config.py 44.6 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 os
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from collections.abc import Callable, Iterator
from contextlib import contextmanager
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from dataclasses import asdict
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from functools import cache, partial
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from importlib.metadata import version
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from pathlib import Path
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from typing import Any, Literal, TypeAlias
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import huggingface_hub
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import torch
from huggingface_hub import constants, get_safetensors_metadata
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from packaging.version import Version
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from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
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from transformers import GenerationConfig, PretrainedConfig
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from transformers.models.auto.image_processing_auto import get_image_processor_config
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from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_MAPPING_NAMES,
)
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from transformers.models.auto.tokenization_auto import get_tokenizer_config
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from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
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from vllm import envs
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from vllm.logger import init_logger
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from vllm.transformers_utils.repo_utils import is_mistral_model_repo
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from vllm.transformers_utils.utils import (
    parse_safetensors_file_metadata,
    without_trust_remote_code,
)
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from vllm.utils.torch_utils import common_broadcastable_dtype
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from .config_parser_base import ConfigParserBase
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from .gguf_utils import (
    check_gguf_file,
    is_gguf,
    is_remote_gguf,
    split_remote_gguf,
)
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from .repo_utils import (
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    file_or_path_exists,
    get_hf_file_to_dict,
    list_repo_files,
    try_get_local_file,
    with_retry,
)
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try:
    # Transformers v5
    from transformers.configuration_utils import ALLOWED_ATTENTION_LAYER_TYPES
except ImportError:
    # Transformers v4
    from transformers.configuration_utils import (
        ALLOWED_LAYER_TYPES as ALLOWED_ATTENTION_LAYER_TYPES,
    )

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if envs.VLLM_USE_MODELSCOPE:
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    from modelscope import AutoConfig
else:
    from transformers import AutoConfig
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MISTRAL_CONFIG_NAME = "params.json"

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logger = init_logger(__name__)

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class LazyConfigDict(dict):
    def __getitem__(self, key):
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        if isinstance(value := super().__getitem__(key), type):
            return value

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        import vllm.transformers_utils.configs as configs
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        return getattr(configs, value)
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_CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
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    afmoe="AfmoeConfig",
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    bagel="BagelConfig",
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    chatglm="ChatGLMConfig",
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    colmodernvbert="ColModernVBertConfig",
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    colpali="ColPaliConfig",
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    colqwen3="ColQwen3Config",
    ops_colqwen3="OpsColQwen3Config",
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    qwen3_vl_nemotron_embed="Qwen3VLNemotronEmbedConfig",
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    deepseek_vl_v2="DeepseekVLV2Config",
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    deepseek_v32="DeepseekV3Config",
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    flex_olmo="FlexOlmoConfig",
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    funaudiochat="FunAudioChatConfig",
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    hunyuan_vl="HunYuanVLConfig",
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    isaac="IsaacConfig",
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    kimi_k2="DeepseekV3Config",  # Kimi K2 uses same architecture as DeepSeek V3
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    kimi_linear="KimiLinearConfig",
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    kimi_vl="KimiVLConfig",
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    kimi_k25="KimiK25Config",
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    RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)
    RefinedWebModel="RWConfig",  # For tiiuae/falcon-7b(-instruct)
    jais="JAISConfig",
    mlp_speculator="MLPSpeculatorConfig",
    medusa="MedusaConfig",
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    midashenglm="MiDashengLMConfig",
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    eagle="EAGLEConfig",
    speculators="SpeculatorsConfig",
    nemotron="NemotronConfig",
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    olmo_hybrid="OlmoHybridConfig",
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    ovis="OvisConfig",
    ultravox="UltravoxConfig",
    step3_vl="Step3VLConfig",
    step3_text="Step3TextConfig",
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    step3p5="Step3p5Config",
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    qwen3_asr="Qwen3ASRConfig",
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    qwen3_next="Qwen3NextConfig",
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    qwen3_5="Qwen3_5Config",
    qwen3_5_moe="Qwen3_5MoeConfig",
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    lfm2_moe="Lfm2MoeConfig",
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    tarsier2="Tarsier2Config",
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)
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_SPECULATIVE_DECODING_CONFIGS: set[str] = {"eagle", "speculators"}

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_CONFIG_ATTRS_MAPPING: dict[str, str] = {
    "llm_config": "text_config",
}

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_AUTO_CONFIG_KWARGS_OVERRIDES: dict[str, dict[str, Any]] = {
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    "internvl_chat": {"has_no_defaults_at_init": True},
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    "Llama_Nemotron_Nano_VL": {"attn_implementation": "eager"},
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    "NVLM_D": {"has_no_defaults_at_init": True},
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}

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def is_rope_parameters_nested(rope_parameters: dict[str, Any]) -> bool:
    """Check if rope_parameters is nested by layer types."""
    # Cannot be nested if rope_parameters is empty
    if not rope_parameters:
        return False
    return set(rope_parameters.keys()).issubset(ALLOWED_ATTENTION_LAYER_TYPES)


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@contextmanager
def _mistral_patch_hf_hub_constants() -> Iterator[None]:
    hf_safetensors_single_file = constants.SAFETENSORS_SINGLE_FILE
    hf_safetensors_index_file = constants.SAFETENSORS_INDEX_FILE
    constants.SAFETENSORS_SINGLE_FILE = "consolidated.safetensors"
    constants.SAFETENSORS_INDEX_FILE = "consolidated.safetensors.index.json"
    try:
        yield
    finally:
        constants.SAFETENSORS_SINGLE_FILE = hf_safetensors_single_file
        constants.SAFETENSORS_INDEX_FILE = hf_safetensors_index_file


