vllm.transformers_utils.gguf_utils ¶

@cache
def check_gguf_file(model: str | PathLike) -> bool:
    """Check if the file is a GGUF model."""
    model = Path(model)
    if not model.is_file():
        return False
    elif model.suffix == ".gguf":
        return True

    try:
        with model.open("rb") as f:
            header = f.read(4)

        return header == b"GGUF"
    except Exception as e:
        logger.debug("Error reading file %s: %s", model, e)
        return False

def detect_gguf_multimodal(model: str) -> Path | None:
    """Check if GGUF model has multimodal projector file.

    Args:
        model: Model path string

    Returns:
        Path to mmproj file if found, None otherwise
    """
    if not model.endswith(".gguf"):
        return None

    try:
        model_path = Path(model)
        if not model_path.is_file():
            return None

        model_dir = model_path.parent
        mmproj_patterns = ["mmproj.gguf", "mmproj-*.gguf", "*mmproj*.gguf"]
        for pattern in mmproj_patterns:
            mmproj_files = list(model_dir.glob(pattern))
            if mmproj_files:
                return mmproj_files[0]
        return None
    except Exception:
        return None

def extract_vision_config_from_gguf(mmproj_path: str) -> "SiglipVisionConfig | None":
    """Extract vision config parameters from mmproj.gguf metadata.

    Reads vision encoder configuration from GGUF metadata fields using
    standardized GGUF constants. Automatically detects the projector type
    (e.g., gemma3, llama4) and applies model-specific parameters accordingly.

    The function extracts standard CLIP vision parameters from GGUF metadata
    and applies projector-type-specific customizations. For unknown projector
    types, it uses safe defaults from SiglipVisionConfig.

    Args:
        mmproj_path: Path to mmproj.gguf file (str or Path)

    Returns:
        SiglipVisionConfig if extraction succeeds, None if any required
        field is missing from the GGUF metadata

    Raises:
        Exception: Exceptions from GGUF reading (file not found, corrupted
            file, etc.) propagate directly from gguf.GGUFReader
    """
    reader = gguf.GGUFReader(str(mmproj_path))

    # Detect projector type to apply model-specific parameters
    projector_type = None
    projector_type_field = reader.get_field(Keys.Clip.PROJECTOR_TYPE)
    if projector_type_field:
        try:
            projector_type = bytes(projector_type_field.parts[-1]).decode("utf-8")
        except (AttributeError, UnicodeDecodeError) as e:
            logger.warning("Failed to decode projector type from GGUF: %s", e)

    # Map GGUF field constants to SiglipVisionConfig parameters.
    # Uses official GGUF constants from gguf-py for standardization.
    # Format: {gguf_constant: (param_name, dtype)}
    VISION_CONFIG_FIELDS = {
        Keys.ClipVision.EMBEDDING_LENGTH: ("hidden_size", int),
        Keys.ClipVision.FEED_FORWARD_LENGTH: ("intermediate_size", int),
        Keys.ClipVision.BLOCK_COUNT: ("num_hidden_layers", int),
        Keys.ClipVision.Attention.HEAD_COUNT: ("num_attention_heads", int),
        Keys.ClipVision.IMAGE_SIZE: ("image_size", int),
        Keys.ClipVision.PATCH_SIZE: ("patch_size", int),
        Keys.ClipVision.Attention.LAYERNORM_EPS: ("layer_norm_eps", float),
    }

    # Extract and validate all required fields
    config_params = {}
    for gguf_key, (param_name, dtype) in VISION_CONFIG_FIELDS.items():
        field = reader.get_field(gguf_key)
        if field is None:
            logger.warning(
                "Missing required vision config field '%s' in mmproj.gguf",
                gguf_key,
            )
            return None
        # Extract scalar value from GGUF field and convert to target type
        config_params[param_name] = dtype(field.parts[-1])

    # Apply model-specific parameters based on projector type
    if projector_type == VisionProjectorType.GEMMA3:
        # Gemma3 doesn't use the vision pooling head (multihead attention)
        # This is a vLLM-specific parameter used in SiglipVisionTransformer
        config_params["vision_use_head"] = False
        logger.info("Detected Gemma3 projector, disabling vision pooling head")
    # Add other projector-type-specific customizations here as needed
    # elif projector_type == VisionProjectorType.LLAMA4:
    #     config_params["vision_use_head"] = ...

