config.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import fnmatch
import json
import os
import time
from collections.abc import Callable
from dataclasses import asdict
from functools import cache, partial
from importlib.metadata import version
from pathlib import Path
from typing import Any, Literal, TypeAlias, TypeVar

import huggingface_hub
from huggingface_hub import (
    get_safetensors_metadata,
    hf_hub_download,
    try_to_load_from_cache,
)
from huggingface_hub import list_repo_files as hf_list_repo_files
from huggingface_hub.utils import (
    EntryNotFoundError,
    HfHubHTTPError,
    LocalEntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
)
from packaging.version import Version
from transformers import DeepseekV3Config, GenerationConfig, PretrainedConfig
from transformers.configuration_utils import ALLOWED_LAYER_TYPES
from transformers.models.auto.image_processing_auto import get_image_processor_config
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_MAPPING_NAMES,
)
from transformers.models.auto.tokenization_auto import get_tokenizer_config
from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME

from vllm import envs
from vllm.logger import init_logger
from vllm.transformers_utils.config_parser_base import ConfigParserBase
from vllm.transformers_utils.utils import (
    check_gguf_file,
    parse_safetensors_file_metadata,
)

if envs.VLLM_USE_MODELSCOPE:
    from modelscope import AutoConfig
else:
    from transformers import AutoConfig

MISTRAL_CONFIG_NAME = "params.json"

logger = init_logger(__name__)


def _get_hf_token() -> str | None:
    """
    Get the HuggingFace token from environment variable.

    Returns None if the token is not set, is an empty string,
    or contains only whitespace.
    This follows the same pattern as huggingface_hub library which
    treats empty string tokens as None to avoid authentication errors.
    """
    token = os.getenv("HF_TOKEN")
    if token and token.strip():
        return token
    return None


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

        import vllm.transformers_utils.configs as configs

        return getattr(configs, value)


_CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
    afmoe="AfmoeConfig",
    chatglm="ChatGLMConfig",
    deepseek_vl_v2="DeepseekVLV2Config",
    deepseek_v32=DeepseekV3Config,
    flex_olmo="FlexOlmoConfig",
    kimi_linear="KimiLinearConfig",
    kimi_vl="KimiVLConfig",
    RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)
    RefinedWebModel="RWConfig",  # For tiiuae/falcon-7b(-instruct)
    jais="JAISConfig",
    mlp_speculator="MLPSpeculatorConfig",
    medusa="MedusaConfig",
    midashenglm="MiDashengLMConfig",
    eagle="EAGLEConfig",
    speculators="SpeculatorsConfig",
    nemotron="NemotronConfig",
    olmo3="Olmo3Config",
    ovis="OvisConfig",
    ultravox="UltravoxConfig",
    step3_vl="Step3VLConfig",
    step3_text="Step3TextConfig",
    qwen3_next="Qwen3NextConfig",
    lfm2_moe="Lfm2MoeConfig",
)

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

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


class HFConfigParser(ConfigParserBase):
    def parse(
        self,
        model: str | Path,
        trust_remote_code: bool,
        revision: str | None = None,
        code_revision: str | None = None,
        **kwargs,
    ) -> tuple[dict, PretrainedConfig]:
        kwargs["local_files_only"] = huggingface_hub.constants.HF_HUB_OFFLINE
        config_dict, _ = PretrainedConfig.get_config_dict(
            model,
            revision=revision,
            code_revision=code_revision,
            token=_get_hf_token(),
            **kwargs,
        )
        # Use custom model class if it's in our registry
        model_type = config_dict.get("model_type")
        if model_type is None:
            model_type = (
                "speculators"
                if config_dict.get("speculators_config") is not None
                else model_type
            )

        if model_type in _CONFIG_REGISTRY:
            config_class = _CONFIG_REGISTRY[model_type]
            config = config_class.from_pretrained(
                model,
                revision=revision,
                code_revision=code_revision,
                token=_get_hf_token(),
                **kwargs,
            )
        else:
            try:
                kwargs = _maybe_update_auto_config_kwargs(kwargs, model_type=model_type)
                config = AutoConfig.from_pretrained(
                    model,
                    trust_remote_code=trust_remote_code,
                    revision=revision,
                    code_revision=code_revision,
                    token=_get_hf_token(),
                    **kwargs,
                )
            except ValueError as e:
                if (
                    not trust_remote_code
                    and "requires you to execute the configuration file" in str(e)
                ):
                    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 "
                        "`--trust-remote-code` flag in the CLI."
                    )
                    raise RuntimeError(err_msg) from e
                else:
                    raise e
        config = _maybe_remap_hf_config_attrs(config)
        return config_dict, config