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class HFConfigParser(ConfigParserBase):
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    def parse(
        self,
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        model: str | Path,
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        trust_remote_code: bool,
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        revision: str | None = None,
        code_revision: str | None = None,
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        **kwargs,
    ) -> tuple[dict, PretrainedConfig]:
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        kwargs["local_files_only"] = huggingface_hub.constants.HF_HUB_OFFLINE
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        trust_remote_code |= kwargs.get("trust_remote_code", False)
        kwargs = without_trust_remote_code(kwargs)
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        config_dict, _ = PretrainedConfig.get_config_dict(
            model,
            revision=revision,
            code_revision=code_revision,
            **kwargs,
        )
        # Use custom model class if it's in our registry
        model_type = config_dict.get("model_type")
        if model_type is None:
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            model_type = (
                "speculators"
                if config_dict.get("speculators_config") is not None
                else model_type
            )
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        # Allow hf_overrides to override model_type before checking _CONFIG_REGISTRY
        if (hf_overrides := kwargs.pop("hf_overrides", None)) is not None:
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            if isinstance(hf_overrides, dict) and "model_type" in hf_overrides:
                model_type = hf_overrides["model_type"]
            elif callable(hf_overrides):
                # If hf_overrides doesn't modify model_type, it will be passed straight
                # through and remain unchanged by this elif block
                dummy_model_type = f"dummy_{model_type}"
                dummy_kwargs = dict(architectures=[""], model_type=dummy_model_type)
                dummy_config = PretrainedConfig(**dummy_kwargs)
                dummy_model_type = hf_overrides(dummy_config).model_type
                model_type = dummy_model_type.removeprefix("dummy_")
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        if model_type in _SPECULATIVE_DECODING_CONFIGS:
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            config_class = _CONFIG_REGISTRY[model_type]
            config = config_class.from_pretrained(
                model,
                revision=revision,
                code_revision=code_revision,
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                trust_remote_code=trust_remote_code,
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                **kwargs,
            )
        else:
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            if model_type in _CONFIG_REGISTRY:
                # Register the config class to AutoConfig to ensure it's used in future
                # calls to `from_pretrained`
                config_class = _CONFIG_REGISTRY[model_type]
                config_class.model_type = model_type
                AutoConfig.register(model_type, config_class, exist_ok=True)
                # Now that it is registered, it is not considered remote code anymore
                trust_remote_code = False
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            try:
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                kwargs = _maybe_update_auto_config_kwargs(kwargs, model_type=model_type)
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                config = AutoConfig.from_pretrained(
                    model,
                    trust_remote_code=trust_remote_code,
                    revision=revision,
                    code_revision=code_revision,
                    **kwargs,
                )
            except ValueError as e:
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                if (
                    not trust_remote_code
                    and "requires you to execute the configuration file" in str(e)
                ):
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                    err_msg = (
                        "Failed to load the model config. If the model "
                        "is a custom model not yet available in the "
                        "HuggingFace transformers library, consider setting "
                        "`trust_remote_code=True` in LLM or using the "
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                        "`--trust-remote-code` flag in the CLI."
                    )
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                    raise RuntimeError(err_msg) from e
                else:
                    raise e
        config = _maybe_remap_hf_config_attrs(config)
        return config_dict, config


class MistralConfigParser(ConfigParserBase):
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    def parse(
        self,
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        model: str | Path,
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        trust_remote_code: bool,
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        revision: str | None = None,
        code_revision: str | None = None,
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        **kwargs,
    ) -> tuple[dict, PretrainedConfig]:
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        # This function loads a params.json config which
        # should be used when loading models in mistral format
        config_dict = _download_mistral_config_file(model, revision)
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        if (
            max_position_embeddings := config_dict.get("max_position_embeddings")
        ) is None:
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            max_position_embeddings = _maybe_retrieve_max_pos_from_hf(
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                model, revision, **kwargs
            )
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            config_dict["max_position_embeddings"] = max_position_embeddings

        from vllm.transformers_utils.configs.mistral import adapt_config_dict

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        # Get missing fields from HF config if available
        try:
            hf_config_dict, _ = PretrainedConfig.get_config_dict(
                model,
                revision=revision,
                code_revision=code_revision,
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                **without_trust_remote_code(kwargs),
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            )
        except OSError:  # Not found
            hf_config_dict = {}

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        if config_dict.get("dtype") is None:
            with _mistral_patch_hf_hub_constants():
                model_str = model if isinstance(model, str) else model.as_posix()
                param_mt = get_safetensors_params_metadata(model_str, revision=revision)
            if param_mt:
                param_dtypes: set[torch.dtype] = {
                    _SAFETENSORS_TO_TORCH_DTYPE[dtype]
                    for info in param_mt.values()
                    if (dtype := info.get("dtype", None))
                    and dtype in _SAFETENSORS_TO_TORCH_DTYPE
                }

                if param_dtypes:
                    config_dict["dtype"] = common_broadcastable_dtype(param_dtypes)
                    logger.info_once(
                        "Inferred from consolidated*.safetensors files "
                        f"{config_dict['dtype']} dtype."
                    )

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        config = adapt_config_dict(config_dict, defaults=hf_config_dict)
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        return config_dict, config


_CONFIG_FORMAT_TO_CONFIG_PARSER: dict[str, type[ConfigParserBase]] = {
    "hf": HFConfigParser,
    "mistral": MistralConfigParser,
}

