromptType | Sequence[PromptType],
1195
        *,
1196
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1197
        use_tqdm: bool | Callable[..., tqdm] = True,
1198
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1199
        tokenization_kwargs: dict[str, Any] | None = None,
1200
    ) -> list[ClassificationRequestOutput]:
1201
1202
1203
1204
1205
1206
1207
1208
1209
        """
        Generate class logits for each prompt.

        This class automatically batches the given prompts, considering
        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
            prompts: The prompts to the LLM. You may pass a sequence of prompts
1210
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1211
                for more details about the format of each prompt.
1212
1213
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1214
1215
1216
1217
            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
1218
            lora_request: LoRA request to use for generation, if any.
1219
1220
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1221
        Returns:
1222
            A list of `ClassificationRequestOutput` objects containing the
1223
1224
            embedding vectors in the same order as the input prompts.
        """
1225
        if "classify" not in self.supported_tasks:
1226
            raise ValueError(
1227
                "Classification API is not supported by this model. "
1228
1229
                "Try converting the model using `--convert classify`."
            )
1230

1231
1232
1233
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
1234
            pooling_params=pooling_params,
1235
1236
            lora_request=lora_request,
            pooling_task="classify",
1237
            tokenization_kwargs=tokenization_kwargs,
1238
        )
1239
1240
1241

        return [ClassificationRequestOutput.from_base(item) for item in items]

1242
1243
    def reward(
        self,
1244
        prompts: PromptType | Sequence[PromptType],
1245
1246
        /,
        *,
1247
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1248
1249
1250
        truncate_prompt_tokens: int | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1251
        tokenization_kwargs: dict[str, Any] | None = None,
1252
1253
1254
1255
1256
1257
1258
    ) -> list[PoolingRequestOutput]:
        """
        Generate rewards for each prompt.

        Args:
            prompts: The prompts to the LLM. You may pass a sequence of prompts
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1259
                for more details about the format of each prompt.
1260
1261
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1262
1263
1264
1265
1266
            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
            lora_request: LoRA request to use for generation, if any.
1267
1268
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
        Returns:
            A list of `PoolingRequestOutput` objects containing the
            pooled hidden states in the same order as the input prompts.
        """

        return self.encode(
            prompts,
            use_tqdm=use_tqdm,
            lora_request=lora_request,
            pooling_params=pooling_params,
            truncate_prompt_tokens=truncate_prompt_tokens,
1280
            pooling_task="token_classify",
1281
            tokenization_kwargs=tokenization_kwargs,
1282
1283
        )

1284
1285
    def _embedding_score(
        self,
1286
1287
        data_1: list[ScoreData],
        data_2: list[ScoreData],
1288
1289
1290
1291
1292
        *,
        use_tqdm: bool | Callable[..., tqdm],
        pooling_params: PoolingParams | None,
        lora_request: list[LoRARequest] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any],
1293
    ) -> list[ScoringRequestOutput]:
1294
1295
        tokenizer = self.get_tokenizer()

1296
1297
1298
1299
1300
1301
1302
1303
        input_texts: list[str] = []
        for text in data_1 + data_2:
            if not isinstance(text, str):
                raise NotImplementedError(
                    "Embedding scores currently do not support multimodal input."
                )
            input_texts.append(text)

1304
        encoded_output = self.encode(
1305
            input_texts,
1306
1307
            use_tqdm=use_tqdm,
            lora_request=lora_request,
1308
            pooling_params=pooling_params,
1309
            pooling_task="embed",
1310
            tokenization_kwargs=tokenization_kwargs,
1311
        )
1312

1313
1314
        encoded_output_1 = encoded_output[0 : len(data_1)]
        encoded_output_2 = encoded_output[len(data_1) :]
1315
1316
1317
1318

        if len(encoded_output_1) == 1:
            encoded_output_1 = encoded_output_1 * len(encoded_output_2)

