llm.py 83.7 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, 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.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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_P = TypeVar("_P", bound=SamplingParams | PoolingParams | None)
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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.renderer = self.llm_engine.renderer
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        self.io_processor = self.llm_engine.io_processor
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        self.input_processor = self.llm_engine.input_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.renderer.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 enqueue(
        self,
        prompts: PromptType | Sequence[PromptType],
        sampling_params: SamplingParams | Sequence[SamplingParams] | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
        priority: list[int] | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        tokenization_kwargs: dict[str, Any] | None = None,
    ) -> list[str]:
        """Enqueue prompts for generation without waiting for completion.

        This method adds requests to the engine queue but does not start
        processing them. Use wait_for_completion() to process the queued
        requests and get results.

        Args:
            prompts: The prompts to the LLM. See generate() for details.
            sampling_params: The sampling parameters for text generation.
            lora_request: LoRA request to use for generation, if any.
            priority: The priority of the requests, if any.
            use_tqdm: If True, shows a tqdm progress bar while adding requests.
            tokenization_kwargs: Overrides for `tokenizer.encode`.

        Returns:
            A list of request IDs for the enqueued requests.
        """
        model_config = self.model_config
        runner_type = model_config.runner_type
        if runner_type != "generate":
            raise ValueError("LLM.enqueue() is only supported for generative models.")

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

        # Use the same preprocessing as _run_completion
        seq_prompts = prompt_to_seq(prompts)
        seq_params = self._params_to_seq(sampling_params, len(seq_prompts))

        if any(param.truncate_prompt_tokens is not None for param in seq_params):
            engine_prompts: Sequence[DictPrompt | TokPrompt] = [
                engine_prompt
                for prompt, param in zip(seq_prompts, seq_params)
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                for engine_prompt in self._preprocess_cmpl(
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                    [prompt],
                    tokenization_kwargs=merge_kwargs(
                        tokenization_kwargs,
                        dict(truncate_prompt_tokens=param.truncate_prompt_tokens),
                    ),
                )
            ]
        else:
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            engine_prompts = self._preprocess_cmpl(
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                seq_prompts,
                tokenization_kwargs=tokenization_kwargs,
            )

        request_ids = 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 request_ids

    def wait_for_completion(
        self,
        use_tqdm: bool | Callable[..., tqdm] = True,
    ) -> list[RequestOutput]:
        """Wait for all enqueued requests to complete and return results.

        This method processes all requests currently in the engine queue
        and returns their outputs. Use after enqueue() to get results.

        Args:
            use_tqdm: If True, shows a tqdm progress bar.

        Returns:
            A list of RequestOutput objects for all completed requests.
        """
        outputs = self._run_engine(use_tqdm=use_tqdm)
        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 _preprocess_cmpl(
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        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:
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            A list of `TokPrompt` objects containing the tokenized prompt
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            after chat template interpolation, and the raw multi-modal inputs.
        """
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        renderer = self.renderer
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        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 = renderer.default_cmpl_tok_params.with_kwargs(
            **(tokenization_kwargs or {})
        )
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        return renderer.render_cmpl(parsed_prompts, tok_params)
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    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 `TokPrompt` objects containing the tokenized prompt
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            after chat template interpolation, and the raw multi-modal inputs.
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        """
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        renderer = self.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),
                ),
            ),
        )
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        tok_params = renderer.default_chat_tok_params.with_kwargs(
            **(tokenization_kwargs or {})
        )
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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:
1070
            prompts: The prompts to the LLM. You may pass a sequence of prompts
1071
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1072
                for more details about the format of each prompt.
1073
1074
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1075
1076
1077
1078
            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.
1079
            lora_request: LoRA request to use for generation, if any.
1080
            pooling_task: Override the pooling task to use.
1081
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1082
1083

        Returns:
1084
            A list of `PoolingRequestOutput` objects containing the
1085
            pooled hidden states in the same order as the input prompts.
1086
        """
1087

1088
        if pooling_task is None:
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
            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"`'
            )
1104

1105
        model_config = self.model_config
1106
        runner_type = model_config.runner_type
1107
        if runner_type != "pooling":
1108
1109
1110
            raise ValueError(
                "LLM.encode() is only supported for pooling models. "
                "Try passing `--runner pooling` to use the model as a "
1111
1112
                "pooling model."
            )
1113

1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
        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),
            )

1128
        if use_io_processor := (isinstance(prompts, dict) and "data" in prompts):
1129
1130
1131
1132
1133
            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' "
1134
1135
                    "offline inference example for more details."
                )
1136
1137