ConfigFormat = Literal[
    "auto",
    "hf",
    "mistral",
]


def get_config_parser(config_format: str) -> ConfigParserBase:
    """Get the config parser for a given config format."""
    if config_format not in _CONFIG_FORMAT_TO_CONFIG_PARSER:
        raise ValueError(f"Unknown config format `{config_format}`.")
    return _CONFIG_FORMAT_TO_CONFIG_PARSER[config_format]()


def register_config_parser(config_format: str):
    """Register a customized vllm config parser.
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     When a config format is not supported by vllm, you can register a customized
    config parser to support it.
     Args:
         config_format (str): The config parser format name.
     Examples:

         >>> from vllm.transformers_utils.config import (get_config_parser,
                                                         register_config_parser)
         >>> from vllm.transformers_utils.config_parser_base import ConfigParserBase
         >>>
         >>> @register_config_parser("custom_config_parser")
         ... class CustomConfigParser(ConfigParserBase):
         ...     def parse(
         ...         self,
         ...         model: Union[str, Path],
         ...         trust_remote_code: bool,
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         ...         revision: str | None = None,
         ...         code_revision: str | None = None,
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         ...         **kwargs,
         ...     ) -> tuple[dict, PretrainedConfig]:
         ...         raise NotImplementedError
         >>>
         >>> type(get_config_parser("custom_config_parser"))
         <class 'CustomConfigParser'>
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    """  # noqa: E501

    def _wrapper(config_parser_cls):
        if config_format in _CONFIG_FORMAT_TO_CONFIG_PARSER:
            logger.warning(
                "Config format `%s` is already registered, and will be "
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                "overwritten by the new parser class `%s`.",
                config_format,
                config_parser_cls,
            )
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        if not issubclass(config_parser_cls, ConfigParserBase):
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            raise ValueError(
                "The config parser must be a subclass of `ConfigParserBase`."
            )
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        _CONFIG_FORMAT_TO_CONFIG_PARSER[config_format] = config_parser_cls
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        logger.info(
            "Registered config parser `%s` with config format `%s`",
            config_parser_cls,
            config_format,
        )
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        return config_parser_cls

    return _wrapper
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def set_default_rope_theta(config: PretrainedConfig, default_theta: float) -> None:
    """Some models may have no rope_theta in their config but still use RoPE.
    This function sets a default rope_theta if it's missing."""
    if getattr(config, "rope_parameters", None) is None:
        config.rope_parameters = {"rope_type": "default"}
    if "rope_theta" not in config.rope_parameters:
        config.rope_parameters["rope_theta"] = default_theta


def patch_rope_parameters(config: PretrainedConfig) -> None:
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    """Provide backwards compatibility for RoPE."""
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    from vllm.config.utils import getattr_iter

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    # Older custom models may use non-standard field names
    # which need patching for both Transformers v4 and v5.
    names = ["rope_theta", "rotary_emb_base"]
    rope_theta = getattr_iter(config, names, None, warn=True)
    names = ["partial_rotary_factor", "rotary_pct", "rotary_emb_fraction"]
    partial_rotary_factor = getattr_iter(config, names, None, warn=True)
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    ompe = getattr(config, "original_max_position_embeddings", None)
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    if Version(version("transformers")) < Version("5.0.0"):
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        # Transformers v4 installed, legacy config fields may be present
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        if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
            config.rope_parameters = rope_scaling
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        if (
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            rope_theta is not None
            or partial_rotary_factor is not None
            or ompe is not None
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        ) and not getattr(config, "rope_parameters", None):
            config.rope_parameters = {"rope_type": "default"}
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        # Patch legacy fields into rope_parameters
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        if rope_theta is not None:
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            config.rope_parameters["rope_theta"] = rope_theta
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        if partial_rotary_factor is not None:
            config.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
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        if ompe is not None:
            config.rope_parameters["original_max_position_embeddings"] = ompe
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    elif rope_theta is not None or getattr(config, "rope_parameters", None):
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        # Transformers v5 installed
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        # Patch these fields in case they used non-standard names
        if rope_theta is not None:
            config.rope_theta = rope_theta
        if partial_rotary_factor is not None:
            config.partial_rotary_factor = partial_rotary_factor
        # Standardize and validate RoPE parameters
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        config.standardize_rope_params()
        config.validate_rope()
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    # No RoPE parameters to patch
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    if getattr(config, "rope_parameters", None) is None:
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        return

    # Handle nested rope_parameters in interleaved sliding attention models
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    if is_rope_parameters_nested(config.rope_parameters):
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        for rope_parameters_layer_type in config.rope_parameters.values():
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            patch_rope_parameters_dict(rope_parameters_layer_type)
    else:
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        patch_rope_parameters_dict(config.rope_parameters)
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def patch_rope_parameters_dict(rope_parameters: dict[str, Any]) -> None:
    if "rope_type" in rope_parameters and "type" in rope_parameters:
        rope_type = rope_parameters["rope_type"]
        rope_type_legacy = rope_parameters["type"]
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        if (rope_type_legacy == "su" and rope_type == "longrope") or (
            rope_type_legacy == "mrope" and rope_type == "default"
        ):
            pass  # No action needed
        elif rope_type != rope_type_legacy:
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            raise ValueError(
                f"Found conflicts between 'rope_type={rope_type}' (modern "
                f"field) and 'type={rope_type_legacy}' (legacy field). "
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                "You should only specify one of them."
            )
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    if "rope_type" not in rope_parameters and "type" in rope_parameters:
        rope_parameters["rope_type"] = rope_parameters["type"]
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        logger.info("Replacing legacy 'type' key with 'rope_type'")