1319
        scores = _cosine_similarity(
1320
1321
1322
            tokenizer=tokenizer,
            embed_1=encoded_output_1,
            embed_2=encoded_output_2,
1323
        )
1324

1325
        items = self.engine_class.validate_outputs(scores, PoolingRequestOutput)
1326
1327
        return [ScoringRequestOutput.from_base(item) for item in items]

1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
    def _late_interaction_score(
        self,
        data_1: list[ScoreData],
        data_2: list[ScoreData],
        *,
        use_tqdm: bool | Callable[..., tqdm],
        pooling_params: PoolingParams | None,
        lora_request: list[LoRARequest] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any],
    ) -> list[ScoringRequestOutput]:
        """
        Late interaction scoring (ColBERT MaxSim).

        Encodes queries and documents into per-token embeddings, then computes
        MaxSim: sum over query tokens of max similarity to any document token.
        """
        from vllm.outputs import PoolingOutput

        tokenizer = self.get_tokenizer()

        # Extract text from ScoreData
        text_1: list[str] = []
        for text in data_1:
            if not isinstance(text, str):
                raise NotImplementedError(
                    "Late interaction scores currently do not support multimodal input."
                )
            text_1.append(text)

        text_2: list[str] = []
        for text in data_2:
            if not isinstance(text, str):
                raise NotImplementedError(
                    "Late interaction scores currently do not support multimodal input."
                )
            text_2.append(text)

        encoded_output: list[PoolingRequestOutput] = self.encode(
            text_1 + text_2,
            use_tqdm=use_tqdm,
            lora_request=lora_request,
            pooling_params=pooling_params,
            pooling_task="token_embed",
            tokenization_kwargs=tokenization_kwargs,
        )

        encoded_output_1: list[PoolingRequestOutput] = encoded_output[0 : len(text_1)]
        encoded_output_2: list[PoolingRequestOutput] = encoded_output[len(text_1) :]

        if len(encoded_output_1) == 1:
            encoded_output_1 = encoded_output_1 * len(encoded_output_2)

        # Compute MaxSim scores
        scores: list[PoolingRequestOutput] = []
        padding: list[int] = []
        if (pad_token_id := tokenizer.pad_token_id) is not None:
            padding = [pad_token_id]

        for emb_1, emb_2 in zip(encoded_output_1, encoded_output_2):
            # emb_1.outputs.data: [query_len, dim]
            # emb_2.outputs.data: [doc_len, dim]
            q_emb = emb_1.outputs.data
            d_emb = emb_2.outputs.data

            maxsim_score = compute_maxsim_score(q_emb, d_emb)

            tokens = emb_1.prompt_token_ids + padding + emb_2.prompt_token_ids

            scores.append(
                PoolingRequestOutput(
                    request_id=f"{emb_1.request_id}_{emb_2.request_id}",
                    outputs=PoolingOutput(data=maxsim_score),
                    prompt_token_ids=tokens,
                    num_cached_tokens=emb_1.num_cached_tokens + emb_2.num_cached_tokens,
                    finished=True,
                )
            )

        items = self.engine_class.validate_outputs(scores, PoolingRequestOutput)
        return [ScoringRequestOutput.from_base(item) for item in items]

1409
1410
    def _cross_encoding_score(
        self,
1411
1412
        data_1: list[ScoreData],
        data_2: list[ScoreData],
1413
1414
1415
1416
1417
1418
        *,
        use_tqdm: bool | Callable[..., tqdm],
        pooling_params: PoolingParams | None,
        lora_request: list[LoRARequest] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any],
        score_template: str | None,
1419
    ) -> list[ScoringRequestOutput]:
1420
        model_config = self.model_config
1421
        tokenizer = self.get_tokenizer()
1422
1423

        if isinstance(tokenizer, MistralTokenizer):
1424
            raise ValueError("Score API is not supported for Mistral tokenizer")
1425