            # Validate the request data is valid for the loaded plugin
1138
            validated_prompt = self.io_processor.parse_data(prompts)
1139
1140
1141

            # obtain the actual model prompts from the pre-processor
            prompts = self.io_processor.pre_process(prompt=validated_prompt)
1142
            prompts_seq = prompt_to_seq(prompts)
1143

1144
1145
1146
1147
1148
            params_seq: Sequence[PoolingParams] = [
                self.io_processor.merge_pooling_params(param)
                for param in self._params_to_seq(
                    pooling_params,
                    len(prompts_seq),
1149
                )
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
            ]
            for p in params_seq:
                if p.task is None:
                    p.task = "plugin"
        else:
            if pooling_params is None:
                # Use default pooling params.
                pooling_params = PoolingParams()

            prompts_seq = prompt_to_seq(prompts)
            params_seq = self._params_to_seq(pooling_params, len(prompts_seq))

            for param in params_seq:
                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)
1170

1171
        outputs = self._run_completion(
1172
1173
            prompts=prompts_seq,
            params=params_seq,
1174
            use_tqdm=use_tqdm,
1175
            lora_request=lora_request,
1176
            tokenization_kwargs=tokenization_kwargs,
1177
1178
        )

1179
        model_outputs = self.engine_class.validate_outputs(
1180
1181
            outputs, PoolingRequestOutput
        )
1182

1183
        if use_io_processor:
1184
1185
            # get the post-processed model outputs
            assert self.io_processor is not None
1186
            processed_outputs = self.io_processor.post_process(model_outputs)
1187
1188

            return [
1189
1190
1191
                PoolingRequestOutput[Any](
                    request_id="",
                    outputs=processed_outputs,
1192
1193
1194
                    num_cached_tokens=getattr(
                        processed_outputs, "num_cached_tokens", 0
                    ),
1195
1196
1197
                    prompt_token_ids=[],
                    finished=True,
                )
1198
1199
1200
            ]
        else:
            return model_outputs
1201

1202
1203
    def embed(
        self,
1204
        prompts: PromptType | Sequence[PromptType],
1205
        *,
1206
1207
1208
1209
        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,
1210
        tokenization_kwargs: dict[str, Any] | None = None,
1211
    ) -> list[EmbeddingRequestOutput]:
1212
1213
1214
1215
1216
1217
1218
1219
1220
        """
        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
1221
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1222
                for more details about the format of each prompt.
1223
1224
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1225
1226
1227
1228
            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.
1229
            lora_request: LoRA request to use for generation, if any.
1230
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1231
1232

        Returns:
1233
            A list of `EmbeddingRequestOutput` objects containing the
1234
1235
            embedding vectors in the same order as the input prompts.
        """
1236
        if "embed" not in self.supported_tasks:
1237
1238
            raise ValueError(
                "Embedding API is not supported by this model. "
1239
1240
                "Try converting the model using `--convert embed`."
            )
1241

1242
1243
1244
1245
1246
1247
        if truncate_prompt_tokens is not None:
            tokenization_kwargs = merge_kwargs(
                tokenization_kwargs,
                dict(truncate_prompt_tokens=truncate_prompt_tokens),
            )

1248
1249
1250
1251
1252
1253
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
            pooling_params=pooling_params,
            lora_request=lora_request,
            pooling_task="embed",
1254
            tokenization_kwargs=tokenization_kwargs,
1255
        )
1256
1257
1258
1259
1260

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

    def classify(
        self,
1261
        prompts: PromptType | Sequence[PromptType],
1262
        *,
1263
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1264
        use_tqdm: bool | Callable[..., tqdm] = True,
1265
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1266
        tokenization_kwargs: dict[str, Any] | None = None,
1267
    ) -> list[ClassificationRequestOutput]:
1268
1269
1270
1271
1272
1273
1274
1275
1276
        """
        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
1277
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1278
                for more details about the format of each prompt.
1279
1280
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1281
1282
1283
1284
            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.
1285
            lora_request: LoRA request to use for generation, if any.
1286
1287
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1288
        Returns:
1289
            A list of `ClassificationRequestOutput` objects containing the
1290
1291
            embedding vectors in the same order as the input prompts.
        """
1292
        if "classify" not in self.supported_tasks:
1293
            raise ValueError(
1294
                "Classification API is not supported by this model. "
1295
1296
                "Try converting the model using `--convert classify`."
            )
1297

1298
1299
1300
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
1301
            pooling_params=pooling_params,
1302
1303
            lora_request=lora_request,
            pooling_task="classify",
1304
            tokenization_kwargs=tokenization_kwargs,
1305
        )
1306
1307
1308