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    if "rope_type" not in rope_parameters:
        raise ValueError("rope_parameters should have a 'rope_type' key")
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    if rope_parameters["rope_type"] == "su":
        rope_parameters["rope_type"] = "longrope"
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        logger.warning("Replacing legacy rope_type 'su' with 'longrope'")
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    elif rope_parameters["rope_type"] == "mrope":
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        if "mrope_section" not in rope_parameters:
            raise ValueError(
                "Legacy rope_type 'mrope' requires 'mrope_section' in rope_parameters"
            )
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        rope_parameters["rope_type"] = "default"
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        logger.warning("Replacing legacy rope_type 'mrope' with 'default'")


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def _uses_mrope(config: PretrainedConfig) -> bool:
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    rope_parameters = getattr(config, "rope_parameters", None)
    if rope_parameters is None:
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        return False

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    return "mrope_section" in rope_parameters
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def uses_mrope(config: PretrainedConfig) -> bool:
    """Detect if the model with this config uses M-ROPE."""
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    return (
        _uses_mrope(config)
        or _uses_mrope(config.get_text_config())
        or thinker_uses_mrope(config)
    )
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def thinker_uses_mrope(config: PretrainedConfig) -> bool:
    """Detect if the model contains a thinker config and it uses M-ROPE."""
    thinker_config = getattr(config, "thinker_config", None)
    if thinker_config is None:
        return False

    thinker_text_config = getattr(thinker_config, "text_config", None)
    if thinker_text_config is None:
        return False

    return uses_mrope(thinker_text_config)


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def uses_xdrope_dim(config: PretrainedConfig) -> int:
    """Detect if the model with this config uses XD-ROPE."""
    xdrope_section = getattr(config, "xdrope_section", None)
    if xdrope_section is not None and isinstance(xdrope_section, list):
        return len(xdrope_section)
    rope_scaling = getattr(config, "rope_scaling", None)
    if rope_scaling is None:
        return 0

    if isinstance(rope_scaling, dict) and "xdrope_section" in rope_scaling:
        xdrope_section = rope_scaling["xdrope_section"]
        if xdrope_section is not None and isinstance(xdrope_section, list):
            return len(xdrope_section)

    return 0


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def is_encoder_decoder(config: PretrainedConfig) -> bool:
    """Detect if the model with this config is used as an encoder/decoder."""

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    def _is_encoder_decoder(config: PretrainedConfig) -> bool:
        return getattr(config, "is_encoder_decoder", False)

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    return _is_encoder_decoder(config) or _is_encoder_decoder(config.get_text_config())
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def is_interleaved(config: PretrainedConfig) -> bool:
    """
    Detect if the model with this config is used with interleaved attention.
    """
    text_config = config.get_text_config()
    if layer_types := getattr(text_config, "layer_types", None):
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        return len(set(layer_types)) > 1
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    return False


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def _maybe_update_auto_config_kwargs(kwargs: dict[str, Any], model_type: str):
    """
    Update kwargs for AutoConfig initialization based on model_type
    """
    if model_type in _AUTO_CONFIG_KWARGS_OVERRIDES:
        kwargs.update(_AUTO_CONFIG_KWARGS_OVERRIDES[model_type])
    return kwargs


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543
def _maybe_remap_hf_config_attrs(config: PretrainedConfig) -> PretrainedConfig:
    """Remap config attributes to match the expected names."""
    for old_attr, new_attr in _CONFIG_ATTRS_MAPPING.items():
        if hasattr(config, old_attr):
            if not hasattr(config, new_attr):
                config.update({new_attr: getattr(config, old_attr)})
544
            logger.debug("Remapped config attribute '%s' to '%s'", old_attr, new_attr)
545
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547
    return config


548
def maybe_override_with_speculators(
549
    model: str,
550
    tokenizer: str | None,
551
    trust_remote_code: bool,
552
553
    revision: str | None = None,
    vllm_speculative_config: dict[str, Any] | None = None,
554
    hf_token: bool | str | None = None,
555
    **kwargs,
556
) -> tuple[str, str | None, dict[str, Any] | None]:
557
    """
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568
    Resolve model configuration when speculators are detected.

    Checks if the provided model is a speculators model and if so, extracts
    the target model configuration and builds the speculative config.

    Args:
        model: Model name or path
        tokenizer: Tokenizer name or path
        trust_remote_code: Whether to trust remote code
        revision: Model revision
        vllm_speculative_config: Existing vLLM speculative config
569
        hf_token: HuggingFace token for authenticated model access
570
571
572

    Returns:
        Tuple of (resolved_model, resolved_tokenizer, speculative_config)
573
    """
574
    if check_gguf_file(model):
575
576
        kwargs["gguf_file"] = Path(model).name
        gguf_model_repo = Path(model).parent
577
578
579
    elif is_remote_gguf(model):
        repo_id, _ = split_remote_gguf(model)
        gguf_model_repo = Path(repo_id)
580
581
    else:
        gguf_model_repo = None
582
    kwargs["local_files_only"] = huggingface_hub.constants.HF_HUB_OFFLINE
583
    config_dict, _ = PretrainedConfig.get_config_dict(
584
        model if gguf_model_repo is None else gguf_model_repo,
585
        revision=revision,
586
        token=hf_token,
587
        **without_trust_remote_code(kwargs),
588
    )
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    speculators_config = config_dict.get("speculators_config")

    if speculators_config is None:
        # No speculators config found, return original values
        return model, tokenizer, vllm_speculative_config

    # Speculators format detected - process overrides
596
    from vllm.transformers_utils.configs.speculators.base import SpeculatorsConfig
597

598
    speculative_config = SpeculatorsConfig.extract_vllm_speculative_config(
599
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        config_dict=config_dict
    )
601
602

    # Set the draft model to the speculators model
603
    speculative_config["model"] = model
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608

    # Override model and tokenizer with the verifier model from config
    verifier_model = speculators_config["verifier"]["name_or_path"]
    model = tokenizer = verifier_model