1426
1427
        if len(data_1) == 1:
            data_1 = data_1 * len(data_2)
1428

1429
1430
        if pooling_params is None:
            pooling_params = PoolingParams(task="score")
1431
1432
        elif pooling_params.task is None:
            pooling_params.task = "score"
1433

1434
        pooling_params_list = list[PoolingParams]()
1435

1436
        prompts = list[PromptType]()
1437

1438
1439
        input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)]

1440
1441
        for q, d in input_pairs:
            _, engine_prompt = get_score_prompt(
1442
                model_config=model_config,
1443
1444
1445
1446
                data_1=q,
                data_2=d,
                tokenizer=tokenizer,
                tokenization_kwargs=tokenization_kwargs,
1447
                score_template=score_template,
1448
1449
            )

1450
            if token_type_ids := engine_prompt.pop("token_type_ids", None):
1451
1452
1453
1454
1455
1456
1457
                params = pooling_params.clone()
                compressed = compress_token_type_ids(token_type_ids)
                params.extra_kwargs = {"compressed_token_type_ids": compressed}
                pooling_params_list.append(params)
            else:
                pooling_params_list.append(pooling_params)

1458
            prompts.append(engine_prompt)
1459

1460
        outputs = self._run_completion(
1461
            prompts=prompts,
1462
            params=pooling_params_list,
1463
            use_tqdm=use_tqdm,
1464
1465
1466
            lora_request=lora_request,
        )

1467
        items = self.engine_class.validate_outputs(outputs, PoolingRequestOutput)
1468
1469
1470

        return [ScoringRequestOutput.from_base(item) for item in items]

1471
1472
    def score(
        self,
1473
1474
1475
1476
1477
1478
1479
1480
        data_1: SingletonPrompt
        | Sequence[SingletonPrompt]
        | ScoreMultiModalParam
        | list[ScoreMultiModalParam],
        data_2: SingletonPrompt
        | Sequence[SingletonPrompt]
        | ScoreMultiModalParam
        | list[ScoreMultiModalParam],
1481
        /,
1482
        *,
1483
1484
1485
        use_tqdm: bool | Callable[..., tqdm] = True,
        pooling_params: PoolingParams | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1486
        tokenization_kwargs: dict[str, Any] | None = None,
1487
        chat_template: str | None = None,
1488
    ) -> list[ScoringRequestOutput]:
1489
1490
        """Generate similarity scores for all pairs `<text,text_pair>` or
          `<multi-modal data, multi-modal data pair>`.
1491

1492
        The inputs can be `1 -> 1`, `1 -> N` or `N -> N`.
1493
1494
        In the `1 - N` case the `data_1` input will be replicated `N`
        times to pair with the `data_2` inputs.
1495
        The input pairs are used to build a list of prompts for the
1496
1497
        cross encoder model. This class automatically batches the prompts,
        considering the memory constraint. For the best performance, put all
1498
1499
1500
        of your inputs into a single list and pass it to this method.

        Supports both text and multi-modal data (images, etc.) when used with
1501
        appropriate multi-modal models. For multi-modal inputs, ensure the
1502
        prompt structure matches the model's expected input format.
1503
1504

        Args:
1505
1506
1507
            data_1: Can be a single prompt, a list of prompts or
                `ScoreMultiModalParam`, which can contain either text or
                multi-modal data. When a list, it must have the same length as
1508
                the `data_2` list.
1509
            data_2: The data to pair with the query to form the input to
1510
                the LLM. Can be text or multi-modal data. See [PromptType]
1511
                [vllm.inputs.PromptType] for more details about the format of
1512
                each prompt.
1513
1514
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1515
1516
1517
1518
            use_tqdm: If `True`, shows a tqdm progress bar.
                If a callable (e.g., `functools.partial(tqdm, leave=False)`),
                it is used to create the progress bar.
                If `False`, no progress bar is created.
1519
            lora_request: LoRA request to use for generation, if any.
1520
1521
            chat_template: The chat template to use for the scoring. If None, we
                use the model's default chat template.
1522
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1523
        Returns:
1524
            A list of `ScoringRequestOutput` objects containing the
1525
1526
            generated scores in the same order as the input prompts.
        """
1527
        model_config = self.model_config
1528