class MistralConfigParser(ConfigParserBase):
    def parse(
        self,
        model: str | Path,
        trust_remote_code: bool,
        revision: str | None = None,
        code_revision: str | None = None,
        **kwargs,
    ) -> tuple[dict, PretrainedConfig]:
        # 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)
        if (
            max_position_embeddings := config_dict.get("max_position_embeddings")
        ) is None:
            max_position_embeddings = _maybe_retrieve_max_pos_from_hf(
                model, revision, **kwargs
            )
            config_dict["max_position_embeddings"] = max_position_embeddings

        from vllm.transformers_utils.configs.mistral import adapt_config_dict

        config = adapt_config_dict(config_dict)

        # Mistral configs may define sliding_window as list[int]. Convert it
        # to int and add the layer_types list[str] to make it HF compatible
        if (sliding_window := getattr(config, "sliding_window", None)) and isinstance(
            sliding_window, list
        ):
            pattern_repeats = config.num_hidden_layers // len(sliding_window)
            layer_types = sliding_window * pattern_repeats
            config.layer_types = [
                "full_attention" if layer_type is None else "sliding_attention"
                for layer_type in layer_types
            ]
            config.sliding_window = next(filter(None, sliding_window), None)

        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.
     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,
         ...         revision: str | None = None,
         ...         code_revision: str | None = None,
         ...         **kwargs,
         ...     ) -> tuple[dict, PretrainedConfig]:
         ...         raise NotImplementedError
         >>>
         >>> type(get_config_parser("custom_config_parser"))
         <class 'CustomConfigParser'>
    """  # 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 "
                "overwritten by the new parser class `%s`.",
                config_format,
                config_parser_cls,
            )
        if not issubclass(config_parser_cls, ConfigParserBase):
            raise ValueError(
                "The config parser must be a subclass of `ConfigParserBase`."
            )
        _CONFIG_FORMAT_TO_CONFIG_PARSER[config_format] = config_parser_cls
        logger.info(
            "Registered config parser `%s` with config format `%s`",
            config_parser_cls,
            config_format,
        )
        return config_parser_cls

    return _wrapper


_R = TypeVar("_R")


def with_retry(
    func: Callable[[], _R],
    log_msg: str,
    max_retries: int = 2,
    retry_delay: int = 2,
) -> _R:
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                logger.error("%s: %s", log_msg, e)
                raise
            logger.error(
                "%s: %s, retrying %d of %d", log_msg, e, attempt + 1, max_retries
            )
            time.sleep(retry_delay)
            retry_delay *= 2

    raise AssertionError("Should not be reached")


# @cache doesn't cache exceptions
@cache
def list_repo_files(
    repo_id: str,
    *,
    revision: str | None = None,
    repo_type: str | None = None,
    token: str | bool | None = None,
) -> list[str]:
    def lookup_files() -> list[str]:
        # directly list files if model is local
        if (local_path := Path(repo_id)).exists():
            return [
                str(file.relative_to(local_path))
                for file in local_path.rglob("*")
                if file.is_file()
            ]
        # if model is remote, use hf_hub api to list files
        try:
            if envs.VLLM_USE_MODELSCOPE:
                from vllm.transformers_utils.utils import modelscope_list_repo_files

                return modelscope_list_repo_files(
                    repo_id,
                    revision=revision,
                    token=os.getenv("MODELSCOPE_API_TOKEN", None),
                )
            return hf_list_repo_files(
                repo_id, revision=revision, repo_type=repo_type, token=token
            )
        except huggingface_hub.errors.OfflineModeIsEnabled:
            # Don't raise in offline mode,
            # all we know is that we don't have this
            # file cached.
            return []

    return with_retry(lookup_files, "Error retrieving file list")


def list_filtered_repo_files(
    model_name_or_path: str,
    allow_patterns: list[str],
    revision: str | None = None,
    repo_type: str | None = None,
    token: str | bool | None = None,
) -> list[str]:
    try:
        all_files = list_repo_files(
            repo_id=model_name_or_path,
            revision=revision,
            token=token,
            repo_type=repo_type,
        )
    except Exception:
        logger.error(
            "Error retrieving file list. Please ensure your `model_name_or_path`"
            "`repo_type`, `token` and `revision` arguments are correctly set. "
            "Returning an empty list."
        )
        return []

    file_list = []
    # Filter patterns on filenames
    for pattern in allow_patterns:
        file_list.extend(
            [
                file
                for file in all_files
                if fnmatch.fnmatch(os.path.basename(file), pattern)
            ]
        )
    return file_list


def file_exists(
    repo_id: str,
    file_name: str,
    *,
    repo_type: str | None = None,
    revision: str | None = None,
    token: str | bool | None = None,
) -> bool:
    file_list = list_repo_files(
        repo_id, repo_type=repo_type, revision=revision, token=token
    )
    return file_name in file_list