609
    return model, tokenizer, speculative_config
610
611


612
def get_config(
613
    model: str | Path,
614
    trust_remote_code: bool,
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617
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619
    revision: str | None = None,
    code_revision: str | None = None,
    config_format: str | ConfigFormat = "auto",
    hf_overrides_kw: dict[str, Any] | None = None,
    hf_overrides_fn: Callable[[PretrainedConfig], PretrainedConfig] | None = None,
620
621
622
    **kwargs,
) -> PretrainedConfig:
    # Separate model folder from file path for GGUF models
623

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635
    _is_gguf = is_gguf(model)
    _is_remote_gguf = is_remote_gguf(model)
    if _is_gguf:
        if check_gguf_file(model):
            # Local GGUF file
            kwargs["gguf_file"] = Path(model).name
            model = Path(model).parent
        elif _is_remote_gguf:
            # Remote GGUF - extract repo_id from repo_id:quant_type format
            # The actual GGUF file will be downloaded later by GGUFModelLoader
            # Keep model as repo_id:quant_type for download, but use repo_id for config
            model, _ = split_remote_gguf(model)
636

637
    if config_format == "auto":
638
        try:
639
640
            # First check for Mistral to avoid defaulting to
            # Transformers implementation.
641
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643
644
645
            if is_mistral_model_repo(
                model_name_or_path=str(model), revision=revision
            ) and file_or_path_exists(
                model=model, config_name=MISTRAL_CONFIG_NAME, revision=revision
            ):
646
                config_format = "mistral"
647
            elif (_is_gguf and not _is_remote_gguf) or file_or_path_exists(
648
649
650
                model, HF_CONFIG_NAME, revision=revision
            ):
                config_format = "hf"
651
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664
665
            # Remote GGUF models must have config.json in repo,
            # otherwise the config can't be parsed correctly.
            # FIXME(Isotr0py): Support remote GGUF repos without config.json
            elif _is_remote_gguf and not file_or_path_exists(
                model, HF_CONFIG_NAME, revision=revision
            ):
                err_msg = (
                    "Could not find config.json for remote GGUF model repo. "
                    "To load remote GGUF model through `<repo_id>:<quant_type>`, "
                    "ensure your model has config.json (HF format) file. "
                    "Otherwise please specify --hf-config-path <original_repo> "
                    "in engine args to fetch config from unquantized hf model."
                )
                logger.error(err_msg)
                raise ValueError(err_msg)
666
667
668
            else:
                raise ValueError(
                    "Could not detect config format for no config file found. "
669
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671
                    "With config_format 'auto', ensure your model has either "
                    "config.json (HF format) or params.json (Mistral format). "
                    "Otherwise please specify your_custom_config_format "
672
673
                    "in engine args for customized config parser."
                )
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683
684

        except Exception as e:
            error_message = (
                "Invalid repository ID or local directory specified:"
                " '{model}'.\nPlease verify the following requirements:\n"
                "1. Provide a valid Hugging Face repository ID.\n"
                "2. Specify a local directory that contains a recognized "
                "configuration file.\n"
                "   - For Hugging Face models: ensure the presence of a "
                "'config.json'.\n"
                "   - For Mistral models: ensure the presence of a "
685
                "'params.json'.\n"
686
            ).format(model=model)
687
688

            raise ValueError(error_message) from e
689

690
691
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693
694
695
    config_parser = get_config_parser(config_format)
    config_dict, config = config_parser.parse(
        model,
        trust_remote_code=trust_remote_code,
        revision=revision,
        code_revision=code_revision,
696
        hf_overrides=hf_overrides_kw or hf_overrides_fn,
697
698
        **kwargs,
    )
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712
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715
716
717
718
719
720

    # Patching defaults for GGUF models
    if _is_gguf:
        # Some models have different default values between GGUF and HF.
        def apply_gguf_default(key: str, gguf_default: Any):
            """
            Apply GGUF defaults unless explicitly configured.

            This function reads/writes external `config` and `config_dict`.
            If the specified `key` is not in `config_dict` (i.e. not explicitly
            configured and the default HF value is used), it updates the
            corresponding `config` value to `gguf_default`.
            """
            if key not in config_dict:
                config.update({key: gguf_default})

        # Apply architecture-specific GGUF defaults.
        if config.model_type in {"qwen3_moe"}:
            # Qwen3 MoE: norm_topk_prob is always true.
            # Note that, this parameter is always false (HF default) on Qwen2 MoE.
            apply_gguf_default("norm_topk_prob", True)

721
    # Special architecture mapping check for GGUF models
722
    if _is_gguf:
723
        if config.model_type not in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
724
            raise RuntimeError(f"Can't get gguf config for {config.model_type}.")
725
726
727
        model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
        config.update({"architectures": [model_type]})

728
729
730
    # Architecture mapping for models without explicit architectures field
    if not config.architectures:
        if config.model_type not in MODEL_MAPPING_NAMES:
731
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733
734
735
736
737
738
            logger.warning(
                "Model config does not have a top-level 'architectures' field: "
                "expecting `hf_overrides={'architectures': ['...']}` to be passed "
                "in engine args."
            )
        else:
            model_type = MODEL_MAPPING_NAMES[config.model_type]
            config.update({"architectures": [model_type]})
739

740
741
742
743
744
745
    # ModelOpt 0.31.0 and after saves the quantization config in the model
    # config file.
    quantization_config = config_dict.get("quantization_config", None)

