llm.py 79.5 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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from collections.abc import Callable, Iterable, Sequence
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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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, overload
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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.config.quantization import (
    OnlineQuantizationConfigArgs,
)
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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,
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    ChatTemplateConfig,
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    ChatTemplateContentFormatOption,
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    load_chat_template,
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)
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from vllm.entrypoints.pooling.factories import init_pooling_io_processors
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from vllm.entrypoints.pooling.scoring.io_processor import ScoringIOProcessor
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from vllm.entrypoints.pooling.scoring.typing import ScoreInput
from vllm.entrypoints.pooling.typing import OfflineInputsContext, OfflineOutputsContext
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from vllm.entrypoints.utils import log_non_default_args
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from vllm.inputs import (
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    DataPrompt,
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    EngineInput,
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    PromptType,
    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.preprocess import (
    conversation_to_seq,
    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 SCORE_TYPE_MAP, PoolingTask
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from vllm.tokenizers import TokenizerLike
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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.utils.mistral import is_mistral_tokenizer
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from vllm.utils.tqdm_utils import maybe_tqdm
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from vllm.v1.engine import PauseMode
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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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_O = TypeVar(
    "_O",
    bound=RequestOutput | PoolingRequestOutput,
    default=RequestOutput | PoolingRequestOutput,
)
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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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        chat_template: The chat template to apply.
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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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        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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        offload_group_size: Prefetch offloading: Group every N layers
            together. Offload last `offload_num_in_group` layers of each group.
            Default is 0 (disabled).
        offload_num_in_group: Prefetch offloading: Number of layers to
            offload per group. Default is 1.
        offload_prefetch_step: Prefetch offloading: Number of layers to
            prefetch ahead. Higher values hide more latency but use more GPU
            memory. Default is 1.
        offload_params: Prefetch offloading: Set of parameter name segments
            to selectively offload. Only parameters whose names contain one of
            these segments will be offloaded (e.g., {"gate_up_proj", "down_proj"}
            for MLP weights, or {"w13_weight", "w2_weight"} for MoE expert
            weights). If None or empty, all parameters are offloaded.
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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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        chat_template: Path | str | None = None,
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        seed: int = 0,
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        gpu_memory_utilization: float = 0.9,
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        cpu_offload_gb: float = 0,
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        offload_group_size: int = 0,
        offload_num_in_group: int = 1,
        offload_prefetch_step: int = 1,
        offload_params: set[str] | None = None,
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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,
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        quantization_config: dict[str, Any]
        | OnlineQuantizationConfigArgs
        | None = None,
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        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 "swap_space" in kwargs:
            kwargs.pop("swap_space")
            import warnings

            warnings.warn(
                "The 'swap_space' parameter is deprecated and ignored. "
                "It will be removed in a future version.",
                DeprecationWarning,
                stacklevel=2,
            )

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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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            cpu_offload_gb=cpu_offload_gb,
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            offload_group_size=offload_group_size,
            offload_num_in_group=offload_num_in_group,
            offload_prefetch_step=offload_prefetch_step,
            offload_params=offload_params or set(),
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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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            quantization_config=quantization_config,
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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.model_config = self.llm_engine.model_config
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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()
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        self.supported_tasks = supported_tasks
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        self.pooling_task = self.model_config.get_pooling_task(supported_tasks)
        if self.pooling_task is not None:
            logger.info("Supported pooling task: %s", self.pooling_task)
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        self.runner_type = self.model_config.runner_type
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        self.renderer = self.llm_engine.renderer
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        self.chat_template = load_chat_template(chat_template)
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        self.input_processor = self.llm_engine.input_processor
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        self.chat_template_config = ChatTemplateConfig(chat_template=self.chat_template)
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        self.pooling_io_processors = init_pooling_io_processors(
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            supported_tasks=supported_tasks,
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            vllm_config=self.llm_engine.vllm_config,
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            renderer=self.renderer,
            chat_template_config=self.chat_template_config,
        )
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        # Cache for __repr__ to avoid repeated collective_rpc calls
        self._cached_repr: str | None = None