1529
        runner_type = model_config.runner_type
1530
        if runner_type != "pooling":
1531
1532
1533
            raise ValueError(
                "LLM.score() is only supported for pooling models. "
                "Try passing `--runner pooling` to use the model as a "
1534
1535
                "pooling model."
            )
1536

1537
        supported_tasks = self.supported_tasks
1538
1539
1540
1541
1542
        # Late interaction models (e.g., ColBERT) use token_embed for scoring
        is_late_interaction = model_config.is_late_interaction
        if not is_late_interaction and all(
            t not in supported_tasks for t in ("embed", "classify")
        ):
1543
1544
1545
1546
1547
            raise ValueError(
                "Score API is not supported by this model. "
                "Try converting the model using "
                "`--convert embed` or `--convert classify`."
            )
1548

1549
1550
1551
1552
        if (
            model_config.is_cross_encoder
            and getattr(model_config.hf_config, "num_labels", 0) != 1
        ):
1553
            raise ValueError("Score API is only enabled for num_labels == 1.")
1554

1555
1556
1557
1558
1559
        if not model_config.is_cross_encoder and chat_template is not None:
            raise ValueError(
                "chat_template is only supported for cross-encoder models."
            )

1560
1561
        is_multimodal_model = model_config.is_multimodal_model
        architecture = model_config.architecture
1562

1563
1564
1565
1566
1567
1568
        score_data_1, score_data_2 = validate_score_input(
            data_1,  # type: ignore[arg-type]
            data_2,  # type: ignore[arg-type]
            is_multimodal_model=is_multimodal_model,
            architecture=architecture,
        )
1569

1570
1571
1572
        tok_params = self._get_cmpl_tok_params(tokenization_kwargs)
        encode_kwargs = tok_params.get_encode_kwargs()

1573
        if model_config.is_cross_encoder:
1574
            return self._cross_encoding_score(
1575
1576
                score_data_1,
                score_data_2,
1577
1578
1579
1580
                use_tqdm=use_tqdm,
                pooling_params=pooling_params,
                lora_request=lora_request,
                tokenization_kwargs=encode_kwargs,
1581
                score_template=chat_template,
1582
            )
1583
1584
1585
1586
1587
1588
1589
1590
1591
        elif is_late_interaction:
            return self._late_interaction_score(
                score_data_1,
                score_data_2,
                use_tqdm=use_tqdm,
                pooling_params=pooling_params,
                lora_request=lora_request,
                tokenization_kwargs=encode_kwargs,
            )
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        else:
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            return self._embedding_score(
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                score_data_1,
                score_data_2,
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                use_tqdm=use_tqdm,
                pooling_params=pooling_params,
                lora_request=lora_request,
                tokenization_kwargs=encode_kwargs,
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            )
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    def start_profile(self) -> None:
        self.llm_engine.start_profile()

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

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    def reset_prefix_cache(
        self, reset_running_requests: bool = False, reset_connector: bool = False
    ) -> bool:
        return self.llm_engine.reset_prefix_cache(
            reset_running_requests, reset_connector
        )
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    def sleep(self, level: int = 1):
        """
        Put the engine to sleep. The engine should not process any requests.
        The caller should guarantee that no requests are being processed
        during the sleep period, before `wake_up` is called.