# In offline mode the result can be a false negative
def file_or_path_exists(
    model: str | Path, config_name: str, revision: str | None
) -> bool:
    if (local_path := Path(model)).exists():
        return (local_path / config_name).is_file()

    # Offline mode support: Check if config file is cached already
    cached_filepath = try_to_load_from_cache(
        repo_id=model, filename=config_name, revision=revision
    )
    if isinstance(cached_filepath, str):
        # The config file exists in cache- we can continue trying to load
        return True

    # NB: file_exists will only check for the existence of the config file on
    # hf_hub. This will fail in offline mode.

    # Call HF to check if the file exists
    return file_exists(
        str(model), config_name, revision=revision, token=_get_hf_token()
    )


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:
    """Provide backwards compatibility for RoPE."""
    # Retrieve rope_parameters differently based on Transformers version
    if Version(version("transformers")) >= Version("5.0.0.dev0"):
        from transformers.modeling_rope_utils import RopeParameters

        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = getattr(
            config, "rope_parameters", None
        )
    elif hasattr(config, "rope_parameters"):
        # We are in Transformers v4 and rope_parameters
        # has already been patched for this config
        return
    else:
        # Convert Transformers v4 rope_theta and rope_scaling into rope_parameters
        rope_theta: float | None = getattr(config, "rope_theta", None)
        rope_scaling: dict | None = getattr(config, "rope_scaling", None)
        rope_parameters = rope_scaling
        # Move rope_theta into rope_parameters
        if rope_theta is not None:
            rope_parameters = rope_parameters or {"rope_type": "default"}
            rope_parameters["rope_theta"] = rope_theta
        # Add original_max_position_embeddings if present
        if rope_parameters and (
            ompe := getattr(config, "original_max_position_embeddings", None)
        ):
            rope_parameters["original_max_position_embeddings"] = ompe
        # Write back to config
        config.rope_parameters = rope_parameters

    # No RoPE parameters to patch
    if rope_parameters is None:
        return

    # Handle nested rope_parameters in interleaved sliding attention models
    if set(rope_parameters.keys()).issubset(ALLOWED_LAYER_TYPES):
        for rope_parameters_layer_type in rope_parameters.values():
            patch_rope_parameters_dict(rope_parameters_layer_type)
    else:
        patch_rope_parameters_dict(rope_parameters)


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"]
        if rope_type != rope_type_legacy:
            raise ValueError(
                f"Found conflicts between 'rope_type={rope_type}' (modern "
                f"field) and 'type={rope_type_legacy}' (legacy field). "
                "You should only specify one of them."
            )

    if "rope_type" not in rope_parameters and "type" in rope_parameters:
        rope_parameters["rope_type"] = rope_parameters["type"]
        logger.info("Replacing legacy 'type' key with 'rope_type'")

    if "rope_type" not in rope_parameters:
        raise ValueError("rope_parameters should have a 'rope_type' key")

    if rope_parameters["rope_type"] == "su":
        rope_parameters["rope_type"] = "longrope"
        logger.warning("Replacing legacy rope_type 'su' with 'longrope'")
    elif rope_parameters["rope_type"] == "mrope":
        assert "mrope_section" in rope_parameters
        rope_parameters["rope_type"] = "default"
        logger.warning("Replacing legacy rope_type 'mrope' with 'default'")


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

    return "mrope_section" in rope_parameters


def uses_mrope(config: PretrainedConfig) -> bool:
    """Detect if the model with this config uses M-ROPE."""
    return (
        _uses_mrope(config)
        or _uses_mrope(config.get_text_config())
        or thinker_uses_mrope(config)
    )


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)


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

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

    return _is_encoder_decoder(config) or _is_encoder_decoder(config.get_text_config())


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):
        return len(set(layer_types)) > 1
    return False