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    @classmethod
    def from_engine_args(cls, engine_args: EngineArgs) -> "LLM":
        """Create an LLM instance from EngineArgs."""
        return cls(**vars(engine_args))

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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,
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        lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
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        priority: list[int] | 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]:
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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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            mm_processor_kwargs: Overrides for `processor.__call__`.
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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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        runner_type = self.model_config.runner_type
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        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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        return self._run_completion(
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            prompts=prompts,
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            params=sampling_params,
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            output_type=RequestOutput,
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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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            mm_processor_kwargs=mm_processor_kwargs,
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        )
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    def enqueue(
        self,
        prompts: PromptType | Sequence[PromptType],
        sampling_params: SamplingParams | Sequence[SamplingParams] | None = None,
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        lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
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        priority: list[int] | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> 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`.
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            mm_processor_kwargs: Overrides for `processor.__call__`.
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        Returns:
            A list of request IDs for the enqueued requests.
        """
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        runner_type = self.model_config.runner_type
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        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()

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        return self._add_completion_requests(
            prompts=prompts,
            params=sampling_params,
            use_tqdm=use_tqdm,
            lora_request=lora_request,
            priority=priority,
            tokenization_kwargs=tokenization_kwargs,
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            mm_processor_kwargs=mm_processor_kwargs,
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        )

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    @overload
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    def wait_for_completion(
        self,
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        *,
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        use_tqdm: bool | Callable[..., tqdm] = True,
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    ) -> list[RequestOutput | PoolingRequestOutput]: ...

    @overload
    def wait_for_completion(
        self,
        output_type: type[_O] | tuple[type[_O], ...],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
    ) -> list[_O]: ...

    def wait_for_completion(
        self,
        output_type: type[Any] | tuple[type[Any], ...] | None = None,
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
    ) -> list[Any]:
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        """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:
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            output_type: The expected output type, defaults to RequestOutput.
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            use_tqdm: If True, shows a tqdm progress bar.

        Returns:
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            A list of output objects for all completed requests.
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        """
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        if output_type is None:
            output_type = (RequestOutput, PoolingRequestOutput)

        return self._run_engine(output_type, use_tqdm=use_tqdm)
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    def _resolve_mm_lora(
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        self,
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        prompt: EngineInput,
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        lora_request: LoRARequest | None,
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    ) -> LoRARequest | None:
        if prompt["type"] != "multimodal":
            return lora_request

        lora_config = self.llm_engine.vllm_config.lora_config
        default_mm_loras = None if lora_config is None else lora_config.default_mm_loras
        if not default_mm_loras:
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            return lora_request

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        prompt_modalities = prompt["mm_placeholders"].keys()
        intersection = set(prompt_modalities).intersection(default_mm_loras.keys())
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        if not intersection:
            return lora_request
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        if len(intersection) > 1:
            # TODO: Would be nice to be able to have multiple loras per prompt
            logger.warning(
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                "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 will be skipped",
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                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 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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        tokenizer = self.renderer.get_tokenizer()
        eos_token_id = tokenizer.eos_token_id
        sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)
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        engine_inputs = self._preprocess_cmpl(prompts)
        lora_requests = self._lora_request_to_seq(lora_request, len(engine_inputs))
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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:
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            concurrency_limit = len(engine_inputs)
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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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        sampling_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, engine_inputs):
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            if prompt["type"] == "embeds":
                raise NotImplementedError(
                    "Embedding prompt not supported for beam search"
                )
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            instances.append(
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                BeamSearchInstance(
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                    prompt,
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                    lora_request=lora_req,
                    logprobs=None,
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                ),
            )
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        for prompt_start in range(0, len(instances), 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

                # only runs for one step
                # we don't need to use tqdm here
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                output = self._render_and_run_requests(
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                    prompts=(beam.get_prompt() for beam in all_beams),
                    params=self._params_to_seq(sampling_params, len(all_beams)),
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                    output_type=RequestOutput,
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                    lora_requests=[beam.lora_request for beam in all_beams],
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                    use_tqdm=False,
                )
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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(
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                                    current_beam.orig_prompt,
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                                    tokens=current_beam.tokens + [token_id],
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                                    lora_request=current_beam.lora_request,
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                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                )