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        Args:
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            level: The sleep level. Level 1 sleep will offload the model
                weights and discard the kv cache. The content of kv cache
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                is forgotten. Level 1 sleep is good for sleeping and waking
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                up the engine to run the same model again. The model weights
                are backed up in CPU memory. Please make sure there's enough
                CPU memory to store the model weights. Level 2 sleep will
                discard both the model weights and the kv cache. The content
                of both the model weights and kv cache is forgotten. Level 2
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                sleep is good for sleeping and waking up the engine to run a
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                different model or update the model, where previous model
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                weights are not needed. It reduces CPU memory pressure.
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        """
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        self.reset_prefix_cache()
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        self.llm_engine.sleep(level=level)

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    def wake_up(self, tags: list[str] | None = None):
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        """
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        Wake up the engine from sleep mode. See the [sleep][vllm.LLM.sleep]
        method for more details.
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        Args:
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            tags: An optional list of tags to reallocate the engine memory
                for specific memory allocations. Values must be in
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                `("weights", "kv_cache")`. If None, all memory is reallocated.
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                wake_up should be called with all tags (or None) before the
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                engine is used again.
        """
        self.llm_engine.wake_up(tags)
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    def get_metrics(self) -> list["Metric"]:
        """Return a snapshot of aggregated metrics from Prometheus.

        Returns:
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            A `MetricSnapshot` instance capturing the current state
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            of all aggregated metrics from Prometheus.

        Note:
            This method is only available with the V1 LLM engine.
        """
        return self.llm_engine.get_metrics()

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    def _params_to_seq(
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        self,
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        params: SamplingParams
        | PoolingParams
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        | Sequence[SamplingParams | PoolingParams],
        num_requests: int,
    ) -> Sequence[SamplingParams | PoolingParams]:
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        if isinstance(params, Sequence):
            if len(params) != num_requests:
                raise ValueError(
                    f"The lengths of prompts ({params}) "
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                    f"and params ({len(params)}) must be the same."
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                )

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            return params
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        return [params] * num_requests

    def _lora_request_to_seq(
        self,
        lora_request: LoRARequest | None | Sequence[LoRARequest | None],
        num_requests: int,
    ) -> Sequence[LoRARequest | None]:
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        if isinstance(lora_request, Sequence):
            if len(lora_request) != num_requests:
                raise ValueError(
                    f"The lengths of prompts ({num_requests}) "
                    f"and lora_request ({len(lora_request)}) must be the same."
                )

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            return lora_request

        return [lora_request] * num_requests
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    def _priority_to_seq(
        self,
        priority: list[int] | None,
        num_requests: int,
    ) -> Sequence[int]:
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        if priority is not None:
            if len(priority) != num_requests:
                raise ValueError(
                    f"The lengths of prompts ({num_requests}) "
                    f"and priority ({len(priority)}) must be the same."
                )

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            return priority

        return [0] * num_requests

    def _run_completion(
        self,
        prompts: PromptType | Sequence[PromptType],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
        priority: list[int] | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
    ):
        seq_prompts = prompt_to_seq(prompts)
        seq_params = self._params_to_seq(params, len(seq_prompts))

        if any(param.truncate_prompt_tokens is not None for param in seq_params):
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            # TODO: Remove this after deprecating `param.truncate_prompt_tokens`
            # Then, move the code from the `else` block to the top and let
            # `self._preprocess_completion` handle prompt normalization
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            engine_prompts: Sequence[DictPrompt | TokPrompt] = [
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                engine_prompt
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                for prompt, param in zip(seq_prompts, seq_params)
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                for engine_prompt in self._preprocess_completion(
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                    [prompt],
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                    tokenization_kwargs=merge_kwargs(
                        tokenization_kwargs,
                        dict(truncate_prompt_tokens=param.truncate_prompt_tokens),
                    ),
                )
            ]
        else:
            engine_prompts = self._preprocess_completion(
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                seq_prompts,
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                tokenization_kwargs=tokenization_kwargs,
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            )
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        self._validate_and_add_requests(
            prompts=engine_prompts,
            params=seq_params,
            use_tqdm=use_tqdm,
            lora_request=self._get_modality_specific_lora_reqs(
                engine_prompts, lora_request
            ),
            tokenization_kwargs=tokenization_kwargs,
            priority=priority,
        )