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


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)})
            logger.debug("Remapped config attribute '%s' to '%s'", old_attr, new_attr)
    return config


def maybe_override_with_speculators(
    model: str,
    tokenizer: str,
    trust_remote_code: bool,
    revision: str | None = None,
    vllm_speculative_config: dict[str, Any] | None = None,
    **kwargs,
) -> tuple[str, str, dict[str, Any] | None]:
    """
    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

    Returns:
        Tuple of (resolved_model, resolved_tokenizer, speculative_config)
    """
    is_gguf = check_gguf_file(model)
    if is_gguf:
        kwargs["gguf_file"] = Path(model).name
        gguf_model_repo = Path(model).parent
    else:
        gguf_model_repo = None
    kwargs["local_files_only"] = huggingface_hub.constants.HF_HUB_OFFLINE
    config_dict, _ = PretrainedConfig.get_config_dict(
        model if gguf_model_repo is None else gguf_model_repo,
        revision=revision,
        trust_remote_code=trust_remote_code,
        token=_get_hf_token(),
        **kwargs,
    )
    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
    from vllm.transformers_utils.configs.speculators.base import SpeculatorsConfig

    speculative_config = SpeculatorsConfig.extract_vllm_speculative_config(
        config_dict=config_dict
    )

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

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

    return model, tokenizer, speculative_config


def get_config(
    model: str | Path,
    trust_remote_code: bool,
    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,
    **kwargs,
) -> PretrainedConfig:
    # Separate model folder from file path for GGUF models

    is_gguf = check_gguf_file(model)
    if is_gguf:
        kwargs["gguf_file"] = Path(model).name
        model = Path(model).parent

    if config_format == "auto":
        try:
            # First check for Mistral to avoid defaulting to
            # Transformers implementation.
            if file_or_path_exists(model, MISTRAL_CONFIG_NAME, revision=revision):
                config_format = "mistral"
            elif is_gguf or file_or_path_exists(
                model, HF_CONFIG_NAME, revision=revision
            ):
                config_format = "hf"
            else:
                raise ValueError(
                    "Could not detect config format for no config file found. "
                    "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 "
                    "in engine args for customized config parser."
                )

        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 "
                "'params.json'.\n"
                "3. For GGUF: pass the local path of the GGUF checkpoint.\n"
                "   Loading GGUF from a remote repo directly is not yet "
                "supported.\n"
            ).format(model=model)

            raise ValueError(error_message) from e

    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,
        **kwargs,
    )
    # Special architecture mapping check for GGUF models
    if is_gguf:
        if config.model_type not in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
            raise RuntimeError(f"Can't get gguf config for {config.model_type}.")
        model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
        config.update({"architectures": [model_type]})

    # Architecture mapping for models without explicit architectures field
    if not config.architectures:
        if config.model_type not in MODEL_MAPPING_NAMES:
            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]})

    # 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.
    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
        )

    if quantization_config is not None:
        config.quantization_config = quantization_config
        # auto-enable DeepGEMM UE8M0 if model config requests it
        scale_fmt = quantization_config.get("scale_fmt", None)
        if scale_fmt in ("ue8m0",):
            if not envs.is_set("VLLM_USE_DEEP_GEMM_E8M0"):
                os.environ["VLLM_USE_DEEP_GEMM_E8M0"] = "1"
                logger.info_once(
                    (
                        "Detected quantization_config.scale_fmt=%s; "
                        "enabling UE8M0 for DeepGEMM."
                    ),
                    scale_fmt,
                )
            elif not envs.VLLM_USE_DEEP_GEMM_E8M0:
                logger.warning_once(
                    (
                        "Model config requests UE8M0 "
                        "(quantization_config.scale_fmt=%s), but "
                        "VLLM_USE_DEEP_GEMM_E8M0=0 is set; "
                        "UE8M0 for DeepGEMM disabled."
                    ),
                    scale_fmt,
                )

    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)

    # 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))

    if trust_remote_code:
        maybe_register_config_serialize_by_value()

    return config


def try_get_local_file(
    model: str | Path, file_name: str, revision: str | None = "main"
) -> Path | None:
    file_path = Path(model) / file_name
    if file_path.is_file():
        return file_path
    else:
        try:
            cached_filepath = try_to_load_from_cache(
                repo_id=model, filename=file_name, revision=revision
            )
            if isinstance(cached_filepath, str):
                return Path(cached_filepath)
        except ValueError:
            ...
    return None


def get_hf_file_to_dict(
    file_name: str, model: str | Path, revision: str | None = "main"
):
    """
    Downloads a file from the Hugging Face Hub and returns
    its contents as a dictionary.