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                                if token_id == 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)
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            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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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> Sequence[EngineInput]:
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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 `EngineInput` objects ready to be passed into LLMEngine.
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        """
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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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        prompt_extras = (
            None
            if mm_processor_kwargs is None
            else {"mm_processor_kwargs": mm_processor_kwargs}
        )
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        return renderer.render_cmpl(
            parsed_prompts,
            tok_params,
            prompt_extras=prompt_extras,
        )
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    def _preprocess_cmpl_one(
        self,
        prompt: PromptType,
        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> EngineInput:
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        (engine_input,) = self._preprocess_cmpl(
            [prompt],
            tokenization_kwargs,
            mm_processor_kwargs=mm_processor_kwargs,
        )
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        return engine_input
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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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        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> Sequence[EngineInput]:
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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 `EngineInput` objects ready to be passed into LLMEngine.
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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,
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                ),
            ),
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        )
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        tok_params = renderer.default_chat_tok_params.with_kwargs(
            **(tokenization_kwargs or {})
        )
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        prompt_extras = (
            None
            if mm_processor_kwargs is None
            else {"mm_processor_kwargs": mm_processor_kwargs}
        )
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        _, engine_inputs = renderer.render_chat(
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            conversations,
            chat_params,
            tok_params,
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            prompt_extras=prompt_extras,
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        )
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        return engine_inputs
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    def _preprocess_chat_one(
        self,
        conversation: list[ChatCompletionMessageParam],
        chat_template: str | None = None,
        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
        chat_template_kwargs: dict[str, Any] | None = None,
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
        tools: list[dict[str, Any]] | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> EngineInput:
        (engine_input,) = self._preprocess_chat(
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            [conversation],
            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,
        )

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        return engine_input
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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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        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()

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        return self._run_chat(
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            messages=messages,
            params=sampling_params,
1059
            output_type=RequestOutput,
1060
1061
            use_tqdm=use_tqdm,
            lora_request=lora_request,
1062
1063
            chat_template=chat_template,
            chat_template_content_format=chat_template_content_format,
1064
            chat_template_kwargs=chat_template_kwargs,
1065
1066
1067
            add_generation_prompt=add_generation_prompt,
            continue_final_message=continue_final_message,
            tools=tools,
1068
            tokenization_kwargs=tokenization_kwargs,
1069
1070
1071
            mm_processor_kwargs=mm_processor_kwargs,
        )

1072
1073
    def encode(
        self,
1074
1075
        prompts: PromptType | Sequence[PromptType] | DataPrompt,
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1076
        *,
1077
1078
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1079
        pooling_task: PoolingTask | None = None,
1080
        tokenization_kwargs: dict[str, Any] | None = None,
1081
    ) -> list[PoolingRequestOutput]:
1082
1083
        """Apply pooling to the hidden states corresponding to the input
        prompts.
1084

1085
        This class automatically batches the given prompts, considering
1086
1087
1088
1089
        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
1090
            prompts: The prompts to the LLM. You may pass a sequence of prompts
1091
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1092
                for more details about the format of each prompt.
1093
1094
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1095
1096
1097
1098
            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.
1099
            lora_request: LoRA request to use for generation, if any.
1100
            pooling_task: Override the pooling task to use.
1101
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1102
1103

        Returns:
1104
            A list of `PoolingRequestOutput` objects containing the
1105
            pooled hidden states in the same order as the input prompts.
1106
        """
1107

1108
1109
1110
1111
        if isinstance(prompts, dict) and "data" in prompts and pooling_task != "plugin":
            raise ValueError(
                "The 'data' field is only supported for the 'plugin' pooling task."
            )
1112
        self._verify_pooling_task(pooling_task)
1113
        assert pooling_task is not None and pooling_task in self.pooling_io_processors
1114

1115
        io_processor = self.pooling_io_processors[pooling_task]
1116

1117
1118
        if pooling_params is None:
            pooling_params = PoolingParams()
1119