        return self._run_engine(use_tqdm=use_tqdm)

    def _run_chat(
        self,
        messages: list[ChatCompletionMessageParam]
        | Sequence[list[ChatCompletionMessageParam]],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: LoRARequest | None = None,
        chat_template: str | None = None,
        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
        tools: list[dict[str, Any]] | None = None,
        chat_template_kwargs: dict[str, Any] | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        mm_processor_kwargs: dict[str, Any] | None = None,
    ):
        engine_prompts = self._preprocess_chat(
            conversation_to_seq(messages),
            chat_template=chat_template,
            chat_template_content_format=chat_template_content_format,
            chat_template_kwargs=chat_template_kwargs,
            add_generation_prompt=add_generation_prompt,
            continue_final_message=continue_final_message,
            tools=tools,
            tokenization_kwargs=tokenization_kwargs,
            mm_processor_kwargs=mm_processor_kwargs,
        )

        self._validate_and_add_requests(
            prompts=engine_prompts,
            params=params,
            use_tqdm=use_tqdm,
            lora_request=self._get_modality_specific_lora_reqs(
                engine_prompts, lora_request
            ),
            tokenization_kwargs=tokenization_kwargs,
        )

        return self._run_engine(use_tqdm=use_tqdm)

    def _validate_and_add_requests(
        self,
        prompts: Sequence[DictPrompt | TokPrompt],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: Sequence[LoRARequest | None] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any] | None = None,
        priority: list[int] | None = None,
    ) -> None:
        num_requests = len(prompts)
        seq_params = self._params_to_seq(params, num_requests)
        seq_lora_requests = self._lora_request_to_seq(lora_request, num_requests)
        seq_priority = self._priority_to_seq(priority, num_requests)

        for sp in seq_params:
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            if isinstance(sp, SamplingParams):
                # We only care about the final output
                sp.output_kind = RequestOutputKind.FINAL_ONLY
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        # Add requests to the engine.
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        it = prompts
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        if use_tqdm:
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            tqdm_func = use_tqdm if callable(use_tqdm) else tqdm
            it = tqdm_func(it, desc="Adding requests")
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        added_request_ids: list[str] = []
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        try:
            for i, prompt in enumerate(it):
                request_id = self._add_request(
                    prompt,
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                    seq_params[i],
                    lora_request=seq_lora_requests[i],
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                    tokenization_kwargs=tokenization_kwargs,
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                    priority=seq_priority[i],
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                )
                added_request_ids.append(request_id)
        except Exception as e:
            if added_request_ids:
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                self.llm_engine.abort_request(added_request_ids, internal=True)
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            raise e
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    def _add_request(
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        self,
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        prompt: PromptType | DictPrompt | TokPrompt,
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        params: SamplingParams | PoolingParams,
        lora_request: LoRARequest | None = None,
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        tokenization_kwargs: dict[str, Any] | None = None,
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        priority: int = 0,
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    ) -> str:
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        prompt_text, _, _ = extract_prompt_components(self.model_config, prompt)
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        request_id = str(next(self.request_counter))
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        if params.truncate_prompt_tokens is not None:
            params_type = type(params).__name__
            warnings.warn(
                f"The `truncate_prompt_tokens` parameter in `{params_type}` "
                "is deprecated and will be removed in v0.16. "
                "Please pass it via `tokenization_kwargs` instead.",
                DeprecationWarning,
                stacklevel=2,
            )

            tokenization_kwargs = merge_kwargs(
                tokenization_kwargs,
                dict(truncate_prompt_tokens=params.truncate_prompt_tokens),
            )

        tok_params = self._get_cmpl_tok_params(tokenization_kwargs)