    # ModelOpt 0.29.0 and before saves the quantization config in a separate
    # "hf_quant_config.json" in the same directory as the model config file.
746
747
748
749
750
751
    if quantization_config is None and file_or_path_exists(
        model, "hf_quant_config.json", revision
    ):
        quantization_config = get_hf_file_to_dict(
            "hf_quant_config.json", model, revision
        )
752
753
754

    if quantization_config is not None:
        config.quantization_config = quantization_config
755
        # auto-enable DeepGEMM UE8M0 if model config requests it
756
        scale_fmt = quantization_config.get("scale_fmt", None)
757
        if scale_fmt in ("ue8m0",):
758
759
            if not envs.is_set("VLLM_USE_DEEP_GEMM_E8M0"):
                os.environ["VLLM_USE_DEEP_GEMM_E8M0"] = "1"
760
                logger.info_once(
761
762
                    (
                        "Detected quantization_config.scale_fmt=%s; "
763
                        "enabling UE8M0 for DeepGEMM."
764
                    ),
765
766
                    scale_fmt,
                )
767
            elif not envs.VLLM_USE_DEEP_GEMM_E8M0:
768
                logger.warning_once(
769
770
771
                    (
                        "Model config requests UE8M0 "
                        "(quantization_config.scale_fmt=%s), but "
772
773
                        "VLLM_USE_DEEP_GEMM_E8M0=0 is set; "
                        "UE8M0 for DeepGEMM disabled."
774
                    ),
775
776
                    scale_fmt,
                )
777

778
779
780
781
782
783
784
    if hf_overrides_kw:
        logger.debug("Overriding HF config with %s", hf_overrides_kw)
        config.update(hf_overrides_kw)
    if hf_overrides_fn:
        logger.debug("Overriding HF config with %s", hf_overrides_fn)
        config = hf_overrides_fn(config)

785
786
787
788
789
790
791
792
    # Exhaustively patch RoPE parameters everywhere they might be
    patch_rope_parameters(config)
    patch_rope_parameters(config.get_text_config())
    SubConfigs: TypeAlias = dict[str, PretrainedConfig]
    sub_configs: SubConfigs | None = getattr(config, "sub_configs", None)
    if sub_configs:
        for sub_config in sub_configs:
            patch_rope_parameters(getattr(config, sub_config))
793

794
795
796
    if trust_remote_code:
        maybe_register_config_serialize_by_value()

797
    return config
798
799


800
@cache
801
802
803
804
def get_pooling_config(
    model: str,
    revision: str | None = "main",
) -> dict[str, Any] | None:
805
    """
806
807
808
    This function gets the pooling and normalize
    config from the model - only applies to
    sentence-transformers models.
809
810

    Args:
811
        model: The name of the Hugging Face model.
812
        revision: The specific version of the model to use.
813
            Defaults to 'main'.
814
815

    Returns:
816
        A dictionary containing the pooling type and whether
817
            normalization is used, or None if no pooling configuration is found.
818
    """
819
820
    if is_remote_gguf(model):
        model, _ = split_remote_gguf(model)
821
822

    modules_file_name = "modules.json"
823
824

    modules_dict = None
825
826
827
    if file_or_path_exists(
        model=model, config_name=modules_file_name, revision=revision
    ):
828
        modules_dict = get_hf_file_to_dict(modules_file_name, model, revision)
829
830
831
832

    if modules_dict is None:
        return None

833
834
    logger.info("Found sentence-transformers modules configuration.")

835
836
837
838
839
840
841
842
    pooling = next(
        (
            item
            for item in modules_dict
            if item["type"] == "sentence_transformers.models.Pooling"
        ),
        None,
    )
843
    normalize = bool(
844
845
846
847
848
849
850
851
852
        next(
            (
                item
                for item in modules_dict
                if item["type"] == "sentence_transformers.models.Normalize"
            ),
            False,
        )
    )
853
854

    if pooling:
855
        from vllm.config.pooler import SEQ_POOLING_TYPES, TOK_POOLING_TYPES
856

857
858
        pooling_file_name = "{}/config.json".format(pooling["path"])
        pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision) or {}
859

860
        logger.info("Found pooling configuration.")
861

862
        config: dict[str, Any] = {"use_activation": normalize}
863
864
865
866
867
868
869
870
871
872
873
        for key, val in pooling_dict.items():
            if val is True:
                pooling_type = parse_pooling_type(key)
                if pooling_type in SEQ_POOLING_TYPES:
                    config["seq_pooling_type"] = pooling_type
                elif pooling_type in TOK_POOLING_TYPES:
                    config["tok_pooling_type"] = pooling_type
                else:
                    logger.debug("Skipping unrelated field: %r=%r", key, val)

        return config
874
875
876
877

    return None


878
def parse_pooling_type(pooling_name: str):
879
880
881
882
    if "pooling_mode_" in pooling_name:
        pooling_name = pooling_name.replace("pooling_mode_", "")

    if "_" in pooling_name:
883
        pooling_name = pooling_name.split("_", 1)[0]
884
885
886
887

    if "lasttoken" in pooling_name:
        pooling_name = "last"

888
    return pooling_name.upper()
889
890


891
@cache
892
def get_sentence_transformer_tokenizer_config(
893
    model: str | Path, revision: str | None = "main"
894
) -> dict[str, Any] | None:
895
    """
896
    Returns the tokenization configuration dictionary for a
897
898
899
    given Sentence Transformer BERT model.

    Parameters:
900
    - model (str|Path): The name of the Sentence Transformer
901
902
903
904
905
    BERT model.
    - revision (str, optional): The revision of the m
    odel to use. Defaults to 'main'.