1120
1121
1122
1123
1124
        ctx = OfflineInputsContext(
            prompts=prompts,
            pooling_params=pooling_params,
            tokenization_kwargs=tokenization_kwargs,
        )
1125

1126
1127
1128
        engine_inputs = io_processor.pre_process_offline(ctx)
        n_inputs = len(engine_inputs)
        assert ctx.pooling_params is not None
1129

1130
        params_seq = self._params_to_seq(ctx.pooling_params, n_inputs)
1131

1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
        for param in params_seq:
            if param.task is None:
                param.task = pooling_task
            elif pooling_task == "plugin":
                # `plugin` task uses io_processor.parse_request to verify inputs.
                # We actually allow plugin to overwrite pooling_task.
                pass
            elif param.task != pooling_task:
                msg = f"You cannot overwrite {param.task=!r} with {pooling_task=!r}!"
                raise ValueError(msg)
1142

1143
1144
        seq_lora_requests = self._lora_request_to_seq(lora_request, n_inputs)
        seq_priority = self._priority_to_seq(None, n_inputs)
1145

1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
        self._render_and_add_requests(
            prompts=engine_inputs,
            params=params_seq,
            lora_requests=seq_lora_requests,
            priorities=seq_priority,
        )

        outputs = self._run_engine(use_tqdm=use_tqdm, output_type=PoolingRequestOutput)
        outputs = io_processor.post_process_offline(
            ctx=OfflineOutputsContext(outputs=outputs)
        )
1157
        return outputs
1158

1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
    def _verify_pooling_task(self, pooling_task: PoolingTask | None):
        if self.runner_type != "pooling":
            raise ValueError(
                "LLM.encode() is only supported for pooling models. "
                "Try passing `--runner pooling` to use the model as a "
                "pooling model."
            )

        if pooling_task is None:
            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"`'
            )

        if (
            pooling_task in ("embed", "token_embed")
            and pooling_task not in self.supported_tasks
        ):
            raise ValueError(
                "Embedding API is not supported by this model. "
                "Try converting the model using `--convert embed`."
            )

        if (
            pooling_task in ("classify", "token_classify")
            and pooling_task not in self.supported_tasks
        ):
            raise ValueError(
                "Classification API is not supported by this model. "
                "Try converting the model using `--convert classify`."
            )

        # plugin task uses io_processor.parse_request to verify inputs
        if pooling_task != "plugin" and pooling_task != self.pooling_task:
            if pooling_task not in self.supported_tasks:
                raise ValueError(
                    f"Unsupported task: {pooling_task!r} "
                    f"Supported tasks: {self.supported_tasks}"
                )
            else:
1210
1211
1212
                raise ValueError(
                    f"Try switching the model's pooling_task "
                    f'via `PoolerConfig(task="{pooling_task}")`'
1213
1214
                )

1215
1216
1217
1218
1219
1220
1221
1222
        if pooling_task == "plugin" and "plugin" not in self.pooling_io_processors:
            raise ValueError(
                "No IOProcessor plugin installed. Please refer "
                "to the documentation and to the "
                "'prithvi_geospatial_mae_io_processor' "
                "offline inference example for more details."
            )

1223
1224
    def embed(
        self,
1225
        prompts: PromptType | Sequence[PromptType],
1226
        *,
1227
1228
1229
        use_tqdm: bool | Callable[..., tqdm] = True,
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1230
        tokenization_kwargs: dict[str, Any] | None = None,
1231
    ) -> list[EmbeddingRequestOutput]:
1232
1233
1234
1235
1236
1237
1238
1239
1240
        """
        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
1241
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1242
                for more details about the format of each prompt.
1243
1244
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1245
1246
1247
1248
            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.
1249
            lora_request: LoRA request to use for generation, if any.
1250
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1251
1252

        Returns:
1253
            A list of `EmbeddingRequestOutput` objects containing the
1254
1255
1256
            embedding vectors in the same order as the input prompts.
        """

1257
1258
1259
1260
1261
1262
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
            pooling_params=pooling_params,
            lora_request=lora_request,
            pooling_task="embed",
1263
            tokenization_kwargs=tokenization_kwargs,
1264
        )
1265
1266
1267
1268
1269