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

1309
1310
    def reward(
        self,
1311
        prompts: PromptType | Sequence[PromptType],
1312
1313
        /,
        *,
1314
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1315
1316
1317
        truncate_prompt_tokens: int | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1318
        tokenization_kwargs: dict[str, Any] | None = None,
1319
1320
1321
1322
1323
1324
1325
    ) -> 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]
1326
                for more details about the format of each prompt.
1327
1328
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1329
1330
1331
1332
1333
            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.
1334
1335
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
        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,
1347
            pooling_task="token_classify",
1348
            tokenization_kwargs=tokenization_kwargs,
1349
1350
        )

1351
1352
    def _embedding_score(
        self,
1353
1354
        data_1: list[ScoreData],
        data_2: list[ScoreData],
1355
1356
1357
1358
1359
        *,
        use_tqdm: bool | Callable[..., tqdm],
        pooling_params: PoolingParams | None,
        lora_request: list[LoRARequest] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any],
1360
    ) -> list[ScoringRequestOutput]:
1361
1362
        tokenizer = self.get_tokenizer()

1363
1364
1365
1366
1367
1368
1369
1370
        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)

1371
        encoded_output = self.encode(
1372
            input_texts,
1373
1374
            use_tqdm=use_tqdm,
            lora_request=lora_request,
1375
            pooling_params=pooling_params,
1376
            pooling_task="embed",
1377
            tokenization_kwargs=tokenization_kwargs,
1378
        )
1379

1380
1381
        encoded_output_1 = encoded_output[0 : len(data_1)]
        encoded_output_2 = encoded_output[len(data_1) :]
1382
1383
1384
1385

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

1386
        scores = _cosine_similarity(
1387
1388
1389
            tokenizer=tokenizer,
            embed_1=encoded_output_1,
            embed_2=encoded_output_2,
1390
        )
1391

1392
        items = self.engine_class.validate_outputs(scores, PoolingRequestOutput)
1393
1394
        return [ScoringRequestOutput.from_base(item) for item in items]

1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
    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]

1476
1477
    def _cross_encoding_score(
        self,
1478
1479
        data_1: list[ScoreData],
        data_2: list[ScoreData],
1480
1481
1482
1483
1484
1485
        *,
        use_tqdm: bool | Callable[..., tqdm],
        pooling_params: PoolingParams | None,
        lora_request: list[LoRARequest] | LoRARequest | None,
        tokenization_kwargs: dict[str, Any],
        score_template: str | None,
1486
    ) -> list[ScoringRequestOutput]:
1487
        model_config = self.model_config
1488
        tokenizer = self.get_tokenizer()
1489
1490

        if isinstance(tokenizer, MistralTokenizer):
1491
            raise ValueError("Score API is not supported for Mistral tokenizer")
1492

1493
1494
        if len(data_1) == 1:
            data_1 = data_1 * len(data_2)
1495

1496
1497
        if pooling_params is None:
            pooling_params = PoolingParams(task="score")
1498
1499
        elif pooling_params.task is None:
            pooling_params.task = "score"
1500

1501
        pooling_params_list = list[PoolingParams]()
1502

1503
        prompts = list[PromptType]()
1504

1505
1506
        input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)]

1507
1508
        for q, d in input_pairs:
            _, engine_prompt = get_score_prompt(
1509
                model_config=model_config,
1510
1511
1512
1513
                data_1=q,
                data_2=d,
                tokenizer=tokenizer,
                tokenization_kwargs=tokenization_kwargs,
1514
                score_template=score_template,
1515
1516
            )

1517
            if token_type_ids := engine_prompt.pop("token_type_ids", None):
1518
1519
1520
1521
1522
1523
1524
                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)

1525
            prompts.append(engine_prompt)
1526

1527
        outputs = self._run_completion(
1528
            prompts=prompts,
1529
            params=pooling_params_list,
1530
            use_tqdm=use_tqdm,
1531
1532
1533
            lora_request=lora_request,
        )

1534
        items = self.engine_class.validate_outputs(outputs, PoolingRequestOutput)
1535
1536
1537

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

1538
1539
    def score(
        self,
1540
1541
1542
1543
1544
1545
1546
1547
        data_1: SingletonPrompt
        | Sequence[SingletonPrompt]
        | ScoreMultiModalParam
        | list[ScoreMultiModalParam],
        data_2: SingletonPrompt
        | Sequence[SingletonPrompt]
        | ScoreMultiModalParam
        | list[ScoreMultiModalParam],
1548
        /,
1549
        *,
1550
1551
1552
        use_tqdm: bool | Callable[..., tqdm] = True,
        pooling_params: PoolingParams | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1553
        tokenization_kwargs: dict[str, Any] | None = None,
1554
        chat_template: str | None = None,
1555
    ) -> list[ScoringRequestOutput]:
1556
1557
        """Generate similarity scores for all pairs `<text,text_pair>` or
          `<multi-modal data, multi-modal data pair>`.
1558