    Returns:
906
    - dict: A dictionary containing the configuration parameters
907
908
    for the Sentence Transformer BERT model.
    """
909
910
911
912
913
914
915
916
917
918
    sentence_transformer_config_files = [
        "sentence_bert_config.json",
        "sentence_roberta_config.json",
        "sentence_distilbert_config.json",
        "sentence_camembert_config.json",
        "sentence_albert_config.json",
        "sentence_xlm-roberta_config.json",
        "sentence_xlnet_config.json",
    ]
    encoder_dict = None
919
920

    for config_file in sentence_transformer_config_files:
921
922
923
924
        if (
            try_get_local_file(model=model, file_name=config_file, revision=revision)
            is not None
        ):
925
            encoder_dict = get_hf_file_to_dict(config_file, model, revision)
926
927
            if encoder_dict:
                break
928

929
    if not encoder_dict and not Path(model).is_absolute():
930
931
        try:
            # If model is on HuggingfaceHub, get the repo files
932
            repo_files = list_repo_files(model, revision=revision)
933
        except Exception:
934
935
936
937
            repo_files = []

        for config_name in sentence_transformer_config_files:
            if config_name in repo_files:
938
                encoder_dict = get_hf_file_to_dict(config_name, model, revision)
939
940
941
                if encoder_dict:
                    break

942
943
944
    if not encoder_dict:
        return None

945
946
    logger.info("Found sentence-transformers tokenize configuration.")

947
948
949
950
951
    if all(k in encoder_dict for k in ("max_seq_length", "do_lower_case")):
        return encoder_dict
    return None


952
def maybe_register_config_serialize_by_value() -> None:
953
954
    """Try to register HF model configuration class to serialize by value

955
956
957
    If trust_remote_code is set, and the model's config file specifies an
    `AutoConfig` class, then the config class is typically an instance of
    a custom class imported from the HF modules cache.
958

959
    Examples:
960

961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
    >>> from transformers import AutoConfig
    >>> klass = AutoConfig.from_pretrained(
    ...     "meta-llama/Meta-Llama-3-8B", trust_remote_code=True
    ... )
    >>> klass.__class__  # transformers.models.llama.configuration_llama.LlamaConfig
    >>> import transformers_modules  # error, not initialized
    >>> klass = AutoConfig.from_pretrained(
    ...     "deepseek-ai/DeepSeek-V2.5", trust_remote_code=True
    ... )
    >>> import transformers_modules  # success, initialized
    >>> klass.__class__  # transformers_modules.deepseek-ai.DeepSeek-V2.5.98b11844770b2c3ffc18b175c758a803640f4e77.configuration_deepseek.DeepseekV2Config

    In the DeepSeek example, the config class is an instance of a custom
    class that is not serializable by default. This class will not be
    importable in spawned workers, and won't exist at all on
    other nodes, which breaks serialization of the config.

    In this function we tell the cloudpickle serialization library to pass
    instances of these generated classes by value instead of by reference,
    i.e. the class definition is serialized along with its data so that the
    class module does not need to be importable on the receiving end.

    See: https://github.com/cloudpipe/cloudpickle?tab=readme-ov-file#overriding-pickles-serialization-mechanism-for-importable-constructs
    """  # noqa
985
986
    try:
        import transformers_modules
987

988
        transformers_modules_available = True
989
    except ImportError:
990
        transformers_modules_available = False
991
992
993
994
995

    try:
        import multiprocessing
        import pickle

996
997
        import cloudpickle

998
        from vllm.config import VllmConfig
999

1000
1001
1002
        # Register multiprocessing reducers to handle cross-process
        # serialization of VllmConfig objects that may contain custom configs
        # from transformers_modules
1003
        def _reduce_config(config: VllmConfig):
1004
            return (pickle.loads, (cloudpickle.dumps(config),))
1005

1006
        multiprocessing.reducer.register(VllmConfig, _reduce_config)
1007

1008
1009
1010
1011
1012
        # Register transformers_modules with cloudpickle if available
        if transformers_modules_available:
            cloudpickle.register_pickle_by_value(transformers_modules)

            # ray vendors its own version of cloudpickle
1013
            from vllm.v1.executor.ray_utils import ray
1014

1015
1016
1017
            if ray:
                ray.cloudpickle.register_pickle_by_value(transformers_modules)

1018
1019
1020
1021
1022
1023
    except Exception as e:
        logger.warning(
            "Unable to register remote classes used by"
            " trust_remote_code with by-value serialization. This may"
            " lead to a later error. If remote code is not needed"
            " remove `--trust-remote-code`",
1024
1025
            exc_info=e,
        )
1026
1027


1028
def get_hf_image_processor_config(
1029
1030
1031
    model: str | Path,
    hf_token: bool | str | None = None,
    revision: str | None = None,
1032
    **kwargs,
1033
) -> dict[str, Any]:
1034
    # ModelScope does not provide an interface for image_processor
1035
    if envs.VLLM_USE_MODELSCOPE:
1036
        return dict()
1037
    # Separate model folder from file path for GGUF models
1038
    if check_gguf_file(model):
1039
        model = Path(model).parent
1040
1041
    elif is_remote_gguf(model):
        model, _ = split_remote_gguf(model)
1042
1043
1044
    return get_image_processor_config(
        model, token=hf_token, revision=revision, **kwargs
    )
1045
1046


1047
1048
def get_hf_text_config(config: PretrainedConfig):
    """Get the "sub" config relevant to llm for multi modal models.
1049
    No op for pure text models.
1050
    """
1051
1052
    text_config = config.get_text_config()