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

    def classify(
        self,
1270
        prompts: PromptType | Sequence[PromptType],
1271
        *,
1272
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1273
        use_tqdm: bool | Callable[..., tqdm] = True,
1274
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1275
        tokenization_kwargs: dict[str, Any] | None = None,
1276
    ) -> list[ClassificationRequestOutput]:
1277
1278
1279
1280
1281
1282
1283
1284
1285
        """
        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
1286
                for batch inference. See [PromptType][vllm.inputs.PromptType]
1287
                for more details about the format of each prompt.
1288
1289
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1290
1291
1292
1293
            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.
1294
            lora_request: LoRA request to use for generation, if any.
1295
1296
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1297
        Returns:
1298
            A list of `ClassificationRequestOutput` objects containing the
1299
1300
1301
            embedding vectors in the same order as the input prompts.
        """

1302
1303
1304
        items = self.encode(
            prompts,
            use_tqdm=use_tqdm,
1305
            pooling_params=pooling_params,
1306
1307
            lora_request=lora_request,
            pooling_task="classify",
1308
            tokenization_kwargs=tokenization_kwargs,
1309
        )
1310
1311
1312

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

1313
1314
    def reward(
        self,
1315
        prompts: PromptType | Sequence[PromptType],
1316
1317
        /,
        *,
1318
        pooling_params: PoolingParams | Sequence[PoolingParams] | None = None,
1319
1320
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1321
        tokenization_kwargs: dict[str, Any] | None = None,
1322
1323
1324
1325
1326
1327
1328
    ) -> 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]
1329
                for more details about the format of each prompt.
1330
1331
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1332
1333
1334
1335
1336
            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.
1337
1338
            tokenization_kwargs: Overrides for `tokenizer.encode`.

1339
1340
1341
1342
1343
1344
1345
1346
1347
        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,
1348
            pooling_task="token_classify",
1349
            tokenization_kwargs=tokenization_kwargs,
1350
1351
        )

1352
1353
    def score(
        self,
1354
1355
        data_1: ScoreInput | list[ScoreInput],
        data_2: ScoreInput | list[ScoreInput],
1356
        /,
1357
        *,
1358
1359
1360
        use_tqdm: bool | Callable[..., tqdm] = True,
        pooling_params: PoolingParams | None = None,
        lora_request: list[LoRARequest] | LoRARequest | None = None,
1361
        tokenization_kwargs: dict[str, Any] | None = None,
1362
        chat_template: str | None = None,
1363
    ) -> list[ScoringRequestOutput]:
1364
1365
        """Generate similarity scores for all pairs `<text,text_pair>` or
          `<multi-modal data, multi-modal data pair>`.
1366

1367
        The inputs can be `1 -> 1`, `1 -> N` or `N -> N`.
1368
1369
        In the `1 - N` case the `data_1` input will be replicated `N`
        times to pair with the `data_2` inputs.
1370
        The input pairs are used to build a list of prompts for the
1371
1372
        cross encoder model. This class automatically batches the prompts,
        considering the memory constraint. For the best performance, put all
1373
1374
1375
        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
1376
        appropriate multi-modal models. For multi-modal inputs, ensure the
1377
        prompt structure matches the model's expected input format.
1378
1379

        Args:
1380
1381
1382
            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
1383
                the `data_2` list.
1384
            data_2: The data to pair with the query to form the input to
1385
                the LLM. Can be text or multi-modal data. See [PromptType]
1386
                [vllm.inputs.PromptType] for more details about the format of
1387
                each prompt.
1388
1389
            pooling_params: The pooling parameters for pooling. If None, we
                use the default pooling parameters.
1390
1391
1392
1393
            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.
1394
            lora_request: LoRA request to use for generation, if any.
1395
1396
            chat_template: The chat template to use for the scoring. If None, we
                use the model's default chat template.
1397
            tokenization_kwargs: Overrides for `tokenizer.encode`.
1398
        Returns:
1399
            A list of `ScoringRequestOutput` objects containing the
1400
1401
            generated scores in the same order as the input prompts.
        """
1402