    Parameters:
    - file_name (str): The name of the file to download.
    - model (str): The name of the model on the Hugging Face Hub.
    - revision (str): The specific version of the model.

    Returns:
    - config_dict (dict): A dictionary containing
    the contents of the downloaded file.
    """

    file_path = try_get_local_file(model=model, file_name=file_name, revision=revision)

    if file_path is None:
        try:
            hf_hub_file = hf_hub_download(model, file_name, revision=revision)
        except huggingface_hub.errors.OfflineModeIsEnabled:
            return None
        except (
            RepositoryNotFoundError,
            RevisionNotFoundError,
            EntryNotFoundError,
            LocalEntryNotFoundError,
        ) as e:
            logger.debug("File or repository not found in hf_hub_download", e)
            return None
        except HfHubHTTPError as e:
            logger.warning(
                "Cannot connect to Hugging Face Hub. Skipping file download for '%s':",
                file_name,
                exc_info=e,
            )
            return None
        file_path = Path(hf_hub_file)

    if file_path is not None and file_path.is_file():
        with open(file_path) as file:
            return json.load(file)

    return None


@cache
def get_pooling_config(model: str, revision: str | None = "main") -> dict | None:
    """
    This function gets the pooling and normalize
    config from the model - only applies to
    sentence-transformers models.

    Args:
        model: The name of the Hugging Face model.
        revision: The specific version of the model to use.
            Defaults to 'main'.

    Returns:
        A dictionary containing the pooling type and whether
            normalization is used, or None if no pooling configuration is found.
    """

    modules_file_name = "modules.json"

    modules_dict = None
    if file_or_path_exists(
        model=model, config_name=modules_file_name, revision=revision
    ):
        modules_dict = get_hf_file_to_dict(modules_file_name, model, revision)

    if modules_dict is None:
        return None

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

    pooling = next(
        (
            item
            for item in modules_dict
            if item["type"] == "sentence_transformers.models.Pooling"
        ),
        None,
    )
    normalize = bool(
        next(
            (
                item
                for item in modules_dict
                if item["type"] == "sentence_transformers.models.Normalize"
            ),
            False,
        )
    )

    if pooling:
        pooling_file_name = "{}/config.json".format(pooling["path"])
        pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision)
        pooling_type_name = next(
            (item for item, val in pooling_dict.items() if val is True), None
        )

        if pooling_type_name is not None:
            pooling_type_name = get_pooling_config_name(pooling_type_name)

        logger.info("Found pooling configuration.")
        return {"pooling_type": pooling_type_name, "normalize": normalize}

    return None


def get_pooling_config_name(pooling_name: str) -> str | None:
    if "pooling_mode_" in pooling_name:
        pooling_name = pooling_name.replace("pooling_mode_", "")

    if "_" in pooling_name:
        pooling_name = pooling_name.split("_")[0]

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

    supported_pooling_types = ["LAST", "ALL", "CLS", "STEP", "MEAN"]
    pooling_type_name = pooling_name.upper()

    if pooling_type_name in supported_pooling_types:
        return pooling_type_name

    raise NotImplementedError(f"Pooling type {pooling_type_name} not supported")


@cache
def get_sentence_transformer_tokenizer_config(
    model: str | Path, revision: str | None = "main"
):
    """
    Returns the tokenization configuration dictionary for a
    given Sentence Transformer BERT model.

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

    Returns:
    - dict: A dictionary containing the configuration parameters
    for the Sentence Transformer BERT model.
    """
    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

    for config_file in sentence_transformer_config_files:
        if (
            try_get_local_file(model=model, file_name=config_file, revision=revision)
            is not None
        ):
            encoder_dict = get_hf_file_to_dict(config_file, model, revision)
            if encoder_dict:
                break

    if not encoder_dict and not Path(model).is_absolute():
        try:
            # If model is on HuggingfaceHub, get the repo files
            repo_files = list_repo_files(
                model, revision=revision, token=_get_hf_token()
            )
        except Exception:
            repo_files = []

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

    if not encoder_dict:
        return None

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

    if all(k in encoder_dict for k in ("max_seq_length", "do_lower_case")):
        return encoder_dict
    return None


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

    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.