1053
1054
1055
1056
1057
1058
1059
    if text_config is not config and not hasattr(text_config, "num_attention_heads"):
        raise ValueError(
            "The text_config extracted from the model config does not have "
            "`num_attention_heads` attribute. This indicates a mismatch "
            "between the model config and vLLM's expectations. Please "
            "ensure that the model config is compatible with vLLM."
        )
1060
1061

    return text_config
1062
1063
1064
1065
1066


def try_get_generation_config(
    model: str,
    trust_remote_code: bool,
1067
1068
    revision: str | None = None,
    config_format: str | ConfigFormat = "auto",
1069
    hf_token: bool | str | None = None,
1070
) -> GenerationConfig | None:
1071
1072
1073
1074
1075
1076
1077
    # GGUF files don't have generation_config.json - their config is embedded
    # in the file header. Skip all filesystem lookups to avoid re-reading the
    # memory-mapped file, which can hang in multi-process scenarios when the
    # EngineCore process already has the file mapped.
    if is_gguf(model):
        return None

1078
1079
1080
1081
    try:
        return GenerationConfig.from_pretrained(
            model,
            revision=revision,
1082
            token=hf_token,
1083
1084
1085
1086
1087
1088
1089
        )
    except OSError:  # Not found
        try:
            config = get_config(
                model,
                trust_remote_code=trust_remote_code,
                revision=revision,
1090
                config_format=config_format,
1091
                token=hf_token,
1092
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1094
1095
            )
            return GenerationConfig.from_model_config(config)
        except OSError:  # Not found
            return None
1096
1097


1098
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1100
def try_get_safetensors_metadata(
    model: str,
    *,
1101
    revision: str | None = None,
1102
1103
):
    get_safetensors_metadata_partial = partial(
1104
        get_safetensors_metadata, model, revision=revision
1105
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1107
    )

    try:
1108
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1110
        return with_retry(
            get_safetensors_metadata_partial, "Error retrieving safetensors"
        )
1111
1112
    except Exception:
        return None
1113
1114
1115


def try_get_tokenizer_config(
1116
    pretrained_model_name_or_path: str | os.PathLike,
1117
    trust_remote_code: bool,
1118
1119
    revision: str | None = None,
) -> dict[str, Any] | None:
1120
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1123
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    try:
        return get_tokenizer_config(
            pretrained_model_name_or_path,
            trust_remote_code=trust_remote_code,
            revision=revision,
        )
    except Exception:
        return None
1128
1129


1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
@cache
def try_get_dense_modules(
    model: str | Path,
    revision: str | None = None,
) -> list[dict[str, Any]] | None:
    try:
        modules = get_hf_file_to_dict("modules.json", model, revision)
        if not modules:
            return None

        if isinstance(modules, dict):
            modules = modules.get("modules", [])

1143
1144
1145
1146
1147
        _DENSE_MODULE_TYPES = {
            "sentence_transformers.models.Dense",
            "pylate.models.Dense.Dense",
        }
        dense_modules = [m for m in modules if m.get("type") in _DENSE_MODULE_TYPES]
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
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        if not dense_modules:
            return None

        layer_configs = []
        for module in dense_modules:
            folder = module.get("path", "")

            config_path = f"{folder}/config.json" if folder else "config.json"
            layer_config = get_hf_file_to_dict(config_path, model, revision)
            if not layer_config:
                continue
            layer_config["folder"] = folder
            layer_configs.append(layer_config)
        return layer_configs
    except Exception:
        return None


1166
1167
1168
def get_safetensors_params_metadata(
    model: str,
    *,
1169
    revision: str | None = None,
1170
1171
) -> dict[str, Any]:
    """
1172
    Get the safetensors parameters metadata for remote/local model repository.
1173
1174
1175
1176
1177
1178
    """
    full_metadata = {}
    if (model_path := Path(model)).exists():
        safetensors_to_check = model_path.glob("*.safetensors")
        full_metadata = {
            param_name: info
1179
1180
1181
            for file_path in safetensors_to_check
            if file_path.is_file()
            for param_name, info in parse_safetensors_file_metadata(file_path).items()
1182
1183
1184
1185
1186
1187
1188
1189
1190
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1193
        }
    else:
        repo_mt = try_get_safetensors_metadata(model, revision=revision)
        if repo_mt and (files_mt := repo_mt.files_metadata):
            full_metadata = {
                param_name: asdict(info)
                for file_mt in files_mt.values()
                for param_name, info in file_mt.tensors.items()
            }
    return full_metadata


1194
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1196
1197
1198
1199
1200
def _download_mistral_config_file(model, revision) -> dict:
    config_file_name = "params.json"
    config_dict = get_hf_file_to_dict(config_file_name, model, revision)
    if config_dict is None:
        raise ValueError(
            f"Failed to load mistral '{config_file_name}' config for model "
            f"{model}. Please check if the model is a mistral-format model "
1201
1202
            f"and if the config file exists."
        )
1203
1204
1205
1206
1207
1208
1209
1210
    assert isinstance(config_dict, dict)
    return config_dict


def _maybe_retrieve_max_pos_from_hf(model, revision, **kwargs) -> int:
    max_position_embeddings = 128_000
    try:
        trust_remote_code_val = kwargs.get("trust_remote_code", False)
1211
1212
1213
1214
1215
1216
        hf_config = get_config(
            model=model,
            trust_remote_code=trust_remote_code_val,
            revision=revision,
            config_format="hf",
        )
1217
1218
1219
1220
1221
1222
1223
        if hf_value := hf_config.get_text_config().max_position_embeddings:
            max_position_embeddings = hf_value
    except Exception as e:
        logger.warning(
            "The params.json file is missing 'max_position_embeddings'"
            " and could not get a value from the HF config."
            " Defaulting to 128000",
1224
1225
            exc_info=e,
        )
1226
1227

    return max_position_embeddings