1403
        if self.runner_type != "pooling":
1404
1405
1406
            raise ValueError(
                "LLM.score() is only supported for pooling models. "
                "Try passing `--runner pooling` to use the model as a "
1407
1408
                "pooling model."
            )
1409

1410
        score_type: str | None = SCORE_TYPE_MAP.get(self.pooling_task, None)  # type: ignore[arg-type]
1411
1412
1413
        if (
            score_type == "cross-encoder"
            and getattr(self.model_config.hf_config, "num_labels", 0) != 1
1414
        ):
1415
            raise ValueError("Scoring API is only enabled for num_labels == 1.")
1416

1417
1418
        if score_type is None or score_type not in self.pooling_io_processors:
            raise ValueError("This model does not support the Scoring API.")
1419

1420
1421
        io_processor = self.pooling_io_processors[score_type]
        assert isinstance(io_processor, ScoringIOProcessor)
1422

1423
1424
        pooling_task = io_processor.pooling_task
        scoring_data = io_processor.valid_inputs(data_1, data_2)
1425
        n_queries = len(scoring_data.data_1)
1426

1427
1428
1429
        if pooling_params is None:
            pooling_params = PoolingParams()

1430
1431
1432
1433
1434
        ctx = OfflineInputsContext(
            prompts=scoring_data,
            pooling_params=pooling_params,
            tokenization_kwargs=tokenization_kwargs,
            chat_template=chat_template,
1435
            n_queries=n_queries,
1436
        )
1437

1438
1439
        engine_inputs = io_processor.pre_process_offline(ctx)
        n_inputs = len(engine_inputs)
1440

1441
1442
        seq_lora_requests = self._lora_request_to_seq(lora_request, n_inputs)
        params_seq = self._params_to_seq(ctx.pooling_params, n_inputs)
1443
1444
1445
1446
1447
1448
1449
1450

        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)

1451
        seq_priority = self._priority_to_seq(None, n_inputs)
1452
1453

        self._render_and_add_requests(
1454
            prompts=engine_inputs,
1455
1456
1457
1458
1459
1460
1461
            params=params_seq,
            lora_requests=seq_lora_requests,
            priorities=seq_priority,
        )

        outputs = self._run_engine(use_tqdm=use_tqdm, output_type=PoolingRequestOutput)
        outputs = io_processor.post_process_offline(
1462
            ctx=OfflineOutputsContext(outputs=outputs, n_queries=n_queries),
1463
1464
1465
        )

        return [ScoringRequestOutput.from_base(item) for item in outputs]
1466

1467
1468
1469
1470
1471
1472
1473
1474
1475
    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)
1476
1477
1478
1479

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

1480
1481
1482
1483
1484
1485
    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
        )
1486

1487
    def sleep(self, level: int = 1, mode: PauseMode = "abort"):
1488
1489
1490
1491
1492
        """
        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.

1493
        Args:
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
            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.
1507
1508
            mode: How to handle any existing requests, can be "abort", "wait",
                or "keep".
1509
        """
1510
        self.llm_engine.sleep(level=level, mode=mode)
1511

1512
    def wake_up(self, tags: list[str] | None = None):
1513
        """
1514
1515
        Wake up the engine from sleep mode. See the [sleep][vllm.LLM.sleep]
        method for more details.
1516

1517
        Args:
1518
1519
            tags: An optional list of tags to reallocate the engine memory
                for specific memory allocations. Values must be in
1520
1521
1522
1523
                `("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.
1524
1525
        """
        self.llm_engine.wake_up(tags)
1526

1527
1528
1529
1530
    def get_metrics(self) -> list["Metric"]:
        """Return a snapshot of aggregated metrics from Prometheus.

        Returns:
1531
            A `MetricSnapshot` instance capturing the current state
1532
1533
1534
1535
1536
1537
1538
            of all aggregated metrics from Prometheus.