    # Create config with extracted parameters
    # Note: num_channels and attention_dropout use SiglipVisionConfig defaults
    # (3 and 0.0 respectively) which are correct for all models
    config = SiglipVisionConfig(**config_params)

    if projector_type:
        logger.info(
            "Extracted vision config from mmproj.gguf (projector_type: %s)",
            projector_type,
        )
    else:
        logger.info("Extracted vision config from mmproj.gguf metadata")

    return config

def get_gguf_file_path_from_hf(
    repo_id: str | Path,
    quant_type: str,
    revision: str | None = None,
) -> str:
    """Get the GGUF file path from HuggingFace Hub based on repo_id and quant_type.

    Args:
        repo_id: The HuggingFace repository ID (e.g., "Qwen/Qwen3-0.6B")
        quant_type: The quantization type (e.g., "Q4_K_M", "F16")
        revision: Optional revision/branch name

    Returns:
        The path to the GGUF file on HuggingFace Hub (e.g., "filename.gguf"),
    """
    repo_id = str(repo_id)
    gguf_patterns = [
        f"*-{quant_type}.gguf",
        f"*-{quant_type}-*.gguf",
        f"*/*-{quant_type}.gguf",
        f"*/*-{quant_type}-*.gguf",
    ]
    matching_files = list_filtered_repo_files(
        repo_id,
        allow_patterns=gguf_patterns,
        revision=revision,
    )

    if len(matching_files) == 0:
        raise ValueError(
            "Could not find GGUF file for repo %s with quantization %s.",
            repo_id,
            quant_type,
        )

    # Sort to ensure consistent ordering (prefer non-sharded files)
    matching_files.sort(key=lambda x: (x.count("-"), x))
    gguf_filename = matching_files[0]
    return gguf_filename

def is_gguf(model: str | Path) -> bool:
    """Check if the model is a GGUF model.

    Args:
        model: Model name, path, or Path object to check.

    Returns:
        True if the model is a GGUF model, False otherwise.
    """
    model = str(model)

    # Check if it's a local GGUF file
    if check_gguf_file(model):
        return True

    # Check if it's a remote GGUF model (repo_id:quant_type format)
    return is_remote_gguf(model)

@cache
def is_remote_gguf(model: str | Path) -> bool:
    """Check if the model is a remote GGUF model."""
    pattern = r"^[a-zA-Z0-9][a-zA-Z0-9._-]*/[a-zA-Z0-9][a-zA-Z0-9._-]*:[A-Za-z0-9_+-]+$"
    model = str(model)
    if re.fullmatch(pattern, model):
        _, quant_type = model.rsplit(":", 1)
        return is_valid_gguf_quant_type(quant_type)
    return False

def is_valid_gguf_quant_type(gguf_quant_type: str) -> bool:
    """Check if the quant type is a valid GGUF quant type."""
    return getattr(GGMLQuantizationType, gguf_quant_type, None) is not None

def maybe_patch_hf_config_from_gguf(
    model: str,
    hf_config: PretrainedConfig,
) -> PretrainedConfig:
    """Patch HF config for GGUF models.

    Applies GGUF-specific patches to HuggingFace config:
    1. For multimodal models: patches architecture and vision config
    2. For all GGUF models: overrides vocab_size from embedding tensor

    This ensures compatibility with GGUF models that have extended
    vocabularies (e.g., Unsloth) where the GGUF file contains more
    tokens than the HuggingFace tokenizer config specifies.

    Args:
        model: Model path string
        hf_config: HuggingFace config to patch in-place

    Returns:
        Updated HuggingFace config
    """
    # Patch multimodal config if mmproj.gguf exists
    mmproj_path = detect_gguf_multimodal(model)
    if mmproj_path is not None:
        vision_config = extract_vision_config_from_gguf(str(mmproj_path))

        # Create HF config for Gemma3 multimodal
        text_config = hf_config.get_text_config()
        is_gemma3 = hf_config.model_type in ("gemma3", "gemma3_text")
        if vision_config is not None and is_gemma3:
            new_hf_config = Gemma3Config.from_text_vision_configs(
                text_config=text_config,
                vision_config=vision_config,
                architectures=["Gemma3ForConditionalGeneration"],
            )
            hf_config = new_hf_config

    return hf_config

def split_remote_gguf(model: str | Path) -> tuple[str, str]:
    """Split the model into repo_id and quant type."""
    model = str(model)
    if is_remote_gguf(model):
        parts = model.rsplit(":", 1)
        return (parts[0], parts[1])
    raise ValueError(
        f"Wrong GGUF model or invalid GGUF quant type: {model}.\n"
        "- It should be in repo_id:quant_type format.\n"
        f"- Valid GGMLQuantizationType values: {GGMLQuantizationType._member_names_}",
    )