        tokenization_kwargs = tok_params.get_encode_kwargs()
        engine_request = self.input_processor.process_inputs(
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            request_id,
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            prompt,
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            params,
            lora_request=lora_request,
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            tokenization_kwargs=tokenization_kwargs,
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            priority=priority,
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            supported_tasks=self.supported_tasks,
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        )

        self.llm_engine.add_request(
            request_id,
            engine_request,
            params,
            lora_request=lora_request,
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            tokenization_kwargs=tokenization_kwargs,
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            priority=priority,
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            prompt_text=prompt_text,
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        )
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        return engine_request.request_id
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    def _run_engine(
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        self, *, use_tqdm: bool | Callable[..., tqdm] = True
    ) -> list[RequestOutput | PoolingRequestOutput]:
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        # Initialize tqdm.
        if use_tqdm:
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            num_requests = self.llm_engine.get_num_unfinished_requests()
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            tqdm_func = use_tqdm if callable(use_tqdm) else tqdm
            pbar = tqdm_func(
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                total=num_requests,
                desc="Processed prompts",
                dynamic_ncols=True,
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                postfix=(f"est. speed input: {0:.2f} toks/s, output: {0:.2f} toks/s"),
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            )
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        # Run the engine.
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        outputs: list[RequestOutput | PoolingRequestOutput] = []
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        total_in_toks = 0
        total_out_toks = 0
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        while self.llm_engine.has_unfinished_requests():
            step_outputs = self.llm_engine.step()
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            for output in step_outputs:
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                if output.finished:
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                    outputs.append(output)
                    if use_tqdm:
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                        if isinstance(output, RequestOutput):
                            # Calculate tokens only for RequestOutput
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                            n = len(output.outputs)
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                            assert output.prompt_token_ids is not None
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                            total_in_toks += len(output.prompt_token_ids) * n
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                            in_spd = total_in_toks / pbar.format_dict["elapsed"]
                            total_out_toks += sum(
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                                len(stp.token_ids) for stp in output.outputs
                            )
                            out_spd = total_out_toks / pbar.format_dict["elapsed"]
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                            pbar.postfix = (
                                f"est. speed input: {in_spd:.2f} toks/s, "
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                                f"output: {out_spd:.2f} toks/s"
                            )
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                            pbar.update(n)
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                        else:
                            pbar.update(1)
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                        if pbar.n == num_requests:
                            pbar.refresh()
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        if use_tqdm:
            pbar.close()
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        # Sort the outputs by request ID.
        # This is necessary because some requests may be finished earlier than
        # its previous requests.
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        return sorted(outputs, key=lambda x: int(x.request_id))
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    def init_weight_transfer_engine(
        self, request: WeightTransferInitRequest | dict
    ) -> None:
        """
        Initialize weight transfer for RL training.

        Args:
            request: Weight transfer initialization request with backend-specific info
        """
        init_info_dict = (
            request["init_info"] if isinstance(request, dict) else request.init_info
        )

        self.llm_engine.collective_rpc(
            "init_weight_transfer_engine", kwargs={"init_info": init_info_dict}
        )

    def update_weights(self, request: WeightTransferUpdateRequest | dict) -> None:
        """
        Update the weights of the model.

        Args:
            request: Weight update request with backend-specific update info
        """
        update_info_dict = (
            request["update_info"] if isinstance(request, dict) else request.update_info
        )

        self.llm_engine.collective_rpc(
            "update_weights", kwargs={"update_info": update_info_dict}
        )

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    def __repr__(self) -> str:
        """Return a transformers-style hierarchical view of the model."""
        # Cache the result to avoid repeated collective_rpc calls
        if self._cached_repr is None:
            results = self.llm_engine.collective_rpc("get_model_inspection")
            # In distributed settings, we get results from all workers
            # Just return the first one (they should all be the same)
            if results:
                self._cached_repr = results[0]
            else:
                self._cached_repr = f"LLM(model={self.model_config.model!r})"
        return self._cached_repr