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

1539
    def _params_to_seq(
1540
        self,
1541
        params: _P | Sequence[_P],
1542
        num_requests: int,
1543
    ) -> Sequence[_P]:
1544
1545
1546
        if isinstance(params, Sequence):
            if len(params) != num_requests:
                raise ValueError(
1547
                    f"The lengths of prompts ({num_requests}) "
1548
                    f"and params ({len(params)}) must be the same."
1549
1550
                )

1551
            return params
1552

1553
1554
1555
1556
1557
1558
1559
        return [params] * num_requests

    def _lora_request_to_seq(
        self,
        lora_request: LoRARequest | None | Sequence[LoRARequest | None],
        num_requests: int,
    ) -> Sequence[LoRARequest | None]:
1560
1561
1562
1563
1564
1565
1566
        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."
                )

1567
1568
1569
            return lora_request

        return [lora_request] * num_requests
1570

1571
1572
1573
1574
1575
    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

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    def _add_completion_requests(
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        self,
        prompts: PromptType | Sequence[PromptType],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
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        lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
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        priority: list[int] | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ) -> list[str]:
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        seq_prompts = prompt_to_seq(prompts)
        seq_params = self._params_to_seq(params, len(seq_prompts))
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        seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_prompts))
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        seq_priority = self._priority_to_seq(priority, len(seq_prompts))
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        return self._render_and_add_requests(
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            prompts=(
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                self._preprocess_cmpl_one(
                    prompt,
                    tokenization_kwargs,
                    mm_processor_kwargs=mm_processor_kwargs,
                )
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                for prompt in maybe_tqdm(
                    seq_prompts,
                    use_tqdm=use_tqdm,
                    desc="Rendering prompts",
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                )
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            ),
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            params=seq_params,
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            lora_requests=seq_lora_requests,
            priorities=seq_priority,
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        )

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    def _run_completion(
        self,
        prompts: PromptType | Sequence[PromptType],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
        output_type: type[_O],
        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
        lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
        priority: list[int] | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
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        mm_processor_kwargs: dict[str, Any] | None = None,
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    ):
        self._add_completion_requests(
            prompts=prompts,
            params=params,
            use_tqdm=use_tqdm,
            lora_request=lora_request,
            priority=priority,
            tokenization_kwargs=tokenization_kwargs,
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            mm_processor_kwargs=mm_processor_kwargs,
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        )
        return self._run_engine(use_tqdm=use_tqdm, output_type=output_type)

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    def _run_chat(
        self,
        messages: list[ChatCompletionMessageParam]
        | Sequence[list[ChatCompletionMessageParam]],
        params: SamplingParams
        | PoolingParams
        | Sequence[SamplingParams | PoolingParams],
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        output_type: type[_O],
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        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
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        lora_request: Sequence[LoRARequest] | LoRARequest | None = None,
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        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,
    ):
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        seq_convs = conversation_to_seq(messages)
        seq_params = self._params_to_seq(params, len(seq_convs))
        seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_convs))

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        # When thinking is enabled or tools are provided, and the model
        # uses special tokens for structured output (e.g. Gemma4's
        # <|channel>, <|tool_call>, <|"|>), automatically set
        # skip_special_tokens=False so these tokens are preserved in
        # output.text for downstream parsing.
        needs_parsing = (
            chat_template_kwargs and chat_template_kwargs.get("enable_thinking")
        ) or tools
        if needs_parsing:
            self._adjust_params_for_parsing(seq_params)

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        return self._render_and_run_requests(
            prompts=(
                self._preprocess_chat_one(
                    conversation,
                    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,
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                    tokenization_kwargs=tokenization_kwargs,
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                    mm_processor_kwargs=mm_processor_kwargs,
                )
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                for conversation in maybe_tqdm(
                    seq_convs,
                    use_tqdm=use_tqdm,
                    desc="Rendering conversations",
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                )
            ),
            params=seq_params,
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            output_type=output_type,
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            lora_requests=seq_lora_requests,
            use_tqdm=use_tqdm,
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        )

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    def _adjust_params_for_parsing(
        self, params: Sequence[SamplingParams | PoolingParams]
    ) -> None:
        """Set ``skip_special_tokens=False`` when the model encodes
        structured output syntax as special tokens.