    Examples:

    >>> 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
    try:
        import transformers_modules

        transformers_modules_available = True
    except ImportError:
        transformers_modules_available = False

    try:
        import multiprocessing
        import pickle

        import cloudpickle

        from vllm.config import VllmConfig

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

        multiprocessing.reducer.register(VllmConfig, _reduce_config)

        # 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
            from vllm.v1.executor.ray_utils import ray

            if ray:
                ray.cloudpickle.register_pickle_by_value(transformers_modules)

    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`",
            exc_info=e,
        )


def get_hf_image_processor_config(
    model: str | Path,
    hf_token: bool | str | None = None,
    revision: str | None = None,
    **kwargs,
) -> dict[str, Any]:
    # ModelScope does not provide an interface for image_processor
    if envs.VLLM_USE_MODELSCOPE:
        return dict()
    # Separate model folder from file path for GGUF models
    if check_gguf_file(model):
        model = Path(model).parent
    return get_image_processor_config(
        model, token=hf_token, revision=revision, **kwargs
    )


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

    if text_config is not config:
        # The code operates under the assumption that text_config should have
        # `num_attention_heads` (among others). Assert here to fail early
        # if transformers config doesn't align with this assumption.
        assert hasattr(text_config, "num_attention_heads")

    return text_config


def try_get_generation_config(
    model: str,
    trust_remote_code: bool,
    revision: str | None = None,
    config_format: str | ConfigFormat = "auto",
) -> GenerationConfig | None:
    try:
        return GenerationConfig.from_pretrained(
            model,
            revision=revision,
        )
    except OSError:  # Not found
        try:
            config = get_config(
                model,
                trust_remote_code=trust_remote_code,
                revision=revision,
                config_format=config_format,
            )
            return GenerationConfig.from_model_config(config)
        except OSError:  # Not found
            return None


def try_get_safetensors_metadata(
    model: str,
    *,
    revision: str | None = None,
):
    get_safetensors_metadata_partial = partial(
        get_safetensors_metadata,
        model,
        revision=revision,
        token=_get_hf_token(),
    )

    try:
        return with_retry(
            get_safetensors_metadata_partial, "Error retrieving safetensors"
        )
    except Exception:
        return None


def try_get_tokenizer_config(
    pretrained_model_name_or_path: str | os.PathLike,
    trust_remote_code: bool,
    revision: str | None = None,
) -> dict[str, Any] | None:
    try:
        return get_tokenizer_config(
            pretrained_model_name_or_path,
            trust_remote_code=trust_remote_code,
            revision=revision,
        )
    except Exception:
        return None


@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", [])

        dense_modules = [
            m for m in modules if m.get("type") == "sentence_transformers.models.Dense"
        ]
        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


def get_safetensors_params_metadata(
    model: str,
    *,
    revision: str | None = None,
) -> dict[str, Any]:
    """
    Get the safetensors metadata for remote model repository.
    """
    full_metadata = {}
    if (model_path := Path(model)).exists():
        safetensors_to_check = model_path.glob("*.safetensors")
        full_metadata = {
            param_name: info
            for file_path in safetensors_to_check
            if file_path.is_file()
            for param_name, info in parse_safetensors_file_metadata(file_path).items()
        }
    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


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 "
            f"and if the config file exists."
        )
    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)
        hf_config = get_config(
            model=model,
            trust_remote_code=trust_remote_code_val,
            revision=revision,
            config_format="hf",
        )
        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",
            exc_info=e,
        )

    return max_position_embeddings


def get_model_path(model: str | Path, revision: str | None = None):
    if os.path.exists(model):
        return model
    assert huggingface_hub.constants.HF_HUB_OFFLINE
    common_kwargs = {
        "local_files_only": huggingface_hub.constants.HF_HUB_OFFLINE,
        "revision": revision,
    }

    if envs.VLLM_USE_MODELSCOPE:
        from modelscope.hub.snapshot_download import snapshot_download

        return snapshot_download(model_id=model, **common_kwargs)

    from huggingface_hub import snapshot_download

    return snapshot_download(repo_id=model, **common_kwargs)


def get_hf_file_bytes(
    file_name: str, model: str | Path, revision: str | None = "main"
) -> bytes | None:
    """Get file contents from HuggingFace repository as bytes."""
    file_path = try_get_local_file(model=model, file_name=file_name, revision=revision)

    if file_path is None:
        hf_hub_file = hf_hub_download(
            model, file_name, revision=revision, token=_get_hf_token()
        )
        file_path = Path(hf_hub_file)

    if file_path is not None and file_path.is_file():
        with open(file_path, "rb") as file:
            return file.read()

    return None