1559
        The inputs can be `1 -> 1`, `1 -> N` or `N -> N`.
1560
1561
        In the `1 - N` case the `data_1` input will be replicated `N`
        times to pair with the `data_2` inputs.
1562
        The input pairs are used to build a list of prompts for the
1563
1564
        cross encoder model. This class automatically batches the prompts,
        considering the memory constraint. For the best performance, put all
1565
1566
1567
        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
1568
        appropriate multi-modal models. For multi-modal inputs, ensure the
1569
        prompt structure matches the model's expected input format.
1570
1571

        Args:
1572
1573
1574
            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
1575
                the `data_2` list.
1576
            data_2: The data to pair with the query to form the input to
1577
                the LLM. Can be text or multi-modal data. See [PromptType]
1578
                [vllm.inputs.PromptType] for more details about the format of
1579
                each prompt.
1580
1581
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1582
1583
1584
1585
            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.
1586
            lora_request: LoRA request to use for generation, if any.
1587
1588
            chat_template: The chat template to use for the scoring. If None, we
                use the model's default chat template.
1589
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1590
        Returns:
1591
            A list of `ScoringRequestOutput` objects containing the
1592
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            generated scores 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
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        if runner_type != "pooling":
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            raise ValueError(
                "LLM.score() 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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        supported_tasks = self.supported_tasks
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        # 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")
        ):
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            raise ValueError(
                "Score API is not supported by this model. "
                "Try converting the model using "
                "`--convert embed` or `--convert classify`."
            )
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        if (
            model_config.is_cross_encoder
            and getattr(model_config.hf_config, "num_labels", 0) != 1
        ):
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            raise ValueError("Score API is only enabled for num_labels == 1.")
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        if not model_config.is_cross_encoder and chat_template is not None:
            raise ValueError(
                "chat_template is only supported for cross-encoder models."
            )

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        is_multimodal_model = model_config.is_multimodal_model
        architecture = model_config.architecture
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        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,
        )
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        renderer = self.renderer
        tok_params = renderer.default_cmpl_tok_params.with_kwargs(
            **(tokenization_kwargs or {})
        )
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        encode_kwargs = tok_params.get_encode_kwargs()

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        if model_config.is_cross_encoder:
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            return self._cross_encoding_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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                score_template=chat_template,
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            )
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        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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                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, profile_prefix: str | None = None) -> None:
        """Start profiling with optional custom trace prefix.

        Args:
            profile_prefix: Optional prefix for the trace file names. If provided,
                           trace files will be named as "<prefix>_dp<X>_pp<Y>_tp<Z>".
                           If not provided, default naming will be used.
        """
        self.llm_engine.start_profile(profile_prefix)
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    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 0: Pause scheduling but continue accepting requests.
                           Requests are queued but not processed.
                - Level 1: Offload model weights to CPU, discard KV cache.
                           The content of kv cache is forgotten. Good for
                           sleeping and waking up the engine to run the same
                           model again. Please make sure there's enough CPU
                           memory to store the model weights.
                - Level 2: Discard all GPU memory (weights + KV cache).
                           Good for sleeping and waking up the engine to run
                           a different model or update the model, where
                           previous model weights are not needed. It reduces
                           CPU memory pressure.
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        """
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        if level > 0:
            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", "scheduling")`. If None, all memory
                is reallocated. wake_up should be called with all tags
                (or None) before the engine is used again.
                Use tags=["scheduling"] to resume from level 0 sleep.
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        """
        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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            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: _P | Sequence[_P],
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        num_requests: int,
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    ) -> Sequence[_P]:
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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
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            # `self._preprocess_cmpl` 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_cmpl(
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                    [prompt],
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                    tokenization_kwargs=merge_kwargs(
                        tokenization_kwargs,
                        dict(truncate_prompt_tokens=param.truncate_prompt_tokens),
                    ),
                )
            ]
        else:
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            engine_prompts = self._preprocess_cmpl(
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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,
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    ) -> list[str]:
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        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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        return added_request_ids

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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),
            )

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        renderer = self.renderer
        tok_params = renderer.default_cmpl_tok_params.with_kwargs(
            **(tokenization_kwargs or {})
        )
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        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,
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    ) -> 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