        Models like Gemma4 register thinking delimiters
        (``<|channel>``/``<channel|>``) and tool call tokens
        (``<|tool_call>``/``<tool_call|>``/``<|"|>``) as special tokens.
        The default ``skip_special_tokens=True`` strips them from
        ``output.text``, breaking parsing of both reasoning blocks and
        tool calls.

        This is a no-op for models whose structured tokens are regular
        text tokens (e.g. DeepSeek's ``<think>``/``</think>``).
        """
        # The offline API currently lacks a unified rendering pipeline.
        # Until the planned Renderer refactor is complete, we hardcode
        # this token preservation logic specifically for Gemma4 models
        # to avoid regressions on other models.
        hf_config = getattr(self.model_config, "hf_config", None)
        architectures = getattr(hf_config, "architectures", [])

        if any("Gemma4" in arch for arch in architectures):
            tokenizer = self.renderer.get_tokenizer()
            vocab = tokenizer.get_vocab()
            special_ids = set(getattr(tokenizer, "all_special_ids", []))

            # Tokens used for thinking delimiters and tool call syntax
            # that some models (Gemma4) register as special tokens.
            structured_tokens = (
                "<|channel>",
                "<channel|>",  # thinking delimiters
                "<|tool_call>",
                "<tool_call|>",  # tool call delimiters
                '<|"|>',  # string quoting in tool args
            )
            needs_special = any(
                vocab.get(tok) in special_ids
                for tok in structured_tokens
                if tok in vocab
            )
            if needs_special:
                for sp in params:
                    if isinstance(sp, SamplingParams) and sp.skip_special_tokens:
                        sp.skip_special_tokens = False

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    def _render_and_run_requests(
        self,
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        prompts: Iterable[EngineInput],
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        params: Sequence[SamplingParams | PoolingParams],
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        output_type: type[_O],
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        *,
        lora_requests: Sequence[LoRARequest | None] | None = None,
        priorities: Sequence[int] | None = None,
        use_tqdm: bool | Callable[..., tqdm] = True,
    ):
        if isinstance(prompts, (list, tuple)):
            logger.warning_once(
                "Rendering all prompts before adding them to the engine "
                "is less efficient than performing both on the same prompt "
                "before processing the next prompt. You should instead pass "
                "a generator that renders one prompt per iteration, as that allows "
                "engine execution to begin for the first prompt while processing "
                "the next prompt."
            )

        self._render_and_add_requests(
            prompts=prompts,
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            params=params,
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            lora_requests=lora_requests,
            priorities=priorities,
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        )

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        return self._run_engine(output_type, use_tqdm=use_tqdm)
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    def _render_and_add_requests(
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        self,
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        prompts: Iterable[EngineInput],
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        params: Sequence[SamplingParams | PoolingParams],
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        *,
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        lora_requests: Sequence[LoRARequest | None] | None = None,
        priorities: Sequence[int] | None = None,
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    ) -> list[str]:
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        added_request_ids: list[str] = []
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        try:
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            for i, prompt in enumerate(prompts):
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                request_id = self._add_request(
                    prompt,
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                    params[i],
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                    lora_request=self._resolve_mm_lora(
                        prompt,
                        None if lora_requests is None else lora_requests[i],
                    ),
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                    priority=0 if priorities is None else priorities[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: EngineInput,
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        params: SamplingParams | PoolingParams,
        lora_request: LoRARequest | None = None,
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        priority: int = 0,
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    ) -> str:
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        if isinstance(params, SamplingParams):
            # We only care about the final output
            params.output_kind = RequestOutputKind.FINAL_ONLY

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        request_id = str(next(self.request_counter))
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        return self.llm_engine.add_request(
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            request_id,
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            prompt,
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            params,
            lora_request=lora_request,
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            priority=priority,
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        )
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    def _run_engine(
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        self,
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        output_type: type[_O] | tuple[type[_O], ...],
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        *,
        use_tqdm: bool | Callable[..., tqdm] = True,
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    ) -> list[_O]:
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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[_O] = []
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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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                assert isinstance(output, output_type)
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                if output.finished:
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                    outputs.append(output)  # type: ignore[arg-type]
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                    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